cmake : bump version to 1.1.0

* whisper : prepare infra for new decoding strategies

* whisper : apply logit filters and compute logprobs

* whisper : add whisper_get_logits()

* whisper : separate self and cross attention memory

Initial step needed for supporting parallel decoders

* whisper : move probs_id buffer to whisper_context

* whisper : refactor kv cache into separate struct

* whisper : move self-attention kv cache to whisper_decoder

* whisper : wip decoding parameters + strategies

* whisper : wip decoding parameters + strategies (part 2)

* whisper : wip decoding parameters + strategies (part 3)

* whisper : wip decoding parameters + strategies (part 4)

* whisper : fix prompt_past update to not include prompt_init

* whisper : temperature + best_of support

* whisper : support for compression_ration_threshold

We actually use entropy, but it is similar

* command : fix example to use logits instead of obsolete probs

* whisper : handle empty sequence ranking

* whisper : add WHISPER_DEBUG + diagnostic prints + new main args

* whisper : minor fixes

* whisper : add beam-search support

* whisper : bug fix when there no previous context

* whisper : add comments

* stream : disable temperature fallback

For real-time processing, we always want a single decoder running at T=0

* whisper.swiftui : update example - fix paths + add empty folders

.github/workflows/bindings.yml

@@ -0,0 +1,17 @@
+name: Bindings Tests
+on:
+  push:
+    paths:
+      - bindings/go/**
+
+jobs:
+    ubuntu-latest:
+      runs-on: ubuntu-latest
+      steps:
+      - uses: actions/setup-go@v3
+        with:
+          go-version: '^1.19'
+      - uses: actions/checkout@v1
+      - run: |
+          cd bindings/go
+          make test

.github/workflows/build.yml

@@ -113,3 +113,155 @@ jobs:
               run: |
                 make
                 ctest -L gh --output-on-failure
+
+    windows:
+        runs-on: windows-latest
+
+        strategy:
+            matrix:
+                build: [Release]
+                arch: [Win32, x64]
+                sdl2: [ON]
+                include:
+                  - arch: Win32
+                    s2arc: x86
+                  - arch: x64
+                    s2arc: x64
+                  - sdl2: ON
+                    s2ver: 2.26.0
+
+        steps:
+            - name: Clone
+              uses: actions/checkout@v1
+
+            - name: Add msbuild to PATH
+              uses: microsoft/setup-msbuild@v1
+
+            - name: Fetch SDL2 and set SDL2_DIR
+              if: matrix.sdl2 == 'ON'
+              run: |
+                C:/msys64/usr/bin/wget.exe -qO sdl2.zip https://github.com/libsdl-org/SDL/releases/download/release-${{ matrix.s2ver }}/SDL2-devel-${{ matrix.s2ver }}-VC.zip
+                7z x sdl2.zip
+                echo "SDL2_DIR=$env:GITHUB_WORKSPACE/SDL2-${{ matrix.s2ver }}/cmake" >> $env:GITHUB_ENV
+
+            - name: Configure
+              run: >
+                cmake -S . -B ./build -A ${{ matrix.arch }}
+                -DCMAKE_BUILD_TYPE=${{ matrix.build }}
+                -DWHISPER_SUPPORT_SDL2=${{ matrix.sdl2 }}
+
+            - name: Build
+              run: |
+                cd ./build
+                msbuild ALL_BUILD.vcxproj -t:build -p:configuration=${{ matrix.build }} -p:platform=${{ matrix.arch }}
+
+            - name: Copy SDL2.dll
+              if: matrix.sdl2 == 'ON'
+              run: copy "$env:SDL2_DIR/../lib/${{ matrix.s2arc }}/SDL2.dll" build/bin/${{ matrix.build }}
+
+            - name: Upload binaries
+              if: matrix.sdl2 == 'ON'
+              uses: actions/upload-artifact@v1
+              with:
+                name: whisper-bin-${{ matrix.arch }}
+                path: build/bin/${{ matrix.build }}
+
+    windows-blas:
+        runs-on: windows-latest
+
+        strategy:
+            matrix:
+                build: [Release]
+                arch: [Win32, x64]
+                blas: [ON]
+                sdl2: [ON]
+                include:
+                  - arch: Win32
+                    obzip: https://github.com/xianyi/OpenBLAS/releases/download/v0.3.21/OpenBLAS-0.3.21-x86.zip
+                    s2arc: x86
+                  - arch: x64
+                    obzip: https://github.com/xianyi/OpenBLAS/releases/download/v0.3.21/OpenBLAS-0.3.21-x64.zip
+                    s2arc: x64
+                  - sdl2: ON
+                    s2ver: 2.26.0
+
+        steps:
+            - name: Clone
+              uses: actions/checkout@v1
+
+            - name: Add msbuild to PATH
+              uses: microsoft/setup-msbuild@v1
+
+            - name: Fetch OpenBLAS
+              if: matrix.blas == 'ON'
+              run: |
+                C:/msys64/usr/bin/wget.exe -qO blas.zip ${{ matrix.obzip }}
+                7z x blas.zip -oblas -y
+                copy blas/include/cblas.h .
+                copy blas/include/openblas_config.h .
+                echo "blasdir=$env:GITHUB_WORKSPACE/blas" >> $env:GITHUB_ENV
+
+            - name: Fetch SDL2 and set SDL2_DIR
+              if: matrix.sdl2 == 'ON'
+              run: |
+                C:/msys64/usr/bin/wget.exe -qO sdl2.zip https://github.com/libsdl-org/SDL/releases/download/release-${{ matrix.s2ver }}/SDL2-devel-${{ matrix.s2ver }}-VC.zip
+                7z x sdl2.zip
+                echo "SDL2_DIR=$env:GITHUB_WORKSPACE/SDL2-${{ matrix.s2ver }}/cmake" >> $env:GITHUB_ENV
+
+            - name: Configure
+              run: >
+                cmake -S . -B ./build -A ${{ matrix.arch }}
+                -DCMAKE_BUILD_TYPE=${{ matrix.build }}
+                -DWHISPER_SUPPORT_OPENBLAS=${{ matrix.blas }}
+                -DCMAKE_LIBRARY_PATH="$env:blasdir/lib"
+                -DWHISPER_SUPPORT_SDL2=${{ matrix.sdl2 }}
+
+            - name: Build
+              run: |
+                cd ./build
+                msbuild ALL_BUILD.vcxproj -t:build -p:configuration=${{ matrix.build }} -p:platform=${{ matrix.arch }}
+
+            - name: Copy libopenblas.dll
+              if: matrix.blas == 'ON'
+              run: copy "$env:blasdir/bin/libopenblas.dll" build/bin/${{ matrix.build }}
+
+            - name: Copy SDL2.dll
+              if: matrix.sdl2 == 'ON'
+              run: copy "$env:SDL2_DIR/../lib/${{ matrix.s2arc }}/SDL2.dll" build/bin/${{ matrix.build }}
+
+            - name: Upload binaries
+              if: matrix.blas == 'ON' && matrix.sdl2 == 'ON'
+              uses: actions/upload-artifact@v1
+              with:
+                name: whisper-blas-bin-${{ matrix.arch }}
+                path: build/bin/${{ matrix.build }}
+
+    emscripten:
+        runs-on: ubuntu-latest
+
+        strategy:
+            matrix:
+                build: [Release]
+
+        steps:
+            - name: Clone
+              uses: actions/checkout@v1
+
+            - name: Dependencies
+              run: |
+                wget -q https://github.com/emscripten-core/emsdk/archive/master.tar.gz
+                tar -xvf master.tar.gz
+                emsdk-master/emsdk update
+                emsdk-master/emsdk install latest
+                emsdk-master/emsdk activate latest
+
+            - name: Configure
+              run: echo "tmp"
+
+            - name: Build
+              run: |
+                pushd emsdk-master
+                source ./emsdk_env.sh
+                popd
+                emcmake cmake . -DCMAKE_BUILD_TYPE=${{ matrix.build }}
+                make

.gitignore

@@ -1,7 +1,31 @@
-sync.sh
-main
-stream
 *.o
-.cache
+.cache/
+.vs/
+.vscode/
+.DS_Store
+
 build/
+build-em/
+build-debug/
+build-release/
+build-static/
+build-sanitize-addr/
+build-sanitize-thread/
+
+/main
+/stream
+/command
+/talk
+/bench
+
+sync.sh
+libwhisper.a
+libwhisper.so
 compile_commands.json
+
+examples/arm_neon.h
+examples/whisper.objc/whisper.objc.xcodeproj/xcshareddata
+examples/whisper.objc/whisper.objc.xcodeproj/xcuserdata/
+examples/whisper.objc/whisper.objc.xcodeproj/project.xcworkspace/xcuserdata
+
+extra/bench-gg.txt

.gitmodules

@@ -0,0 +1,3 @@
+[submodule "bindings/ios"]
+	path = bindings/ios
+	url = https://github.com/ggerganov/whisper.spm

CMakeLists.txt

@@ -1,44 +1,82 @@
 cmake_minimum_required (VERSION 3.0)
-project(whisper.cpp VERSION 1.0.0)
 
-set(CMAKE_EXPORT_COMPILE_COMMANDS "on")
+project(whisper.cpp VERSION 1.1.0)
+
+# Add path to modules
+list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/")
+
 set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
-set(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_PREFIX}/lib")
 
 if(CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)
     set(WHISPER_STANDALONE ON)
+    include(GitVars)
+    include(BuildTypes)
+
+    # configure project version
+    if (EXISTS "${CMAKE_SOURCE_DIR}/bindings/ios/Makefile-tmpl")
+        configure_file(${CMAKE_SOURCE_DIR}/bindings/ios/Makefile-tmpl ${CMAKE_SOURCE_DIR}/bindings/ios/Makefile @ONLY)
+    endif()
+    configure_file(${CMAKE_SOURCE_DIR}/bindings/javascript/package-tmpl.json ${CMAKE_SOURCE_DIR}/bindings/javascript/package.json @ONLY)
 else()
     set(WHISPER_STANDALONE OFF)
 endif()
 
+if (EMSCRIPTEN)
+    set(BUILD_SHARED_LIBS_DEFAULT OFF)
+
+    option(WHISPER_WASM_SINGLE_FILE "whisper: embed WASM inside the generated whisper.js" ON)
+else()
+    if (MINGW)
+        set(BUILD_SHARED_LIBS_DEFAULT OFF)
+    else()
+        set(BUILD_SHARED_LIBS_DEFAULT ON)
+    endif()
+endif()
+
 # options
 
-option(WHISPER_ALL_WARNINGS            "whisper: enable all compiler warnings" ON)
+option(BUILD_SHARED_LIBS               "whisper: build shared libs" ${BUILD_SHARED_LIBS_DEFAULT})
+
+option(WHISPER_ALL_WARNINGS            "whisper: enable all compiler warnings"                   ON)
 option(WHISPER_ALL_WARNINGS_3RD_PARTY  "whisper: enable all compiler warnings in 3rd party libs" OFF)
 
-option(WHISPER_SANITIZE_THREAD         "whisper: enable thread sanitizer" OFF)
-option(WHISPER_SANITIZE_ADDRESS        "whisper: enable address sanitizer" OFF)
+option(WHISPER_SANITIZE_THREAD         "whisper: enable thread sanitizer"    OFF)
+option(WHISPER_SANITIZE_ADDRESS        "whisper: enable address sanitizer"   OFF)
 option(WHISPER_SANITIZE_UNDEFINED      "whisper: enable undefined sanitizer" OFF)
 
-option(WHISPER_BUILD_TESTS             "whisper: build tests" ${WHISPER_STANDALONE})
+option(WHISPER_BUILD_TESTS             "whisper: build tests"    ${WHISPER_STANDALONE})
+option(WHISPER_BUILD_EXAMPLES          "whisper: build examples" ${WHISPER_STANDALONE})
 
 option(WHISPER_SUPPORT_SDL2            "whisper: support for libSDL2" OFF)
 
+if (APPLE)
+    option(WHISPER_NO_ACCELERATE       "whisper: disable Accelerate framework" OFF)
+    option(WHISPER_NO_AVX              "whisper: disable AVX" OFF)
+    option(WHISPER_NO_AVX2             "whisper: disable AVX2" OFF)
+    option(WHISPER_NO_FMA              "whisper: disable FMA" OFF)
+else()
+    option(WHISPER_SUPPORT_OPENBLAS    "whisper: support for OpenBLAS" OFF)
+endif()
+
+option(WHISPER_PERF                    "whisper: enable perf timings" OFF)
+
 # sanitizers
 
-if (WHISPER_SANITIZE_THREAD)
-    set(CMAKE_C_FLAGS   "${CMAKE_C_FLAGS} -fsanitize=thread")
-    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=thread")
-endif()
+if (NOT MSVC)
+    if (WHISPER_SANITIZE_THREAD)
+        set(CMAKE_C_FLAGS   "${CMAKE_C_FLAGS}   -fsanitize=thread")
+        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=thread")
+    endif()
 
-if (WHISPER_SANITIZE_ADDRESS)
-    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}     -fsanitize=address -fno-omit-frame-pointer")
-    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=address -fno-omit-frame-pointer")
-endif()
+    if (WHISPER_SANITIZE_ADDRESS)
+        set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}     -fsanitize=address -fno-omit-frame-pointer")
+        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=address -fno-omit-frame-pointer")
+    endif()
 
-if (WHISPER_SANITIZE_UNDEFINED)
-    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}     -fsanitize=undefined")
-    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=undefined")
+    if (WHISPER_SANITIZE_UNDEFINED)
+        set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}     -fsanitize=undefined")
+        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=undefined")
+    endif()
 endif()
 
 #set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -ffast-math")
@@ -46,19 +84,33 @@ endif()
 
 # dependencies
 
-set(CMAKE_C_STANDARD   11)
-set(CMAKE_CXX_STANDARD 11)
-
 find_package(Threads REQUIRED)
 
-if (WHISPER_SUPPORT_SDL2)
-    # SDL2
-    find_package(SDL2 REQUIRED)
+# on APPLE - include Accelerate framework
+if (APPLE AND NOT WHISPER_NO_ACCELERATE)
+    find_library(ACCELERATE_FRAMEWORK Accelerate)
+    if (ACCELERATE_FRAMEWORK)
+        message(STATUS "Accelerate framework found")
 
-    string(STRIP "${SDL2_LIBRARIES}" SDL2_LIBRARIES)
+        set(WHISPER_EXTRA_LIBS  ${WHISPER_EXTRA_LIBS}  ${ACCELERATE_FRAMEWORK})
+        set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DGGML_USE_ACCELERATE)
+    else()
+        message(WARNING "Accelerate framework not found")
+    endif()
+endif()
 
-    message(STATUS "SDL2_INCLUDE_DIRS = ${SDL2_INCLUDE_DIRS}")
-    message(STATUS "SDL2_LIBRARIES = ${SDL2_LIBRARIES}")
+if (WHISPER_SUPPORT_OPENBLAS)
+    find_library(OPENBLAS_LIB
+        NAMES openblas libopenblas
+        )
+    if (OPENBLAS_LIB)
+        message(STATUS "OpenBLAS found")
+
+        set(WHISPER_EXTRA_LIBS  ${WHISPER_EXTRA_LIBS}  ${OPENBLAS_LIB})
+        set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DGGML_USE_OPENBLAS)
+    else()
+        message(WARNING "OpenBLAS not found")
+    endif()
 endif()
 
 # compiler flags
@@ -69,7 +121,7 @@ if (NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
 endif ()
 
 if (WHISPER_ALL_WARNINGS)
-    if (CMAKE_COMPILER_IS_GNUCC OR CMAKE_C_COMPILER_ID MATCHES "Clang")
+    if (NOT MSVC)
         set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} \
             -Wall                           \
             -Wextra                         \
@@ -78,14 +130,23 @@ if (WHISPER_ALL_WARNINGS)
             -Wcast-qual                     \
             -Wstrict-prototypes             \
             -Wpointer-arith                 \
+            -Wno-unused-function            \
+        ")
+        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} \
+            -Wall                           \
+            -Wextra                         \
+            -Wpedantic                      \
+            -Wcast-qual                     \
         ")
     else()
-        # todo : windows
+        # todo : msvc
     endif()
 endif()
 
-set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Werror=vla")
-#set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fno-math-errno -ffinite-math-only -funsafe-math-optimizations")
+if (NOT MSVC)
+    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Werror=vla")
+    #set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fno-math-errno -ffinite-math-only -funsafe-math-optimizations")
+endif()
 
 message(STATUS "CMAKE_SYSTEM_PROCESSOR: ${CMAKE_SYSTEM_PROCESSOR}")
 
@@ -93,23 +154,59 @@ if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm" OR ${CMAKE_SYSTEM_PROCESSOR} MATCHES
     message(STATUS "ARM detected")
 else()
     message(STATUS "x86 detected")
-    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mavx -mavx2 -mfma -mf16c")
+    if (MSVC)
+        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /arch:AVX2")
+        set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /arch:AVX2")
+        set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /arch:AVX2")
+    else()
+        if (EMSCRIPTEN)
+            set(CMAKE_C_FLAGS   "${CMAKE_C_FLAGS}   -pthread")
+            set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread")
+        else()
+            if(NOT WHISPER_NO_AVX)
+                set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mavx")
+            endif()
+            if(NOT WHISPER_NO_AVX2)
+                set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mavx2")
+            endif()
+            if(NOT WHISPER_NO_FMA)
+                set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mfma")
+            endif()
+            set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mf16c")
+        endif()
+    endif()
 endif()
 
+if (WHISPER_PERF)
+    set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DGGML_PERF)
+endif()
+
+#
 # whisper - this is the main library of the project
+#
 
 set(TARGET whisper)
 
 add_library(${TARGET}
+    ggml.h
     ggml.c
+    whisper.h
     whisper.cpp
     )
 
+include(DefaultTargetOptions)
+
 target_include_directories(${TARGET} PUBLIC
     .
     )
 
-target_link_libraries(${TARGET} PRIVATE ${CMAKE_THREAD_LIBS_INIT})
+if (MSVC)
+    target_link_libraries(${TARGET} PRIVATE ${WHISPER_EXTRA_LIBS} ${CMAKE_THREAD_LIBS_INIT})
+
+    set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -D_CRT_SECURE_NO_WARNINGS)
+else()
+    target_link_libraries(${TARGET} PRIVATE m ${WHISPER_EXTRA_LIBS} ${CMAKE_THREAD_LIBS_INIT})
+endif()
 
 if (BUILD_SHARED_LIBS)
     target_link_libraries(${TARGET} PUBLIC
@@ -121,6 +218,10 @@ if (BUILD_SHARED_LIBS)
         )
 endif()
 
+if (EMSCRIPTEN)
+    set_target_properties(${TARGET} PROPERTIES COMPILE_FLAGS "-msimd128")
+endif()
+
 target_compile_definitions(${TARGET} PUBLIC
     ${WHISPER_EXTRA_FLAGS}
     )
@@ -128,26 +229,24 @@ target_compile_definitions(${TARGET} PUBLIC
 install(TARGETS ${TARGET}
     LIBRARY DESTINATION lib
     ARCHIVE DESTINATION lib/static
+    RUNTIME DESTINATION bin
     )
 
+#
+# bindings
+#
+
+add_subdirectory(bindings)
+
+#
 # programs, examples and tests
+#
 
-if (WHISPER_STANDALONE)
-    # main
-    set(TARGET main)
-    add_executable(${TARGET} main.cpp)
-    target_link_libraries(${TARGET} PRIVATE whisper ${CMAKE_THREAD_LIBS_INIT})
-
-    if (WHISPER_SUPPORT_SDL2)
-        # stream
-        set(TARGET stream)
-        add_executable(${TARGET} stream.cpp)
-        target_include_directories(${TARGET} PRIVATE ${SDL2_INCLUDE_DIRS})
-        target_link_libraries(${TARGET} PRIVATE whisper ${SDL2_LIBRARIES} ${CMAKE_THREAD_LIBS_INIT})
-    endif ()
-
-    if (WHISPER_BUILD_TESTS)
-        enable_testing()
-        add_subdirectory(tests)
-    endif ()
+if (WHISPER_BUILD_TESTS)
+    enable_testing()
+    add_subdirectory(tests)
 endif ()
+
+if (WHISPER_BUILD_EXAMPLES)
+    add_subdirectory(examples)
+endif()

Makefile

@@ -1,16 +1,38 @@
+ifndef UNAME_S
 UNAME_S := $(shell uname -s)
+endif
+
+ifndef UNAME_P
 UNAME_P := $(shell uname -p)
+endif
+
+ifndef UNAME_M
 UNAME_M := $(shell uname -m)
+endif
+
+CCV := $(shell $(CC) --version | head -n 1)
+CXXV := $(shell $(CXX) --version | head -n 1)
+
+# Mac OS + Arm can report x86_64
+# ref: https://github.com/ggerganov/whisper.cpp/issues/66#issuecomment-1282546789
+ifeq ($(UNAME_S),Darwin)
+	ifneq ($(UNAME_P),arm)
+		SYSCTL_M := $(shell sysctl -n hw.optional.arm64)
+		ifeq ($(SYSCTL_M),1)
+			# UNAME_P := arm
+			# UNAME_M := arm64
+			warn := $(warning Your arch is announced as x86_64, but it seems to actually be ARM64. Not fixing that can lead to bad performance. For more info see: https://github.com/ggerganov/whisper.cpp/issues/66\#issuecomment-1282546789)
+		endif
+	endif
+endif
 
 #
 # Compile flags
 #
 
-CFLAGS   = -O3 -std=c11
-CXXFLAGS = -O3 -std=c++11
-
-CFLAGS   += -Wall -Wextra -Wno-unused-parameter -Wno-unused-function
-CXXFLAGS += -Wall -Wextra -Wno-unused-parameter -Wno-unused-function
+CFLAGS   = -I.              -O3 -std=c11   -fPIC
+CXXFLAGS = -I. -I./examples -O3 -std=c++11 -fPIC
+LDFLAGS  =
 
 # OS specific
 # TODO: support Windows
@@ -22,17 +44,101 @@ ifeq ($(UNAME_S),Darwin)
 	CFLAGS   += -pthread
 	CXXFLAGS += -pthread
 endif
+ifeq ($(UNAME_S),FreeBSD)
+	CFLAGS   += -pthread
+	CXXFLAGS += -pthread
+endif
+ifeq ($(UNAME_S),Haiku)
+	CFLAGS   += -pthread
+	CXXFLAGS += -pthread
+endif
 
 # Architecture specific
-ifeq ($(UNAME_P),x86_64)
+# TODO: probably these flags need to be tweaked on some architectures
+#       feel free to update the Makefile for your architecture and send a pull request or issue
+ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686))
+	ifeq ($(UNAME_S),Darwin)
+		CFLAGS += -mf16c
+		AVX1_M := $(shell sysctl machdep.cpu.features)
+		ifneq (,$(findstring FMA,$(AVX1_M)))
+			CFLAGS += -mfma
+		endif
+		ifneq (,$(findstring AVX1.0,$(AVX1_M)))
+			CFLAGS += -mavx
+		endif
+		AVX2_M := $(shell sysctl machdep.cpu.leaf7_features)
+		ifneq (,$(findstring AVX2,$(AVX2_M)))
+			CFLAGS += -mavx2
+		endif
+	else ifeq ($(UNAME_S),Linux)
+		AVX1_M := $(shell grep "avx " /proc/cpuinfo)
+		ifneq (,$(findstring avx,$(AVX1_M)))
+			CFLAGS += -mavx
+		endif
+		AVX2_M := $(shell grep "avx2 " /proc/cpuinfo)
+		ifneq (,$(findstring avx2,$(AVX2_M)))
+			CFLAGS += -mavx2
+		endif
+		FMA_M := $(shell grep "fma " /proc/cpuinfo)
+		ifneq (,$(findstring fma,$(FMA_M)))
+			CFLAGS += -mfma
+		endif
+		F16C_M := $(shell grep "f16c " /proc/cpuinfo)
+		ifneq (,$(findstring f16c,$(F16C_M)))
+			CFLAGS += -mf16c
+		endif
+		SSE3_M := $(shell grep "sse3 " /proc/cpuinfo)
+		ifneq (,$(findstring sse3,$(SSE3_M)))
+			CFLAGS += -msse3
+		endif
+	else ifeq ($(UNAME_S),Haiku)
+		AVX1_M := $(shell sysinfo -cpu | grep "AVX ")
+		ifneq (,$(findstring avx,$(AVX1_M)))
+			CFLAGS += -mavx
+		endif
+		AVX2_M := $(shell sysinfo -cpu | grep "AVX2 ")
+		ifneq (,$(findstring avx2,$(AVX2_M)))
+			CFLAGS += -mavx2
+		endif
+		FMA_M := $(shell sysinfo -cpu | grep "FMA ")
+		ifneq (,$(findstring fma,$(FMA_M)))
+			CFLAGS += -mfma
+		endif
+		F16C_M := $(shell sysinfo -cpu | grep "F16C ")
+		ifneq (,$(findstring f16c,$(F16C_M)))
+			CFLAGS += -mf16c
+		endif
+	else
+		CFLAGS += -mfma -mf16c -mavx -mavx2
+	endif
+endif
+ifeq ($(UNAME_M),amd64)
 	CFLAGS += -mavx -mavx2 -mfma -mf16c
 endif
-ifneq ($(filter arm%,$(UNAME_P)),)
-	# Mac M1
-endif
-ifneq ($(filter aarch64%,$(UNAME_P)),)
+ifeq ($(UNAME_M),ppc64le)
+	POWER9_M := $(shell grep "POWER9" /proc/cpuinfo)
+	ifneq (,$(findstring POWER9,$(POWER9_M)))
+		CFLAGS += -mpower9-vector
 	endif
-	ifneq ($(filter armv6%,$(UNAME_M)),)
+endif
+ifndef WHISPER_NO_ACCELERATE
+	# Mac M1 - include Accelerate framework
+	ifeq ($(UNAME_S),Darwin)
+		CFLAGS  += -DGGML_USE_ACCELERATE
+		LDFLAGS += -framework Accelerate
+	endif
+endif
+ifdef WHISPER_OPENBLAS
+	CFLAGS  += -DGGML_USE_OPENBLAS -I/usr/local/include/openblas
+	LDFLAGS += -lopenblas
+endif
+ifdef WHISPER_GPROF
+	CFLAGS  += -pg
+	CXXFLAGS  += -pg
+endif
+ifneq ($(filter aarch64%,$(UNAME_M)),)
+endif
+ifneq ($(filter armv6%,$(UNAME_M)),)
 	# Raspberry Pi 1, 2, 3
 	CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access
 endif
@@ -46,21 +152,40 @@ ifneq ($(filter armv8%,$(UNAME_M)),)
 endif
 
 #
-# Build library + main
+# Print build information
 #
 
-main: main.cpp ggml.o whisper.o
-	$(CXX) $(CXXFLAGS) main.cpp whisper.o ggml.o -o main
-	./main -h
+$(info I whisper.cpp build info: )
+$(info I UNAME_S:  $(UNAME_S))
+$(info I UNAME_P:  $(UNAME_P))
+$(info I UNAME_M:  $(UNAME_M))
+$(info I CFLAGS:   $(CFLAGS))
+$(info I CXXFLAGS: $(CXXFLAGS))
+$(info I LDFLAGS:  $(LDFLAGS))
+$(info I CC:       $(CCV))
+$(info I CXX:      $(CXXV))
+$(info )
+
+default: main
+
+#
+# Build library
+#
 
 ggml.o: ggml.c ggml.h
-	$(CC)  $(CFLAGS)   -c ggml.c
+	$(CC)  $(CFLAGS)   -c ggml.c -o ggml.o
 
 whisper.o: whisper.cpp whisper.h
-	$(CXX) $(CXXFLAGS) -c whisper.cpp
+	$(CXX) $(CXXFLAGS) -c whisper.cpp -o whisper.o
+
+libwhisper.a: ggml.o whisper.o
+	$(AR) rcs libwhisper.a ggml.o whisper.o
+
+libwhisper.so: ggml.o whisper.o
+	$(CXX) $(CXXFLAGS) -shared -o libwhisper.so ggml.o whisper.o $(LDFLAGS)
 
 clean:
-	rm -f *.o main
+	rm -f *.o main stream command talk bench libwhisper.a libwhisper.so
 
 #
 # Examples
@@ -68,8 +193,21 @@ clean:
 
 CC_SDL=`sdl2-config --cflags --libs`
 
-stream: stream.cpp ggml.o whisper.o
-	$(CXX) $(CXXFLAGS) stream.cpp ggml.o whisper.o -o stream $(CC_SDL)
+main: examples/main/main.cpp ggml.o whisper.o
+	$(CXX) $(CXXFLAGS) examples/main/main.cpp ggml.o whisper.o -o main $(LDFLAGS)
+	./main -h
+
+stream: examples/stream/stream.cpp ggml.o whisper.o
+	$(CXX) $(CXXFLAGS) examples/stream/stream.cpp ggml.o whisper.o -o stream $(CC_SDL) $(LDFLAGS)
+
+command: examples/command/command.cpp ggml.o whisper.o
+	$(CXX) $(CXXFLAGS) examples/command/command.cpp ggml.o whisper.o -o command $(CC_SDL) $(LDFLAGS)
+
+talk: examples/talk/talk.cpp  examples/talk/gpt-2.cpp ggml.o whisper.o
+	$(CXX) $(CXXFLAGS) examples/talk/talk.cpp examples/talk/gpt-2.cpp ggml.o whisper.o -o talk $(CC_SDL) $(LDFLAGS)
+
+bench: examples/bench/bench.cpp ggml.o whisper.o
+	$(CXX) $(CXXFLAGS) examples/bench/bench.cpp ggml.o whisper.o -o bench $(LDFLAGS)
 
 #
 # Audio samples
@@ -106,10 +244,11 @@ samples:
 .PHONY: small
 .PHONY: medium.en
 .PHONY: medium
+.PHONY: large-v1
 .PHONY: large
 
-tiny.en tiny base.en base small.en small medium.en medium large: main
-	bash ./download-ggml-model.sh $@
+tiny.en tiny base.en base small.en small medium.en medium large-v1 large: main
+	bash ./models/download-ggml-model.sh $@
 	@echo ""
 	@echo "==============================================="
 	@echo "Running $@ on all samples in ./samples ..."
@@ -117,9 +256,17 @@ tiny.en tiny base.en base small.en small medium.en medium large: main
 	@echo ""
 	@for f in samples/*.wav; do \
 		echo "----------------------------------------------" ; \
-		echo "[+] Running base.en on $$f ... (run 'ffplay $$f' to listen)" ; \
+		echo "[+] Running $@ on $$f ... (run 'ffplay $$f' to listen)" ; \
 	    echo "----------------------------------------------" ; \
 		echo "" ; \
 		./main -m models/ggml-$@.bin -f $$f ; \
 		echo "" ; \
 	done
+
+#
+# Tests
+#
+
+.PHONY: tests
+tests:
+	bash ./tests/run-tests.sh

README.md

@@ -2,30 +2,76 @@
 
 [![Actions Status](https://github.com/ggerganov/whisper.cpp/workflows/CI/badge.svg)](https://github.com/ggerganov/whisper.cpp/actions)
 [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
+[![npm](https://img.shields.io/npm/v/whisper.cpp.svg)](https://www.npmjs.com/package/whisper.cpp/)
+
+Stable: [1.0.4](https://github.com/ggerganov/whisper.cpp/releases/tag/1.0.4) / Beta: [1.1.0](https://github.com/ggerganov/whisper.cpp/releases/tag/1.1.0) / [Roadmap | F.A.Q.](https://github.com/ggerganov/whisper.cpp/discussions/126)
 
 High-performance inference of [OpenAI's Whisper](https://github.com/openai/whisper) automatic speech recognition (ASR) model:
 
 - Plain C/C++ implementation without dependencies
-- ARM_NEON and AVX intrinsics support
+- Apple silicon first-class citizen - optimized via Arm Neon and Accelerate framework
+- AVX intrinsics support for x86 architectures
+- VSX intrinsics support for POWER architectures
 - Mixed F16 / F32 precision
 - Low memory usage (Flash Attention + Flash Forward)
 - Zero memory allocations at runtime
 - Runs on the CPU
 - [C-style API](https://github.com/ggerganov/whisper.cpp/blob/master/whisper.h)
-- Supported platforms: Linux, Mac OS (Intel and Arm), Raspberry Pi, Android
 
-## Usage
+Supported platforms:
 
-To build the main program, run `make`. You can then transcribe a `.wav` file like this:
+- [x] Mac OS (Intel and Arm)
+- [x] [iOS](examples/whisper.objc)
+- [x] [Android](examples/whisper.android)
+- [x] Linux / [FreeBSD](https://github.com/ggerganov/whisper.cpp/issues/56#issuecomment-1350920264)
+- [x] [WebAssembly](examples/whisper.wasm)
+- [x] Windows ([MSVC](https://github.com/ggerganov/whisper.cpp/blob/master/.github/workflows/build.yml#L117-L144) and [MinGW](https://github.com/ggerganov/whisper.cpp/issues/168)]
+- [x] [Raspberry Pi](https://github.com/ggerganov/whisper.cpp/discussions/166)
+
+The entire implementation of the model is contained in 2 source files:
+
+- Tensor operations: [ggml.h](ggml.h) / [ggml.c](ggml.c)
+- Transformer inference: [whisper.h](whisper.h) / [whisper.cpp](whisper.cpp)
+
+Having such a lightweight implementation of the model allows to easily integrate it in different platforms and applications.
+As an example, here is a video of running the model on an iPhone 13 device - fully offline, on-device: [whisper.objc](examples/whisper.objc)
+
+https://user-images.githubusercontent.com/1991296/197385372-962a6dea-bca1-4d50-bf96-1d8c27b98c81.mp4
+
+You can also easily make your own offline voice assistant application: [command](examples/command)
+
+https://user-images.githubusercontent.com/1991296/204038393-2f846eae-c255-4099-a76d-5735c25c49da.mp4
+
+Or you can even run it straight in the browser: [talk.wasm](examples/talk.wasm)
+
+## Implementation details
+
+- The core tensor operations are implemented in C ([ggml.h](ggml.h) / [ggml.c](ggml.c))
+- The transformer model and the high-level C-style API are implemented in C++ ([whisper.h](whisper.h) / [whisper.cpp](whisper.cpp))
+- Sample usage is demonstrated in [main.cpp](examples/main)
+- Sample real-time audio transcription from the microphone is demonstrated in [stream.cpp](examples/stream)
+- Various other examples are available in the [examples](examples) folder
+
+The tensor operators are optimized heavily for Apple silicon CPUs. Depending on the computation size, Arm Neon SIMD
+instrisics or CBLAS Accelerate framework routines are used. The latter are especially effective for bigger sizes since
+the Accelerate framework utilizes the special-purpose AMX coprocessor available in modern Apple products.
+
+## Quick start
+
+First, download one of the Whisper models converted in [ggml format](models). For example:
 
 ```bash
-$ ./main -f input.wav
+bash ./models/download-ggml-model.sh base.en
 ```
 
-Before running the program, make sure to download one of the ggml Whisper models. For example:
+Now build the [main](examples/main) example and transcribe an audio file like this:
 
 ```bash
-bash ./download-ggml-model.sh base.en
+# build the main example
+make
+
+# transcribe an audio file
+./main -f samples/jfk.wav
 ```
 
 ---
@@ -34,28 +80,49 @@ For a quick demo, simply run `make base.en`:
 
 ```java
 $ make base.en
-cc  -O3 -std=c11 -Wall -Wextra -Wno-unused-parameter -Wno-unused-function -pthread   -c ggml.c
-c++ -O3 -std=c++11 -Wall -Wextra -Wno-unused-parameter -Wno-unused-function -pthread -c whisper.cpp
-c++ -O3 -std=c++11 -Wall -Wextra -Wno-unused-parameter -Wno-unused-function -pthread main.cpp whisper.o ggml.o -o main
+
+cc  -I.              -O3 -std=c11   -pthread -DGGML_USE_ACCELERATE   -c ggml.c -o ggml.o
+c++ -I. -I./examples -O3 -std=c++11 -pthread -c whisper.cpp -o whisper.o
+c++ -I. -I./examples -O3 -std=c++11 -pthread examples/main/main.cpp whisper.o ggml.o -o main  -framework Accelerate
 ./main -h
 
 usage: ./main [options] file0.wav file1.wav ...
 
 options:
-  -h,       --help           show this help message and exit
-  -s SEED,  --seed SEED      RNG seed (default: -1)
-  -t N,     --threads N      number of threads to use during computation (default: 4)
-  -v,       --verbose        verbose output
-            --translate      translate from source language to english
-  -ps,      --print_special  print special tokens
-  -nt,      --no_timestamps  do not print timestamps
-  -l LANG,  --language LANG  spoken language (default: en)
-  -m FNAME, --model FNAME    model path (default: models/ggml-base.en.bin)
-  -f FNAME, --file FNAME     input WAV file path
+  -h,       --help            [default] show this help message and exit
+  -t N,     --threads N       [4      ] number of threads to use during computation
+  -p N,     --processors N    [1      ] number of processors to use during computation
+  -ot N,    --offset-t N      [0      ] time offset in milliseconds
+  -on N,    --offset-n N      [0      ] segment index offset
+  -d  N,    --duration N      [0      ] duration of audio to process in milliseconds
+  -mc N,    --max-context N   [-1     ] maximum number of text context tokens to store
+  -ml N,    --max-len N       [0      ] maximum segment length in characters
+  -bo N,    --best-of N       [5      ] number of best candidates to keep
+  -bs N,    --beam-size N     [-1     ] beam size for beam search
+  -wt N,    --word-thold N    [0.01   ] word timestamp probability threshold
+  -et N,    --entropy-thold N [2.40   ] entropy threshold for decoder fail
+  -lpt N,   --logprob-thold N [-1.00  ] log probability threshold for decoder fail
+  -su,      --speed-up        [false  ] speed up audio by x2 (reduced accuracy)
+  -tr,      --translate       [false  ] translate from source language to english
+  -di,      --diarize         [false  ] stereo audio diarization
+  -otxt,    --output-txt      [false  ] output result in a text file
+  -ovtt,    --output-vtt      [false  ] output result in a vtt file
+  -osrt,    --output-srt      [false  ] output result in a srt file
+  -owts,    --output-words    [false  ] output script for generating karaoke video
+  -ocsv,    --output-csv      [false  ] output result in a CSV file
+  -ps,      --print-special   [false  ] print special tokens
+  -pc,      --print-colors    [false  ] print colors
+  -pp,      --print-progress  [false  ] print progress
+  -nt,      --no-timestamps   [true   ] do not print timestamps
+  -l LANG,  --language LANG   [en     ] spoken language ('auto' for auto-detect)
+            --prompt PROMPT   [       ] initial prompt
+  -m FNAME, --model FNAME     [models/ggml-base.en.bin] model path
+  -f FNAME, --file FNAME      [       ] input WAV file path
 
-bash ./download-ggml-model.sh base.en
+
+bash ./models/download-ggml-model.sh base.en
 Downloading ggml model base.en ...
-models/ggml-base.en.bin            100%[===================================>] 141.11M  6.49MB/s    in 23s
+ggml-base.en.bin               100%[========================>] 141.11M  6.34MB/s    in 24s
 Done! Model 'base.en' saved in 'models/ggml-base.en.bin'
 You can now use it like this:
 
@@ -83,30 +150,33 @@ whisper_model_load: n_text_layer  = 6
 whisper_model_load: n_mels        = 80
 whisper_model_load: f16           = 1
 whisper_model_load: type          = 2
-whisper_model_load: mem_required  = 377.00 MB
 whisper_model_load: adding 1607 extra tokens
-whisper_model_load: ggml ctx size = 163.43 MB
-whisper_model_load: memory size =    22.83 MB
-whisper_model_load: model size  =   140.54 MB
+whisper_model_load: mem_required  =  506.00 MB
+whisper_model_load: ggml ctx size =  140.60 MB
+whisper_model_load: memory size   =   22.83 MB
+whisper_model_load: model size    =  140.54 MB
 
-main: processing 'samples/jfk.wav' (176000 samples, 11.0 sec), 4 threads, lang = en, task = transcribe, timestamps = 1 ...
+system_info: n_threads = 4 / 10 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | NEON = 1 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 |
 
-[00:00.000 --> 00:11.000]   And so my fellow Americans, ask not what your country can do for you, ask what you can do for your country.
+main: processing 'samples/jfk.wav' (176000 samples, 11.0 sec), 4 threads, 1 processors, lang = en, task = transcribe, timestamps = 1 ...
 
 
-whisper_print_timings:     load time =    77.48 ms
-whisper_print_timings:      mel time =    26.10 ms
-whisper_print_timings:   sample time =     2.19 ms
-whisper_print_timings:   encode time =   632.95 ms / 105.49 ms per layer
-whisper_print_timings:   decode time =    85.11 ms / 14.18 ms per layer
-whisper_print_timings:    total time =   824.14 ms
+[00:00:00.000 --> 00:00:11.000]   And so my fellow Americans, ask not what your country can do for you, ask what you can do for your country.
+
+
+whisper_print_timings:     load time =   105.91 ms
+whisper_print_timings:      mel time =    24.62 ms
+whisper_print_timings:   sample time =     3.63 ms
+whisper_print_timings:   encode time =   324.71 ms / 54.12 ms per layer
+whisper_print_timings:   decode time =    83.58 ms / 13.93 ms per layer
+whisper_print_timings:    total time =   542.81 ms
 ```
 
 The command downloads the `base.en` model converted to custom `ggml` format and runs the inference on all `.wav` samples in the folder `samples`.
 
 For detailed usage instructions, run: `./main -h`
 
-Note that `whisper.cpp` currently runs only with 16-bit WAV files, so make sure to convert your input before running the tool.
+Note that the [main](examples/main) example currently runs only with 16-bit WAV files, so make sure to convert your input before running the tool.
 For example, you can use `ffmpeg` like this:
 
 ```java
@@ -134,13 +204,32 @@ make small.en
 make small
 make medium.en
 make medium
+make large-v1
 make large
 ```
 
+## Memory usage
+
+| Model  | Disk   | Mem     | SHA                                        |
+| ---    | ---    | ---     | ---                                        |
+| tiny   |  75 MB | ~390 MB | `bd577a113a864445d4c299885e0cb97d4ba92b5f` |
+| base   | 142 MB | ~500 MB | `465707469ff3a37a2b9b8d8f89f2f99de7299dac` |
+| small  | 466 MB | ~1.0 GB | `55356645c2b361a969dfd0ef2c5a50d530afd8d5` |
+| medium | 1.5 GB | ~2.6 GB | `fd9727b6e1217c2f614f9b698455c4ffd82463b4` |
+| large  | 2.9 GB | ~4.7 GB | `0f4c8e34f21cf1a914c59d8b3ce882345ad349d6` |
+
+## Limitations
+
+- Inference only
+- No GPU support (yet)
+
 ## Another example
 
 Here is another example of transcribing a [3:24 min speech](https://upload.wikimedia.org/wikipedia/commons/1/1f/George_W_Bush_Columbia_FINAL.ogg)
-in less than a minute on a MacBook M1 Pro, using `medium.en` model:
+in about half a minute on a MacBook M1 Pro, using `medium.en` model:
+
+<details>
+  <summary>Expand to see the result</summary>
 
 ```java
 $ ./main -m models/ggml-medium.en.bin -f samples/gb1.wav -t 8
@@ -158,86 +247,188 @@ whisper_model_load: n_text_layer  = 24
 whisper_model_load: n_mels        = 80
 whisper_model_load: f16           = 1
 whisper_model_load: type          = 4
-whisper_model_load: mem_required  = 2502.00 MB
+whisper_model_load: mem_required  = 2610.00 MB
 whisper_model_load: adding 1607 extra tokens
 whisper_model_load: ggml ctx size = 1644.97 MB
 whisper_model_load: memory size =   182.62 MB
 whisper_model_load: model size  =  1462.12 MB
-log_mel_spectrogram: n_sample = 3179750, n_len = 19873
-log_mel_spectrogram: recording length: 198.734375 s
 
-main: processing 3179750 samples (198.7 sec), 8 threads, lang = english, task = transcribe, timestamps = 1 ...
+main: processing 'samples/gb1.wav' (3179750 samples, 198.7 sec), 8 threads, lang = en, task = transcribe, timestamps = 1 ...
 
 [00:00.000 --> 00:08.000]   My fellow Americans, this day has brought terrible news and great sadness to our country.
-[00:08.000 --> 00:17.000]   At 9 o'clock this morning, Mission Control in Houston lost contact with our Space Shuttle Columbia.
-[00:17.000 --> 00:24.000]   A short time later, debris was seen falling from the skies above Texas.
-[00:24.000 --> 00:29.000]   The Columbia's lost. There are no survivors.
+[00:08.000 --> 00:17.000]   At nine o'clock this morning, Mission Control in Houston lost contact with our Space Shuttle Columbia.
+[00:17.000 --> 00:23.000]   A short time later, debris was seen falling from the skies above Texas.
+[00:23.000 --> 00:29.000]   The Columbia's lost. There are no survivors.
 [00:29.000 --> 00:32.000]   On board was a crew of seven.
-[00:32.000 --> 00:43.000]   Colonel Rick Husband, Lieutenant Colonel Michael Anderson, Commander Laurel Clark, Captain David Brown, Commander William McCool,
-[00:43.000 --> 00:52.000]   Dr. Kultner Aschavla, and Elon Ramon, a Colonel in the Israeli Air Force.
+[00:32.000 --> 00:39.000]   Colonel Rick Husband, Lieutenant Colonel Michael Anderson, Commander Laurel Clark,
+[00:39.000 --> 00:48.000]   Captain David Brown, Commander William McCool, Dr. Kultna Shavla, and Ilan Ramon,
+[00:48.000 --> 00:52.000]   a colonel in the Israeli Air Force.
 [00:52.000 --> 00:58.000]   These men and women assumed great risk in the service to all humanity.
-[00:58.000 --> 01:06.000]   In an age when space flight has come to seem almost routine, it is easy to overlook the dangers of travel by rocket
-[01:06.000 --> 01:12.000]   and the difficulties of navigating the fierce outer atmosphere of the Earth.
-[01:12.000 --> 01:22.000]   These astronauts knew the dangers, and they faced them willingly, knowing they had a high and noble purpose in life.
-[01:22.000 --> 01:30.000]   Because of their courage, endearing, and idealism, we will miss them all the more.
-[01:30.000 --> 01:40.000]   All Americans today are thinking as well of the families of these men and women who have been given this sudden shock and grief.
-[01:40.000 --> 01:45.000]   You're not alone. Our entire nation agrees with you.
-[01:45.000 --> 01:52.000]   And those you love will always have the respect and gratitude of this country.
+[00:58.000 --> 01:03.000]   In an age when space flight has come to seem almost routine,
+[01:03.000 --> 01:07.000]   it is easy to overlook the dangers of travel by rocket
+[01:07.000 --> 01:12.000]   and the difficulties of navigating the fierce outer atmosphere of the Earth.
+[01:12.000 --> 01:18.000]   These astronauts knew the dangers, and they faced them willingly,
+[01:18.000 --> 01:23.000]   knowing they had a high and noble purpose in life.
+[01:23.000 --> 01:31.000]   Because of their courage and daring and idealism, we will miss them all the more.
+[01:31.000 --> 01:36.000]   All Americans today are thinking as well of the families of these men and women
+[01:36.000 --> 01:40.000]   who have been given this sudden shock and grief.
+[01:40.000 --> 01:45.000]   You're not alone. Our entire nation grieves with you,
+[01:45.000 --> 01:52.000]   and those you love will always have the respect and gratitude of this country.
 [01:52.000 --> 01:56.000]   The cause in which they died will continue.
-[01:56.000 --> 02:07.000]   Mankind is led into the darkness beyond our world by the inspiration of discovery and the longing to understand.
-[02:07.000 --> 02:11.000]   Our journey into space will go on.
+[01:56.000 --> 02:04.000]   Mankind is led into the darkness beyond our world by the inspiration of discovery
+[02:04.000 --> 02:11.000]   and the longing to understand. Our journey into space will go on.
 [02:11.000 --> 02:16.000]   In the skies today, we saw destruction and tragedy.
 [02:16.000 --> 02:22.000]   Yet farther than we can see, there is comfort and hope.
-[02:22.000 --> 02:31.000]   In the words of the prophet Isaiah, "Lift your eyes and look to the heavens who created all these.
-[02:31.000 --> 02:39.000]   He who brings out the starry hosts one by one and calls them each by name."
-[02:39.000 --> 02:46.000]   Because of his great power and mighty strength, not one of them is missing.
-[02:46.000 --> 02:55.000]   The same creator who names the stars also knows the names of the seven souls we mourn today.
-[02:55.000 --> 03:05.000]   The crew of the shuttle Columbia did not return safely to Earth, yet we can pray that all are safely home.
-[03:05.000 --> 03:14.000]   May God bless the grieving families and may God continue to bless America.
-[03:14.000 --> 03:24.000]   [Music]
+[02:22.000 --> 02:29.000]   In the words of the prophet Isaiah, "Lift your eyes and look to the heavens
+[02:29.000 --> 02:35.000]   who created all these. He who brings out the starry hosts one by one
+[02:35.000 --> 02:39.000]   and calls them each by name."
+[02:39.000 --> 02:46.000]   Because of His great power and mighty strength, not one of them is missing.
+[02:46.000 --> 02:55.000]   The same Creator who names the stars also knows the names of the seven souls we mourn today.
+[02:55.000 --> 03:01.000]   The crew of the shuttle Columbia did not return safely to earth,
+[03:01.000 --> 03:05.000]   yet we can pray that all are safely home.
+[03:05.000 --> 03:13.000]   May God bless the grieving families, and may God continue to bless America.
+[03:13.000 --> 03:41.000]   Audio
 
 
-main:     load time =   522.18 ms
-main:      mel time =   423.43 ms
-main:   sample time =    31.42 ms
-main:   encode time = 41518.51 ms / 1729.94 ms per layer
-main:   decode time = 14907.22 ms
-main:    total time = 57416.63 ms
+whisper_print_timings:     load time =   575.92 ms
+whisper_print_timings:      mel time =   230.60 ms
+whisper_print_timings:   sample time =    73.19 ms
+whisper_print_timings:   encode time = 19552.61 ms / 814.69 ms per layer
+whisper_print_timings:   decode time = 13249.96 ms / 552.08 ms per layer
+whisper_print_timings:    total time = 33686.27 ms
 ```
+</details>
 
 ## Real-time audio input example
 
 This is a naive example of performing real-time inference on audio from your microphone.
-The `stream` tool samples the audio every 3 seconds and runs the transcription continously. More info is available in [issue #10](https://github.com/ggerganov/whisper.cpp/issues/10).
+The [stream](examples/stream) tool samples the audio every half a second and runs the transcription continously.
+More info is available in [issue #10](https://github.com/ggerganov/whisper.cpp/issues/10).
 
 ```java
-$ ./stream -m models/ggml-small.en.bin -t 8
+make stream
+./stream -m ./models/ggml-base.en.bin -t 8 --step 500 --length 5000
 ```
 
-https://user-images.githubusercontent.com/1991296/193465125-c163d304-64f6-4f5d-83e5-72239c9a203e.mp4
+https://user-images.githubusercontent.com/1991296/194935793-76afede7-cfa8-48d8-a80f-28ba83be7d09.mp4
 
-## Implementation details
+## Confidence color-coding
 
-- The core tensor operations are implemented in C ([ggml.h](ggml.h) / [ggml.c](ggml.c))
-- The high-level C-style API is implemented in C++ ([whisper.h](whisper.h) / [whisper.cpp](whisper.cpp))
-- Simple usage is demonstrated in [main.cpp](main.cpp)
-- Sample real-time audio transcription from the microphone is demonstrated in [stream.cpp](stream.cpp)
+Adding the `--print-colors` argument will print the transcribed text using an experimental color coding strategy
+to highlight words with high or low confidence:
 
-## Limitations
+<img width="965" alt="image" src="https://user-images.githubusercontent.com/1991296/197356445-311c8643-9397-4e5e-b46e-0b4b4daa2530.png">
 
-- Very basic greedy sampling scheme - always pick up the top token. You can implement your own strategy
-- Inference only
-- No GPU support
+## Controlling the length of the generated text segments (experimental)
 
-## Memory usage
+For example, to limit the line length to a maximum of 16 characters, simply add `-ml 16`: 
 
-| Model  | Disk   | Mem     |
-| ---    | ---    | ---     |
-| tiny   |  75 MB | ~240 MB |
-| base   | 142 MB | ~380 MB |
-| small  | 466 MB | ~970 MB |
-| medium | 1.5 GB | ~2.5 GB |
-| large  | 2.9 GB | ~4.6 GB |
+```java
+./main -m ./models/ggml-base.en.bin -f ./samples/jfk.wav -ml 16
+
+whisper_model_load: loading model from './models/ggml-base.en.bin'
+...
+system_info: n_threads = 4 / 10 | AVX2 = 0 | AVX512 = 0 | NEON = 1 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | 
+
+main: processing './samples/jfk.wav' (176000 samples, 11.0 sec), 4 threads, 1 processors, lang = en, task = transcribe, timestamps = 1 ...
+
+[00:00:00.000 --> 00:00:00.850]   And so my
+[00:00:00.850 --> 00:00:01.590]   fellow
+[00:00:01.590 --> 00:00:04.140]   Americans, ask
+[00:00:04.140 --> 00:00:05.660]   not what your
+[00:00:05.660 --> 00:00:06.840]   country can do
+[00:00:06.840 --> 00:00:08.430]   for you, ask
+[00:00:08.430 --> 00:00:09.440]   what you can do
+[00:00:09.440 --> 00:00:10.020]   for your
+[00:00:10.020 --> 00:00:11.000]   country.
+```
+
+## Word-level timestamp
+
+The `--max-len` argument can be used to obtain word-level timestamps. Simply use `-ml 1`:
+
+```java
+./main -m ./models/ggml-base.en.bin -f ./samples/jfk.wav -ml 1
+
+whisper_model_load: loading model from './models/ggml-base.en.bin'
+...
+system_info: n_threads = 4 / 10 | AVX2 = 0 | AVX512 = 0 | NEON = 1 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | 
+
+main: processing './samples/jfk.wav' (176000 samples, 11.0 sec), 4 threads, 1 processors, lang = en, task = transcribe, timestamps = 1 ...
+
+[00:00:00.000 --> 00:00:00.320]  
+[00:00:00.320 --> 00:00:00.370]   And
+[00:00:00.370 --> 00:00:00.690]   so
+[00:00:00.690 --> 00:00:00.850]   my
+[00:00:00.850 --> 00:00:01.590]   fellow
+[00:00:01.590 --> 00:00:02.850]   Americans
+[00:00:02.850 --> 00:00:03.300]  ,
+[00:00:03.300 --> 00:00:04.140]   ask
+[00:00:04.140 --> 00:00:04.990]   not
+[00:00:04.990 --> 00:00:05.410]   what
+[00:00:05.410 --> 00:00:05.660]   your
+[00:00:05.660 --> 00:00:06.260]   country
+[00:00:06.260 --> 00:00:06.600]   can
+[00:00:06.600 --> 00:00:06.840]   do
+[00:00:06.840 --> 00:00:07.010]   for
+[00:00:07.010 --> 00:00:08.170]   you
+[00:00:08.170 --> 00:00:08.190]  ,
+[00:00:08.190 --> 00:00:08.430]   ask
+[00:00:08.430 --> 00:00:08.910]   what
+[00:00:08.910 --> 00:00:09.040]   you
+[00:00:09.040 --> 00:00:09.320]   can
+[00:00:09.320 --> 00:00:09.440]   do
+[00:00:09.440 --> 00:00:09.760]   for
+[00:00:09.760 --> 00:00:10.020]   your
+[00:00:10.020 --> 00:00:10.510]   country
+[00:00:10.510 --> 00:00:11.000]  .
+```
+
+## Karaoke-style movie generation (experimental)
+
+The [main](examples/main) example provides support for output of karaoke-style movies, where the
+currently pronounced word is highlighted. Use the `-wts` argument and run the generated bash script.
+This requires to have `ffmpeg` installed.
+
+Here are a few *"typical"* examples:
+
+```java
+./main -m ./models/ggml-base.en.bin -f ./samples/jfk.wav -owts
+source ./samples/jfk.wav.wts
+ffplay ./samples/jfk.wav.mp4
+```
+
+https://user-images.githubusercontent.com/1991296/199337465-dbee4b5e-9aeb-48a3-b1c6-323ac4db5b2c.mp4
+
+---
+
+```java
+./main -m ./models/ggml-base.en.bin -f ./samples/mm0.wav -owts
+source ./samples/mm0.wav.wts
+ffplay ./samples/mm0.wav.mp4
+```
+
+https://user-images.githubusercontent.com/1991296/199337504-cc8fd233-0cb7-4920-95f9-4227de3570aa.mp4
+
+---
+
+```java
+./main -m ./models/ggml-base.en.bin -f ./samples/gb0.wav -owts
+source ./samples/gb0.wav.wts
+ffplay ./samples/gb0.wav.mp4
+```
+
+https://user-images.githubusercontent.com/1991296/199337538-b7b0c7a3-2753-4a88-a0cd-f28a317987ba.mp4
+
+---
+
+## Benchmarks
+
+In order to have an objective comparison of the performance of the inference across different system configurations,
+use the [bench](examples/bench) tool. The tool simply runs the Encoder part of the model and prints how much time it
+took to execute it. The results are summarized in the following Github issue:
+
+[Benchmark results](https://github.com/ggerganov/whisper.cpp/issues/89)
 
 ## ggml format
 
@@ -248,6 +439,46 @@ The original models are converted to a custom binary format. This allows to pack
 - vocabulary
 - weights
 
-You can download the converted models using the [download-ggml-model.sh](download-ggml-model.sh) script.
+You can download the converted models using the [models/download-ggml-model.sh](models/download-ggml-model.sh) script
+or manually from here:
 
-For more details, see the conversion script [convert-pt-to-ggml.py](convert-pt-to-ggml.py) or the README in [models](models).
+- https://huggingface.co/datasets/ggerganov/whisper.cpp
+- https://ggml.ggerganov.com
+
+For more details, see the conversion script [models/convert-pt-to-ggml.py](models/convert-pt-to-ggml.py) or the README
+in [models](models).
+
+## [Bindings](https://github.com/ggerganov/whisper.cpp/discussions/categories/bindings)
+
+- [X] Rust: [tazz4843/whisper-rs](https://github.com/tazz4843/whisper-rs) | [#310](https://github.com/ggerganov/whisper.cpp/discussions/310)
+- [X] Javascript: [bindings/javascript](bindings/javascript) | [#309](https://github.com/ggerganov/whisper.cpp/discussions/309)
+- [X] Go: [bindings/go](bindings/go) | [#312](https://github.com/ggerganov/whisper.cpp/discussions/312)
+- [X] Objective-C / Swift: [ggerganov/whisper.spm](https://github.com/ggerganov/whisper.spm) | [#313](https://github.com/ggerganov/whisper.cpp/discussions/313)
+- [ ] Python: soon | [WIP](https://github.com/ggerganov/whisper.cpp/issues/9)
+
+## Examples
+
+There are various examples of using the library for different projects in the [examples](examples) folder.
+Some of the examples are even ported to run in the browser using WebAssembly. Check them out!
+
+| Example | Web | Description |
+| ---     | --- | ---         |
+| [main](examples/main) | [whisper.wasm](examples/whisper.wasm) | Tool for translating and transcribing audio using Whisper |
+| [bench](examples/bench) | [bench.wasm](examples/bench.wasm) | Benchmark the performance of Whisper on your machine |
+| [stream](examples/stream) | [stream.wasm](examples/stream.wasm) | Real-time transcription of raw microphone capture |
+| [command](examples/command) | [command.wasm](examples/command.wasm) | Basic voice assistant example for receiving voice commands from the mic |
+| [talk](examples/talk) | [talk.wasm](examples/talk.wasm) | Talk with a GPT-2 bot |
+| [whisper.objc](examples/whisper.objc) | | iOS mobile application using whisper.cpp |
+| [whisper.swiftui](examples/whisper.swiftui) | | SwiftUI iOS / macOS application using whisper.cpp |
+| [whisper.android](examples/whisper.android) | | Android mobile application using whisper.cpp |
+| [whisper.nvim](examples/whisper.nvim) | | Speech-to-text plugin for Neovim |
+| [generate-karaoke.sh](examples/generate-karaoke.sh) | | Helper script to easily [generate a karaoke video](https://youtu.be/uj7hVta4blM) of raw audio capture |
+| [livestream.sh](examples/livestream.sh) | | [Livestream audio transcription](https://github.com/ggerganov/whisper.cpp/issues/185) |
+| [yt-wsp.sh](examples/yt-wsp.sh) | | Download + transcribe and/or translate any VOD [(original)](https://gist.github.com/DaniruKun/96f763ec1a037cc92fe1a059b643b818) |
+
+## [Discussions](https://github.com/ggerganov/whisper.cpp/discussions)
+
+If you have any kind of feedback about this project feel free to use the Discussions section and open a new topic.
+You can use the [Show and tell](https://github.com/ggerganov/whisper.cpp/discussions/categories/show-and-tell) category
+to share your own projects that use `whisper.cpp`. If you have a question, make sure to check the
+[Frequently asked questions (#126)](https://github.com/ggerganov/whisper.cpp/discussions/126) discussion.

bindings/CMakeLists.txt

@@ -0,0 +1,19 @@
+if (EMSCRIPTEN)
+    add_subdirectory(javascript)
+
+    add_custom_command(
+        OUTPUT ${CMAKE_CURRENT_SOURCE_DIR}/javascript/publish.log
+        DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/javascript/whisper.js
+        DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/javascript/libwhisper.worker.js
+        DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/javascript/package.json
+        WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/javascript
+        COMMAND npm publish
+        COMMAND touch publish.log
+        COMMENT "Publishing npm module v${PROJECT_VERSION}"
+        VERBATIM
+        )
+
+    add_custom_target(publish-npm
+        DEPENDS javascript/publish.log
+        )
+endif()

bindings/go/.gitignore

@@ -0,0 +1,2 @@
+build
+models

bindings/go/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 David Thorpe
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

bindings/go/Makefile

@@ -0,0 +1,38 @@
+BUILD_DIR := build
+MODELS_DIR := models
+EXAMPLES_DIR := $(wildcard examples/*)
+INCLUDE_PATH := $(abspath ../..)
+LIBRARY_PATH := $(abspath ../..)
+
+all: clean whisper examples
+
+whisper: mkdir
+	@echo Build whisper
+	@${MAKE} -C ../.. libwhisper.a
+
+test: model-small whisper modtidy
+	@C_INCLUDE_PATH=${INCLUDE_PATH} LIBRARY_PATH=${LIBRARY_PATH} go test -v .
+	@C_INCLUDE_PATH=${INCLUDE_PATH} LIBRARY_PATH=${LIBRARY_PATH} go test -v ./pkg/whisper/...
+
+examples: $(EXAMPLES_DIR)
+
+model-small: mkdir examples/go-model-download
+	@${BUILD_DIR}/go-model-download -out models ggml-small.en.bin
+
+$(EXAMPLES_DIR): mkdir whisper modtidy
+	@echo Build example $(notdir $@)
+	@C_INCLUDE_PATH=${INCLUDE_PATH} LIBRARY_PATH=${LIBRARY_PATH} go build ${BUILD_FLAGS} -o ${BUILD_DIR}/$(notdir $@) ./$@
+
+mkdir:
+	@echo Mkdir ${BUILD_DIR}
+	@install -d ${BUILD_DIR}
+	@echo Mkdir ${MODELS_DIR}
+	@install -d ${MODELS_DIR}
+
+modtidy:
+	@go mod tidy
+
+clean: 
+	@echo Clean
+	@rm -fr $(BUILD_DIR)
+	@go clean

bindings/go/README.md

@@ -0,0 +1,100 @@
+# Go bindings for Whisper
+
+This package provides Go bindings for whisper.cpp. They have been tested on:
+
+  * Darwin (OS X) 12.6 on x64_64
+  * Debian Linux on arm64
+  * Fedora Linux on x86_64
+
+The "low level" bindings are in the `bindings/go` directory and there is a more
+Go-style package in the `bindings/go/pkg/whisper` directory. The most simple usage
+is as follows:
+
+```go
+import (
+	"github.com/ggerganov/whisper.cpp/bindings/go/pkg/whisper"
+)
+
+func main() {
+	var modelpath string // Path to the model
+	var samples []float32 // Samples to process
+
+	// Load the model
+	model, err := whisper.New(modelpath)
+	if err != nil {
+		panic(err)
+	}
+	defer model.Close()
+
+	// Process samples
+	context, err := model.NewContext()
+	if err != nil {
+		panic(err)
+	}
+	if err := context.Process(samples, nil); err != nil {
+		return err
+	}
+
+	// Print out the results
+	for {
+		segment, err := context.NextSegment()
+		if err != nil {
+			break
+		}
+		fmt.Printf("[%6s->%6s] %s\n", segment.Start, segment.End, segment.Text)
+	}
+}
+```
+
+## Building & Testing
+
+In order to build, you need to have the Go compiler installed. You can get it from [here](https://golang.org/dl/). Run the tests with:
+
+```bash
+git clone https://github.com/ggerganov/whisper.cpp.git
+cd whisper.cpp/bindings/go
+make test
+```
+
+This will compile a static `libwhisper.a` in a `build` folder, download a model file, then run the tests. To build the examples:
+
+```bash
+make examples
+```
+
+The examples are placed in the `build` directory. Once built, you can download all the models with the following command:
+
+```bash
+./build/go-model-download -out models
+```
+
+And you can then test a model against samples with the following command:
+
+```bash
+./build/go-whisper -model models/ggml-tiny.en.bin samples/jfk.wav 
+```
+
+## Using the bindings
+
+To use the bindings in your own software,
+
+  1. Import `github.com/ggerganov/whisper.cpp/bindings/go/pkg/whisper` (or `github.com/ggerganov/whisper.cpp/bindings/go` into your package;
+  2. Compile `libwhisper.a` (you can use `make whisper` in the `bindings/go` directory);
+  3. Link your go binary against whisper by setting the environment variables `C_INCLUDE_PATH` and `LIBRARY_PATH`
+     to point to the `whisper.h` file directory and `libwhisper.a` file directory respectively.
+
+Look at the `Makefile` in the `bindings/go` directory for an example.
+
+The API Documentation:
+
+  * https://pkg.go.dev/github.com/ggerganov/whisper.cpp/bindings/go
+  * https://pkg.go.dev/github.com/ggerganov/whisper.cpp/bindings/go/pkg/whisper
+
+Getting help:
+
+  * Follow the discussion for the go bindings [here](https://github.com/ggerganov/whisper.cpp/discussions/312)
+
+## License
+
+The license for the Go bindings is the same as the license for the rest of the whisper.cpp project, which is the MIT License. See the `LICENSE` file for more details.
+

bindings/go/doc.go

@@ -0,0 +1,5 @@
+/*
+github.com/ggerganov/whisper.cpp/bindings/go
+provides a speech-to-text service bindings for the Go programming language.
+*/
+package whisper

bindings/go/examples/go-model-download/context.go

@@ -0,0 +1,30 @@
+package main
+
+import (
+	"context"
+	"os"
+	"os/signal"
+)
+
+// ContextForSignal returns a context object which is cancelled when a signal
+// is received. It returns nil if no signal parameter is provided
+func ContextForSignal(signals ...os.Signal) context.Context {
+	if len(signals) == 0 {
+		return nil
+	}
+
+	ch := make(chan os.Signal)
+	ctx, cancel := context.WithCancel(context.Background())
+
+	// Send message on channel when signal received
+	signal.Notify(ch, signals...)
+
+	// When any signal received, call cancel
+	go func() {
+		<-ch
+		cancel()
+	}()
+
+	// Return success
+	return ctx
+}

bindings/go/examples/go-model-download/main.go

@@ -0,0 +1,208 @@
+package main
+
+import (
+	"context"
+	"flag"
+	"fmt"
+	"io"
+	"net/http"
+	"net/url"
+	"os"
+	"path/filepath"
+	"syscall"
+	"time"
+)
+
+///////////////////////////////////////////////////////////////////////////////
+// CONSTANTS
+
+const (
+	srcUrl  = "https://huggingface.co/datasets/ggerganov/whisper.cpp/resolve/main" // The location of the models
+	srcExt  = ".bin"                                                               // Filename extension
+	bufSize = 1024 * 64                                                            // Size of the buffer used for downloading the model
+)
+
+var (
+	// The models which will be downloaded, if no model is specified as an argument
+	modelNames = []string{"ggml-tiny.en", "ggml-tiny", "ggml-base.en", "ggml-base", "ggml-small.en", "ggml-small", "ggml-medium.en", "ggml-medium", "ggml-large-v1", "ggml-large"}
+)
+
+var (
+	// The output folder. When not set, use current working directory.
+	flagOut = flag.String("out", "", "Output folder")
+
+	// HTTP timeout parameter - will timeout if takes longer than this to download a model
+	flagTimeout = flag.Duration("timeout", 30*time.Minute, "HTTP timeout")
+
+	// Quiet parameter - will not print progress if set
+	flagQuiet = flag.Bool("quiet", false, "Quiet mode")
+)
+
+///////////////////////////////////////////////////////////////////////////////
+// MAIN
+
+func main() {
+	flag.Usage = func() {
+		name := filepath.Base(flag.CommandLine.Name())
+		fmt.Fprintf(flag.CommandLine.Output(), "Usage: %s [options] <model>\n\n", name)
+		flag.PrintDefaults()
+	}
+	flag.Parse()
+
+	// Get output path
+	out, err := GetOut()
+	if err != nil {
+		fmt.Fprintln(os.Stderr, "Error:", err)
+		os.Exit(-1)
+	}
+
+	// Create context which quits on SIGINT or SIGQUIT
+	ctx := ContextForSignal(os.Interrupt, syscall.SIGQUIT)
+
+	// Progress filehandle
+	progress := os.Stdout
+	if *flagQuiet {
+		progress, err = os.Open(os.DevNull)
+		if err != nil {
+			fmt.Fprintln(os.Stderr, "Error:", err)
+			os.Exit(-1)
+		}
+		defer progress.Close()
+	}
+
+	// Download models - exit on error or interrupt
+	for _, model := range GetModels() {
+		url, err := URLForModel(model)
+		if err != nil {
+			fmt.Fprintln(os.Stderr, "Error:", err)
+			continue
+		} else if path, err := Download(ctx, progress, url, out); err == nil || err == io.EOF {
+			continue
+		} else if err == context.Canceled {
+			os.Remove(path)
+			fmt.Fprintln(progress, "\nInterrupted")
+			break
+		} else if err == context.DeadlineExceeded {
+			os.Remove(path)
+			fmt.Fprintln(progress, "Timeout downloading model")
+			continue
+		} else {
+			os.Remove(path)
+			fmt.Fprintln(os.Stderr, "Error:", err)
+			break
+		}
+	}
+}
+
+///////////////////////////////////////////////////////////////////////////////
+// PUBLIC METHODS
+
+// GetOut returns the path to the output directory
+func GetOut() (string, error) {
+	if *flagOut == "" {
+		return os.Getwd()
+	}
+	if info, err := os.Stat(*flagOut); err != nil {
+		return "", err
+	} else if !info.IsDir() {
+		return "", fmt.Errorf("not a directory: %s", info.Name())
+	} else {
+		return *flagOut, nil
+	}
+}
+
+// GetModels returns the list of models to download
+func GetModels() []string {
+	if flag.NArg() == 0 {
+		return modelNames
+	} else {
+		return flag.Args()
+	}
+}
+
+// URLForModel returns the URL for the given model on huggingface.co
+func URLForModel(model string) (string, error) {
+	if filepath.Ext(model) != srcExt {
+		model += srcExt
+	}
+	url, err := url.Parse(srcUrl)
+	if err != nil {
+		return "", err
+	} else {
+		url.Path = filepath.Join(url.Path, model)
+	}
+	return url.String(), nil
+}
+
+// Download downloads the model from the given URL to the given output directory
+func Download(ctx context.Context, p io.Writer, model, out string) (string, error) {
+	// Create HTTP client
+	client := http.Client{
+		Timeout: *flagTimeout,
+	}
+
+	// Initiate the download
+	req, err := http.NewRequest("GET", model, nil)
+	if err != nil {
+		return "", err
+	}
+	resp, err := client.Do(req)
+	if err != nil {
+		return "", err
+	}
+	defer resp.Body.Close()
+	if resp.StatusCode != http.StatusOK {
+		return "", fmt.Errorf("%s: %s", model, resp.Status)
+	}
+
+	// If output file exists and is the same size as the model, skip
+	path := filepath.Join(out, filepath.Base(model))
+	if info, err := os.Stat(path); err == nil && info.Size() == resp.ContentLength {
+		fmt.Fprintln(p, "Skipping", model, "as it already exists")
+		return "", nil
+	}
+
+	// Create file
+	w, err := os.Create(path)
+	if err != nil {
+		return "", err
+	}
+	defer w.Close()
+
+	// Report
+	fmt.Fprintln(p, "Downloading", model, "to", out)
+
+	// Progressively download the model
+	data := make([]byte, bufSize)
+	count, pct := int64(0), int64(0)
+	ticker := time.NewTicker(5 * time.Second)
+	for {
+		select {
+		case <-ctx.Done():
+			// Cancelled, return error
+			return path, ctx.Err()
+		case <-ticker.C:
+			pct = DownloadReport(p, pct, count, resp.ContentLength)
+		default:
+			// Read body
+			n, err := resp.Body.Read(data)
+			if err != nil {
+				DownloadReport(p, pct, count, resp.ContentLength)
+				return path, err
+			} else if m, err := w.Write(data[:n]); err != nil {
+				return path, err
+			} else {
+				count += int64(m)
+			}
+		}
+	}
+}
+
+// Report periodically reports the download progress when percentage changes
+func DownloadReport(w io.Writer, pct, count, total int64) int64 {
+	pct_ := count * 100 / total
+	if pct_ > pct {
+		fmt.Fprintf(w, "  ...%d MB written (%d%%)\n", count/1e6, pct_)
+	}
+	return pct_
+}

bindings/go/examples/go-whisper/color.go

@@ -0,0 +1,22 @@
+package main
+
+import "fmt"
+
+///////////////////////////////////////////////////////////////////////////////
+// CONSTANTS
+
+const (
+	Reset     = "\033[0m"
+	RGBPrefix = "\033[38;5;" // followed by RGB values in decimal format separated by colons
+	RGBSuffix = "m"
+)
+
+///////////////////////////////////////////////////////////////////////////////
+// PUBLIC METHODS
+
+// Colorize text with RGB values, from 0 to 23
+func Colorize(text string, v int) string {
+	// https://en.wikipedia.org/wiki/ANSI_escape_code#8-bit
+	// Grayscale colors are in the range 232-255
+	return RGBPrefix + fmt.Sprint(v%24+232) + RGBSuffix + text + Reset
+}

bindings/go/examples/go-whisper/flags.go

@@ -0,0 +1,156 @@
+package main
+
+import (
+	"flag"
+	"fmt"
+	"strings"
+	"time"
+
+	// Packages
+	whisper "github.com/ggerganov/whisper.cpp/bindings/go/pkg/whisper"
+)
+
+///////////////////////////////////////////////////////////////////////////////
+// TYPES
+
+type Flags struct {
+	*flag.FlagSet
+}
+
+///////////////////////////////////////////////////////////////////////////////
+// LIFECYCLE
+
+func NewFlags(name string, args []string) (*Flags, error) {
+	flags := &Flags{
+		FlagSet: flag.NewFlagSet(name, flag.ContinueOnError),
+	}
+
+	// Register the command line arguments
+	registerFlags(flags)
+
+	// Parse command line
+	if err := flags.Parse(args); err != nil {
+		return nil, err
+	}
+
+	// Return success
+	return flags, nil
+}
+
+///////////////////////////////////////////////////////////////////////////////
+// PUBLIC METHODS
+
+func (flags *Flags) GetModel() string {
+	return flags.Lookup("model").Value.String()
+}
+
+func (flags *Flags) GetLanguage() string {
+	return flags.Lookup("language").Value.String()
+}
+
+func (flags *Flags) IsTranslate() bool {
+	return flags.Lookup("translate").Value.(flag.Getter).Get().(bool)
+}
+
+func (flags *Flags) GetOffset() time.Duration {
+	return flags.Lookup("offset").Value.(flag.Getter).Get().(time.Duration)
+}
+
+func (flags *Flags) GetDuration() time.Duration {
+	return flags.Lookup("duration").Value.(flag.Getter).Get().(time.Duration)
+}
+
+func (flags *Flags) GetThreads() uint {
+	return flags.Lookup("threads").Value.(flag.Getter).Get().(uint)
+}
+
+func (flags *Flags) GetOut() string {
+	return strings.ToLower(flags.Lookup("out").Value.String())
+}
+
+func (flags *Flags) IsSpeedup() bool {
+	return flags.Lookup("speedup").Value.String() == "true"
+}
+
+func (flags *Flags) IsTokens() bool {
+	return flags.Lookup("tokens").Value.String() == "true"
+}
+
+func (flags *Flags) IsColorize() bool {
+	return flags.Lookup("colorize").Value.String() == "true"
+}
+
+func (flags *Flags) GetMaxLen() uint {
+	return flags.Lookup("max-len").Value.(flag.Getter).Get().(uint)
+}
+
+func (flags *Flags) GetMaxTokens() uint {
+	return flags.Lookup("max-tokens").Value.(flag.Getter).Get().(uint)
+}
+
+func (flags *Flags) GetWordThreshold() float32 {
+	return float32(flags.Lookup("word-thold").Value.(flag.Getter).Get().(float64))
+}
+
+func (flags *Flags) SetParams(context whisper.Context) error {
+	if lang := flags.GetLanguage(); lang != "" && lang != "auto" {
+		fmt.Fprintf(flags.Output(), "Setting language to %q\n", lang)
+		if err := context.SetLanguage(lang); err != nil {
+			return err
+		}
+	}
+	if flags.IsTranslate() && context.IsMultilingual() {
+		fmt.Fprintf(flags.Output(), "Setting translate to true\n")
+		context.SetTranslate(true)
+	}
+	if offset := flags.GetOffset(); offset != 0 {
+		fmt.Fprintf(flags.Output(), "Setting offset to %v\n", offset)
+		context.SetOffset(offset)
+	}
+	if duration := flags.GetDuration(); duration != 0 {
+		fmt.Fprintf(flags.Output(), "Setting duration to %v\n", duration)
+		context.SetDuration(duration)
+	}
+	if flags.IsSpeedup() {
+		fmt.Fprintf(flags.Output(), "Setting speedup to true\n")
+		context.SetSpeedup(true)
+	}
+	if threads := flags.GetThreads(); threads != 0 {
+		fmt.Fprintf(flags.Output(), "Setting threads to %d\n", threads)
+		context.SetThreads(threads)
+	}
+	if max_len := flags.GetMaxLen(); max_len != 0 {
+		fmt.Fprintf(flags.Output(), "Setting max_segment_length to %d\n", max_len)
+		context.SetMaxSegmentLength(max_len)
+	}
+	if max_tokens := flags.GetMaxTokens(); max_tokens != 0 {
+		fmt.Fprintf(flags.Output(), "Setting max_tokens to %d\n", max_tokens)
+		context.SetMaxTokensPerSegment(max_tokens)
+	}
+	if word_threshold := flags.GetWordThreshold(); word_threshold != 0 {
+		fmt.Fprintf(flags.Output(), "Setting word_threshold to %f\n", word_threshold)
+		context.SetTokenThreshold(word_threshold)
+	}
+
+	// Return success
+	return nil
+}
+
+///////////////////////////////////////////////////////////////////////////////
+// PRIVATE METHODS
+
+func registerFlags(flag *Flags) {
+	flag.String("model", "", "Path to the model file")
+	flag.String("language", "", "Spoken language")
+	flag.Bool("translate", false, "Translate from source language to english")
+	flag.Duration("offset", 0, "Time offset")
+	flag.Duration("duration", 0, "Duration of audio to process")
+	flag.Uint("threads", 0, "Number of threads to use")
+	flag.Bool("speedup", false, "Enable speedup")
+	flag.Uint("max-len", 0, "Maximum segment length in characters")
+	flag.Uint("max-tokens", 0, "Maximum tokens per segment")
+	flag.Float64("word-thold", 0, "Maximum segment score")
+	flag.Bool("tokens", false, "Display tokens")
+	flag.Bool("colorize", false, "Colorize tokens")
+	flag.String("out", "", "Output format (srt, none or leave as empty string)")
+}

bindings/go/examples/go-whisper/main.go

@@ -0,0 +1,43 @@
+package main
+
+import (
+	"flag"
+	"fmt"
+	"os"
+	"path/filepath"
+
+	// Packages
+	whisper "github.com/ggerganov/whisper.cpp/bindings/go/pkg/whisper"
+)
+
+func main() {
+	flags, err := NewFlags(filepath.Base(os.Args[0]), os.Args[1:])
+	if err == flag.ErrHelp {
+		os.Exit(0)
+	} else if err != nil {
+		fmt.Fprintln(os.Stderr, err)
+		os.Exit(1)
+	} else if flags.GetModel() == "" {
+		fmt.Fprintln(os.Stderr, "Use -model flag to specify which model file to use")
+		os.Exit(1)
+	} else if flags.NArg() == 0 {
+		fmt.Fprintln(os.Stderr, "No input files specified")
+		os.Exit(1)
+	}
+
+	// Load model
+	model, err := whisper.New(flags.GetModel())
+	if err != nil {
+		fmt.Fprintln(os.Stderr, err)
+		os.Exit(1)
+	}
+	defer model.Close()
+
+	// Process files
+	for _, filename := range flags.Args() {
+		if err := Process(model, filename, flags); err != nil {
+			fmt.Fprintln(os.Stderr, err)
+			continue
+		}
+	}
+}

bindings/go/examples/go-whisper/process.go

@@ -0,0 +1,127 @@
+package main
+
+import (
+	"fmt"
+	"io"
+	"os"
+	"time"
+
+	// Package imports
+	whisper "github.com/ggerganov/whisper.cpp/bindings/go/pkg/whisper"
+	wav "github.com/go-audio/wav"
+)
+
+func Process(model whisper.Model, path string, flags *Flags) error {
+	var data []float32
+
+	// Create processing context
+	context, err := model.NewContext()
+	if err != nil {
+		return err
+	}
+
+	// Set the parameters
+	if err := flags.SetParams(context); err != nil {
+		return err
+	}
+
+	// Open the file
+	fmt.Fprintf(flags.Output(), "Loading %q\n", path)
+	fh, err := os.Open(path)
+	if err != nil {
+		return err
+	}
+	defer fh.Close()
+
+	// Decode the WAV file - load the full buffer
+	dec := wav.NewDecoder(fh)
+	if buf, err := dec.FullPCMBuffer(); err != nil {
+		return err
+	} else if dec.SampleRate != whisper.SampleRate {
+		return fmt.Errorf("unsupported sample rate: %d", dec.SampleRate)
+	} else if dec.NumChans != 1 {
+		return fmt.Errorf("unsupported number of channels: %d", dec.NumChans)
+	} else {
+		data = buf.AsFloat32Buffer().Data
+	}
+
+	// Segment callback when -tokens is specified
+	var cb whisper.SegmentCallback
+	if flags.IsTokens() {
+		cb = func(segment whisper.Segment) {
+			fmt.Fprintf(flags.Output(), "%02d [%6s->%6s] ", segment.Num, segment.Start.Truncate(time.Millisecond), segment.End.Truncate(time.Millisecond))
+			for _, token := range segment.Tokens {
+				if flags.IsColorize() && context.IsText(token) {
+					fmt.Fprint(flags.Output(), Colorize(token.Text, int(token.P*24.0)), " ")
+				} else {
+					fmt.Fprint(flags.Output(), token.Text, " ")
+				}
+			}
+			fmt.Fprintln(flags.Output(), "")
+			fmt.Fprintln(flags.Output(), "")
+		}
+	}
+
+	// Process the data
+	fmt.Fprintf(flags.Output(), "  ...processing %q\n", path)
+	if err := context.Process(data, cb); err != nil {
+		return err
+	}
+
+	// Print out the results
+	switch {
+	case flags.GetOut() == "srt":
+		return OutputSRT(os.Stdout, context)
+	case flags.GetOut() == "none":
+		return nil
+	default:
+		return Output(os.Stdout, context, flags.IsColorize())
+	}
+}
+
+// Output text as SRT file
+func OutputSRT(w io.Writer, context whisper.Context) error {
+	n := 1
+	for {
+		segment, err := context.NextSegment()
+		if err == io.EOF {
+			return nil
+		} else if err != nil {
+			return err
+		}
+		fmt.Fprintln(w, n)
+		fmt.Fprintln(w, srtTimestamp(segment.Start), " --> ", srtTimestamp(segment.End))
+		fmt.Fprintln(w, segment.Text)
+		fmt.Fprintln(w, "")
+		n++
+	}
+}
+
+// Output text to terminal
+func Output(w io.Writer, context whisper.Context, colorize bool) error {
+	for {
+		segment, err := context.NextSegment()
+		if err == io.EOF {
+			return nil
+		} else if err != nil {
+			return err
+		}
+		fmt.Fprintf(w, "[%6s->%6s]", segment.Start.Truncate(time.Millisecond), segment.End.Truncate(time.Millisecond))
+		if colorize {
+			for _, token := range segment.Tokens {
+				if !context.IsText(token) {
+					continue
+				}
+				fmt.Fprint(w, " ", Colorize(token.Text, int(token.P*24.0)))
+			}
+			fmt.Fprint(w, "\n")
+		} else {
+			fmt.Fprintln(w, " ", segment.Text)
+		}
+	}
+}
+
+// Return srtTimestamp
+func srtTimestamp(t time.Duration) string {
+	return fmt.Sprintf("%02d:%02d:%02d,%03d", t/time.Hour, (t%time.Hour)/time.Minute, (t%time.Minute)/time.Second, (t%time.Second)/time.Millisecond)
+}

bindings/go/go.mod

@@ -0,0 +1,16 @@
+module github.com/ggerganov/whisper.cpp/bindings/go
+
+go 1.19
+
+require (
+	github.com/go-audio/wav v1.1.0
+	github.com/stretchr/testify v1.8.1
+)
+
+require (
+	github.com/davecgh/go-spew v1.1.1 // indirect
+	github.com/go-audio/audio v1.0.0 // indirect
+	github.com/go-audio/riff v1.0.0 // indirect
+	github.com/pmezard/go-difflib v1.0.0 // indirect
+	gopkg.in/yaml.v3 v3.0.1 // indirect
+)

bindings/go/go.sum

@@ -0,0 +1,23 @@
+github.com/davecgh/go-spew v1.1.0/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
+github.com/davecgh/go-spew v1.1.1 h1:vj9j/u1bqnvCEfJOwUhtlOARqs3+rkHYY13jYWTU97c=
+github.com/davecgh/go-spew v1.1.1/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
+github.com/go-audio/audio v1.0.0 h1:zS9vebldgbQqktK4H0lUqWrG8P0NxCJVqcj7ZpNnwd4=
+github.com/go-audio/audio v1.0.0/go.mod h1:6uAu0+H2lHkwdGsAY+j2wHPNPpPoeg5AaEFh9FlA+Zs=
+github.com/go-audio/riff v1.0.0 h1:d8iCGbDvox9BfLagY94fBynxSPHO80LmZCaOsmKxokA=
+github.com/go-audio/riff v1.0.0/go.mod h1:l3cQwc85y79NQFCRB7TiPoNiaijp6q8Z0Uv38rVG498=
+github.com/go-audio/wav v1.1.0 h1:jQgLtbqBzY7G+BM8fXF7AHUk1uHUviWS4X39d5rsL2g=
+github.com/go-audio/wav v1.1.0/go.mod h1:mpe9qfwbScEbkd8uybLuIpTgHyrISw/OTuvjUW2iGtE=
+github.com/pmezard/go-difflib v1.0.0 h1:4DBwDE0NGyQoBHbLQYPwSUPoCMWR5BEzIk/f1lZbAQM=
+github.com/pmezard/go-difflib v1.0.0/go.mod h1:iKH77koFhYxTK1pcRnkKkqfTogsbg7gZNVY4sRDYZ/4=
+github.com/stretchr/objx v0.1.0/go.mod h1:HFkY916IF+rwdDfMAkV7OtwuqBVzrE8GR6GFx+wExME=
+github.com/stretchr/objx v0.4.0/go.mod h1:YvHI0jy2hoMjB+UWwv71VJQ9isScKT/TqJzVSSt89Yw=
+github.com/stretchr/objx v0.5.0/go.mod h1:Yh+to48EsGEfYuaHDzXPcE3xhTkx73EhmCGUpEOglKo=
+github.com/stretchr/testify v1.7.1/go.mod h1:6Fq8oRcR53rry900zMqJjRRixrwX3KX962/h/Wwjteg=
+github.com/stretchr/testify v1.8.0/go.mod h1:yNjHg4UonilssWZ8iaSj1OCr/vHnekPRkoO+kdMU+MU=
+github.com/stretchr/testify v1.8.1 h1:w7B6lhMri9wdJUVmEZPGGhZzrYTPvgJArz7wNPgYKsk=
+github.com/stretchr/testify v1.8.1/go.mod h1:w2LPCIKwWwSfY2zedu0+kehJoqGctiVI29o6fzry7u4=
+gopkg.in/check.v1 v0.0.0-20161208181325-20d25e280405 h1:yhCVgyC4o1eVCa2tZl7eS0r+SDo693bJlVdllGtEeKM=
+gopkg.in/check.v1 v0.0.0-20161208181325-20d25e280405/go.mod h1:Co6ibVJAznAaIkqp8huTwlJQCZ016jof/cbN4VW5Yz0=
+gopkg.in/yaml.v3 v3.0.0-20200313102051-9f266ea9e77c/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
+gopkg.in/yaml.v3 v3.0.1 h1:fxVm/GzAzEWqLHuvctI91KS9hhNmmWOoWu0XTYJS7CA=
+gopkg.in/yaml.v3 v3.0.1/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=

bindings/go/params.go

@@ -0,0 +1,156 @@
+package whisper
+
+import (
+	"fmt"
+)
+
+///////////////////////////////////////////////////////////////////////////////
+// CGO
+
+/*
+#include <whisper.h>
+*/
+import "C"
+
+///////////////////////////////////////////////////////////////////////////////
+// PUBLIC METHODS
+
+func (p *Params) SetTranslate(v bool) {
+	p.translate = toBool(v)
+}
+
+func (p *Params) SetNoContext(v bool) {
+	p.no_context = toBool(v)
+}
+
+func (p *Params) SetSingleSegment(v bool) {
+	p.single_segment = toBool(v)
+}
+
+func (p *Params) SetPrintSpecial(v bool) {
+	p.print_special = toBool(v)
+}
+
+func (p *Params) SetPrintProgress(v bool) {
+	p.print_progress = toBool(v)
+}
+
+func (p *Params) SetPrintRealtime(v bool) {
+	p.print_realtime = toBool(v)
+}
+
+func (p *Params) SetPrintTimestamps(v bool) {
+	p.print_timestamps = toBool(v)
+}
+
+func (p *Params) SetSpeedup(v bool) {
+	p.speed_up = toBool(v)
+}
+
+// Set language id
+func (p *Params) SetLanguage(lang int) error {
+	str := C.whisper_lang_str(C.int(lang))
+	if str == nil {
+		return ErrInvalidLanguage
+	} else {
+		p.language = str
+	}
+	return nil
+}
+
+// Get language id
+func (p *Params) Language() int {
+	if p.language == nil {
+		return -1
+	}
+	return int(C.whisper_lang_id(p.language))
+}
+
+// Set number of threads to use
+func (p *Params) SetThreads(threads int) {
+	p.n_threads = C.int(threads)
+}
+
+// Set start offset in ms
+func (p *Params) SetOffset(offset_ms int) {
+	p.offset_ms = C.int(offset_ms)
+}
+
+// Set audio duration to process in ms
+func (p *Params) SetDuration(duration_ms int) {
+	p.duration_ms = C.int(duration_ms)
+}
+
+// Set timestamp token probability threshold (~0.01)
+func (p *Params) SetTokenThreshold(t float32) {
+	p.thold_pt = C.float(t)
+}
+
+// Set timestamp token sum probability threshold (~0.01)
+func (p *Params) SetTokenSumThreshold(t float32) {
+	p.thold_ptsum = C.float(t)
+}
+
+// Set max segment length in characters
+func (p *Params) SetMaxSegmentLength(n int) {
+	p.max_len = C.int(n)
+}
+
+// Set max tokens per segment (0 = no limit)
+func (p *Params) SetMaxTokensPerSegment(n int) {
+	p.max_tokens = C.int(n)
+}
+
+///////////////////////////////////////////////////////////////////////////////
+// PRIVATE METHODS
+
+func toBool(v bool) C.bool {
+	if v {
+		return C.bool(true)
+	}
+	return C.bool(false)
+}
+
+///////////////////////////////////////////////////////////////////////////////
+// STRINGIFY
+
+func (p *Params) String() string {
+	str := "<whisper.params"
+	str += fmt.Sprintf(" strategy=%v", p.strategy)
+	str += fmt.Sprintf(" n_threads=%d", p.n_threads)
+	if p.language != nil {
+		str += fmt.Sprintf(" language=%s", C.GoString(p.language))
+	}
+	str += fmt.Sprintf(" n_max_text_ctx=%d", p.n_max_text_ctx)
+	str += fmt.Sprintf(" offset_ms=%d", p.offset_ms)
+	str += fmt.Sprintf(" duration_ms=%d", p.duration_ms)
+	if p.translate {
+		str += " translate"
+	}
+	if p.no_context {
+		str += " no_context"
+	}
+	if p.single_segment {
+		str += " single_segment"
+	}
+	if p.print_special {
+		str += " print_special"
+	}
+	if p.print_progress {
+		str += " print_progress"
+	}
+	if p.print_realtime {
+		str += " print_realtime"
+	}
+	if p.print_timestamps {
+		str += " print_timestamps"
+	}
+	if p.token_timestamps {
+		str += " token_timestamps"
+	}
+	if p.speed_up {
+		str += " speed_up"
+	}
+
+	return str + ">"
+}

bindings/go/pkg/whisper/consts.go

@@ -0,0 +1,28 @@
+package whisper
+
+import (
+	"errors"
+
+	// Bindings
+	whisper "github.com/ggerganov/whisper.cpp/bindings/go"
+)
+
+///////////////////////////////////////////////////////////////////////////////
+// ERRORS
+
+var (
+	ErrUnableToLoadModel    = errors.New("unable to load model")
+	ErrInternalAppError     = errors.New("internal application error")
+	ErrProcessingFailed     = errors.New("processing failed")
+	ErrUnsupportedLanguage  = errors.New("unsupported language")
+	ErrModelNotMultilingual = errors.New("model is not multilingual")
+)
+
+///////////////////////////////////////////////////////////////////////////////
+// CONSTANTS
+
+// SampleRate is the sample rate of the audio data.
+const SampleRate = whisper.SampleRate
+
+// SampleBits is the number of bytes per sample.
+const SampleBits = whisper.SampleBits

bindings/go/pkg/whisper/context.go

@@ -0,0 +1,251 @@
+package whisper
+
+import (
+	"io"
+	"strings"
+	"time"
+
+	// Bindings
+	whisper "github.com/ggerganov/whisper.cpp/bindings/go"
+)
+
+///////////////////////////////////////////////////////////////////////////////
+// TYPES
+
+type context struct {
+	n      int
+	model  *model
+	params whisper.Params
+}
+
+// Make sure context adheres to the interface
+var _ Context = (*context)(nil)
+
+///////////////////////////////////////////////////////////////////////////////
+// LIFECYCLE
+
+func newContext(model *model, params whisper.Params) (Context, error) {
+	context := new(context)
+	context.model = model
+	context.params = params
+
+	// Return success
+	return context, nil
+}
+
+///////////////////////////////////////////////////////////////////////////////
+// PUBLIC METHODS
+
+// Set the language to use for speech recognition.
+func (context *context) SetLanguage(lang string) error {
+	if context.model.ctx == nil {
+		return ErrInternalAppError
+	}
+	if !context.model.IsMultilingual() {
+		return ErrModelNotMultilingual
+	}
+	if id := context.model.ctx.Whisper_lang_id(lang); id < 0 {
+		return ErrUnsupportedLanguage
+	} else if err := context.params.SetLanguage(id); err != nil {
+		return err
+	}
+	// Return success
+	return nil
+}
+
+func (context *context) IsMultilingual() bool {
+	return context.model.IsMultilingual()
+}
+
+// Get language
+func (context *context) Language() string {
+	return whisper.Whisper_lang_str(context.params.Language())
+}
+
+// Set translate flag
+func (context *context) SetTranslate(v bool) {
+	context.params.SetTranslate(v)
+}
+
+// Set speedup flag
+func (context *context) SetSpeedup(v bool) {
+	context.params.SetSpeedup(v)
+}
+
+// Set number of threads to use
+func (context *context) SetThreads(v uint) {
+	context.params.SetThreads(int(v))
+}
+
+// Set time offset
+func (context *context) SetOffset(v time.Duration) {
+	context.params.SetOffset(int(v.Milliseconds()))
+}
+
+// Set duration of audio to process
+func (context *context) SetDuration(v time.Duration) {
+	context.params.SetOffset(int(v.Milliseconds()))
+}
+
+// Set timestamp token probability threshold (~0.01)
+func (context *context) SetTokenThreshold(t float32) {
+	context.params.SetTokenThreshold(t)
+}
+
+// Set timestamp token sum probability threshold (~0.01)
+func (context *context) SetTokenSumThreshold(t float32) {
+	context.params.SetTokenSumThreshold(t)
+}
+
+// Set max segment length in characters
+func (context *context) SetMaxSegmentLength(n uint) {
+	context.params.SetMaxSegmentLength(int(n))
+}
+
+// Set max tokens per segment (0 = no limit)
+func (context *context) SetMaxTokensPerSegment(n uint) {
+	context.params.SetMaxTokensPerSegment(int(n))
+}
+
+// Process new sample data and return any errors
+func (context *context) Process(data []float32, cb SegmentCallback) error {
+	if context.model.ctx == nil {
+		return ErrInternalAppError
+	}
+	// If the callback is defined then we force on single_segment mode
+	if cb != nil {
+		context.params.SetSingleSegment(true)
+	}
+
+	// We don't do parallel processing at the moment
+	processors := 0
+	if processors > 1 {
+		if err := context.model.ctx.Whisper_full_parallel(context.params, data, processors, nil, func(new int) {
+			if cb != nil {
+				num_segments := context.model.ctx.Whisper_full_n_segments()
+				s0 := num_segments - new
+				for i := s0; i < num_segments; i++ {
+					cb(toSegment(context.model.ctx, i))
+				}
+			}
+		}); err != nil {
+			return err
+		}
+	} else if err := context.model.ctx.Whisper_full(context.params, data, nil, func(new int) {
+		if cb != nil {
+			num_segments := context.model.ctx.Whisper_full_n_segments()
+			s0 := num_segments - new
+			for i := s0; i < num_segments; i++ {
+				cb(toSegment(context.model.ctx, i))
+			}
+		}
+	}); err != nil {
+		return err
+	}
+
+	// Return success
+	return nil
+}
+
+// Return the next segment of tokens
+func (context *context) NextSegment() (Segment, error) {
+	if context.model.ctx == nil {
+		return Segment{}, ErrInternalAppError
+	}
+	if context.n >= context.model.ctx.Whisper_full_n_segments() {
+		return Segment{}, io.EOF
+	}
+
+	// Populate result
+	result := toSegment(context.model.ctx, context.n)
+
+	// Increment the cursor
+	context.n++
+
+	// Return success
+	return result, nil
+}
+
+// Test for text tokens
+func (context *context) IsText(t Token) bool {
+	switch {
+	case context.IsBEG(t):
+		return false
+	case context.IsSOT(t):
+		return false
+	case whisper.Token(t.Id) >= context.model.ctx.Whisper_token_eot():
+		return false
+	case context.IsPREV(t):
+		return false
+	case context.IsSOLM(t):
+		return false
+	case context.IsNOT(t):
+		return false
+	default:
+		return true
+	}
+}
+
+// Test for "begin" token
+func (context *context) IsBEG(t Token) bool {
+	return whisper.Token(t.Id) == context.model.ctx.Whisper_token_beg()
+}
+
+// Test for "start of transcription" token
+func (context *context) IsSOT(t Token) bool {
+	return whisper.Token(t.Id) == context.model.ctx.Whisper_token_sot()
+}
+
+// Test for "end of transcription" token
+func (context *context) IsEOT(t Token) bool {
+	return whisper.Token(t.Id) == context.model.ctx.Whisper_token_eot()
+}
+
+// Test for "start of prev" token
+func (context *context) IsPREV(t Token) bool {
+	return whisper.Token(t.Id) == context.model.ctx.Whisper_token_prev()
+}
+
+// Test for "start of lm" token
+func (context *context) IsSOLM(t Token) bool {
+	return whisper.Token(t.Id) == context.model.ctx.Whisper_token_solm()
+}
+
+// Test for "No timestamps" token
+func (context *context) IsNOT(t Token) bool {
+	return whisper.Token(t.Id) == context.model.ctx.Whisper_token_not()
+}
+
+// Test for token associated with a specific language
+func (context *context) IsLANG(t Token, lang string) bool {
+	if id := context.model.ctx.Whisper_lang_id(lang); id >= 0 {
+		return whisper.Token(t.Id) == context.model.ctx.Whisper_token_lang(id)
+	} else {
+		return false
+	}
+}
+
+///////////////////////////////////////////////////////////////////////////////
+// PRIVATE METHODS
+
+func toSegment(ctx *whisper.Context, n int) Segment {
+	return Segment{
+		Num:    n,
+		Text:   strings.TrimSpace(ctx.Whisper_full_get_segment_text(n)),
+		Start:  time.Duration(ctx.Whisper_full_get_segment_t0(n)) * time.Millisecond * 10,
+		End:    time.Duration(ctx.Whisper_full_get_segment_t1(n)) * time.Millisecond * 10,
+		Tokens: toTokens(ctx, n),
+	}
+}
+
+func toTokens(ctx *whisper.Context, n int) []Token {
+	result := make([]Token, ctx.Whisper_full_n_tokens(n))
+	for i := 0; i < len(result); i++ {
+		result[i] = Token{
+			Id:   int(ctx.Whisper_full_get_token_id(n, i)),
+			Text: strings.TrimSpace(ctx.Whisper_full_get_token_text(n, i)),
+			P:    ctx.Whisper_full_get_token_p(n, i),
+		}
+	}
+	return result
+}

bindings/go/pkg/whisper/context_test.go

@@ -0,0 +1,55 @@
+package whisper_test
+
+import (
+	"os"
+	"testing"
+
+	// Packages
+	whisper "github.com/ggerganov/whisper.cpp/bindings/go/pkg/whisper"
+	assert "github.com/stretchr/testify/assert"
+)
+
+const (
+	ModelPath  = "../../models/ggml-tiny.bin"
+	SamplePath = "../../samples/jfk.wav"
+)
+
+func Test_Whisper_000(t *testing.T) {
+	assert := assert.New(t)
+	if _, err := os.Stat(ModelPath); os.IsNotExist(err) {
+		t.Skip("Skipping test, model not found:", ModelPath)
+	}
+	if _, err := os.Stat(SamplePath); os.IsNotExist(err) {
+		t.Skip("Skipping test, sample not found:", SamplePath)
+	}
+
+	// Load model
+	model, err := whisper.New(ModelPath)
+	assert.NoError(err)
+	assert.NotNil(model)
+	assert.NoError(model.Close())
+
+	t.Log("languages=", model.Languages())
+}
+
+func Test_Whisper_001(t *testing.T) {
+	assert := assert.New(t)
+	if _, err := os.Stat(ModelPath); os.IsNotExist(err) {
+		t.Skip("Skipping test, model not found:", ModelPath)
+	}
+	if _, err := os.Stat(SamplePath); os.IsNotExist(err) {
+		t.Skip("Skipping test, sample not found:", SamplePath)
+	}
+
+	// Load model
+	model, err := whisper.New(ModelPath)
+	assert.NoError(err)
+	assert.NotNil(model)
+	defer model.Close()
+
+	// Get context for decoding
+	ctx, err := model.NewContext()
+	assert.NoError(err)
+	assert.NotNil(ctx)
+
+}

bindings/go/pkg/whisper/doc.go

@@ -0,0 +1,4 @@
+/*
+This is the higher-level speech-to-text whisper.cpp API for go
+*/
+package whisper

bindings/go/pkg/whisper/interface.go

@@ -0,0 +1,85 @@
+package whisper
+
+import (
+	"io"
+	"time"
+)
+
+///////////////////////////////////////////////////////////////////////////////
+// TYPES
+
+// SegmentCallback is the callback function for processing segments in real
+// time. It is called during the Process function
+type SegmentCallback func(Segment)
+
+// Model is the interface to a whisper model. Create a new model with the
+// function whisper.New(string)
+type Model interface {
+	io.Closer
+
+	// Return a new speech-to-text context.
+	NewContext() (Context, error)
+
+	// Return true if the model is multilingual.
+	IsMultilingual() bool
+
+	// Return all languages supported.
+	Languages() []string
+}
+
+// Context is the speach recognition context.
+type Context interface {
+	SetLanguage(string) error // Set the language to use for speech recognition.
+	SetTranslate(bool)        // Set translate flag
+	IsMultilingual() bool     // Return true if the model is multilingual.
+	Language() string         // Get language
+
+	SetOffset(time.Duration)      // Set offset
+	SetDuration(time.Duration)    // Set duration
+	SetThreads(uint)              // Set number of threads to use
+	SetSpeedup(bool)              // Set speedup flag
+	SetTokenThreshold(float32)    // Set timestamp token probability threshold
+	SetTokenSumThreshold(float32) // Set timestamp token sum probability threshold
+	SetMaxSegmentLength(uint)     // Set max segment length in characters
+	SetMaxTokensPerSegment(uint)  // Set max tokens per segment (0 = no limit)
+
+	// Process mono audio data and return any errors.
+	// If defined, newly generated segments are passed to the
+	// callback function during processing.
+	Process([]float32, SegmentCallback) error
+
+	// After process is called, return segments until the end of the stream
+	// is reached, when io.EOF is returned.
+	NextSegment() (Segment, error)
+
+	IsBEG(Token) bool          // Test for "begin" token
+	IsSOT(Token) bool          // Test for "start of transcription" token
+	IsEOT(Token) bool          // Test for "end of transcription" token
+	IsPREV(Token) bool         // Test for "start of prev" token
+	IsSOLM(Token) bool         // Test for "start of lm" token
+	IsNOT(Token) bool          // Test for "No timestamps" token
+	IsLANG(Token, string) bool // Test for token associated with a specific language
+	IsText(Token) bool         // Test for text token
+}
+
+// Segment is the text result of a speech recognition.
+type Segment struct {
+	// Segment Number
+	Num int
+
+	// Time beginning and end timestamps for the segment.
+	Start, End time.Duration
+
+	// The text of the segment.
+	Text string
+
+	// The tokens of the segment.
+	Tokens []Token
+}
+
+// Token is a text or special token
+type Token struct {
+	Id   int
+	Text string
+	P    float32
+}

bindings/go/pkg/whisper/model.go

@@ -0,0 +1,100 @@
+package whisper
+
+import (
+	"fmt"
+	"os"
+	"runtime"
+
+	// Bindings
+	whisper "github.com/ggerganov/whisper.cpp/bindings/go"
+)
+
+///////////////////////////////////////////////////////////////////////////////
+// TYPES
+
+type model struct {
+	path string
+	ctx  *whisper.Context
+}
+
+// Make sure model adheres to the interface
+var _ Model = (*model)(nil)
+
+///////////////////////////////////////////////////////////////////////////////
+// LIFECYCLE
+
+func New(path string) (Model, error) {
+	model := new(model)
+	if _, err := os.Stat(path); err != nil {
+		return nil, err
+	} else if ctx := whisper.Whisper_init(path); ctx == nil {
+		return nil, ErrUnableToLoadModel
+	} else {
+		model.ctx = ctx
+		model.path = path
+	}
+
+	// Return success
+	return model, nil
+}
+
+func (model *model) Close() error {
+	if model.ctx != nil {
+		model.ctx.Whisper_free()
+	}
+
+	// Release resources
+	model.ctx = nil
+
+	// Return success
+	return nil
+}
+
+///////////////////////////////////////////////////////////////////////////////
+// STRINGIFY
+
+func (model *model) String() string {
+	str := "<whisper.model"
+	if model.ctx != nil {
+		str += fmt.Sprintf(" model=%q", model.path)
+	}
+	return str + ">"
+}
+
+///////////////////////////////////////////////////////////////////////////////
+// PUBLIC METHODS
+
+// Return true if model is multilingual (language and translation options are supported)
+func (model *model) IsMultilingual() bool {
+	return model.ctx.Whisper_is_multilingual() != 0
+}
+
+// Return all recognized languages. Initially it is set to auto-detect
+func (model *model) Languages() []string {
+	result := make([]string, 0, whisper.Whisper_lang_max_id())
+	for i := 0; i < whisper.Whisper_lang_max_id(); i++ {
+		str := whisper.Whisper_lang_str(i)
+		if model.ctx.Whisper_lang_id(str) >= 0 {
+			result = append(result, str)
+		}
+	}
+	return result
+}
+
+func (model *model) NewContext() (Context, error) {
+	if model.ctx == nil {
+		return nil, ErrInternalAppError
+	}
+
+	// Create new context
+	params := model.ctx.Whisper_full_default_params(whisper.SAMPLING_GREEDY)
+	params.SetTranslate(false)
+	params.SetPrintSpecial(false)
+	params.SetPrintProgress(false)
+	params.SetPrintRealtime(false)
+	params.SetPrintTimestamps(false)
+	params.SetThreads(runtime.NumCPU())
+
+	// Return new context
+	return newContext(model, params)
+}

bindings/go/whisper.go

@@ -0,0 +1,419 @@
+package whisper
+
+import (
+	"errors"
+	"unsafe"
+)
+
+///////////////////////////////////////////////////////////////////////////////
+// CGO
+
+/*
+#cgo LDFLAGS: -lwhisper -lm -lstdc++
+#cgo darwin LDFLAGS: -framework Accelerate
+#include <whisper.h>
+#include <stdlib.h>
+
+extern void callNewSegment(void* user_data, int new);
+extern bool callEncoderBegin(void* user_data);
+
+// Text segment callback
+// Called on every newly generated text segment
+// Use the whisper_full_...() functions to obtain the text segments
+static void whisper_new_segment_cb(struct whisper_context* ctx, int n_new, void* user_data) {
+    if(user_data != NULL && ctx != NULL) {
+        callNewSegment(user_data, n_new);
+    }
+}
+
+// Encoder begin callback
+// If not NULL, called before the encoder starts
+// If it returns false, the computation is aborted
+static bool whisper_encoder_begin_cb(struct whisper_context* ctx, void* user_data) {
+    if(user_data != NULL && ctx != NULL) {
+        return callEncoderBegin(user_data);
+    }
+    return false;
+}
+
+// Get default parameters and set callbacks
+static struct whisper_full_params whisper_full_default_params_cb(struct whisper_context* ctx, enum whisper_sampling_strategy strategy) {
+	struct whisper_full_params params = whisper_full_default_params(strategy);
+	params.new_segment_callback = whisper_new_segment_cb;
+	params.new_segment_callback_user_data = (void*)(ctx);
+	params.encoder_begin_callback = whisper_encoder_begin_cb;
+	params.encoder_begin_callback_user_data = (void*)(ctx);
+	return params;
+}
+*/
+import "C"
+
+///////////////////////////////////////////////////////////////////////////////
+// TYPES
+
+type (
+	Context          C.struct_whisper_context
+	Token            C.whisper_token
+	TokenData        C.struct_whisper_token_data
+	SamplingStrategy C.enum_whisper_sampling_strategy
+	Params           C.struct_whisper_full_params
+)
+
+///////////////////////////////////////////////////////////////////////////////
+// GLOBALS
+
+const (
+	SAMPLING_GREEDY      SamplingStrategy = C.WHISPER_SAMPLING_GREEDY
+	SAMPLING_BEAM_SEARCH SamplingStrategy = C.WHISPER_SAMPLING_BEAM_SEARCH
+)
+
+const (
+	SampleRate = C.WHISPER_SAMPLE_RATE                 // Expected sample rate, samples per second
+	SampleBits = uint16(unsafe.Sizeof(C.float(0))) * 8 // Sample size in bits
+	NumFFT     = C.WHISPER_N_FFT
+	NumMEL     = C.WHISPER_N_MEL
+	HopLength  = C.WHISPER_HOP_LENGTH
+	ChunkSize  = C.WHISPER_CHUNK_SIZE
+)
+
+var (
+	ErrTokenizerFailed  = errors.New("whisper_tokenize failed")
+	ErrAutoDetectFailed = errors.New("whisper_lang_auto_detect failed")
+	ErrConversionFailed = errors.New("whisper_convert failed")
+	ErrInvalidLanguage  = errors.New("invalid language")
+)
+
+///////////////////////////////////////////////////////////////////////////////
+// PUBLIC METHODS
+
+// Allocates all memory needed for the model and loads the model from the given file.
+// Returns NULL on failure.
+func Whisper_init(path string) *Context {
+	cPath := C.CString(path)
+	defer C.free(unsafe.Pointer(cPath))
+	if ctx := C.whisper_init_from_file(cPath); ctx != nil {
+		return (*Context)(ctx)
+	} else {
+		return nil
+	}
+}
+
+// Frees all memory allocated by the model.
+func (ctx *Context) Whisper_free() {
+	C.whisper_free((*C.struct_whisper_context)(ctx))
+}
+
+// Convert RAW PCM audio to log mel spectrogram.
+// The resulting spectrogram is stored inside the provided whisper context.
+func (ctx *Context) Whisper_pcm_to_mel(data []float32, threads int) error {
+	if C.whisper_pcm_to_mel((*C.struct_whisper_context)(ctx), (*C.float)(&data[0]), C.int(len(data)), C.int(threads)) == 0 {
+		return nil
+	} else {
+		return ErrConversionFailed
+	}
+}
+
+// This can be used to set a custom log mel spectrogram inside the provided whisper context.
+// Use this instead of whisper_pcm_to_mel() if you want to provide your own log mel spectrogram.
+// n_mel must be 80
+func (ctx *Context) Whisper_set_mel(data []float32, n_mel int) error {
+	if C.whisper_set_mel((*C.struct_whisper_context)(ctx), (*C.float)(&data[0]), C.int(len(data)), C.int(n_mel)) == 0 {
+		return nil
+	} else {
+		return ErrConversionFailed
+	}
+}
+
+// Run the Whisper encoder on the log mel spectrogram stored inside the provided whisper context.
+// Make sure to call whisper_pcm_to_mel() or whisper_set_mel() first.
+// offset can be used to specify the offset of the first frame in the spectrogram.
+func (ctx *Context) Whisper_encode(offset, threads int) error {
+	if C.whisper_encode((*C.struct_whisper_context)(ctx), C.int(offset), C.int(threads)) == 0 {
+		return nil
+	} else {
+		return ErrConversionFailed
+	}
+}
+
+// Run the Whisper decoder to obtain the logits and probabilities for the next token.
+// Make sure to call whisper_encode() first.
+// tokens + n_tokens is the provided context for the decoder.
+// n_past is the number of tokens to use from previous decoder calls.
+func (ctx *Context) Whisper_decode(tokens []Token, past, threads int) error {
+	if C.whisper_decode((*C.struct_whisper_context)(ctx), (*C.whisper_token)(&tokens[0]), C.int(len(tokens)), C.int(past), C.int(threads)) == 0 {
+		return nil
+	} else {
+		return ErrConversionFailed
+	}
+}
+
+// whisper_sample_best() returns the token with the highest probability
+func (ctx *Context) Whisper_sample_best() TokenData {
+	return TokenData(C.whisper_sample_best((*C.struct_whisper_context)(ctx)))
+}
+
+// whisper_sample_timestamp() returns the most probable timestamp token
+func (ctx *Context) Whisper_sample_timestamp(is_initial bool) TokenData {
+	return TokenData(C.whisper_sample_timestamp((*C.struct_whisper_context)(ctx), C.bool(is_initial)))
+}
+
+// Convert the provided text into tokens. The tokens pointer must be large enough to hold the resulting tokens.
+// Returns the number of tokens on success
+func (ctx *Context) Whisper_tokenize(text string, tokens []Token) (int, error) {
+	cText := C.CString(text)
+	defer C.free(unsafe.Pointer(cText))
+	if n := C.whisper_tokenize((*C.struct_whisper_context)(ctx), cText, (*C.whisper_token)(&tokens[0]), C.int(len(tokens))); n >= 0 {
+		return int(n), nil
+	} else {
+		return 0, ErrTokenizerFailed
+	}
+}
+
+// Return the id of the specified language, returns -1 if not found
+// Examples:
+//
+//	"de" -> 2
+//	"german" -> 2
+func (ctx *Context) Whisper_lang_id(lang string) int {
+	return int(C.whisper_lang_id(C.CString(lang)))
+}
+
+// Largest language id (i.e. number of available languages - 1)
+func Whisper_lang_max_id() int {
+	return int(C.whisper_lang_max_id())
+}
+
+// Return the short string of the specified language id (e.g. 2 -> "de"),
+// returns empty string if not found
+func Whisper_lang_str(id int) string {
+	return C.GoString(C.whisper_lang_str(C.int(id)))
+}
+
+// Use mel data at offset_ms to try and auto-detect the spoken language
+// Make sure to call whisper_pcm_to_mel() or whisper_set_mel() first.
+// Returns the probabilities of all languages.
+// ref: https://github.com/openai/whisper/blob/main/whisper/decoding.py#L18-L69
+func (ctx *Context) Whisper_lang_auto_detect(offset_ms, n_threads int) ([]float32, error) {
+	probs := make([]float32, Whisper_lang_max_id()+1)
+	if n := int(C.whisper_lang_auto_detect((*C.struct_whisper_context)(ctx), C.int(offset_ms), C.int(n_threads), (*C.float)(&probs[0]))); n < 0 {
+		return nil, ErrAutoDetectFailed
+	} else {
+		return probs, nil
+	}
+}
+
+func (ctx *Context) Whisper_n_len() int {
+	return int(C.whisper_n_len((*C.struct_whisper_context)(ctx)))
+}
+
+func (ctx *Context) Whisper_n_vocab() int {
+	return int(C.whisper_n_vocab((*C.struct_whisper_context)(ctx)))
+}
+
+func (ctx *Context) Whisper_n_text_ctx() int {
+	return int(C.whisper_n_text_ctx((*C.struct_whisper_context)(ctx)))
+}
+
+func (ctx *Context) Whisper_n_audio_ctx() int {
+	return int(C.whisper_n_audio_ctx((*C.struct_whisper_context)(ctx)))
+}
+
+func (ctx *Context) Whisper_is_multilingual() int {
+	return int(C.whisper_is_multilingual((*C.struct_whisper_context)(ctx)))
+}
+
+// The probabilities for the next token
+//func (ctx *Whisper_context) Whisper_get_probs() []float32 {
+//	return (*[1 << 30]float32)(unsafe.Pointer(C.whisper_get_probs((*C.struct_whisper_context)(ctx))))[:ctx.Whisper_n_vocab()]
+//}
+
+// Token Id -> String. Uses the vocabulary in the provided context
+func (ctx *Context) Whisper_token_to_str(token Token) string {
+	return C.GoString(C.whisper_token_to_str((*C.struct_whisper_context)(ctx), C.whisper_token(token)))
+}
+
+// Special tokens
+func (ctx *Context) Whisper_token_eot() Token {
+	return Token(C.whisper_token_eot((*C.struct_whisper_context)(ctx)))
+}
+
+// Special tokens
+func (ctx *Context) Whisper_token_sot() Token {
+	return Token(C.whisper_token_sot((*C.struct_whisper_context)(ctx)))
+}
+
+// Special tokens
+func (ctx *Context) Whisper_token_prev() Token {
+	return Token(C.whisper_token_prev((*C.struct_whisper_context)(ctx)))
+}
+
+// Special tokens
+func (ctx *Context) Whisper_token_solm() Token {
+	return Token(C.whisper_token_solm((*C.struct_whisper_context)(ctx)))
+}
+
+// Special tokens
+func (ctx *Context) Whisper_token_not() Token {
+	return Token(C.whisper_token_not((*C.struct_whisper_context)(ctx)))
+}
+
+// Special tokens
+func (ctx *Context) Whisper_token_beg() Token {
+	return Token(C.whisper_token_beg((*C.struct_whisper_context)(ctx)))
+}
+
+// Special tokens
+func (ctx *Context) Whisper_token_lang(lang_id int) Token {
+	return Token(C.whisper_token_lang((*C.struct_whisper_context)(ctx), C.int(lang_id)))
+}
+
+// Task tokens
+func Whisper_token_translate() Token {
+	return Token(C.whisper_token_translate())
+}
+
+// Task tokens
+func Whisper_token_transcribe() Token {
+	return Token(C.whisper_token_transcribe())
+}
+
+// Performance information
+func (ctx *Context) Whisper_print_timings() {
+	C.whisper_print_timings((*C.struct_whisper_context)(ctx))
+}
+
+// Performance information
+func (ctx *Context) Whisper_reset_timings() {
+	C.whisper_reset_timings((*C.struct_whisper_context)(ctx))
+}
+
+// Print system information
+func Whisper_print_system_info() string {
+	return C.GoString(C.whisper_print_system_info())
+}
+
+// Return default parameters for a strategy
+func (ctx *Context) Whisper_full_default_params(strategy SamplingStrategy) Params {
+	// Get default parameters
+	return Params(C.whisper_full_default_params_cb((*C.struct_whisper_context)(ctx), C.enum_whisper_sampling_strategy(strategy)))
+}
+
+// Run the entire model: PCM -> log mel spectrogram -> encoder -> decoder -> text
+// Uses the specified decoding strategy to obtain the text.
+func (ctx *Context) Whisper_full(params Params, samples []float32, encoderBeginCallback func() bool, newSegmentCallback func(int)) error {
+	registerEncoderBeginCallback(ctx, encoderBeginCallback)
+	registerNewSegmentCallback(ctx, newSegmentCallback)
+	defer registerEncoderBeginCallback(ctx, nil)
+	defer registerNewSegmentCallback(ctx, nil)
+	if C.whisper_full((*C.struct_whisper_context)(ctx), (C.struct_whisper_full_params)(params), (*C.float)(&samples[0]), C.int(len(samples))) == 0 {
+		return nil
+	} else {
+		return ErrConversionFailed
+	}
+}
+
+// Split the input audio in chunks and process each chunk separately using whisper_full()
+// It seems this approach can offer some speedup in some cases.
+// However, the transcription accuracy can be worse at the beginning and end of each chunk.
+func (ctx *Context) Whisper_full_parallel(params Params, samples []float32, processors int, encoderBeginCallback func() bool, newSegmentCallback func(int)) error {
+	registerEncoderBeginCallback(ctx, encoderBeginCallback)
+	registerNewSegmentCallback(ctx, newSegmentCallback)
+	defer registerEncoderBeginCallback(ctx, nil)
+	defer registerNewSegmentCallback(ctx, nil)
+
+	if C.whisper_full_parallel((*C.struct_whisper_context)(ctx), (C.struct_whisper_full_params)(params), (*C.float)(&samples[0]), C.int(len(samples)), C.int(processors)) == 0 {
+		return nil
+	} else {
+		return ErrConversionFailed
+	}
+}
+
+// Number of generated text segments.
+// A segment can be a few words, a sentence, or even a paragraph.
+func (ctx *Context) Whisper_full_n_segments() int {
+	return int(C.whisper_full_n_segments((*C.struct_whisper_context)(ctx)))
+}
+
+// Get the start and end time of the specified segment.
+func (ctx *Context) Whisper_full_get_segment_t0(segment int) int64 {
+	return int64(C.whisper_full_get_segment_t0((*C.struct_whisper_context)(ctx), C.int(segment)))
+}
+
+// Get the start and end time of the specified segment.
+func (ctx *Context) Whisper_full_get_segment_t1(segment int) int64 {
+	return int64(C.whisper_full_get_segment_t1((*C.struct_whisper_context)(ctx), C.int(segment)))
+}
+
+// Get the text of the specified segment.
+func (ctx *Context) Whisper_full_get_segment_text(segment int) string {
+	return C.GoString(C.whisper_full_get_segment_text((*C.struct_whisper_context)(ctx), C.int(segment)))
+}
+
+// Get number of tokens in the specified segment.
+func (ctx *Context) Whisper_full_n_tokens(segment int) int {
+	return int(C.whisper_full_n_tokens((*C.struct_whisper_context)(ctx), C.int(segment)))
+}
+
+// Get the token text of the specified token index in the specified segment.
+func (ctx *Context) Whisper_full_get_token_text(segment int, token int) string {
+	return C.GoString(C.whisper_full_get_token_text((*C.struct_whisper_context)(ctx), C.int(segment), C.int(token)))
+}
+
+// Get the token of the specified token index in the specified segment.
+func (ctx *Context) Whisper_full_get_token_id(segment int, token int) Token {
+	return Token(C.whisper_full_get_token_id((*C.struct_whisper_context)(ctx), C.int(segment), C.int(token)))
+}
+
+// Get token data for the specified token in the specified segment.
+// This contains probabilities, timestamps, etc.
+func (ctx *Context) whisper_full_get_token_data(segment int, token int) TokenData {
+	return TokenData(C.whisper_full_get_token_data((*C.struct_whisper_context)(ctx), C.int(segment), C.int(token)))
+}
+
+// Get the probability of the specified token in the specified segment.
+func (ctx *Context) Whisper_full_get_token_p(segment int, token int) float32 {
+	return float32(C.whisper_full_get_token_p((*C.struct_whisper_context)(ctx), C.int(segment), C.int(token)))
+}
+
+///////////////////////////////////////////////////////////////////////////////
+// CALLBACKS
+
+var (
+	cbNewSegment   = make(map[unsafe.Pointer]func(int))
+	cbEncoderBegin = make(map[unsafe.Pointer]func() bool)
+)
+
+func registerNewSegmentCallback(ctx *Context, fn func(int)) {
+	if fn == nil {
+		delete(cbNewSegment, unsafe.Pointer(ctx))
+	} else {
+		cbNewSegment[unsafe.Pointer(ctx)] = fn
+	}
+}
+
+func registerEncoderBeginCallback(ctx *Context, fn func() bool) {
+	if fn == nil {
+		delete(cbEncoderBegin, unsafe.Pointer(ctx))
+	} else {
+		cbEncoderBegin[unsafe.Pointer(ctx)] = fn
+	}
+}
+
+//export callNewSegment
+func callNewSegment(user_data unsafe.Pointer, new C.int) {
+	if fn, ok := cbNewSegment[user_data]; ok {
+		fn(int(new))
+	}
+}
+
+//export callEncoderBegin
+func callEncoderBegin(user_data unsafe.Pointer) C.bool {
+	if fn, ok := cbEncoderBegin[user_data]; ok {
+		if fn() {
+			return C.bool(true)
+		} else {
+			return C.bool(false)
+		}
+	}
+	return true
+}

bindings/go/whisper_test.go

@@ -0,0 +1,113 @@
+package whisper_test
+
+import (
+	"os"
+	"runtime"
+	"testing"
+	"time"
+
+	// Packages
+	whisper "github.com/ggerganov/whisper.cpp/bindings/go"
+	wav "github.com/go-audio/wav"
+	assert "github.com/stretchr/testify/assert"
+)
+
+const (
+	ModelPath  = "models/ggml-small.en.bin"
+	SamplePath = "samples/jfk.wav"
+)
+
+func Test_Whisper_000(t *testing.T) {
+	assert := assert.New(t)
+	if _, err := os.Stat(ModelPath); os.IsNotExist(err) {
+		t.Skip("Skipping test, model not found:", ModelPath)
+	}
+	ctx := whisper.Whisper_init(ModelPath)
+	assert.NotNil(ctx)
+	ctx.Whisper_free()
+}
+
+func Test_Whisper_001(t *testing.T) {
+	assert := assert.New(t)
+	if _, err := os.Stat(ModelPath); os.IsNotExist(err) {
+		t.Skip("Skipping test, model not found:", ModelPath)
+	}
+	if _, err := os.Stat(SamplePath); os.IsNotExist(err) {
+		t.Skip("Skipping test, sample not found:", SamplePath)
+	}
+
+	// Open samples
+	fh, err := os.Open(SamplePath)
+	assert.NoError(err)
+	defer fh.Close()
+
+	// Read samples
+	d := wav.NewDecoder(fh)
+	buf, err := d.FullPCMBuffer()
+	assert.NoError(err)
+
+	// Run whisper
+	ctx := whisper.Whisper_init(ModelPath)
+	assert.NotNil(ctx)
+	defer ctx.Whisper_free()
+	params := ctx.Whisper_full_default_params(whisper.SAMPLING_GREEDY)
+	data := buf.AsFloat32Buffer().Data
+	err = ctx.Whisper_full(params, data, nil, nil)
+	assert.NoError(err)
+
+	// Print out tokens
+	num_segments := ctx.Whisper_full_n_segments()
+	assert.GreaterOrEqual(num_segments, 1)
+	for i := 0; i < num_segments; i++ {
+		str := ctx.Whisper_full_get_segment_text(i)
+		assert.NotEmpty(str)
+		t0 := time.Duration(ctx.Whisper_full_get_segment_t0(i)) * time.Millisecond
+		t1 := time.Duration(ctx.Whisper_full_get_segment_t1(i)) * time.Millisecond
+		t.Logf("[%6s->%-6s] %q", t0, t1, str)
+	}
+}
+
+func Test_Whisper_002(t *testing.T) {
+	assert := assert.New(t)
+	for i := 0; i < whisper.Whisper_lang_max_id(); i++ {
+		str := whisper.Whisper_lang_str(i)
+		assert.NotEmpty(str)
+		t.Log(str)
+	}
+}
+
+func Test_Whisper_003(t *testing.T) {
+	threads := runtime.NumCPU()
+	assert := assert.New(t)
+	if _, err := os.Stat(ModelPath); os.IsNotExist(err) {
+		t.Skip("Skipping test, model not found:", ModelPath)
+	}
+	if _, err := os.Stat(SamplePath); os.IsNotExist(err) {
+		t.Skip("Skipping test, sample not found:", SamplePath)
+	}
+
+	// Open samples
+	fh, err := os.Open(SamplePath)
+	assert.NoError(err)
+	defer fh.Close()
+
+	// Read samples
+	d := wav.NewDecoder(fh)
+	buf, err := d.FullPCMBuffer()
+	assert.NoError(err)
+
+	// Make the model
+	ctx := whisper.Whisper_init(ModelPath)
+	assert.NotNil(ctx)
+	defer ctx.Whisper_free()
+
+	// Get MEL
+	assert.NoError(ctx.Whisper_pcm_to_mel(buf.AsFloat32Buffer().Data, threads))
+
+	// Get Languages
+	languages, err := ctx.Whisper_lang_auto_detect(0, threads)
+	assert.NoError(err)
+	for i, p := range languages {
+		t.Logf("%s: %f", whisper.Whisper_lang_str(i), p)
+	}
+}

bindings/javascript/.gitignore

@@ -0,0 +1 @@
+publish.log

bindings/javascript/CMakeLists.txt

@@ -0,0 +1,41 @@
+set(TARGET libwhisper)
+
+add_executable(${TARGET}
+    emscripten.cpp
+    )
+
+target_link_libraries(${TARGET} PRIVATE
+    whisper
+    )
+
+unset(EXTRA_FLAGS)
+
+if (WHISPER_WASM_SINGLE_FILE)
+    set(EXTRA_FLAGS "-s SINGLE_FILE=1")
+    message(STATUS "Embedding WASM inside whisper.js")
+
+    add_custom_command(
+        TARGET ${TARGET} POST_BUILD
+        COMMAND ${CMAKE_COMMAND} -E copy
+        ${CMAKE_BINARY_DIR}/bin/libwhisper.js
+        ${CMAKE_CURRENT_SOURCE_DIR}/whisper.js
+        )
+
+    add_custom_command(
+        TARGET ${TARGET} POST_BUILD
+        COMMAND ${CMAKE_COMMAND} -E copy
+        ${CMAKE_BINARY_DIR}/bin/libwhisper.worker.js
+        ${CMAKE_CURRENT_SOURCE_DIR}/libwhisper.worker.js
+        )
+endif()
+
+set_target_properties(${TARGET} PROPERTIES LINK_FLAGS " \
+    --bind \
+    -s MODULARIZE=1 \
+    -s EXPORT_NAME=\"'whisper_factory'\" \
+    -s FORCE_FILESYSTEM=1 \
+    -s USE_PTHREADS=1 \
+    -s PTHREAD_POOL_SIZE=8 \
+    -s ALLOW_MEMORY_GROWTH=1 \
+    ${EXTRA_FLAGS} \
+    ")

bindings/javascript/README.md

@@ -0,0 +1,78 @@
+# whisper.cpp
+
+Node.js package for Whisper speech recognition
+
+Package: https://www.npmjs.com/package/whisper.cpp
+
+## Details
+
+The performance is comparable to when running `whisper.cpp` in the browser via WASM.
+
+The API is currently very rudimentary: [bindings/javascript/emscripten.cpp](/bindings/javascript/emscripten.cpp)
+
+For sample usage check [tests/test-whisper.js](/tests/test-whisper.js)
+
+## Package building + test
+
+```bash
+# load emscripten
+source /path/to/emsdk/emsdk_env.sh
+
+# clone repo
+git clone https://github.com/ggerganov/whisper.cpp
+cd whisper.cpp
+
+# grab base.en model
+./models/download-ggml-model.sh base.en
+
+# prepare PCM sample for testing
+ffmpeg -i samples/jfk.wav -f f32le -acodec pcm_f32le samples/jfk.pcmf32
+
+# build
+mkdir build-em && cd build-em
+emcmake cmake .. && make -j
+
+# run test
+node --experimental-wasm-threads --experimental-wasm-simd ../tests/test-whisper.js
+
+# publish npm package
+make publish-npm
+```
+
+## Sample run
+
+```java
+$ node --experimental-wasm-threads --experimental-wasm-simd ../tests/test-whisper.js
+
+whisper_model_load: loading model from 'whisper.bin'
+whisper_model_load: n_vocab       = 51864
+whisper_model_load: n_audio_ctx   = 1500
+whisper_model_load: n_audio_state = 512
+whisper_model_load: n_audio_head  = 8
+whisper_model_load: n_audio_layer = 6
+whisper_model_load: n_text_ctx    = 448
+whisper_model_load: n_text_state  = 512
+whisper_model_load: n_text_head   = 8
+whisper_model_load: n_text_layer  = 6
+whisper_model_load: n_mels        = 80
+whisper_model_load: f16           = 1
+whisper_model_load: type          = 2
+whisper_model_load: adding 1607 extra tokens
+whisper_model_load: mem_required  =  506.00 MB
+whisper_model_load: ggml ctx size =  140.60 MB
+whisper_model_load: memory size   =   22.83 MB
+whisper_model_load: model size    =  140.54 MB
+
+system_info: n_threads = 8 / 10 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | NEON = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 1 | BLAS = 0 | 
+
+operator(): processing 176000 samples, 11.0 sec, 8 threads, 1 processors, lang = en, task = transcribe ...
+
+[00:00:00.000 --> 00:00:11.000]   And so my fellow Americans, ask not what your country can do for you, ask what you can do for your country.
+
+whisper_print_timings:     load time =   162.37 ms
+whisper_print_timings:      mel time =   183.70 ms
+whisper_print_timings:   sample time =     4.27 ms
+whisper_print_timings:   encode time =  8582.63 ms / 1430.44 ms per layer
+whisper_print_timings:   decode time =   436.16 ms / 72.69 ms per layer
+whisper_print_timings:    total time =  9370.90 ms
+```

bindings/javascript/emscripten.cpp

@@ -0,0 +1,93 @@
+//
+// This is the Javascript API of whisper.cpp
+//
+// Very crude at the moment.
+// Feel free to contribute and make this better!
+//
+// See the tests/test-whisper.js for sample usage
+//
+
+#include "whisper.h"
+
+#include <emscripten.h>
+#include <emscripten/bind.h>
+
+#include <thread>
+#include <vector>
+
+struct whisper_context * g_context;
+
+EMSCRIPTEN_BINDINGS(whisper) {
+    emscripten::function("init", emscripten::optional_override([](const std::string & path_model) {
+        if (g_context == nullptr) {
+            g_context = whisper_init_from_file(path_model.c_str());
+            if (g_context != nullptr) {
+                return true;
+            } else {
+                return false;
+            }
+        }
+
+        return false;
+    }));
+
+    emscripten::function("free", emscripten::optional_override([]() {
+        if (g_context) {
+            whisper_free(g_context);
+            g_context = nullptr;
+        }
+    }));
+
+    emscripten::function("full_default", emscripten::optional_override([](const emscripten::val & audio, const std::string & lang, bool translate) {
+        if (g_context == nullptr) {
+            return -1;
+        }
+
+        struct whisper_full_params params = whisper_full_default_params(whisper_sampling_strategy::WHISPER_SAMPLING_GREEDY);
+
+        params.print_realtime   = true;
+        params.print_progress   = false;
+        params.print_timestamps = true;
+        params.print_special    = false;
+        params.translate        = translate;
+        params.language         = whisper_is_multilingual(g_context) ? lang.c_str() : "en";
+        params.n_threads        = std::min(8, (int) std::thread::hardware_concurrency());
+        params.offset_ms        = 0;
+
+        std::vector<float> pcmf32;
+        const int n = audio["length"].as<int>();
+
+        emscripten::val heap = emscripten::val::module_property("HEAPU8");
+        emscripten::val memory = heap["buffer"];
+
+        pcmf32.resize(n);
+
+        emscripten::val memoryView = audio["constructor"].new_(memory, reinterpret_cast<uintptr_t>(pcmf32.data()), n);
+        memoryView.call<void>("set", audio);
+
+        // print system information
+        {
+            printf("\n");
+            printf("system_info: n_threads = %d / %d | %s\n",
+                    params.n_threads, std::thread::hardware_concurrency(), whisper_print_system_info());
+
+            printf("\n");
+            printf("%s: processing %d samples, %.1f sec, %d threads, %d processors, lang = %s, task = %s ...\n",
+                    __func__, int(pcmf32.size()), float(pcmf32.size())/WHISPER_SAMPLE_RATE,
+                    params.n_threads, 1,
+                    params.language,
+                    params.translate ? "translate" : "transcribe");
+
+            printf("\n");
+        }
+
+        // run whisper
+        {
+            whisper_reset_timings(g_context);
+            whisper_full(g_context, params, pcmf32.data(), pcmf32.size());
+            whisper_print_timings(g_context);
+        }
+
+        return 0;
+    }));
+}

bindings/javascript/libwhisper.worker.js

@@ -0,0 +1 @@
+"use strict";var Module={};var ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require("worker_threads");var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",data=>onmessage({data:data}));var fs=require("fs");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,"utf8")+"//# sourceURL="+f)},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}var initializedJS=false;var pendingNotifiedProxyingQueues=[];function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+"\n");return}console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;self.alert=threadAlert;Module["instantiateWasm"]=(info,receiveInstance)=>{var instance=new WebAssembly.Instance(Module["wasmModule"],info);receiveInstance(instance);Module["wasmModule"]=null;return instance.exports};self.onunhandledrejection=e=>{throw e.reason??e};self.onmessage=e=>{try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;for(const handler of e.data.handlers){Module[handler]=function(){postMessage({cmd:"callHandler",handler:handler,args:[...arguments]})}}Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob=="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}whisper_factory(Module).then(function(instance){Module=instance})}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.pthread_ptr,0,0,1);Module["establishStackSpace"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].threadInitTLS();if(!initializedJS){Module["__embind_initialize_bindings"]();pendingNotifiedProxyingQueues.forEach(queue=>{Module["executeNotifiedProxyingQueue"](queue)});pendingNotifiedProxyingQueues=[];initializedJS=true}try{Module["invokeEntryPoint"](e.data.start_routine,e.data.arg)}catch(ex){if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["keepRuntimeAlive"]()){}else{Module["__emscripten_thread_exit"](ex.status)}}else{throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["__emscripten_thread_exit"](-1)}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processProxyingQueue"){if(initializedJS){Module["executeNotifiedProxyingQueue"](e.data.queue)}else{pendingNotifiedProxyingQueues.push(e.data.queue)}}else if(e.data.cmd){err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){if(Module["__emscripten_thread_crashed"]){Module["__emscripten_thread_crashed"]()}throw ex}};

bindings/javascript/package-tmpl.json

@@ -0,0 +1,26 @@
+{
+  "name": "whisper.cpp",
+  "version": "@PROJECT_VERSION@",
+  "description": "Whisper speech recognition",
+  "main": "whisper.js",
+  "scripts": {
+    "test": "echo \"todo: add tests\" && exit 0"
+  },
+  "repository": {
+    "type": "git",
+    "url": "git+https://github.com/ggerganov/whisper.cpp"
+  },
+  "keywords": [
+    "openai",
+    "whisper",
+    "speech-to-text",
+    "speech-recognition",
+    "transformer"
+  ],
+  "author": "Georgi Gerganov",
+  "license": "MIT",
+  "bugs": {
+    "url": "https://github.com/ggerganov/whisper.cpp/issues"
+  },
+  "homepage": "https://github.com/ggerganov/whisper.cpp#readme"
+}

bindings/javascript/package.json

@@ -0,0 +1,26 @@
+{
+  "name": "whisper.cpp",
+  "version": "1.1.0",
+  "description": "Whisper speech recognition",
+  "main": "whisper.js",
+  "scripts": {
+    "test": "echo \"todo: add tests\" && exit 0"
+  },
+  "repository": {
+    "type": "git",
+    "url": "git+https://github.com/ggerganov/whisper.cpp"
+  },
+  "keywords": [
+    "openai",
+    "whisper",
+    "speech-to-text",
+    "speech-recognition",
+    "transformer"
+  ],
+  "author": "Georgi Gerganov",
+  "license": "MIT",
+  "bugs": {
+    "url": "https://github.com/ggerganov/whisper.cpp/issues"
+  },
+  "homepage": "https://github.com/ggerganov/whisper.cpp#readme"
+}

cmake/BuildTypes.cmake

@@ -0,0 +1,54 @@
+# Add new build types
+
+# ReleaseGG - Release with enabled asserts
+
+SET(CMAKE_CXX_FLAGS_RELEASEGG
+    "-O3"
+    CACHE STRING "Flags used by the c++ compiler during release builds with enabled asserts."
+    FORCE )
+SET(CMAKE_C_FLAGS_RELEASEGG
+    "-O3"
+    CACHE STRING "Flags used by the compiler during release builds with enabled asserts."
+    FORCE )
+SET(CMAKE_EXE_LINKER_FLAGS_RELEASEGG
+    ""
+    CACHE STRING "Flags used for linking binaries during release builds with enabled asserts."
+    FORCE )
+SET(CMAKE_SHARED_LINKER_FLAGS_RELEASEGG
+    ""
+    CACHE STRING "Flags used by the shared libraries linker during release builds with enabled asserts."
+    FORCE )
+MARK_AS_ADVANCED(
+    CMAKE_CXX_FLAGS_RELEASEGG
+    CMAKE_C_FLAGS_RELEASEGG
+    CMAKE_EXE_LINKER_FLAGS_RELEASEGG
+    CMAKE_SHARED_LINKER_FLAGS_RELEASEGG )
+
+# RelWithDebInfoGG - RelWithDebInfo with enabled asserts
+
+SET(CMAKE_CXX_FLAGS_RELWITHDEBINFOGG
+    "-O2 -g"
+    CACHE STRING "Flags used by the c++ compiler during release builds with debug symbols and enabled asserts."
+    FORCE )
+SET(CMAKE_C_FLAGS_RELWITHDEBINFOGG
+    "-O2 -g"
+    CACHE STRING "Flags used by the compiler during release builds with debug symbols and enabled asserts."
+    FORCE )
+SET(CMAKE_EXE_LINKER_FLAGS_RELWITHDEBINFOGG
+    ""
+    CACHE STRING "Flags used for linking binaries during release builds with debug symbols and enabled asserts."
+    FORCE )
+SET(CMAKE_SHARED_LINKER_FLAGS_RELWITHDEBINFOGG
+    ""
+    CACHE STRING "Flags used by the shared libraries linker during release builds with debug symbols and enabled asserts."
+    FORCE )
+MARK_AS_ADVANCED(
+    CMAKE_CXX_FLAGS_RELWITHDEBINFOGG
+    CMAKE_C_FLAGS_RELWITHDEBINFOGG
+    CMAKE_EXE_LINKER_FLAGS_RELWITHDEBINFOGG
+    CMAKE_SHARED_LINKER_FLAGS_RELWITHDEBINFOGG )
+
+if (NOT XCODE AND NOT MSVC AND NOT CMAKE_BUILD_TYPE)
+    set(CMAKE_BUILD_TYPE Release CACHE STRING "Build type" FORCE)
+    set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo" "ReleaseGG" "RelWithDebInfoGG")
+endif()

cmake/DefaultTargetOptions.cmake

@@ -0,0 +1,17 @@
+# Set the default compile features and properties for a target.
+
+if (NOT TARGET)
+    message(FATAL_ERROR "TARGET not set before including DefaultTargetOptions")
+endif()
+
+target_compile_features(${TARGET}
+    PRIVATE
+        cxx_std_11
+    )
+
+set_target_properties(${TARGET}
+    PROPERTIES
+        EXPORT_COMPILE_COMMANDS ON
+        RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin"
+        INSTALL_RPATH "${CMAKE_INSTALL_PREFIX}/lib"
+)

cmake/GitVars.cmake

@@ -0,0 +1,22 @@
+find_package(Git)
+
+# the commit's SHA1
+execute_process(COMMAND
+    "${GIT_EXECUTABLE}" describe --match=NeVeRmAtCh --always --abbrev=8
+    WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
+    OUTPUT_VARIABLE GIT_SHA1
+    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
+
+# the date of the commit
+execute_process(COMMAND
+    "${GIT_EXECUTABLE}" log -1 --format=%ad --date=local
+    WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
+    OUTPUT_VARIABLE GIT_DATE
+    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
+
+# the subject of the commit
+execute_process(COMMAND
+    "${GIT_EXECUTABLE}" log -1 --format=%s
+    WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
+    OUTPUT_VARIABLE GIT_COMMIT_SUBJECT
+    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)

examples/CMakeLists.txt

@@ -0,0 +1,33 @@
+# dependencies
+
+find_package(Threads REQUIRED)
+
+# third-party
+
+if (WHISPER_SUPPORT_SDL2)
+    # SDL2
+    find_package(SDL2 REQUIRED)
+
+    string(STRIP "${SDL2_LIBRARIES}" SDL2_LIBRARIES)
+
+    message(STATUS "SDL2_INCLUDE_DIRS = ${SDL2_INCLUDE_DIRS}")
+    message(STATUS "SDL2_LIBRARIES = ${SDL2_LIBRARIES}")
+endif()
+
+# examples
+
+include_directories(${CMAKE_CURRENT_SOURCE_DIR})
+
+if (EMSCRIPTEN)
+    add_subdirectory(whisper.wasm)
+    add_subdirectory(stream.wasm)
+    add_subdirectory(command.wasm)
+    add_subdirectory(talk.wasm)
+    add_subdirectory(bench.wasm)
+else()
+    add_subdirectory(main)
+    add_subdirectory(stream)
+    add_subdirectory(command)
+    add_subdirectory(bench)
+    add_subdirectory(talk)
+endif()

examples/bench.wasm/CMakeLists.txt

@@ -0,0 +1,49 @@
+#
+# libbench
+#
+
+set(TARGET libbench)
+
+add_executable(${TARGET}
+    emscripten.cpp
+    )
+
+include(DefaultTargetOptions)
+
+target_link_libraries(${TARGET} PRIVATE
+    whisper
+    )
+
+unset(EXTRA_FLAGS)
+
+if (WHISPER_WASM_SINGLE_FILE)
+    set(EXTRA_FLAGS "-s SINGLE_FILE=1")
+    message(STATUS "Embedding WASM inside bench.js")
+
+    add_custom_command(
+        TARGET ${TARGET} POST_BUILD
+        COMMAND ${CMAKE_COMMAND} -E copy
+        ${CMAKE_BINARY_DIR}/bin/libbench.js
+        ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/bench.wasm/bench.js
+        )
+endif()
+
+set_target_properties(${TARGET} PROPERTIES LINK_FLAGS " \
+    --bind \
+    -s USE_PTHREADS=1 \
+    -s PTHREAD_POOL_SIZE=8 \
+    -s INITIAL_MEMORY=1024MB \
+    -s TOTAL_MEMORY=1024MB \
+    -s FORCE_FILESYSTEM=1 \
+    -s EXPORTED_RUNTIME_METHODS=\"['print', 'printErr', 'ccall', 'cwrap']\" \
+    ${EXTRA_FLAGS} \
+    ")
+
+#
+# bench.wasm
+#
+
+set(TARGET bench.wasm)
+
+configure_file(${CMAKE_CURRENT_SOURCE_DIR}/index-tmpl.html  ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${TARGET}/index.html @ONLY)
+configure_file(${CMAKE_CURRENT_SOURCE_DIR}/../helpers.js    ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${TARGET}/helpers.js @ONLY)

examples/bench.wasm/README.md

@@ -0,0 +1,22 @@
+# bench.wasm
+
+Benchmark the performance of whisper.cpp in the browser using WebAssembly
+
+Link: https://whisper.ggerganov.com/bench/
+
+Terminal version: [examples/bench](/examples/bench)
+
+## Build instructions
+
+```bash
+# build using Emscripten (v3.1.2)
+git clone https://github.com/ggerganov/whisper.cpp
+cd whisper.cpp
+mkdir build-em && cd build-em
+emcmake cmake ..
+make -j
+
+# copy the produced page to your HTTP path
+cp bin/bench.wasm/*       /path/to/html/
+cp bin/libbench.worker.js /path/to/html/
+```

examples/bench.wasm/emscripten.cpp

@@ -0,0 +1,85 @@
+#include "whisper.h"
+
+#include <emscripten.h>
+#include <emscripten/bind.h>
+
+#include <cmath>
+#include <string>
+#include <thread>
+#include <vector>
+
+constexpr int N_THREAD = 8;
+
+// TODO: get rid of this vector of contexts - bad idea in the first place
+std::vector<struct whisper_context *> g_contexts(4, nullptr);
+
+std::thread g_worker;
+
+void bench_main(size_t index) {
+    const int n_threads = std::min(N_THREAD, (int) std::thread::hardware_concurrency());
+
+    // whisper context
+    auto & ctx = g_contexts[index];
+
+    fprintf(stderr, "%s: running benchmark with %d threads - please wait...\n", __func__, n_threads);
+
+    if (int ret = whisper_set_mel(ctx, nullptr, 0, WHISPER_N_MEL)) {
+        fprintf(stderr, "error: failed to set mel: %d\n", ret);
+        return;
+    }
+
+    {
+        fprintf(stderr, "\n");
+        fprintf(stderr, "system_info: n_threads = %d / %d | %s\n", n_threads, std::thread::hardware_concurrency(), whisper_print_system_info());
+    }
+
+    if (int ret = whisper_encode(ctx, 0, n_threads) != 0) {
+        fprintf(stderr, "error: failed to encode model: %d\n", ret);
+        return;
+    }
+
+    whisper_print_timings(ctx);
+
+    fprintf(stderr, "\n");
+    fprintf(stderr, "If you wish, you can submit these results here:\n");
+    fprintf(stderr, "\n");
+    fprintf(stderr, "  https://github.com/ggerganov/whisper.cpp/issues/89\n");
+    fprintf(stderr, "\n");
+    fprintf(stderr, "Please include the following information:\n");
+    fprintf(stderr, "\n");
+    fprintf(stderr, "  - CPU model\n");
+    fprintf(stderr, "  - Operating system\n");
+    fprintf(stderr, "  - Browser\n");
+    fprintf(stderr, "\n");
+}
+
+EMSCRIPTEN_BINDINGS(bench) {
+    emscripten::function("init", emscripten::optional_override([](const std::string & path_model) {
+        for (size_t i = 0; i < g_contexts.size(); ++i) {
+            if (g_contexts[i] == nullptr) {
+                g_contexts[i] = whisper_init_from_file(path_model.c_str());
+                if (g_contexts[i] != nullptr) {
+                    if (g_worker.joinable()) {
+                        g_worker.join();
+                    }
+                    g_worker = std::thread([i]() {
+                        bench_main(i);
+                    });
+
+                    return i + 1;
+                } else {
+                    return (size_t) 0;
+                }
+            }
+        }
+
+        return (size_t) 0;
+    }));
+
+    emscripten::function("free", emscripten::optional_override([](size_t index) {
+        if (index < g_contexts.size()) {
+            whisper_free(g_contexts[index]);
+            g_contexts[index] = nullptr;
+        }
+    }));
+}

examples/bench.wasm/index-tmpl.html

@@ -0,0 +1,227 @@
+<!doctype html>
+<html lang="en-us">
+    <head>
+        <title>bench : Benchmark whisper.cpp performance in the browser</title>
+
+        <style>
+            #output {
+                width: 100%;
+                height: 100%;
+                margin: 0 auto;
+                margin-top: 10px;
+                border-left: 0px;
+                border-right: 0px;
+                padding-left: 0px;
+                padding-right: 0px;
+                display: block;
+                background-color: black;
+                color: white;
+                font-size: 10px;
+                font-family: 'Lucida Console', Monaco, monospace;
+                outline: none;
+                white-space: pre;
+                overflow-wrap: normal;
+                overflow-x: scroll;
+            }
+        </style>
+    </head>
+    <body>
+        <div id="main-container">
+            <b>bench : Benchmark whisper.cpp performance in the browser</b>
+
+            <br><br>
+
+            You can find more about this project on <a href="https://github.com/ggerganov/whisper.cpp/tree/master/examples/bench.wasm">GitHub</a>.
+
+            <br><br>
+
+            <hr>
+
+            Select the model you would like to use and click the "Bench" button.<br>
+            The results will be displayed in the textarea below.
+
+            <br><br>
+
+            <div id="model-whisper">
+                Whisper model: <span id="model-whisper-status"></span>
+                <button id="fetch-whisper-tiny-en" onclick="loadWhisper('tiny.en')">tiny.en (75 MB)</button>
+                <button id="fetch-whisper-base-en" onclick="loadWhisper('base.en')">base.en (142 MB)</button>
+                <span id="fetch-whisper-progress"></span>
+
+                <input type="file" id="whisper-file" name="file" onchange="loadFile(event, 'whisper.bin')" />
+            </div>
+
+            <br>
+
+            <div id="input">
+                <button id="bench" onclick="onBench()" disabled>Bench</button>
+                <button id="clear" onclick="clearCache()">Clear Cache</button>
+            </div>
+
+            <hr>
+
+            Debug output:
+            <textarea id="output" rows="20"></textarea>
+
+            <br>
+
+            <b>Troubleshooting</b>
+
+            <br><br>
+
+            The page does some heavy computations, so make sure:
+
+            <ul>
+                <li>To use a modern web browser (e.g. Chrome, Firefox)</li>
+                <li>To use a fast desktop or laptop computer (i.e. not a mobile phone)</li>
+                <li>Your browser supports WASM <a href="https://webassembly.org/roadmap/">Fixed-width SIMD</a></li>
+            </ul>
+
+            <div class="cell-version">
+                <span>
+                    |
+                    Build time: <span class="nav-link">@GIT_DATE@</span> |
+                    Commit hash: <a class="nav-link" href="https://github.com/ggerganov/whisper.cpp/commit/@GIT_SHA1@">@GIT_SHA1@</a> |
+                    Commit subject: <span class="nav-link">@GIT_COMMIT_SUBJECT@</span> |
+                    <a class="nav-link" href="https://github.com/ggerganov/whisper.cpp/tree/master/examples/bench.wasm">Source Code</a> |
+                </span>
+            </div>
+        </div>
+
+        <script type="text/javascript" src="helpers.js"></script>
+        <script type='text/javascript'>
+            // the bench instance
+            var instance = null;
+
+            // model name
+            var model_whisper = null;
+
+            var Module = {
+                print: printTextarea,
+                printErr: printTextarea,
+                setStatus: function(text) {
+                    printTextarea('js: ' + text);
+                },
+                monitorRunDependencies: function(left) {
+                },
+                preRun: function() {
+                    printTextarea('js: Preparing ...');
+                },
+                postRun: function() {
+                    printTextarea('js: Initialized successfully!');
+                }
+            };
+
+            //
+            // fetch models
+            //
+
+            let dbVersion = 1
+            let dbName    = 'whisper.ggerganov.com';
+            let indexedDB = window.indexedDB || window.mozIndexedDB || window.webkitIndexedDB || window.msIndexedDB
+
+            function storeFS(fname, buf) {
+                // write to WASM file using FS_createDataFile
+                // if the file exists, delete it
+                try {
+                    Module.FS_unlink(fname);
+                } catch (e) {
+                    // ignore
+                }
+
+                Module.FS_createDataFile("/", fname, buf, true, true);
+
+                printTextarea('storeFS: stored model: ' + fname + ' size: ' + buf.length);
+
+                model_whisper = fname;
+
+                document.getElementById('model-whisper-status').innerHTML = 'loaded "' + model_whisper + '"!';
+
+                if (model_whisper != null) {
+                    document.getElementById('bench').disabled = false;
+                }
+            }
+
+            function loadFile(event, fname) {
+                var file = event.target.files[0] || null;
+                if (file == null) {
+                    return;
+                }
+
+                printTextarea("loadFile: loading model: " + file.name + ", size: " + file.size + " bytes");
+                printTextarea('loadFile: please wait ...');
+
+                var reader = new FileReader();
+                reader.onload = function(event) {
+                    var buf = new Uint8Array(reader.result);
+                    storeFS(fname, buf);
+                }
+                reader.readAsArrayBuffer(file);
+
+                document.getElementById('fetch-whisper-tiny-en').style.display = 'none';
+                document.getElementById('fetch-whisper-base-en').style.display = 'none';
+                document.getElementById('whisper-file'         ).style.display = 'none';
+                document.getElementById('model-whisper-status' ).innerHTML = 'loaded model: ' + file.name;
+            }
+
+            function loadWhisper(model) {
+                let urls = {
+                    'tiny.en': 'https://whisper.ggerganov.com/ggml-model-whisper-tiny.en.bin',
+                    'base.en': 'https://whisper.ggerganov.com/ggml-model-whisper-base.en.bin',
+                };
+
+                let sizes = {
+                    'tiny.en': 75,
+                    'base.en': 142,
+                };
+
+                let url     = urls[model];
+                let dst     = 'whisper.bin';
+                let size_mb = sizes[model];
+
+                document.getElementById('fetch-whisper-tiny-en').style.display = 'none';
+                document.getElementById('fetch-whisper-base-en').style.display = 'none';
+                document.getElementById('model-whisper-status').innerHTML = 'loading "' + model + '" ... ';
+
+                cbProgress = function(p) {
+                    let el = document.getElementById('fetch-whisper-progress');
+                    el.innerHTML = Math.round(100*p) + '%';
+                };
+
+                cbCancel = function() {
+                    var el;
+                    el = document.getElementById('fetch-whisper-tiny-en'); if (el) el.style.display = 'inline-block';
+                    el = document.getElementById('fetch-whisper-base-en'); if (el) el.style.display = 'inline-block';
+                    el = document.getElementById('model-whisper-status');  if (el) el.innerHTML = '';
+                };
+
+                loadRemote(url, dst, size_mb, cbProgress, storeFS, cbCancel, printTextarea);
+            }
+
+            //
+            // main
+            //
+
+            function onBench() {
+                if (instance) {
+                    Module.free(instance);
+                }
+
+                instance = Module.init('whisper.bin');
+
+                if (instance) {
+                    printTextarea("js: whisper initialized, instance: " + instance);
+                }
+
+                document.getElementById('bench').disabled = true;
+
+                if (!instance) {
+                    printTextarea("js: failed to initialize whisper");
+                    return;
+                }
+            }
+
+        </script>
+        <script type="text/javascript" src="bench.js"></script>
+    </body>
+</html>

examples/bench/CMakeLists.txt

@@ -0,0 +1,6 @@
+set(TARGET bench)
+add_executable(${TARGET} bench.cpp)
+
+include(DefaultTargetOptions)
+
+target_link_libraries(${TARGET} PRIVATE whisper ${CMAKE_THREAD_LIBS_INIT})

examples/bench/README.md

@@ -0,0 +1,54 @@
+# bench
+
+A very basic tool for benchmarking the inference performance on your device. The tool simply runs the Encoder part of
+the transformer on some random audio data and records the execution time. This way we can have an objective comparison
+of the performance of the model for various setups.
+
+Benchmark results are tracked in the following Github issue: https://github.com/ggerganov/whisper.cpp/issues/89
+
+```bash
+# build the bench tool
+$ make bench
+
+# run it on the small.en model using 4 threads
+$ ./bench -m ./models/ggml-small.en.bin -t 4
+
+whisper_model_load: loading model from './models/ggml-small.en.bin'
+whisper_model_load: n_vocab       = 51864
+whisper_model_load: n_audio_ctx   = 1500
+whisper_model_load: n_audio_state = 768
+whisper_model_load: n_audio_head  = 12
+whisper_model_load: n_audio_layer = 12
+whisper_model_load: n_text_ctx    = 448
+whisper_model_load: n_text_state  = 768
+whisper_model_load: n_text_head   = 12
+whisper_model_load: n_text_layer  = 12
+whisper_model_load: n_mels        = 80
+whisper_model_load: f16           = 1
+whisper_model_load: type          = 3
+whisper_model_load: mem_required  = 1048.00 MB
+whisper_model_load: adding 1607 extra tokens
+whisper_model_load: ggml ctx size = 533.05 MB
+whisper_model_load: memory size =    68.48 MB 
+whisper_model_load: model size  =   464.44 MB
+
+whisper_print_timings:     load time =   240.82 ms
+whisper_print_timings:      mel time =     0.00 ms
+whisper_print_timings:   sample time =     0.00 ms
+whisper_print_timings:   encode time =  1062.21 ms / 88.52 ms per layer
+whisper_print_timings:   decode time =     0.00 ms / 0.00 ms per layer
+whisper_print_timings:    total time =  1303.04 ms
+
+system_info: n_threads = 4 | AVX2 = 0 | AVX512 = 0 | NEON = 1 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | 
+
+If you wish, you can submit these results here:
+
+  https://github.com/ggerganov/whisper.cpp/issues/89
+
+Please include the following information:
+
+  - CPU model
+  - Operating system
+  - Compiler
+
+```

examples/bench/bench.cpp

@@ -0,0 +1,94 @@
+#include "whisper.h"
+
+#include <cstdio>
+#include <string>
+#include <thread>
+
+// command-line parameters
+struct whisper_params {
+    int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
+
+    std::string model = "models/ggml-base.en.bin";
+};
+
+void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
+
+bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
+    for (int i = 1; i < argc; i++) {
+        std::string arg = argv[i];
+
+        if (arg == "-h" || arg == "--help") {
+            whisper_print_usage(argc, argv, params);
+            exit(0);
+        }
+        else if (arg == "-t" || arg == "--threads") { params.n_threads = std::stoi(argv[++i]); }
+        else if (arg == "-m" || arg == "--model")   { params.model     = argv[++i]; }
+        else {
+            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
+            whisper_print_usage(argc, argv, params);
+            exit(0);
+        }
+    }
+
+    return true;
+}
+
+void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & params) {
+    fprintf(stderr, "\n");
+    fprintf(stderr, "usage: %s [options]\n", argv[0]);
+    fprintf(stderr, "\n");
+    fprintf(stderr, "options:\n");
+    fprintf(stderr, "  -h,       --help        [default] show this help message and exit\n");
+    fprintf(stderr, "  -t N,     --threads N   [%-7d] number of threads to use during computation\n", params.n_threads);
+    fprintf(stderr, "  -m FNAME, --model FNAME [%-7s] model path\n",                                  params.model.c_str());
+    fprintf(stderr, "\n");
+}
+
+int main(int argc, char ** argv) {
+    whisper_params params;
+
+    if (whisper_params_parse(argc, argv, params) == false) {
+        return 1;
+    }
+
+    // whisper init
+
+    struct whisper_context * ctx = whisper_init_from_file(params.model.c_str());
+
+    {
+        fprintf(stderr, "\n");
+        fprintf(stderr, "system_info: n_threads = %d / %d | %s\n", params.n_threads, std::thread::hardware_concurrency(), whisper_print_system_info());
+    }
+
+    if (ctx == nullptr) {
+        fprintf(stderr, "error: failed to initialize whisper context\n");
+        return 2;
+    }
+
+    if (int ret = whisper_set_mel(ctx, nullptr, 0, WHISPER_N_MEL)) {
+        fprintf(stderr, "error: failed to set mel: %d\n", ret);
+        return 3;
+    }
+
+    if (int ret = whisper_encode(ctx, 0, params.n_threads) != 0) {
+        fprintf(stderr, "error: failed to encode model: %d\n", ret);
+        return 4;
+    }
+
+    whisper_print_timings(ctx);
+    whisper_free(ctx);
+
+    fprintf(stderr, "\n");
+    fprintf(stderr, "If you wish, you can submit these results here:\n");
+    fprintf(stderr, "\n");
+    fprintf(stderr, "  https://github.com/ggerganov/whisper.cpp/issues/89\n");
+    fprintf(stderr, "\n");
+    fprintf(stderr, "Please include the following information:\n");
+    fprintf(stderr, "\n");
+    fprintf(stderr, "  - CPU model\n");
+    fprintf(stderr, "  - Operating system\n");
+    fprintf(stderr, "  - Compiler\n");
+    fprintf(stderr, "\n");
+
+    return 0;
+}

examples/command.wasm/CMakeLists.txt

@@ -0,0 +1,49 @@
+#
+# libcommand
+#
+
+set(TARGET libcommand)
+
+add_executable(${TARGET}
+    emscripten.cpp
+    )
+
+include(DefaultTargetOptions)
+
+target_link_libraries(${TARGET} PRIVATE
+    whisper
+    )
+
+unset(EXTRA_FLAGS)
+
+if (WHISPER_WASM_SINGLE_FILE)
+    set(EXTRA_FLAGS "-s SINGLE_FILE=1")
+    message(STATUS "Embedding WASM inside command.js")
+
+    add_custom_command(
+        TARGET ${TARGET} POST_BUILD
+        COMMAND ${CMAKE_COMMAND} -E copy
+        ${CMAKE_BINARY_DIR}/bin/libcommand.js
+        ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/command.wasm/command.js
+        )
+endif()
+
+set_target_properties(${TARGET} PROPERTIES LINK_FLAGS " \
+    --bind \
+    -s USE_PTHREADS=1 \
+    -s PTHREAD_POOL_SIZE=8 \
+    -s INITIAL_MEMORY=1024MB \
+    -s TOTAL_MEMORY=1024MB \
+    -s FORCE_FILESYSTEM=1 \
+    -s EXPORTED_RUNTIME_METHODS=\"['print', 'printErr', 'ccall', 'cwrap']\" \
+    ${EXTRA_FLAGS} \
+    ")
+
+#
+# command.wasm
+#
+
+set(TARGET command.wasm)
+
+configure_file(${CMAKE_CURRENT_SOURCE_DIR}/index-tmpl.html  ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${TARGET}/index.html @ONLY)
+configure_file(${CMAKE_CURRENT_SOURCE_DIR}/../helpers.js    ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${TARGET}/helpers.js @ONLY)

examples/command.wasm/README.md

@@ -0,0 +1,23 @@
+# command.wasm
+
+This is a basic Voice Assistant example that accepts voice commands from the microphone.
+It runs in fully in the browser via WebAseembly.
+
+Online demo: https://whisper.ggerganov.com/command/
+
+Terminal version: [examples/command](/examples/command)
+
+## Build instructions
+
+```bash
+# build using Emscripten (v3.1.2)
+git clone https://github.com/ggerganov/whisper.cpp
+cd whisper.cpp
+mkdir build-em && cd build-em
+emcmake cmake ..
+make -j
+
+# copy the produced page to your HTTP path
+cp bin/command.wasm/*       /path/to/html/
+cp bin/libcommand.worker.js /path/to/html/
+```

examples/command.wasm/emscripten.cpp

@@ -0,0 +1,408 @@
+#include "ggml.h"
+#include "whisper.h"
+
+#include <emscripten.h>
+#include <emscripten/bind.h>
+
+#include <atomic>
+#include <cmath>
+#include <mutex>
+#include <string>
+#include <thread>
+#include <vector>
+#include <regex>
+
+constexpr int N_THREAD = 8;
+
+std::vector<struct whisper_context *> g_contexts(4, nullptr);
+
+std::mutex  g_mutex;
+std::thread g_worker;
+
+std::atomic<bool> g_running(false);
+
+std::string g_status        = "";
+std::string g_status_forced = "";
+std::string g_transcribed   = "";
+
+std::vector<float> g_pcmf32;
+
+static std::string trim(const std::string & s) {
+    std::regex e("^\\s+|\\s+$");
+    return std::regex_replace(s, e, "");
+}
+
+static void high_pass_filter(std::vector<float> & data, float cutoff, float sample_rate) {
+    const float rc = 1.0f / (2.0f * M_PI * cutoff);
+    const float dt = 1.0f / sample_rate;
+    const float alpha = dt / (rc + dt);
+
+    float y = data[0];
+
+    for (size_t i = 1; i < data.size(); i++) {
+        y = alpha * (y + data[i] - data[i - 1]);
+        data[i] = y;
+    }
+}
+
+// compute similarity between two strings using Levenshtein distance
+static float similarity(const std::string & s0, const std::string & s1) {
+    const size_t len0 = s0.size() + 1;
+    const size_t len1 = s1.size() + 1;
+
+    std::vector<int> col(len1, 0);
+    std::vector<int> prevCol(len1, 0);
+
+    for (size_t i = 0; i < len1; i++) {
+        prevCol[i] = i;
+    }
+
+    for (size_t i = 0; i < len0; i++) {
+        col[0] = i;
+        for (size_t j = 1; j < len1; j++) {
+            col[j] = std::min(std::min(1 + col[j - 1], 1 + prevCol[j]), prevCol[j - 1] + (s0[i - 1] == s1[j - 1] ? 0 : 1));
+        }
+        col.swap(prevCol);
+    }
+
+    const float dist = prevCol[len1 - 1];
+
+    return 1.0f - (dist / std::max(s0.size(), s1.size()));
+}
+
+void command_set_status(const std::string & status) {
+    std::lock_guard<std::mutex> lock(g_mutex);
+    g_status = status;
+}
+
+bool command_vad_simple(std::vector<float> & pcmf32, int sample_rate, int last_ms, float vad_thold, float freq_thold, bool verbose) {
+    const int n_samples      = pcmf32.size();
+    const int n_samples_last = (sample_rate * last_ms) / 1000;
+
+    if (n_samples_last >= n_samples) {
+        // not enough samples - assume no speech
+        return false;
+    }
+
+    if (freq_thold > 0.0f) {
+        high_pass_filter(pcmf32, freq_thold, sample_rate);
+    }
+
+    float energy_all  = 0.0f;
+    float energy_last = 0.0f;
+
+    for (size_t i = 0; i < n_samples; i++) {
+        energy_all += fabsf(pcmf32[i]);
+
+        if (i >= n_samples - n_samples_last) {
+            energy_last += fabsf(pcmf32[i]);
+        }
+    }
+
+    energy_all  /= n_samples;
+    energy_last /= n_samples_last;
+
+    if (verbose) {
+        fprintf(stderr, "%s: energy_all: %f, energy_last: %f, vad_thold: %f, freq_thold: %f\n", __func__, energy_all, energy_last, vad_thold, freq_thold);
+    }
+
+    if (energy_last > vad_thold*energy_all) {
+        return false;
+    }
+
+    return true;
+}
+
+std::string command_transcribe(whisper_context * ctx, const whisper_full_params & wparams, const std::vector<float> & pcmf32, float & prob, int64_t & t_ms) {
+    const auto t_start = std::chrono::high_resolution_clock::now();
+
+    prob = 0.0f;
+    t_ms = 0;
+
+    if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
+        return "";
+    }
+
+    int prob_n = 0;
+    std::string result;
+
+    const int n_segments = whisper_full_n_segments(ctx);
+    for (int i = 0; i < n_segments; ++i) {
+        const char * text = whisper_full_get_segment_text(ctx, i);
+
+        result += text;
+
+        const int n_tokens = whisper_full_n_tokens(ctx, i);
+        for (int j = 0; j < n_tokens; ++j) {
+            const auto token = whisper_full_get_token_data(ctx, i, j);
+
+            prob += token.p;
+            ++prob_n;
+        }
+    }
+
+    if (prob_n > 0) {
+        prob /= prob_n;
+    }
+
+    const auto t_end = std::chrono::high_resolution_clock::now();
+    t_ms = std::chrono::duration_cast<std::chrono::milliseconds>(t_end - t_start).count();
+
+    return result;
+}
+
+void command_get_audio(int ms, int sample_rate, std::vector<float> & audio) {
+    const int64_t n_samples = (ms * sample_rate) / 1000;
+
+    int64_t n_take = 0;
+    if (g_pcmf32.size() < n_samples) {
+        n_take = g_pcmf32.size();
+    } else {
+        n_take = n_samples;
+    }
+
+    audio.resize(n_take);
+    std::copy(g_pcmf32.end() - n_take, g_pcmf32.end(), audio.begin());
+}
+
+void command_main(size_t index) {
+    command_set_status("loading data ...");
+
+    struct whisper_full_params wparams = whisper_full_default_params(whisper_sampling_strategy::WHISPER_SAMPLING_GREEDY);
+
+    wparams.n_threads        = std::min(N_THREAD, (int) std::thread::hardware_concurrency());
+    wparams.offset_ms        = 0;
+    wparams.translate        = false;
+    wparams.no_context       = true;
+    wparams.single_segment   = true;
+    wparams.print_realtime   = false;
+    wparams.print_progress   = false;
+    wparams.print_timestamps = true;
+    wparams.print_special    = false;
+
+    wparams.max_tokens       = 32;
+    wparams.audio_ctx        = 768; // partial encoder context for better performance
+
+    wparams.language         = "en";
+
+    printf("command: using %d threads\n", wparams.n_threads);
+
+    bool is_running   = true;
+    bool have_prompt  = false;
+    bool ask_prompt   = true;
+    bool print_energy = false;
+
+    float prob0 = 0.0f;
+    float prob  = 0.0f;
+
+    std::vector<float> pcmf32_cur;
+    std::vector<float> pcmf32_prompt;
+
+    const std::string k_prompt = "Ok Whisper, start listening for commands.";
+
+    // whisper context
+    auto & ctx = g_contexts[index];
+
+    const int32_t vad_ms     = 2000;
+    const int32_t prompt_ms  = 5000;
+    const int32_t command_ms = 4000;
+
+    const float vad_thold  = 0.1f;
+    const float freq_thold = -1.0f;
+
+    while (g_running) {
+        // delay
+        std::this_thread::sleep_for(std::chrono::milliseconds(100));
+
+        if (ask_prompt) {
+            fprintf(stdout, "\n");
+            fprintf(stdout, "%s: Say the following phrase: '%s%s%s'\n", __func__, "\033[1m", k_prompt.c_str(), "\033[0m");
+            fprintf(stdout, "\n");
+
+            {
+                char txt[1024];
+                snprintf(txt, sizeof(txt), "Say the following phrase: '%s'", k_prompt.c_str());
+                command_set_status(txt);
+            }
+
+            ask_prompt = false;
+        }
+
+        int64_t t_ms = 0;
+
+        {
+            command_get_audio(vad_ms, WHISPER_SAMPLE_RATE, pcmf32_cur);
+
+            if (command_vad_simple(pcmf32_cur, WHISPER_SAMPLE_RATE, 1000, vad_thold, freq_thold, print_energy)) {
+                fprintf(stdout, "%s: Speech detected! Processing ...\n", __func__);
+                command_set_status("Speech detected! Processing ...");
+
+                if (!have_prompt) {
+                    command_get_audio(prompt_ms, WHISPER_SAMPLE_RATE, pcmf32_cur);
+
+                    const auto txt = ::trim(::command_transcribe(ctx, wparams, pcmf32_cur, prob0, t_ms));
+
+                    fprintf(stdout, "%s: Heard '%s%s%s', (t = %d ms)\n", __func__, "\033[1m", txt.c_str(), "\033[0m", (int) t_ms);
+
+                    const float sim = similarity(txt, k_prompt);
+
+                    if (txt.length() < 0.8*k_prompt.length() || txt.length() > 1.2*k_prompt.length() || sim < 0.8f) {
+                        fprintf(stdout, "%s: WARNING: prompt not recognized, try again\n", __func__);
+                        ask_prompt = true;
+                    } else {
+                        fprintf(stdout, "\n");
+                        fprintf(stdout, "%s: The prompt has been recognized!\n", __func__);
+                        fprintf(stdout, "%s: Waiting for voice commands ...\n", __func__);
+                        fprintf(stdout, "\n");
+
+                        {
+                            char txt[1024];
+                            snprintf(txt, sizeof(txt), "Success! Waiting for voice commands ...");
+                            command_set_status(txt);
+                        }
+
+                        // save the audio for the prompt
+                        pcmf32_prompt = pcmf32_cur;
+                        have_prompt = true;
+                    }
+                } else {
+                    command_get_audio(command_ms, WHISPER_SAMPLE_RATE, pcmf32_cur);
+
+                    // prepend the prompt audio
+                    pcmf32_cur.insert(pcmf32_cur.begin(), pcmf32_prompt.begin(), pcmf32_prompt.end());
+
+                    const auto txt = ::trim(::command_transcribe(ctx, wparams, pcmf32_cur, prob, t_ms));
+
+                    prob = 100.0f*(prob - prob0);
+
+                    fprintf(stdout, "%s: heard '%s'\n", __func__, txt.c_str());
+
+                    // find the prompt in the text
+                    float best_sim = 0.0f;
+                    size_t best_len = 0;
+                    for (int n = 0.8*k_prompt.size(); n <= 1.2*k_prompt.size(); ++n) {
+                        const auto prompt = txt.substr(0, n);
+
+                        const float sim = similarity(prompt, k_prompt);
+
+                        //fprintf(stderr, "%s: prompt = '%s', sim = %f\n", __func__, prompt.c_str(), sim);
+
+                        if (sim > best_sim) {
+                            best_sim = sim;
+                            best_len = n;
+                        }
+                    }
+
+                    const std::string command = ::trim(txt.substr(best_len));
+
+                    fprintf(stdout, "%s: Command '%s%s%s', (t = %d ms)\n", __func__, "\033[1m", command.c_str(), "\033[0m", (int) t_ms);
+                    fprintf(stdout, "\n");
+
+                    {
+                        char txt[1024];
+                        snprintf(txt, sizeof(txt), "Command '%s', (t = %d ms)", command.c_str(), (int) t_ms);
+                        command_set_status(txt);
+                    }
+                    {
+                        std::lock_guard<std::mutex> lock(g_mutex);
+                        g_transcribed = command;
+                    }
+                }
+
+                g_pcmf32.clear();
+            }
+        }
+    }
+
+    if (index < g_contexts.size()) {
+        whisper_free(g_contexts[index]);
+        g_contexts[index] = nullptr;
+    }
+}
+
+EMSCRIPTEN_BINDINGS(command) {
+    emscripten::function("init", emscripten::optional_override([](const std::string & path_model) {
+        for (size_t i = 0; i < g_contexts.size(); ++i) {
+            if (g_contexts[i] == nullptr) {
+                g_contexts[i] = whisper_init_from_file(path_model.c_str());
+                if (g_contexts[i] != nullptr) {
+                    g_running = true;
+                    if (g_worker.joinable()) {
+                        g_worker.join();
+                    }
+                    g_worker = std::thread([i]() {
+                        command_main(i);
+                    });
+
+                    return i + 1;
+                } else {
+                    return (size_t) 0;
+                }
+            }
+        }
+
+        return (size_t) 0;
+    }));
+
+    emscripten::function("free", emscripten::optional_override([](size_t index) {
+        if (g_running) {
+            g_running = false;
+        }
+    }));
+
+    emscripten::function("set_audio", emscripten::optional_override([](size_t index, const emscripten::val & audio) {
+        --index;
+
+        if (index >= g_contexts.size()) {
+            return -1;
+        }
+
+        if (g_contexts[index] == nullptr) {
+            return -2;
+        }
+
+        {
+            std::lock_guard<std::mutex> lock(g_mutex);
+            const int n = audio["length"].as<int>();
+
+            emscripten::val heap = emscripten::val::module_property("HEAPU8");
+            emscripten::val memory = heap["buffer"];
+
+            g_pcmf32.resize(n);
+
+            emscripten::val memoryView = audio["constructor"].new_(memory, reinterpret_cast<uintptr_t>(g_pcmf32.data()), n);
+            memoryView.call<void>("set", audio);
+        }
+
+        return 0;
+    }));
+
+    emscripten::function("get_transcribed", emscripten::optional_override([]() {
+        std::string transcribed;
+
+        {
+            std::lock_guard<std::mutex> lock(g_mutex);
+            transcribed = std::move(g_transcribed);
+        }
+
+        return transcribed;
+    }));
+
+    emscripten::function("get_status", emscripten::optional_override([]() {
+        std::string status;
+
+        {
+            std::lock_guard<std::mutex> lock(g_mutex);
+            status = g_status_forced.empty() ? g_status : g_status_forced;
+        }
+
+        return status;
+    }));
+
+    emscripten::function("set_status", emscripten::optional_override([](const std::string & status) {
+        {
+            std::lock_guard<std::mutex> lock(g_mutex);
+            g_status_forced = status;
+        }
+    }));
+}

examples/command.wasm/index-tmpl.html

@@ -0,0 +1,386 @@
+<!doctype html>
+<html lang="en-us">
+    <head>
+        <title>command : Voice assistant example using Whisper + WebAssembly</title>
+
+        <style>
+            #output {
+                width: 100%;
+                height: 100%;
+                margin: 0 auto;
+                margin-top: 10px;
+                border-left: 0px;
+                border-right: 0px;
+                padding-left: 0px;
+                padding-right: 0px;
+                display: block;
+                background-color: black;
+                color: white;
+                font-size: 10px;
+                font-family: 'Lucida Console', Monaco, monospace;
+                outline: none;
+                white-space: pre;
+                overflow-wrap: normal;
+                overflow-x: scroll;
+            }
+        </style>
+    </head>
+    <body>
+        <div id="main-container">
+            <b>command : Voice assistant example using Whisper + WebAssembly</b>
+
+            <br><br>
+
+            You can find more about this project on <a href="https://github.com/ggerganov/whisper.cpp/tree/master/examples/command.wasm">GitHub</a>.
+
+            <br><br>
+
+            <hr>
+
+            Select the model you would like to use, click the "Start" button and follow the instructions.
+
+            <br><br>
+
+            <div id="model-whisper">
+                Whisper model: <span id="model-whisper-status"></span>
+                <button id="fetch-whisper-tiny-en" onclick="loadWhisper('tiny.en')">tiny.en (75 MB)</button>
+                <button id="fetch-whisper-base-en" onclick="loadWhisper('base.en')">base.en (142 MB)</button>
+                <span id="fetch-whisper-progress"></span>
+
+                <!--
+                    <input type="file" id="file" name="file" onchange="loadFile(event, 'whisper.bin')" />
+                -->
+            </div>
+
+            <br>
+
+            <div id="input">
+                <button id="start" onclick="onStart()" disabled>Start</button>
+                <button id="stop"  onclick="onStop()" disabled>Stop</button>
+                <button id="clear" onclick="clearCache()">Clear Cache</button>
+            </div>
+
+            <br>
+
+            <div id="state">
+                Status: <b><span id="state-status">not started</span></b>
+
+                <pre id="state-transcribed">[The recognized voice commands will be displayed here]</pre>
+            </div>
+
+            <hr>
+
+            Debug output:
+            <textarea id="output" rows="20"></textarea>
+
+            <br>
+
+            <b>Troubleshooting</b>
+
+            <br><br>
+
+            The page does some heavy computations, so make sure:
+
+            <ul>
+                <li>To use a modern web browser (e.g. Chrome, Firefox)</li>
+                <li>To use a fast desktop or laptop computer (i.e. not a mobile phone)</li>
+                <li>Your browser supports WASM <a href="https://webassembly.org/roadmap/">Fixed-width SIMD</a></li>
+            </ul>
+
+            <div class="cell-version">
+                <span>
+                    |
+                    Build time: <span class="nav-link">@GIT_DATE@</span> |
+                    Commit hash: <a class="nav-link" href="https://github.com/ggerganov/whisper.cpp/commit/@GIT_SHA1@">@GIT_SHA1@</a> |
+                    Commit subject: <span class="nav-link">@GIT_COMMIT_SUBJECT@</span> |
+                    <a class="nav-link" href="https://github.com/ggerganov/whisper.cpp/tree/master/examples/command.wasm">Source Code</a> |
+                </span>
+            </div>
+        </div>
+
+        <script type="text/javascript" src="helpers.js"></script>
+        <script type='text/javascript'>
+            // web audio context
+            var context = null;
+
+            // audio data
+            var audio = null;
+            var audio0 = null;
+
+            // the command instance
+            var instance = null;
+
+            // model name
+            var model_whisper = null;
+
+            var Module = {
+                print: printTextarea,
+                printErr: printTextarea,
+                setStatus: function(text) {
+                    printTextarea('js: ' + text);
+                },
+                monitorRunDependencies: function(left) {
+                },
+                preRun: function() {
+                    printTextarea('js: Preparing ...');
+                },
+                postRun: function() {
+                    printTextarea('js: Initialized successfully!');
+                }
+            };
+
+            //
+            // fetch models
+            //
+
+            let dbVersion = 1
+            let dbName    = 'whisper.ggerganov.com';
+            let indexedDB = window.indexedDB || window.mozIndexedDB || window.webkitIndexedDB || window.msIndexedDB
+
+            function storeFS(fname, buf) {
+                // write to WASM file using FS_createDataFile
+                // if the file exists, delete it
+                try {
+                    Module.FS_unlink(fname);
+                } catch (e) {
+                    // ignore
+                }
+
+                Module.FS_createDataFile("/", fname, buf, true, true);
+
+                printTextarea('storeFS: stored model: ' + fname + ' size: ' + buf.length);
+
+                document.getElementById('model-whisper-status').innerHTML = 'loaded "' + model_whisper + '"!';
+
+                if (model_whisper != null) {
+                    document.getElementById('start').disabled = false;
+                    document.getElementById('stop' ).disabled = true;
+                }
+            }
+
+            function loadWhisper(model) {
+                let urls = {
+                    'tiny.en': 'https://whisper.ggerganov.com/ggml-model-whisper-tiny.en.bin',
+                    'base.en': 'https://whisper.ggerganov.com/ggml-model-whisper-base.en.bin',
+                };
+
+                let sizes = {
+                    'tiny.en': 75,
+                    'base.en': 142,
+                };
+
+                let url     = urls[model];
+                let dst     = 'whisper.bin';
+                let size_mb = sizes[model];
+
+                model_whisper = model;
+
+                document.getElementById('fetch-whisper-tiny-en').style.display = 'none';
+                document.getElementById('fetch-whisper-base-en').style.display = 'none';
+                document.getElementById('model-whisper-status').innerHTML = 'loading "' + model + '" ... ';
+
+                cbProgress = function(p) {
+                    let el = document.getElementById('fetch-whisper-progress');
+                    el.innerHTML = Math.round(100*p) + '%';
+                };
+
+                cbCancel = function() {
+                    var el;
+                    el = document.getElementById('fetch-whisper-tiny-en'); if (el) el.style.display = 'inline-block';
+                    el = document.getElementById('fetch-whisper-base-en'); if (el) el.style.display = 'inline-block';
+                    el = document.getElementById('model-whisper-status');  if (el) el.innerHTML = '';
+                };
+
+                loadRemote(url, dst, size_mb, cbProgress, storeFS, cbCancel, printTextarea);
+            }
+
+            //
+            // microphone
+            //
+
+            const kSampleRate = 16000;
+            const kRestartRecording_s = 120;
+            const kIntervalAudio_ms = 250; // pass the recorded audio to the C++ instance at this rate
+
+            var mediaRecorder = null;
+            var doRecording = false;
+            var startTime = 0;
+
+            window.AudioContext = window.AudioContext || window.webkitAudioContext;
+            window.OfflineAudioContext = window.OfflineAudioContext || window.webkitOfflineAudioContext;
+
+            function stopRecording() {
+                Module.set_status("paused");
+                doRecording = false;
+                audio0 = null;
+                audio = null;
+                context = null;
+            }
+
+            function startRecording() {
+                if (!context) {
+                    context = new AudioContext({
+                        sampleRate: kSampleRate,
+                        channelCount: 1,
+                        echoCancellation: false,
+                        autoGainControl:  true,
+                        noiseSuppression: true,
+                    });
+                }
+
+                Module.set_status("");
+
+                document.getElementById('start').disabled = true;
+                document.getElementById('stop').disabled = false;
+
+                doRecording = true;
+                startTime = Date.now();
+
+                var chunks = [];
+                var stream = null;
+
+                navigator.mediaDevices.getUserMedia({audio: true, video: false})
+                    .then(function(s) {
+                        stream = s;
+                        mediaRecorder = new MediaRecorder(stream);
+                        mediaRecorder.ondataavailable = function(e) {
+                            chunks.push(e.data);
+
+                            var blob = new Blob(chunks, { 'type' : 'audio/ogg; codecs=opus' });
+                            var reader = new FileReader();
+
+                            reader.onload = function(event) {
+                                var buf = new Uint8Array(reader.result);
+
+                                if (!context) {
+                                    return;
+                                }
+                                context.decodeAudioData(buf.buffer, function(audioBuffer) {
+                                    var offlineContext = new OfflineAudioContext(audioBuffer.numberOfChannels, audioBuffer.length, audioBuffer.sampleRate);
+                                    var source = offlineContext.createBufferSource();
+                                    source.buffer = audioBuffer;
+                                    source.connect(offlineContext.destination);
+                                    source.start(0);
+
+                                    offlineContext.startRendering().then(function(renderedBuffer) {
+                                        audio = renderedBuffer.getChannelData(0);
+
+                                        //printTextarea('js: audio recorded, size: ' + audio.length + ', old size: ' + (audio0 == null ? 0 : audio0.length));
+
+                                        var audioAll = new Float32Array(audio0 == null ? audio.length : audio0.length + audio.length);
+                                        if (audio0 != null) {
+                                            audioAll.set(audio0, 0);
+                                        }
+                                        audioAll.set(audio, audio0 == null ? 0 : audio0.length);
+
+                                        if (instance) {
+                                            Module.set_audio(instance, audioAll);
+                                        }
+                                    });
+                                }, function(e) {
+                                    audio = null;
+                                });
+                            }
+
+                            reader.readAsArrayBuffer(blob);
+                        };
+
+                        mediaRecorder.onstop = function(e) {
+                            if (doRecording) {
+                                setTimeout(function() {
+                                    startRecording();
+                                });
+                            }
+                        };
+
+                        mediaRecorder.start(kIntervalAudio_ms);
+                    })
+                    .catch(function(err) {
+                        printTextarea('js: error getting audio stream: ' + err);
+                    });
+
+                var interval = setInterval(function() {
+                    if (!doRecording) {
+                        clearInterval(interval);
+                        mediaRecorder.stop();
+                        stream.getTracks().forEach(function(track) {
+                            track.stop();
+                        });
+
+                        document.getElementById('start').disabled = false;
+                        document.getElementById('stop').disabled  = true;
+
+                        mediaRecorder = null;
+                    }
+
+                    // if audio length is more than kRestartRecording_s seconds, restart recording
+                    if (audio != null && audio.length > kSampleRate*kRestartRecording_s) {
+                        if (doRecording) {
+                            //printTextarea('js: restarting recording');
+
+                            clearInterval(interval);
+                            audio0 = audio;
+                            audio = null;
+                            mediaRecorder.stop();
+                            stream.getTracks().forEach(function(track) {
+                                track.stop();
+                            });
+                        }
+                    }
+                }, 100);
+            }
+
+            //
+            // main
+            //
+
+            var nLines = 0;
+            var intervalUpdate = null;
+            var transcribedAll = '';
+
+            function onStart() {
+                if (!instance) {
+                    instance = Module.init('whisper.bin');
+
+                    if (instance) {
+                        printTextarea("js: whisper initialized, instance: " + instance);
+                    }
+                }
+
+                if (!instance) {
+                    printTextarea("js: failed to initialize whisper");
+                    return;
+                }
+
+                startRecording();
+
+                intervalUpdate = setInterval(function() {
+                    var transcribed = Module.get_transcribed();
+
+                    if (transcribed != null && transcribed.length > 1) {
+                        transcribedAll += transcribed + '<br>';
+                        nLines++;
+
+                        // if more than 10 lines, remove the first line
+                        if (nLines > 10) {
+                            var i = transcribedAll.indexOf('<br>');
+                            if (i > 0) {
+                                transcribedAll = transcribedAll.substring(i + 4);
+                                nLines--;
+                            }
+                        }
+                    }
+
+                    document.getElementById('state-status').innerHTML = Module.get_status();
+                    document.getElementById('state-transcribed').innerHTML = transcribedAll;
+                }, 100);
+            }
+
+            function onStop() {
+                stopRecording();
+            }
+
+        </script>
+        <script type="text/javascript" src="command.js"></script>
+    </body>
+</html>

examples/command/CMakeLists.txt

@@ -0,0 +1,10 @@
+if (WHISPER_SUPPORT_SDL2)
+    # command
+    set(TARGET command)
+    add_executable(${TARGET} command.cpp)
+
+    include(DefaultTargetOptions)
+
+    target_include_directories(${TARGET} PRIVATE ${SDL2_INCLUDE_DIRS})
+    target_link_libraries(${TARGET} PRIVATE whisper ${SDL2_LIBRARIES} ${CMAKE_THREAD_LIBS_INIT})
+endif ()

examples/command/README.md

@@ -0,0 +1,47 @@
+# command
+
+This is a basic Voice Assistant example that accepts voice commands from the microphone.
+More info is available in [issue #171](https://github.com/ggerganov/whisper.cpp/issues/171).
+
+```bash
+# Run with default arguments and small model
+./command -m ./models/ggml-small.en.bin -t 8
+
+# On Raspberry Pi, use tiny or base models + "-ac 768" for better performance
+./command -m ./models/ggml-tiny.en.bin -ac 768 -t 3 -c 0
+```
+
+https://user-images.githubusercontent.com/1991296/204038393-2f846eae-c255-4099-a76d-5735c25c49da.mp4
+
+Web version: [examples/command.wasm](/examples/command.wasm)
+
+## Guided mode
+
+"Guided mode" allows you to specify a list of commands (i.e. strings) and the transcription will be guided to classify your command into one from the list. This can be useful in situations where a device is listening only for a small subset of commands.
+
+Initial tests show that this approach might be extremely efficient in terms of performance, since it integrates very well with the "partial Encoder" idea from #137.
+
+```bash
+# Run in guided mode, the list of allowed commands is in commands.txt
+./command -m ./models/ggml-base.en.bin -cmd ./examples/command/commands.txt
+
+# On Raspberry Pi, in guided mode you can use "-ac 128" for extra performance
+./command -m ./models/ggml-tiny.en.bin -cmd ./examples/command/commands.txt -ac 128 -t 3 -c 0
+```
+
+https://user-images.githubusercontent.com/1991296/207435352-8fc4ed3f-bde5-4555-9b8b-aeeb76bee969.mp4
+
+
+## Building
+
+The `command` tool depends on SDL2 library to capture audio from the microphone. You can build it like this:
+
+```bash
+# Install SDL2 on Linux
+sudo apt-get install libsdl2-dev
+
+# Install SDL2 on Mac OS
+brew install sdl2
+
+make command
+```

examples/command/commands.txt

@@ -0,0 +1,9 @@
+enable
+disable
+cat
+dog
+apple
+red
+blue
+green
+lightblue

examples/generate-karaoke.sh

@@ -0,0 +1,60 @@
+#!/bin/bash
+
+# Simple tool to record audio from the microphone and generate a karaoke video
+# Usage:
+#
+#  cd whisper.cpp
+#  make
+#
+#  ./examples/generate-karaoke.sh [model] [step_ms]
+#
+# Press Ctrl+C to stop recording
+#
+
+executable="./main"
+model="base.en"
+model_path="models/ggml-$model.bin"
+
+# require sox and ffmpeg to be installed
+if ! command -v sox &> /dev/null
+then
+    echo "sox could not be found"
+    exit 1
+fi
+
+if ! command -v ffmpeg &> /dev/null
+then
+    echo "ffmpeg could not be found"
+    exit 2
+fi
+
+if [ ! -f "$executable" ]; then
+    echo "'$executable' does not exist. Please build it first."
+    exit 3
+fi
+
+if [ ! -f "$model_path" ]; then
+    echo "'$model_path' does not exist. Please download it first."
+    exit 4
+fi
+
+# record some raw audio
+sox -d rec.wav
+
+# resample to 16kHz
+ffmpeg -y -i ./rec.wav -ar 16000 -ac 1 -c:a pcm_s16le ./rec16.wav > /dev/null 2>&1
+
+# run Whisper
+echo "Processing ..."
+./main -m models/ggml-base.en.bin rec16.wav -owts > /dev/null 2>&1
+
+# generate Karaoke video
+echo "Generating video ..."
+source rec16.wav.wts > /dev/null 2>&1
+
+# play the video
+echo "Playing ./rec16.wav.mp4 ..."
+ffplay -loglevel 0 -autoexit ./rec16.wav.mp4
+
+echo "Done"
+exit 0

examples/helpers.js

@@ -0,0 +1,182 @@
+// Common Javascript functions used by the examples
+
+function convertTypedArray(src, type) {
+    var buffer = new ArrayBuffer(src.byteLength);
+    var baseView = new src.constructor(buffer).set(src);
+    return new type(buffer);
+}
+
+var printTextarea = (function() {
+    var element = document.getElementById('output');
+    if (element) element.alue = ''; // clear browser cache
+    return function(text) {
+        if (arguments.length > 1) text = Array.prototype.slice.call(arguments).join(' ');
+        console.log(text);
+        if (element) {
+            element.value += text + "\n";
+            element.scrollTop = element.scrollHeight; // focus on bottom
+        }
+    };
+})();
+
+async function clearCache() {
+    if (confirm('Are you sure you want to clear the cache?\nAll the models will be downloaded again.')) {
+        indexedDB.deleteDatabase(dbName);
+    }
+}
+
+// fetch a remote file from remote URL using the Fetch API
+async function fetchRemote(url, cbProgress, cbPrint) {
+    cbPrint('fetchRemote: downloading with fetch()...');
+
+    const response = await fetch(
+        url,
+        {
+            method: 'GET',
+            headers: {
+                'Content-Type': 'application/octet-stream',
+            },
+        }
+    );
+
+    if (!response.ok) {
+        cbPrint('fetchRemote: failed to fetch ' + url);
+        return;
+    }
+
+    const contentLength = response.headers.get('content-length');
+    const total = parseInt(contentLength, 10);
+    const reader = response.body.getReader();
+
+    var chunks = [];
+    var receivedLength = 0;
+    var progressLast = -1;
+
+    while (true) {
+        const { done, value } = await reader.read();
+
+        if (done) {
+            break;
+        }
+
+        chunks.push(value);
+        receivedLength += value.length;
+
+        if (contentLength) {
+            cbProgress(receivedLength/total);
+
+            var progressCur = Math.round((receivedLength / total) * 10);
+            if (progressCur != progressLast) {
+                cbPrint('fetchRemote: fetching ' + 10*progressCur + '% ...');
+                progressLast = progressCur;
+            }
+        }
+    }
+
+    var position = 0;
+    var chunksAll = new Uint8Array(receivedLength);
+
+    for (var chunk of chunks) {
+        chunksAll.set(chunk, position);
+        position += chunk.length;
+    }
+
+    return chunksAll;
+}
+
+// load remote data
+// - check if the data is already in the IndexedDB
+// - if not, fetch it from the remote URL and store it in the IndexedDB
+function loadRemote(url, dst, size_mb, cbProgress, cbReady, cbCancel, cbPrint) {
+    // query the storage quota and print it
+    navigator.storage.estimate().then(function (estimate) {
+        cbPrint('loadRemote: storage quota: ' + estimate.quota + ' bytes');
+        cbPrint('loadRemote: storage usage: ' + estimate.usage + ' bytes');
+    });
+
+    // check if the data is already in the IndexedDB
+    var rq = indexedDB.open(dbName, dbVersion);
+
+    rq.onupgradeneeded = function (event) {
+        var db = event.target.result;
+        if (db.version == 1) {
+            var os = db.createObjectStore('models', { autoIncrement: false });
+            cbPrint('loadRemote: created IndexedDB ' + db.name + ' version ' + db.version);
+        } else {
+            // clear the database
+            var os = event.currentTarget.transaction.objectStore('models');
+            os.clear();
+            cbPrint('loadRemote: cleared IndexedDB ' + db.name + ' version ' + db.version);
+        }
+    };
+
+    rq.onsuccess = function (event) {
+        var db = event.target.result;
+        var tx = db.transaction(['models'], 'readonly');
+        var os = tx.objectStore('models');
+        var rq = os.get(url);
+
+        rq.onsuccess = function (event) {
+            if (rq.result) {
+                cbPrint('loadRemote: "' + url + '" is already in the IndexedDB');
+                cbReady(dst, rq.result);
+            } else {
+                // data is not in the IndexedDB
+                cbPrint('loadRemote: "' + url + '" is not in the IndexedDB');
+
+                // alert and ask the user to confirm
+                if (!confirm(
+                    'You are about to download ' + size_mb + ' MB of data.\n' +
+                    'The model data will be cached in the browser for future use.\n\n' +
+                    'Press OK to continue.')) {
+                    cbCancel();
+                    return;
+                }
+
+                fetchRemote(url, cbProgress, cbPrint).then(function (data) {
+                    if (data) {
+                        // store the data in the IndexedDB
+                        var rq = indexedDB.open(dbName, dbVersion);
+                        rq.onsuccess = function (event) {
+                            var db = event.target.result;
+                            var tx = db.transaction(['models'], 'readwrite');
+                            var os = tx.objectStore('models');
+                            var rq = os.put(data, url);
+
+                            rq.onsuccess = function (event) {
+                                cbPrint('loadRemote: "' + url + '" stored in the IndexedDB');
+                                cbReady(dst, data);
+                            };
+
+                            rq.onerror = function (event) {
+                                cbPrint('loadRemote: failed to store "' + url + '" in the IndexedDB');
+                                cbCancel();
+                            };
+                        };
+                    }
+                });
+            }
+        };
+
+        rq.onerror = function (event) {
+            cbPrint('loadRemote: failed to get data from the IndexedDB');
+            cbCancel();
+        };
+    };
+
+    rq.onerror = function (event) {
+        cbPrint('loadRemote: failed to open IndexedDB');
+        cbCancel();
+    };
+
+    rq.onblocked = function (event) {
+        cbPrint('loadRemote: failed to open IndexedDB: blocked');
+        cbCancel();
+    };
+
+    rq.onabort = function (event) {
+        cbPrint('loadRemote: failed to open IndexedDB: abort');
+
+    };
+}
+

examples/livestream.sh

@@ -0,0 +1,112 @@
+#!/bin/bash
+#
+# Transcribe audio livestream by feeding ffmpeg output to whisper.cpp at regular intervals
+# Idea by @semiformal-net
+# ref: https://github.com/ggerganov/whisper.cpp/issues/185
+#
+
+set -eo pipefail
+
+url="http://a.files.bbci.co.uk/media/live/manifesto/audio/simulcast/hls/nonuk/sbr_low/ak/bbc_world_service.m3u8"
+fmt=aac # the audio format extension of the stream (TODO: auto detect)
+step_s=30
+model="base.en"
+
+check_requirements()
+{
+    if ! command -v ./main &>/dev/null; then
+        echo "whisper.cpp main executable is required (make)"
+        exit 1
+    fi
+
+    if ! command -v ffmpeg &>/dev/null; then
+        echo "ffmpeg is required (https://ffmpeg.org)"
+        exit 1
+    fi
+}
+
+check_requirements
+
+
+if [ -z "$1" ]; then
+    echo "Usage: $0 stream_url [step_s] [model]"
+    echo ""
+    echo "  Example:"
+    echo "    $0 $url $step_s $model"
+    echo ""
+    echo "No url specified, using default: $url"
+else
+    url="$1"
+fi
+
+if [ -n "$2" ]; then
+    step_s="$2"
+fi
+
+if [ -n "$3" ]; then
+    model="$3"
+fi
+
+# Whisper models
+models=( "tiny.en" "tiny" "base.en" "base" "small.en" "small" "medium.en" "medium" "large-v1" "large" )
+
+# list available models
+function list_models {
+    printf "\n"
+    printf "  Available models:"
+    for model in "${models[@]}"; do
+        printf " $model"
+    done
+    printf "\n\n"
+}
+
+if [[ ! " ${models[@]} " =~ " ${model} " ]]; then
+    printf "Invalid model: $model\n"
+    list_models
+
+    exit 1
+fi
+
+running=1
+
+trap "running=0" SIGINT SIGTERM
+
+printf "[+] Transcribing stream with model '$model', step_s $step_s (press Ctrl+C to stop):\n\n"
+
+# continuous stream in native fmt (this file will grow forever!)
+ffmpeg -loglevel quiet -y -re -probesize 32 -i $url -c copy /tmp/whisper-live0.${fmt} &
+if [ $? -ne 0 ]; then
+    printf "Error: ffmpeg failed to capture audio stream\n"
+    exit 1
+fi
+
+printf "Buffering audio. Please wait...\n\n"
+sleep $(($step_s))
+
+# do not stop script on error
+set +e
+
+i=0
+SECONDS=0
+while [ $running -eq 1 ]; do
+    # extract the next piece from the main file above and transcode to wav. -ss sets start time and nudges it by -0.5s to catch missing words (??)
+    err=1
+    while [ $err -ne 0 ]; do
+        if [ $i -gt 0 ]; then
+            ffmpeg -loglevel quiet -v error -noaccurate_seek -i /tmp/whisper-live0.${fmt} -y -ar 16000 -ac 1 -c:a pcm_s16le -ss $(($i*$step_s-1)).5 -t $step_s /tmp/whisper-live.wav 2> /tmp/whisper-live.err
+        else
+            ffmpeg -loglevel quiet -v error -noaccurate_seek -i /tmp/whisper-live0.${fmt} -y -ar 16000 -ac 1 -c:a pcm_s16le -ss $(($i*$step_s)) -t $step_s /tmp/whisper-live.wav 2> /tmp/whisper-live.err
+        fi
+        err=$(cat /tmp/whisper-live.err | wc -l)
+    done
+
+    ./main -t 8 -m ./models/ggml-base.en.bin -f /tmp/whisper-live.wav --no-timestamps -otxt 2> /tmp/whispererr | tail -n 1
+
+    while [ $SECONDS -lt $((($i+1)*$step_s)) ]; do
+        sleep 1
+    done
+    ((i=i+1))
+done
+
+killall -v ffmpeg
+killall -v main

examples/main/CMakeLists.txt

@@ -0,0 +1,6 @@
+set(TARGET main)
+add_executable(${TARGET} main.cpp)
+
+include(DefaultTargetOptions)
+
+target_link_libraries(${TARGET} PRIVATE whisper ${CMAKE_THREAD_LIBS_INIT})

examples/main/README.md

@@ -0,0 +1,33 @@
+# main
+
+This is the main example demonstrating most of the functionality of the Whisper model.
+It can be used as a reference for using the `whisper.cpp` library in other projects.
+
+```
+./main -h
+
+usage: ./main [options] file0.wav file1.wav ...
+
+options:
+  -h,       --help          [default] show this help message and exit
+  -t N,     --threads N     [4      ] number of threads to use during computation
+  -p N,     --processors N  [1      ] number of processors to use during computation
+  -ot N,    --offset-t N    [0      ] time offset in milliseconds
+  -on N,    --offset-n N    [0      ] segment index offset
+  -d  N,    --duration N    [0      ] duration of audio to process in milliseconds
+  -mc N,    --max-context N [-1     ] maximum number of text context tokens to store
+  -ml N,    --max-len N     [0      ] maximum segment length in characters
+  -wt N,    --word-thold N  [0.01   ] word timestamp probability threshold
+  -su,      --speed-up      [false  ] speed up audio by x2 (reduced accuracy)
+  -tr,      --translate     [false  ] translate from source language to english
+  -otxt,    --output-txt    [false  ] output result in a text file
+  -ovtt,    --output-vtt    [false  ] output result in a vtt file
+  -osrt,    --output-srt    [false  ] output result in a srt file
+  -owts,    --output-words  [false  ] output script for generating karaoke video
+  -ps,      --print-special [false  ] print special tokens
+  -pc,      --print-colors  [false  ] print colors
+  -nt,      --no-timestamps [true   ] do not print timestamps
+  -l LANG,  --language LANG [en     ] spoken language
+  -m FNAME, --model FNAME   [models/ggml-base.en.bin] model path
+  -f FNAME, --file FNAME    [       ] input WAV file path
+```

examples/main/main.cpp

@@ -0,0 +1,730 @@
+#include "whisper.h"
+
+// third-party utilities
+// use your favorite implementations
+#define DR_WAV_IMPLEMENTATION
+#include "dr_wav.h"
+
+#include <cmath>
+#include <fstream>
+#include <cstdio>
+#include <string>
+#include <thread>
+#include <vector>
+
+// Terminal color map. 10 colors grouped in ranges [0.0, 0.1, ..., 0.9]
+// Lowest is red, middle is yellow, highest is green.
+const std::vector<std::string> k_colors = {
+    "\033[38;5;196m", "\033[38;5;202m", "\033[38;5;208m", "\033[38;5;214m", "\033[38;5;220m",
+    "\033[38;5;226m", "\033[38;5;190m", "\033[38;5;154m", "\033[38;5;118m", "\033[38;5;82m",
+};
+
+//  500 -> 00:05.000
+// 6000 -> 01:00.000
+std::string to_timestamp(int64_t t, bool comma = false) {
+    int64_t msec = t * 10;
+    int64_t hr = msec / (1000 * 60 * 60);
+    msec = msec - hr * (1000 * 60 * 60);
+    int64_t min = msec / (1000 * 60);
+    msec = msec - min * (1000 * 60);
+    int64_t sec = msec / 1000;
+    msec = msec - sec * 1000;
+
+    char buf[32];
+    snprintf(buf, sizeof(buf), "%02d:%02d:%02d%s%03d", (int) hr, (int) min, (int) sec, comma ? "," : ".", (int) msec);
+
+    return std::string(buf);
+}
+
+int timestamp_to_sample(int64_t t, int n_samples) {
+    return std::max(0, std::min((int) n_samples - 1, (int) ((t*WHISPER_SAMPLE_RATE)/100)));
+}
+
+// helper function to replace substrings
+void replace_all(std::string & s, const std::string & search, const std::string & replace) {
+    for (size_t pos = 0; ; pos += replace.length()) {
+        pos = s.find(search, pos);
+        if (pos == std::string::npos) break;
+        s.erase(pos, search.length());
+        s.insert(pos, replace);
+    }
+}
+
+// command-line parameters
+struct whisper_params {
+    int32_t n_threads    = std::min(4, (int32_t) std::thread::hardware_concurrency());
+    int32_t n_processors = 1;
+    int32_t offset_t_ms  = 0;
+    int32_t offset_n     = 0;
+    int32_t duration_ms  = 0;
+    int32_t max_context  = -1;
+    int32_t max_len      = 0;
+    int32_t best_of      = 5;
+    int32_t beam_size    = -1;
+
+    float word_thold    = 0.01f;
+    float entropy_thold = 2.4f;
+    float logprob_thold = -1.0f;
+
+    bool speed_up       = false;
+    bool translate      = false;
+    bool diarize        = false;
+    bool output_txt     = false;
+    bool output_vtt     = false;
+    bool output_srt     = false;
+    bool output_wts     = false;
+    bool output_csv     = false;
+    bool print_special  = false;
+    bool print_colors   = false;
+    bool print_progress = false;
+    bool no_timestamps  = false;
+
+    std::string language = "en";
+    std::string prompt;
+    std::string model    = "models/ggml-base.en.bin";
+
+    std::vector<std::string> fname_inp = {};
+};
+
+void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
+
+bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
+    for (int i = 1; i < argc; i++) {
+        std::string arg = argv[i];
+
+        if (arg[0] != '-') {
+            params.fname_inp.push_back(arg);
+            continue;
+        }
+
+        if (arg == "-h" || arg == "--help") {
+            whisper_print_usage(argc, argv, params);
+            exit(0);
+        }
+        else if (arg == "-t"    || arg == "--threads")        { params.n_threads      = std::stoi(argv[++i]); }
+        else if (arg == "-p"    || arg == "--processors")     { params.n_processors   = std::stoi(argv[++i]); }
+        else if (arg == "-ot"   || arg == "--offset-t")       { params.offset_t_ms    = std::stoi(argv[++i]); }
+        else if (arg == "-on"   || arg == "--offset-n")       { params.offset_n       = std::stoi(argv[++i]); }
+        else if (arg == "-d"    || arg == "--duration")       { params.duration_ms    = std::stoi(argv[++i]); }
+        else if (arg == "-mc"   || arg == "--max-context")    { params.max_context    = std::stoi(argv[++i]); }
+        else if (arg == "-ml"   || arg == "--max-len")        { params.max_len        = std::stoi(argv[++i]); }
+        else if (arg == "-bo"   || arg == "--best-of")        { params.best_of        = std::stoi(argv[++i]); }
+        else if (arg == "-bs"   || arg == "--beam-size")      { params.beam_size      = std::stoi(argv[++i]); }
+        else if (arg == "-wt"   || arg == "--word-thold")     { params.word_thold     = std::stof(argv[++i]); }
+        else if (arg == "-et"   || arg == "--entropy-thold")  { params.entropy_thold  = std::stof(argv[++i]); }
+        else if (arg == "-lpt"  || arg == "--logprob-thold")  { params.logprob_thold  = std::stof(argv[++i]); }
+        else if (arg == "-su"   || arg == "--speed-up")       { params.speed_up       = true; }
+        else if (arg == "-tr"   || arg == "--translate")      { params.translate      = true; }
+        else if (arg == "-di"   || arg == "--diarize")        { params.diarize        = true; }
+        else if (arg == "-otxt" || arg == "--output-txt")     { params.output_txt     = true; }
+        else if (arg == "-ovtt" || arg == "--output-vtt")     { params.output_vtt     = true; }
+        else if (arg == "-osrt" || arg == "--output-srt")     { params.output_srt     = true; }
+        else if (arg == "-owts" || arg == "--output-words")   { params.output_wts     = true; }
+        else if (arg == "-ocsv" || arg == "--output-csv")     { params.output_csv     = true; }
+        else if (arg == "-ps"   || arg == "--print-special")  { params.print_special  = true; }
+        else if (arg == "-pc"   || arg == "--print-colors")   { params.print_colors   = true; }
+        else if (arg == "-pp"   || arg == "--print-progress") { params.print_progress = true; }
+        else if (arg == "-nt"   || arg == "--no-timestamps")  { params.no_timestamps  = true; }
+        else if (arg == "-l"    || arg == "--language")       { params.language       = argv[++i]; }
+        else if (                  arg == "--prompt")         { params.prompt         = argv[++i]; }
+        else if (arg == "-m"    || arg == "--model")          { params.model          = argv[++i]; }
+        else if (arg == "-f"    || arg == "--file")           { params.fname_inp.emplace_back(argv[++i]); }
+        else {
+            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
+            whisper_print_usage(argc, argv, params);
+            exit(0);
+        }
+    }
+
+    return true;
+}
+
+void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & params) {
+    fprintf(stderr, "\n");
+    fprintf(stderr, "usage: %s [options] file0.wav file1.wav ...\n", argv[0]);
+    fprintf(stderr, "\n");
+    fprintf(stderr, "options:\n");
+    fprintf(stderr, "  -h,       --help            [default] show this help message and exit\n");
+    fprintf(stderr, "  -t N,     --threads N       [%-7d] number of threads to use during computation\n",    params.n_threads);
+    fprintf(stderr, "  -p N,     --processors N    [%-7d] number of processors to use during computation\n", params.n_processors);
+    fprintf(stderr, "  -ot N,    --offset-t N      [%-7d] time offset in milliseconds\n",                    params.offset_t_ms);
+    fprintf(stderr, "  -on N,    --offset-n N      [%-7d] segment index offset\n",                           params.offset_n);
+    fprintf(stderr, "  -d  N,    --duration N      [%-7d] duration of audio to process in milliseconds\n",   params.duration_ms);
+    fprintf(stderr, "  -mc N,    --max-context N   [%-7d] maximum number of text context tokens to store\n", params.max_context);
+    fprintf(stderr, "  -ml N,    --max-len N       [%-7d] maximum segment length in characters\n",           params.max_len);
+    fprintf(stderr, "  -bo N,    --best-of N       [%-7d] number of best candidates to keep\n",              params.best_of);
+    fprintf(stderr, "  -bs N,    --beam-size N     [%-7d] beam size for beam search\n",                      params.beam_size);
+    fprintf(stderr, "  -wt N,    --word-thold N    [%-7.2f] word timestamp probability threshold\n",         params.word_thold);
+    fprintf(stderr, "  -et N,    --entropy-thold N [%-7.2f] entropy threshold for decoder fail\n",           params.entropy_thold);
+    fprintf(stderr, "  -lpt N,   --logprob-thold N [%-7.2f] log probability threshold for decoder fail\n",   params.logprob_thold);
+    fprintf(stderr, "  -su,      --speed-up        [%-7s] speed up audio by x2 (reduced accuracy)\n",        params.speed_up ? "true" : "false");
+    fprintf(stderr, "  -tr,      --translate       [%-7s] translate from source language to english\n",      params.translate ? "true" : "false");
+    fprintf(stderr, "  -di,      --diarize         [%-7s] stereo audio diarization\n",                       params.diarize ? "true" : "false");
+    fprintf(stderr, "  -otxt,    --output-txt      [%-7s] output result in a text file\n",                   params.output_txt ? "true" : "false");
+    fprintf(stderr, "  -ovtt,    --output-vtt      [%-7s] output result in a vtt file\n",                    params.output_vtt ? "true" : "false");
+    fprintf(stderr, "  -osrt,    --output-srt      [%-7s] output result in a srt file\n",                    params.output_srt ? "true" : "false");
+    fprintf(stderr, "  -owts,    --output-words    [%-7s] output script for generating karaoke video\n",     params.output_wts ? "true" : "false");
+    fprintf(stderr, "  -ocsv,    --output-csv      [%-7s] output result in a CSV file\n",                    params.output_csv ? "true" : "false");
+    fprintf(stderr, "  -ps,      --print-special   [%-7s] print special tokens\n",                           params.print_special ? "true" : "false");
+    fprintf(stderr, "  -pc,      --print-colors    [%-7s] print colors\n",                                   params.print_colors ? "true" : "false");
+    fprintf(stderr, "  -pp,      --print-progress  [%-7s] print progress\n",                                 params.print_progress ? "true" : "false");
+    fprintf(stderr, "  -nt,      --no-timestamps   [%-7s] do not print timestamps\n",                        params.no_timestamps ? "false" : "true");
+    fprintf(stderr, "  -l LANG,  --language LANG   [%-7s] spoken language ('auto' for auto-detect)\n",       params.language.c_str());
+    fprintf(stderr, "            --prompt PROMPT   [%-7s] initial prompt\n",                                 params.prompt.c_str());
+    fprintf(stderr, "  -m FNAME, --model FNAME     [%-7s] model path\n",                                     params.model.c_str());
+    fprintf(stderr, "  -f FNAME, --file FNAME      [%-7s] input WAV file path\n",                            "");
+    fprintf(stderr, "\n");
+}
+
+struct whisper_print_user_data {
+    const whisper_params * params;
+
+    const std::vector<std::vector<float>> * pcmf32s;
+};
+
+void whisper_print_segment_callback(struct whisper_context * ctx, int n_new, void * user_data) {
+    const auto & params  = *((whisper_print_user_data *) user_data)->params;
+    const auto & pcmf32s = *((whisper_print_user_data *) user_data)->pcmf32s;
+
+    const int n_segments = whisper_full_n_segments(ctx);
+
+    std::string speaker = "";
+
+    int64_t t0;
+    int64_t t1;
+
+    // print the last n_new segments
+    const int s0 = n_segments - n_new;
+
+    if (s0 == 0) {
+        printf("\n");
+    }
+
+    for (int i = s0; i < n_segments; i++) {
+        if (!params.no_timestamps || params.diarize) {
+            t0 = whisper_full_get_segment_t0(ctx, i);
+            t1 = whisper_full_get_segment_t1(ctx, i);
+        }
+
+        if (!params.no_timestamps) {
+            printf("[%s --> %s]  ", to_timestamp(t0).c_str(), to_timestamp(t1).c_str());
+        }
+
+        if (params.diarize && pcmf32s.size() == 2) {
+            const int64_t n_samples = pcmf32s[0].size();
+
+            const int64_t is0 = timestamp_to_sample(t0, n_samples);
+            const int64_t is1 = timestamp_to_sample(t1, n_samples);
+
+            double energy0 = 0.0f;
+            double energy1 = 0.0f;
+
+            for (int64_t j = is0; j < is1; j++) {
+                energy0 += fabs(pcmf32s[0][j]);
+                energy1 += fabs(pcmf32s[1][j]);
+            }
+
+            if (energy0 > 1.1*energy1) {
+                speaker = "(speaker 0)";
+            } else if (energy1 > 1.1*energy0) {
+                speaker = "(speaker 1)";
+            } else {
+                speaker = "(speaker ?)";
+            }
+
+            //printf("is0 = %lld, is1 = %lld, energy0 = %f, energy1 = %f, %s\n", is0, is1, energy0, energy1, speaker.c_str());
+        }
+
+        if (params.print_colors) {
+            for (int j = 0; j < whisper_full_n_tokens(ctx, i); ++j) {
+                if (params.print_special == false) {
+                    const whisper_token id = whisper_full_get_token_id(ctx, i, j);
+                    if (id >= whisper_token_eot(ctx)) {
+                        continue;
+                    }
+                }
+
+                const char * text = whisper_full_get_token_text(ctx, i, j);
+                const float  p    = whisper_full_get_token_p   (ctx, i, j);
+
+                const int col = std::max(0, std::min((int) k_colors.size() - 1, (int) (std::pow(p, 3)*float(k_colors.size()))));
+
+                printf("%s%s%s%s", speaker.c_str(), k_colors[col].c_str(), text, "\033[0m");
+            }
+        } else {
+            const char * text = whisper_full_get_segment_text(ctx, i);
+
+            printf("%s%s", speaker.c_str(), text);
+        }
+
+        // with timestamps or speakers: each segment on new line
+        if (!params.no_timestamps || params.diarize) {
+            printf("\n");
+        }
+
+        fflush(stdout);
+    }
+}
+
+bool output_txt(struct whisper_context * ctx, const char * fname) {
+    std::ofstream fout(fname);
+    if (!fout.is_open()) {
+        fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname);
+        return false;
+    }
+
+    fprintf(stderr, "%s: saving output to '%s'\n", __func__, fname);
+
+    const int n_segments = whisper_full_n_segments(ctx);
+    for (int i = 0; i < n_segments; ++i) {
+        const char * text = whisper_full_get_segment_text(ctx, i);
+        fout << text << "\n";
+    }
+
+    return true;
+}
+
+bool output_vtt(struct whisper_context * ctx, const char * fname) {
+    std::ofstream fout(fname);
+    if (!fout.is_open()) {
+        fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname);
+        return false;
+    }
+
+    fprintf(stderr, "%s: saving output to '%s'\n", __func__, fname);
+
+    fout << "WEBVTT\n\n";
+
+    const int n_segments = whisper_full_n_segments(ctx);
+    for (int i = 0; i < n_segments; ++i) {
+        const char * text = whisper_full_get_segment_text(ctx, i);
+        const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
+        const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
+
+        fout << to_timestamp(t0) << " --> " << to_timestamp(t1) << "\n";
+        fout << text << "\n\n";
+    }
+
+    return true;
+}
+
+bool output_srt(struct whisper_context * ctx, const char * fname, const whisper_params & params) {
+    std::ofstream fout(fname);
+    if (!fout.is_open()) {
+        fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname);
+        return false;
+    }
+
+    fprintf(stderr, "%s: saving output to '%s'\n", __func__, fname);
+
+    const int n_segments = whisper_full_n_segments(ctx);
+    for (int i = 0; i < n_segments; ++i) {
+        const char * text = whisper_full_get_segment_text(ctx, i);
+        const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
+        const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
+
+        fout << i + 1 + params.offset_n << "\n";
+        fout << to_timestamp(t0, true) << " --> " << to_timestamp(t1, true) << "\n";
+        fout << text << "\n\n";
+    }
+
+    return true;
+}
+
+bool output_csv(struct whisper_context * ctx, const char * fname) {
+    std::ofstream fout(fname);
+    if (!fout.is_open()) {
+        fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname);
+        return false;
+    }
+
+    fprintf(stderr, "%s: saving output to '%s'\n", __func__, fname);
+
+    const int n_segments = whisper_full_n_segments(ctx);
+    for (int i = 0; i < n_segments; ++i) {
+        const char * text = whisper_full_get_segment_text(ctx, i);
+        if (text[0] == ' ') {
+            text = text + sizeof(char); //whisper_full_get_segment_text() returns a string with leading space, point to the next character.
+        }
+        const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
+        const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
+
+        //need to multiply times returned from whisper_full_get_segment_t{0,1}() by 10 to get milliseconds.
+        fout << 10 * t0 << ", " << 10 * t1 << ", \"" << text    << "\"\n";
+    }
+
+    return true;
+}
+
+// karaoke video generation
+// outputs a bash script that uses ffmpeg to generate a video with the subtitles
+// TODO: font parameter adjustments
+bool output_wts(struct whisper_context * ctx, const char * fname, const char * fname_inp, const whisper_params & /*params*/, float t_sec) {
+    std::ofstream fout(fname);
+
+    fprintf(stderr, "%s: saving output to '%s'\n", __func__, fname);
+
+    // TODO: become parameter
+    static const char * font = "/System/Library/Fonts/Supplemental/Courier New Bold.ttf";
+
+    fout << "#!/bin/bash" << "\n";
+    fout << "\n";
+
+    fout << "ffmpeg -i " << fname_inp << " -f lavfi -i color=size=1200x120:duration=" << t_sec << ":rate=25:color=black -vf \"";
+
+    for (int i = 0; i < whisper_full_n_segments(ctx); i++) {
+        const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
+        const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
+
+        const int n = whisper_full_n_tokens(ctx, i);
+
+        std::vector<whisper_token_data> tokens(n);
+        for (int j = 0; j < n; ++j) {
+            tokens[j] = whisper_full_get_token_data(ctx, i, j);
+        }
+
+        if (i > 0) {
+            fout << ",";
+        }
+
+        // background text
+        fout << "drawtext=fontfile='" << font << "':fontsize=24:fontcolor=gray:x=(w-text_w)/2:y=h/2:text='':enable='between(t," << t0/100.0 << "," << t0/100.0 << ")'";
+
+        bool is_first = true;
+
+        for (int j = 0; j < n; ++j) {
+            const auto & token = tokens[j];
+
+            if (tokens[j].id >= whisper_token_eot(ctx)) {
+                continue;
+            }
+
+            std::string txt_bg;
+            std::string txt_fg; // highlight token
+            std::string txt_ul; // underline
+
+            txt_bg = "> ";
+            txt_fg = "> ";
+            txt_ul = "\\ \\ ";
+
+            {
+                for (int k = 0; k < n; ++k) {
+                    const auto & token2 = tokens[k];
+
+                    if (tokens[k].id >= whisper_token_eot(ctx)) {
+                        continue;
+                    }
+
+                    const std::string txt = whisper_token_to_str(ctx, token2.id);
+
+                    txt_bg += txt;
+
+                    if (k == j) {
+                        for (int l = 0; l < (int) txt.size(); ++l) {
+                            txt_fg += txt[l];
+                            txt_ul += "_";
+                        }
+                        txt_fg += "|";
+                    } else {
+                        for (int l = 0; l < (int) txt.size(); ++l) {
+                            txt_fg += "\\ ";
+                            txt_ul += "\\ ";
+                        }
+                    }
+                }
+
+                ::replace_all(txt_bg, "'", "\u2019");
+                ::replace_all(txt_bg, "\"", "\\\"");
+                ::replace_all(txt_fg, "'", "\u2019");
+                ::replace_all(txt_fg, "\"", "\\\"");
+            }
+
+            if (is_first) {
+                // background text
+                fout << ",drawtext=fontfile='" << font << "':fontsize=24:fontcolor=gray:x=(w-text_w)/2:y=h/2:text='" << txt_bg << "':enable='between(t," << t0/100.0 << "," << t1/100.0 << ")'";
+                is_first = false;
+            }
+
+            // foreground text
+            fout << ",drawtext=fontfile='" << font << "':fontsize=24:fontcolor=lightgreen:x=(w-text_w)/2+8:y=h/2:text='" << txt_fg << "':enable='between(t," << token.t0/100.0 << "," << token.t1/100.0 << ")'";
+
+            // underline
+            fout << ",drawtext=fontfile='" << font << "':fontsize=24:fontcolor=lightgreen:x=(w-text_w)/2+8:y=h/2+16:text='" << txt_ul << "':enable='between(t," << token.t0/100.0 << "," << token.t1/100.0 << ")'";
+        }
+    }
+
+    fout << "\" -c:v libx264 -pix_fmt yuv420p -y " << fname_inp << ".mp4" << "\n";
+
+    fout << "\n\n";
+    fout << "echo \"Your video has been saved to " << fname_inp << ".mp4\"" << "\n";
+    fout << "\n";
+    fout << "echo \"  ffplay " << fname_inp << ".mp4\"\n";
+    fout << "\n";
+
+    fout.close();
+
+    fprintf(stderr, "%s: run 'source %s' to generate karaoke video\n", __func__, fname);
+
+    return true;
+}
+
+int main(int argc, char ** argv) {
+    whisper_params params;
+
+    if (whisper_params_parse(argc, argv, params) == false) {
+        return 1;
+    }
+
+    if (params.fname_inp.empty()) {
+        fprintf(stderr, "error: no input files specified\n");
+        whisper_print_usage(argc, argv, params);
+        return 2;
+    }
+
+    if (params.language != "auto" && whisper_lang_id(params.language.c_str()) == -1) {
+        fprintf(stderr, "error: unknown language '%s'\n", params.language.c_str());
+        whisper_print_usage(argc, argv, params);
+        exit(0);
+    }
+
+    // whisper init
+
+    struct whisper_context * ctx = whisper_init_from_file(params.model.c_str());
+
+    if (ctx == nullptr) {
+        fprintf(stderr, "error: failed to initialize whisper context\n");
+        return 3;
+    }
+
+    // initial prompt
+    std::vector<whisper_token> prompt_tokens;
+
+    if (!params.prompt.empty()) {
+        prompt_tokens.resize(1024);
+        prompt_tokens.resize(whisper_tokenize(ctx, params.prompt.c_str(), prompt_tokens.data(), prompt_tokens.size()));
+
+        fprintf(stderr, "\n");
+        fprintf(stderr, "initial prompt: '%s'\n", params.prompt.c_str());
+        fprintf(stderr, "initial tokens: [ ");
+        for (int i = 0; i < (int) prompt_tokens.size(); ++i) {
+            fprintf(stderr, "%d ", prompt_tokens[i]);
+        }
+        fprintf(stderr, "]\n");
+    }
+
+    for (int f = 0; f < (int) params.fname_inp.size(); ++f) {
+        const auto fname_inp = params.fname_inp[f];
+
+        std::vector<float> pcmf32; // mono-channel F32 PCM
+        std::vector<std::vector<float>> pcmf32s; // stereo-channel F32 PCM
+
+        // WAV input
+        {
+            drwav wav;
+            std::vector<uint8_t> wav_data; // used for pipe input from stdin
+
+            if (fname_inp == "-") {
+                {
+                    uint8_t buf[1024];
+                    while (true)
+                    {
+                        const size_t n = fread(buf, 1, sizeof(buf), stdin);
+                        if (n == 0) {
+                            break;
+                        }
+                        wav_data.insert(wav_data.end(), buf, buf + n);
+                    }
+                }
+
+                if (drwav_init_memory(&wav, wav_data.data(), wav_data.size(), nullptr) == false) {
+                    fprintf(stderr, "error: failed to open WAV file from stdin\n");
+                    return 4;
+                }
+
+                fprintf(stderr, "%s: read %zu bytes from stdin\n", __func__, wav_data.size());
+            }
+            else if (drwav_init_file(&wav, fname_inp.c_str(), nullptr) == false) {
+                fprintf(stderr, "error: failed to open '%s' as WAV file\n", fname_inp.c_str());
+                return 5;
+            }
+
+            if (wav.channels != 1 && wav.channels != 2) {
+                fprintf(stderr, "%s: WAV file '%s' must be mono or stereo\n", argv[0], fname_inp.c_str());
+                return 6;
+            }
+
+            if (params.diarize && wav.channels != 2 && params.no_timestamps == false) {
+                fprintf(stderr, "%s: WAV file '%s' must be stereo for diarization and timestamps have to be enabled\n", argv[0], fname_inp.c_str());
+                return 6;
+            }
+
+            if (wav.sampleRate != WHISPER_SAMPLE_RATE) {
+                fprintf(stderr, "%s: WAV file '%s' must be %i kHz\n", argv[0], fname_inp.c_str(), WHISPER_SAMPLE_RATE/1000);
+                return 8;
+            }
+
+            if (wav.bitsPerSample != 16) {
+                fprintf(stderr, "%s: WAV file '%s' must be 16-bit\n", argv[0], fname_inp.c_str());
+                return 9;
+            }
+
+            const uint64_t n = wav_data.empty() ? wav.totalPCMFrameCount : wav_data.size()/(wav.channels*wav.bitsPerSample/8);
+
+            std::vector<int16_t> pcm16;
+            pcm16.resize(n*wav.channels);
+            drwav_read_pcm_frames_s16(&wav, n, pcm16.data());
+            drwav_uninit(&wav);
+
+            // convert to mono, float
+            pcmf32.resize(n);
+            if (wav.channels == 1) {
+                for (uint64_t i = 0; i < n; i++) {
+                    pcmf32[i] = float(pcm16[i])/32768.0f;
+                }
+            } else {
+                for (uint64_t i = 0; i < n; i++) {
+                    pcmf32[i] = float(pcm16[2*i] + pcm16[2*i + 1])/65536.0f;
+                }
+            }
+
+            if (params.diarize) {
+                // convert to stereo, float
+                pcmf32s.resize(2);
+
+                pcmf32s[0].resize(n);
+                pcmf32s[1].resize(n);
+                for (uint64_t i = 0; i < n; i++) {
+                    pcmf32s[0][i] = float(pcm16[2*i])/32768.0f;
+                    pcmf32s[1][i] = float(pcm16[2*i + 1])/32768.0f;
+                }
+            }
+        }
+
+        // print system information
+        {
+            fprintf(stderr, "\n");
+            fprintf(stderr, "system_info: n_threads = %d / %d | %s\n",
+                    params.n_threads*params.n_processors, std::thread::hardware_concurrency(), whisper_print_system_info());
+        }
+
+        // print some info about the processing
+        {
+            fprintf(stderr, "\n");
+            if (!whisper_is_multilingual(ctx)) {
+                if (params.language != "en" || params.translate) {
+                    params.language = "en";
+                    params.translate = false;
+                    fprintf(stderr, "%s: WARNING: model is not multilingual, ignoring language and translation options\n", __func__);
+                }
+            }
+            fprintf(stderr, "%s: processing '%s' (%d samples, %.1f sec), %d threads, %d processors, lang = %s, task = %s, timestamps = %d ...\n",
+                    __func__, fname_inp.c_str(), int(pcmf32.size()), float(pcmf32.size())/WHISPER_SAMPLE_RATE,
+                    params.n_threads, params.n_processors,
+                    params.language.c_str(),
+                    params.translate ? "translate" : "transcribe",
+                    params.no_timestamps ? 0 : 1);
+
+            fprintf(stderr, "\n");
+        }
+
+        // run the inference
+        {
+            whisper_full_params wparams = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
+
+            wparams.strategy = params.beam_size > 1 ? WHISPER_SAMPLING_BEAM_SEARCH : WHISPER_SAMPLING_GREEDY;
+
+            wparams.print_realtime   = false;
+            wparams.print_progress   = params.print_progress;
+            wparams.print_timestamps = !params.no_timestamps;
+            wparams.print_special    = params.print_special;
+            wparams.translate        = params.translate;
+            wparams.language         = params.language.c_str();
+            wparams.n_threads        = params.n_threads;
+            wparams.n_max_text_ctx   = params.max_context >= 0 ? params.max_context : wparams.n_max_text_ctx;
+            wparams.offset_ms        = params.offset_t_ms;
+            wparams.duration_ms      = params.duration_ms;
+
+            wparams.token_timestamps = params.output_wts || params.max_len > 0;
+            wparams.thold_pt         = params.word_thold;
+            wparams.entropy_thold    = params.entropy_thold;
+            wparams.logprob_thold    = params.logprob_thold;
+            wparams.max_len          = params.output_wts && params.max_len == 0 ? 60 : params.max_len;
+
+            wparams.speed_up         = params.speed_up;
+
+            wparams.greedy.best_of        = params.best_of;
+            wparams.beam_search.beam_size = params.beam_size;
+            wparams.temperature_inc = -1;
+
+            wparams.prompt_tokens     = prompt_tokens.empty() ? nullptr : prompt_tokens.data();
+            wparams.prompt_n_tokens   = prompt_tokens.empty() ? 0       : prompt_tokens.size();
+
+            whisper_print_user_data user_data = { &params, &pcmf32s };
+
+            // this callback is called on each new segment
+            if (!wparams.print_realtime) {
+                wparams.new_segment_callback           = whisper_print_segment_callback;
+                wparams.new_segment_callback_user_data = &user_data;
+            }
+
+            // example for abort mechanism
+            // in this example, we do not abort the processing, but we could if the flag is set to true
+            // the callback is called before every encoder run - if it returns false, the processing is aborted
+            {
+                static bool is_aborted = false; // NOTE: this should be atomic to avoid data race
+
+                wparams.encoder_begin_callback = [](struct whisper_context * /*ctx*/, void * user_data) {
+                    bool is_aborted = *(bool*)user_data;
+                    return !is_aborted;
+                };
+                wparams.encoder_begin_callback_user_data = &is_aborted;
+            }
+
+            if (whisper_full_parallel(ctx, wparams, pcmf32.data(), pcmf32.size(), params.n_processors) != 0) {
+                fprintf(stderr, "%s: failed to process audio\n", argv[0]);
+                return 10;
+            }
+        }
+
+        // output stuff
+        {
+            printf("\n");
+
+            // output to text file
+            if (params.output_txt) {
+                const auto fname_txt = fname_inp + ".txt";
+                output_txt(ctx, fname_txt.c_str());
+            }
+
+            // output to VTT file
+            if (params.output_vtt) {
+                const auto fname_vtt = fname_inp + ".vtt";
+                output_vtt(ctx, fname_vtt.c_str());
+            }
+
+            // output to SRT file
+            if (params.output_srt) {
+                const auto fname_srt = fname_inp + ".srt";
+                output_srt(ctx, fname_srt.c_str(), params);
+            }
+
+            // output to WTS file
+            if (params.output_wts) {
+                const auto fname_wts = fname_inp + ".wts";
+                output_wts(ctx, fname_wts.c_str(), fname_inp.c_str(), params, float(pcmf32.size() + 1000)/WHISPER_SAMPLE_RATE);
+            }
+
+	    // output to CSV file
+            if (params.output_csv) {
+                const auto fname_csv = fname_inp + ".csv";
+                output_csv(ctx, fname_csv.c_str());
+            }
+
+        }
+    }
+
+    whisper_print_timings(ctx);
+    whisper_free(ctx);
+
+    return 0;
+}

examples/stream.wasm/CMakeLists.txt

@@ -0,0 +1,49 @@
+#
+# libstream
+#
+
+set(TARGET libstream)
+
+add_executable(${TARGET}
+    emscripten.cpp
+    )
+
+include(DefaultTargetOptions)
+
+target_link_libraries(${TARGET} PRIVATE
+    whisper
+    )
+
+unset(EXTRA_FLAGS)
+
+if (WHISPER_WASM_SINGLE_FILE)
+    set(EXTRA_FLAGS "-s SINGLE_FILE=1")
+    message(STATUS "Embedding WASM inside stream.js")
+
+    add_custom_command(
+        TARGET ${TARGET} POST_BUILD
+        COMMAND ${CMAKE_COMMAND} -E copy
+        ${CMAKE_BINARY_DIR}/bin/libstream.js
+        ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/stream.wasm/stream.js
+        )
+endif()
+
+set_target_properties(${TARGET} PROPERTIES LINK_FLAGS " \
+    --bind \
+    -s USE_PTHREADS=1 \
+    -s PTHREAD_POOL_SIZE=8 \
+    -s INITIAL_MEMORY=1024MB \
+    -s TOTAL_MEMORY=1024MB \
+    -s FORCE_FILESYSTEM=1 \
+    -s EXPORTED_RUNTIME_METHODS=\"['print', 'printErr', 'ccall', 'cwrap']\" \
+    ${EXTRA_FLAGS} \
+    ")
+
+#
+# stream.wasm
+#
+
+set(TARGET stream.wasm)
+
+configure_file(${CMAKE_CURRENT_SOURCE_DIR}/index-tmpl.html  ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${TARGET}/index.html @ONLY)
+configure_file(${CMAKE_CURRENT_SOURCE_DIR}/../helpers.js    ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${TARGET}/helpers.js @ONLY)

examples/stream.wasm/README.md

@@ -0,0 +1,20 @@
+# stream.wasm
+
+Real-time transcription in the browser using WebAssembly
+
+Online demo: https://whisper.ggerganov.com/stream/
+
+## Build instructions
+
+```bash
+# build using Emscripten (v3.1.2)
+git clone https://github.com/ggerganov/whisper.cpp
+cd whisper.cpp
+mkdir build-em && cd build-em
+emcmake cmake ..
+make -j
+
+# copy the produced page to your HTTP path
+cp bin/stream.wasm/*       /path/to/html/
+cp bin/libstream.worker.js /path/to/html/
+```

examples/stream.wasm/emscripten.cpp

@@ -0,0 +1,216 @@
+#include "ggml.h"
+#include "whisper.h"
+
+#include <emscripten.h>
+#include <emscripten/bind.h>
+
+#include <atomic>
+#include <cmath>
+#include <mutex>
+#include <string>
+#include <thread>
+#include <vector>
+
+constexpr int N_THREAD = 8;
+
+std::vector<struct whisper_context *> g_contexts(4, nullptr);
+
+std::mutex g_mutex;
+std::thread g_worker;
+
+std::atomic<bool> g_running(false);
+
+std::string g_status        = "";
+std::string g_status_forced = "";
+std::string g_transcribed   = "";
+
+std::vector<float> g_pcmf32;
+
+void stream_set_status(const std::string & status) {
+    std::lock_guard<std::mutex> lock(g_mutex);
+    g_status = status;
+}
+
+void stream_main(size_t index) {
+    stream_set_status("loading data ...");
+
+    struct whisper_full_params wparams = whisper_full_default_params(whisper_sampling_strategy::WHISPER_SAMPLING_GREEDY);
+
+    wparams.n_threads        = std::min(N_THREAD, (int) std::thread::hardware_concurrency());
+    wparams.offset_ms        = 0;
+    wparams.translate        = false;
+    wparams.no_context       = true;
+    wparams.single_segment   = true;
+    wparams.print_realtime   = false;
+    wparams.print_progress   = false;
+    wparams.print_timestamps = true;
+    wparams.print_special    = false;
+
+    wparams.max_tokens       = 32;
+    wparams.audio_ctx        = 768; // partial encoder context for better performance
+
+    // disable temperature fallback
+    wparams.temperature_inc  = -1.0f;
+
+    wparams.language         = "en";
+
+    printf("stream: using %d threads\n", wparams.n_threads);
+
+    std::vector<float> pcmf32;
+
+    // whisper context
+    auto & ctx = g_contexts[index];
+
+    // 5 seconds interval
+    const int64_t window_samples = 5*WHISPER_SAMPLE_RATE;
+
+    while (g_running) {
+        stream_set_status("waiting for audio ...");
+
+        {
+            std::unique_lock<std::mutex> lock(g_mutex);
+
+            if (g_pcmf32.size() < 1024) {
+                lock.unlock();
+
+                std::this_thread::sleep_for(std::chrono::milliseconds(10));
+
+                continue;
+            }
+
+            pcmf32 = std::vector<float>(g_pcmf32.end() - std::min((int64_t) g_pcmf32.size(), window_samples), g_pcmf32.end());
+            g_pcmf32.clear();
+        }
+
+        {
+            const auto t_start = std::chrono::high_resolution_clock::now();
+
+            stream_set_status("running whisper ...");
+
+            int ret = whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size());
+            if (ret != 0) {
+                printf("whisper_full() failed: %d\n", ret);
+                break;
+            }
+
+            const auto t_end = std::chrono::high_resolution_clock::now();
+
+            printf("stream: whisper_full() returned %d in %f seconds\n", ret, std::chrono::duration<double>(t_end - t_start).count());
+        }
+
+        {
+            std::string text_heard;
+
+            {
+                const int n_segments = whisper_full_n_segments(ctx);
+                for (int i = n_segments - 1; i < n_segments; ++i) {
+                    const char * text = whisper_full_get_segment_text(ctx, i);
+
+                    const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
+                    const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
+
+                    printf("transcribed: %s\n", text);
+
+                    text_heard += text;
+                }
+            }
+
+            {
+                std::lock_guard<std::mutex> lock(g_mutex);
+                g_transcribed = text_heard;
+            }
+        }
+    }
+
+    if (index < g_contexts.size()) {
+        whisper_free(g_contexts[index]);
+        g_contexts[index] = nullptr;
+    }
+}
+
+EMSCRIPTEN_BINDINGS(stream) {
+    emscripten::function("init", emscripten::optional_override([](const std::string & path_model) {
+        for (size_t i = 0; i < g_contexts.size(); ++i) {
+            if (g_contexts[i] == nullptr) {
+                g_contexts[i] = whisper_init_from_file(path_model.c_str());
+                if (g_contexts[i] != nullptr) {
+                    g_running = true;
+                    if (g_worker.joinable()) {
+                        g_worker.join();
+                    }
+                    g_worker = std::thread([i]() {
+                        stream_main(i);
+                    });
+
+                    return i + 1;
+                } else {
+                    return (size_t) 0;
+                }
+            }
+        }
+
+        return (size_t) 0;
+    }));
+
+    emscripten::function("free", emscripten::optional_override([](size_t index) {
+        if (g_running) {
+            g_running = false;
+        }
+    }));
+
+    emscripten::function("set_audio", emscripten::optional_override([](size_t index, const emscripten::val & audio) {
+        --index;
+
+        if (index >= g_contexts.size()) {
+            return -1;
+        }
+
+        if (g_contexts[index] == nullptr) {
+            return -2;
+        }
+
+        {
+            std::lock_guard<std::mutex> lock(g_mutex);
+            const int n = audio["length"].as<int>();
+
+            emscripten::val heap = emscripten::val::module_property("HEAPU8");
+            emscripten::val memory = heap["buffer"];
+
+            g_pcmf32.resize(n);
+
+            emscripten::val memoryView = audio["constructor"].new_(memory, reinterpret_cast<uintptr_t>(g_pcmf32.data()), n);
+            memoryView.call<void>("set", audio);
+        }
+
+        return 0;
+    }));
+
+    emscripten::function("get_transcribed", emscripten::optional_override([]() {
+        std::string transcribed;
+
+        {
+            std::lock_guard<std::mutex> lock(g_mutex);
+            transcribed = std::move(g_transcribed);
+        }
+
+        return transcribed;
+    }));
+
+    emscripten::function("get_status", emscripten::optional_override([]() {
+        std::string status;
+
+        {
+            std::lock_guard<std::mutex> lock(g_mutex);
+            status = g_status_forced.empty() ? g_status : g_status_forced;
+        }
+
+        return status;
+    }));
+
+    emscripten::function("set_status", emscripten::optional_override([](const std::string & status) {
+        {
+            std::lock_guard<std::mutex> lock(g_mutex);
+            g_status_forced = status;
+        }
+    }));
+}

examples/stream.wasm/index-tmpl.html

@@ -0,0 +1,386 @@
+<!doctype html>
+<html lang="en-us">
+    <head>
+        <title>stream : Real-time Whisper transcription in WebAssembly</title>
+
+        <style>
+            #output {
+                width: 100%;
+                height: 100%;
+                margin: 0 auto;
+                margin-top: 10px;
+                border-left: 0px;
+                border-right: 0px;
+                padding-left: 0px;
+                padding-right: 0px;
+                display: block;
+                background-color: black;
+                color: white;
+                font-size: 10px;
+                font-family: 'Lucida Console', Monaco, monospace;
+                outline: none;
+                white-space: pre;
+                overflow-wrap: normal;
+                overflow-x: scroll;
+            }
+        </style>
+    </head>
+    <body>
+        <div id="main-container">
+            <b>stream : Real-time Whisper transcription in WebAssembly</b>
+
+            <br><br>
+
+            You can find more about this project on <a href="https://github.com/ggerganov/whisper.cpp/tree/master/examples/stream.wasm">GitHub</a>.
+
+            <br><br>
+
+            <hr>
+
+            Select the model you would like to use, click the "Start" button and start speaking
+
+            <br><br>
+
+            <div id="model-whisper">
+                Whisper model: <span id="model-whisper-status"></span>
+                <button id="fetch-whisper-tiny-en" onclick="loadWhisper('tiny.en')">tiny.en (75 MB)</button>
+                <button id="fetch-whisper-base-en" onclick="loadWhisper('base.en')">base.en (142 MB)</button>
+                <span id="fetch-whisper-progress"></span>
+
+                <!--
+                    <input type="file" id="file" name="file" onchange="loadFile(event, 'whisper.bin')" />
+                -->
+            </div>
+
+            <br>
+
+            <div id="input">
+                <button id="start"  onclick="onStart()" disabled>Start</button>
+                <button id="stop"   onclick="onStop()" disabled>Stop</button>
+                <button id="clear"  onclick="clearCache()">Clear Cache</button>
+            </div>
+
+            <br>
+
+            <div id="state">
+                Status: <b><span id="state-status">not started</span></b>
+
+                <pre id="state-transcribed">[The transcribed text will be displayed here]</pre>
+            </div>
+
+            <hr>
+
+            Debug output:
+            <textarea id="output" rows="20"></textarea>
+
+            <br>
+
+            <b>Troubleshooting</b>
+
+            <br><br>
+
+            The page does some heavy computations, so make sure:
+
+            <ul>
+                <li>To use a modern web browser (e.g. Chrome, Firefox)</li>
+                <li>To use a fast desktop or laptop computer (i.e. not a mobile phone)</li>
+                <li>Your browser supports WASM <a href="https://webassembly.org/roadmap/">Fixed-width SIMD</a></li>
+            </ul>
+
+            <div class="cell-version">
+                <span>
+                    |
+                    Build time: <span class="nav-link">@GIT_DATE@</span> |
+                    Commit hash: <a class="nav-link" href="https://github.com/ggerganov/whisper.cpp/commit/@GIT_SHA1@">@GIT_SHA1@</a> |
+                    Commit subject: <span class="nav-link">@GIT_COMMIT_SUBJECT@</span> |
+                    <a class="nav-link" href="https://github.com/ggerganov/whisper.cpp/tree/master/examples/stream.wasm">Source Code</a> |
+                </span>
+            </div>
+        </div>
+
+        <script type="text/javascript" src="helpers.js"></script>
+        <script type='text/javascript'>
+            // web audio context
+            var context = null;
+
+            // audio data
+            var audio = null;
+            var audio0 = null;
+
+            // the stream instance
+            var instance = null;
+
+            // model name
+            var model_whisper = null;
+
+            var Module = {
+                print: printTextarea,
+                printErr: printTextarea,
+                setStatus: function(text) {
+                    printTextarea('js: ' + text);
+                },
+                monitorRunDependencies: function(left) {
+                },
+                preRun: function() {
+                    printTextarea('js: Preparing ...');
+                },
+                postRun: function() {
+                    printTextarea('js: Initialized successfully!');
+                }
+            };
+
+            //
+            // fetch models
+            //
+
+            let dbVersion = 1
+            let dbName    = 'whisper.ggerganov.com';
+            let indexedDB = window.indexedDB || window.mozIndexedDB || window.webkitIndexedDB || window.msIndexedDB
+
+            function storeFS(fname, buf) {
+                // write to WASM file using FS_createDataFile
+                // if the file exists, delete it
+                try {
+                    Module.FS_unlink(fname);
+                } catch (e) {
+                    // ignore
+                }
+
+                Module.FS_createDataFile("/", fname, buf, true, true);
+
+                printTextarea('storeFS: stored model: ' + fname + ' size: ' + buf.length);
+
+                document.getElementById('model-whisper-status').innerHTML = 'loaded "' + model_whisper + '"!';
+
+                if (model_whisper != null) {
+                    document.getElementById('start').disabled = false;
+                    document.getElementById('stop' ).disabled = true;
+                }
+            }
+
+            function loadWhisper(model) {
+                let urls = {
+                    'tiny.en': 'https://whisper.ggerganov.com/ggml-model-whisper-tiny.en.bin',
+                    'base.en': 'https://whisper.ggerganov.com/ggml-model-whisper-base.en.bin',
+                };
+
+                let sizes = {
+                    'tiny.en': 75,
+                    'base.en': 142,
+                };
+
+                let url     = urls[model];
+                let dst     = 'whisper.bin';
+                let size_mb = sizes[model];
+
+                model_whisper = model;
+
+                document.getElementById('fetch-whisper-tiny-en').style.display = 'none';
+                document.getElementById('fetch-whisper-base-en').style.display = 'none';
+                document.getElementById('model-whisper-status').innerHTML = 'loading "' + model + '" ... ';
+
+                cbProgress = function(p) {
+                    let el = document.getElementById('fetch-whisper-progress');
+                    el.innerHTML = Math.round(100*p) + '%';
+                };
+
+                cbCancel = function() {
+                    var el;
+                    el = document.getElementById('fetch-whisper-tiny-en'); if (el) el.style.display = 'inline-block';
+                    el = document.getElementById('fetch-whisper-base-en'); if (el) el.style.display = 'inline-block';
+                    el = document.getElementById('model-whisper-status');  if (el) el.innerHTML = '';
+                };
+
+                loadRemote(url, dst, size_mb, cbProgress, storeFS, cbCancel, printTextarea);
+            }
+
+            //
+            // microphone
+            //
+
+            const kSampleRate = 16000;
+            const kRestartRecording_s = 120;
+            const kIntervalAudio_ms = 5000; // pass the recorded audio to the C++ instance at this rate
+
+            var mediaRecorder = null;
+            var doRecording = false;
+            var startTime = 0;
+
+            window.AudioContext = window.AudioContext || window.webkitAudioContext;
+            window.OfflineAudioContext = window.OfflineAudioContext || window.webkitOfflineAudioContext;
+
+            function stopRecording() {
+                Module.set_status("paused");
+                doRecording = false;
+                audio0 = null;
+                audio = null;
+                context = null;
+            }
+
+            function startRecording() {
+                if (!context) {
+                    context = new AudioContext({
+                        sampleRate: kSampleRate,
+                        channelCount: 1,
+                        echoCancellation: false,
+                        autoGainControl:  true,
+                        noiseSuppression: true,
+                    });
+                }
+
+                Module.set_status("");
+
+                document.getElementById('start').disabled = true;
+                document.getElementById('stop').disabled = false;
+
+                doRecording = true;
+                startTime = Date.now();
+
+                var chunks = [];
+                var stream = null;
+
+                navigator.mediaDevices.getUserMedia({audio: true, video: false})
+                    .then(function(s) {
+                        stream = s;
+                        mediaRecorder = new MediaRecorder(stream);
+                        mediaRecorder.ondataavailable = function(e) {
+                            chunks.push(e.data);
+
+                            var blob = new Blob(chunks, { 'type' : 'audio/ogg; codecs=opus' });
+                            var reader = new FileReader();
+
+                            reader.onload = function(event) {
+                                var buf = new Uint8Array(reader.result);
+
+                                if (!context) {
+                                    return;
+                                }
+                                context.decodeAudioData(buf.buffer, function(audioBuffer) {
+                                    var offlineContext = new OfflineAudioContext(audioBuffer.numberOfChannels, audioBuffer.length, audioBuffer.sampleRate);
+                                    var source = offlineContext.createBufferSource();
+                                    source.buffer = audioBuffer;
+                                    source.connect(offlineContext.destination);
+                                    source.start(0);
+
+                                    offlineContext.startRendering().then(function(renderedBuffer) {
+                                        audio = renderedBuffer.getChannelData(0);
+
+                                        //printTextarea('js: audio recorded, size: ' + audio.length + ', old size: ' + (audio0 == null ? 0 : audio0.length));
+
+                                        var audioAll = new Float32Array(audio0 == null ? audio.length : audio0.length + audio.length);
+                                        if (audio0 != null) {
+                                            audioAll.set(audio0, 0);
+                                        }
+                                        audioAll.set(audio, audio0 == null ? 0 : audio0.length);
+
+                                        if (instance) {
+                                            Module.set_audio(instance, audioAll);
+                                        }
+                                    });
+                                }, function(e) {
+                                    audio = null;
+                                });
+                            }
+
+                            reader.readAsArrayBuffer(blob);
+                        };
+
+                        mediaRecorder.onstop = function(e) {
+                            if (doRecording) {
+                                setTimeout(function() {
+                                    startRecording();
+                                });
+                            }
+                        };
+
+                        mediaRecorder.start(kIntervalAudio_ms);
+                    })
+                    .catch(function(err) {
+                        printTextarea('js: error getting audio stream: ' + err);
+                    });
+
+                var interval = setInterval(function() {
+                    if (!doRecording) {
+                        clearInterval(interval);
+                        mediaRecorder.stop();
+                        stream.getTracks().forEach(function(track) {
+                            track.stop();
+                        });
+
+                        document.getElementById('start').disabled = false;
+                        document.getElementById('stop').disabled  = true;
+
+                        mediaRecorder = null;
+                    }
+
+                    // if audio length is more than kRestartRecording_s seconds, restart recording
+                    if (audio != null && audio.length > kSampleRate*kRestartRecording_s) {
+                        if (doRecording) {
+                            //printTextarea('js: restarting recording');
+
+                            clearInterval(interval);
+                            audio0 = audio;
+                            audio = null;
+                            mediaRecorder.stop();
+                            stream.getTracks().forEach(function(track) {
+                                track.stop();
+                            });
+                        }
+                    }
+                }, 100);
+            }
+
+            //
+            // main
+            //
+
+            var nLines = 0;
+            var intervalUpdate = null;
+            var transcribedAll = '';
+
+            function onStart() {
+                if (!instance) {
+                    instance = Module.init('whisper.bin');
+
+                    if (instance) {
+                        printTextarea("js: whisper initialized, instance: " + instance);
+                    }
+                }
+
+                if (!instance) {
+                    printTextarea("js: failed to initialize whisper");
+                    return;
+                }
+
+                startRecording();
+
+                intervalUpdate = setInterval(function() {
+                    var transcribed = Module.get_transcribed();
+
+                    if (transcribed != null && transcribed.length > 1) {
+                        transcribedAll += transcribed + '<br>';
+                        nLines++;
+
+                        // if more than 10 lines, remove the first line
+                        if (nLines > 10) {
+                            var i = transcribedAll.indexOf('<br>');
+                            if (i > 0) {
+                                transcribedAll = transcribedAll.substring(i + 4);
+                                nLines--;
+                            }
+                        }
+                    }
+
+                    document.getElementById('state-status').innerHTML = Module.get_status();
+                    document.getElementById('state-transcribed').innerHTML = transcribedAll;
+                }, 100);
+            }
+
+            function onStop() {
+                stopRecording();
+            }
+
+        </script>
+        <script type="text/javascript" src="stream.js"></script>
+    </body>
+</html>

examples/stream/CMakeLists.txt

@@ -0,0 +1,10 @@
+if (WHISPER_SUPPORT_SDL2)
+    # stream
+    set(TARGET stream)
+    add_executable(${TARGET} stream.cpp)
+
+    include(DefaultTargetOptions)
+
+    target_include_directories(${TARGET} PRIVATE ${SDL2_INCLUDE_DIRS})
+    target_link_libraries(${TARGET} PRIVATE whisper ${SDL2_LIBRARIES} ${CMAKE_THREAD_LIBS_INIT})
+endif ()

examples/stream/README.md

@@ -0,0 +1,44 @@
+# stream
+
+This is a naive example of performing real-time inference on audio from your microphone.
+The `stream` tool samples the audio every half a second and runs the transcription continously.
+More info is available in [issue #10](https://github.com/ggerganov/whisper.cpp/issues/10).
+
+```java
+./stream -m ./models/ggml-base.en.bin -t 8 --step 500 --length 5000
+```
+
+https://user-images.githubusercontent.com/1991296/194935793-76afede7-cfa8-48d8-a80f-28ba83be7d09.mp4
+
+## Sliding window mode with VAD
+
+Setting the `--step` argument to `0` enables the sliding window mode:
+
+```java
+ ./stream -m ./models/ggml-small.en.bin -t 6 --step 0 --length 30000 -vth 0.6
+```
+
+In this mode, the tool will transcribe only after some speech activity is detected. A very
+basic VAD detector is used, but in theory a more sophisticated approach can be added. The
+`-vth` argument determines the VAD threshold - higher values will make it detect silence more often.
+It's best to tune it to the specific use case, but a value around `0.6` should be OK in general.
+When silence is detected, it will transcribe the last `--length` milliseconds of audio and output
+a transcription block that is suitable for parsing.
+
+## Building
+
+The `stream` tool depends on SDL2 library to capture audio from the microphone. You can build it like this:
+
+```bash
+# Install SDL2 on Linux
+sudo apt-get install libsdl2-dev
+
+# Install SDL2 on Mac OS
+brew install sdl2
+
+make stream
+```
+
+## Web version
+
+This tool can also run in the browser: [examples/stream.wasm](/examples/stream.wasm)

examples/stream/stream.cpp

@@ -0,0 +1,710 @@
+// Real-time speech recognition of input from a microphone
+//
+// A very quick-n-dirty implementation serving mainly as a proof of concept.
+//
+
+#include "whisper.h"
+
+#include <SDL.h>
+#include <SDL_audio.h>
+
+#include <atomic>
+#include <cassert>
+#include <cstdio>
+#include <string>
+#include <thread>
+#include <vector>
+#include <fstream>
+#include <mutex>
+
+//  500 -> 00:05.000
+// 6000 -> 01:00.000
+std::string to_timestamp(int64_t t) {
+    int64_t sec = t/100;
+    int64_t msec = t - sec*100;
+    int64_t min = sec/60;
+    sec = sec - min*60;
+
+    char buf[32];
+    snprintf(buf, sizeof(buf), "%02d:%02d.%03d", (int) min, (int) sec, (int) msec);
+
+    return std::string(buf);
+}
+
+// command-line parameters
+struct whisper_params {
+    int32_t n_threads  = std::min(4, (int32_t) std::thread::hardware_concurrency());
+    int32_t step_ms    = 3000;
+    int32_t length_ms  = 10000;
+    int32_t keep_ms    = 200;
+    int32_t capture_id = -1;
+    int32_t max_tokens = 32;
+    int32_t audio_ctx  = 0;
+
+    float vad_thold    = 0.6f;
+    float freq_thold   = 100.0f;
+
+    bool speed_up      = false;
+    bool translate     = false;
+    bool print_special = false;
+    bool no_context    = true;
+    bool no_timestamps = false;
+
+    std::string language  = "en";
+    std::string model     = "models/ggml-base.en.bin";
+    std::string fname_out;
+};
+
+void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
+
+bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
+    for (int i = 1; i < argc; i++) {
+        std::string arg = argv[i];
+
+        if (arg == "-h" || arg == "--help") {
+            whisper_print_usage(argc, argv, params);
+            exit(0);
+        }
+        else if (arg == "-t"   || arg == "--threads")       { params.n_threads     = std::stoi(argv[++i]); }
+        else if (                 arg == "--step")          { params.step_ms       = std::stoi(argv[++i]); }
+        else if (                 arg == "--length")        { params.length_ms     = std::stoi(argv[++i]); }
+        else if (                 arg == "--keep")          { params.keep_ms       = std::stoi(argv[++i]); }
+        else if (arg == "-c"   || arg == "--capture")       { params.capture_id    = std::stoi(argv[++i]); }
+        else if (arg == "-mt"  || arg == "--max-tokens")    { params.max_tokens    = std::stoi(argv[++i]); }
+        else if (arg == "-ac"  || arg == "--audio-ctx")     { params.audio_ctx     = std::stoi(argv[++i]); }
+        else if (arg == "-vth" || arg == "--vad-thold")     { params.vad_thold     = std::stof(argv[++i]); }
+        else if (arg == "-fth" || arg == "--freq-thold")    { params.freq_thold    = std::stof(argv[++i]); }
+        else if (arg == "-su"  || arg == "--speed-up")      { params.speed_up      = true; }
+        else if (arg == "-tr"  || arg == "--translate")     { params.translate     = true; }
+        else if (arg == "-ps"  || arg == "--print-special") { params.print_special = true; }
+        else if (arg == "-kc"  || arg == "--keep-context")  { params.no_context    = false; }
+        else if (arg == "-l"   || arg == "--language")      { params.language      = argv[++i]; }
+        else if (arg == "-m"   || arg == "--model")         { params.model         = argv[++i]; }
+        else if (arg == "-f"   || arg == "--file")          { params.fname_out     = argv[++i]; }
+        else {
+            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
+            whisper_print_usage(argc, argv, params);
+            exit(0);
+        }
+    }
+
+    return true;
+}
+
+void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & params) {
+    fprintf(stderr, "\n");
+    fprintf(stderr, "usage: %s [options]\n", argv[0]);
+    fprintf(stderr, "\n");
+    fprintf(stderr, "options:\n");
+    fprintf(stderr, "  -h,       --help          [default] show this help message and exit\n");
+    fprintf(stderr, "  -t N,     --threads N     [%-7d] number of threads to use during computation\n", params.n_threads);
+    fprintf(stderr, "            --step N        [%-7d] audio step size in milliseconds\n",             params.step_ms);
+    fprintf(stderr, "            --length N      [%-7d] audio length in milliseconds\n",                params.length_ms);
+    fprintf(stderr, "            --keep N        [%-7d] audio to keep from previous step in ms\n",      params.keep_ms);
+    fprintf(stderr, "  -c ID,    --capture ID    [%-7d] capture device ID\n",                           params.capture_id);
+    fprintf(stderr, "  -mt N,    --max-tokens N  [%-7d] maximum number of tokens per audio chunk\n",    params.max_tokens);
+    fprintf(stderr, "  -ac N,    --audio-ctx N   [%-7d] audio context size (0 - all)\n",                params.audio_ctx);
+    fprintf(stderr, "  -vth N,   --vad-thold N   [%-7.2f] voice activity detection threshold\n",        params.vad_thold);
+    fprintf(stderr, "  -fth N,   --freq-thold N  [%-7.2f] high-pass frequency cutoff\n",                params.freq_thold);
+    fprintf(stderr, "  -su,      --speed-up      [%-7s] speed up audio by x2 (reduced accuracy)\n",     params.speed_up ? "true" : "false");
+    fprintf(stderr, "  -tr,      --translate     [%-7s] translate from source language to english\n",   params.translate ? "true" : "false");
+    fprintf(stderr, "  -ps,      --print-special [%-7s] print special tokens\n",                        params.print_special ? "true" : "false");
+    fprintf(stderr, "  -kc,      --keep-context  [%-7s] keep context between audio chunks\n",           params.no_context ? "false" : "true");
+    fprintf(stderr, "  -l LANG,  --language LANG [%-7s] spoken language\n",                             params.language.c_str());
+    fprintf(stderr, "  -m FNAME, --model FNAME   [%-7s] model path\n",                                  params.model.c_str());
+    fprintf(stderr, "  -f FNAME, --file FNAME    [%-7s] text output file name\n",                       params.fname_out.c_str());
+    fprintf(stderr, "\n");
+}
+
+//
+// SDL Audio capture
+//
+
+class audio_async {
+public:
+    audio_async(int len_ms);
+    ~audio_async();
+
+    bool init(int capture_id, int sample_rate);
+
+    // start capturing audio via the provided SDL callback
+    // keep last len_ms seconds of audio in a circular buffer
+    bool resume();
+    bool pause();
+    bool clear();
+
+    // callback to be called by SDL
+    void callback(uint8_t * stream, int len);
+
+    // get audio data from the circular buffer
+    void get(int ms, std::vector<float> & audio);
+
+private:
+    SDL_AudioDeviceID m_dev_id_in = 0;
+
+    int m_len_ms = 0;
+    int m_sample_rate = 0;
+
+    std::atomic_bool m_running;
+    std::mutex       m_mutex;
+
+    std::vector<float> m_audio;
+    std::vector<float> m_audio_new;
+    size_t             m_audio_pos = 0;
+    size_t             m_audio_len = 0;
+};
+
+audio_async::audio_async(int len_ms) {
+    m_len_ms = len_ms;
+
+    m_running = false;
+}
+
+audio_async::~audio_async() {
+    if (m_dev_id_in) {
+        SDL_CloseAudioDevice(m_dev_id_in);
+    }
+}
+
+bool audio_async::init(int capture_id, int sample_rate) {
+    SDL_LogSetPriority(SDL_LOG_CATEGORY_APPLICATION, SDL_LOG_PRIORITY_INFO);
+
+    if (SDL_Init(SDL_INIT_AUDIO) < 0) {
+        SDL_LogError(SDL_LOG_CATEGORY_APPLICATION, "Couldn't initialize SDL: %s\n", SDL_GetError());
+        return false;
+    }
+
+    SDL_SetHintWithPriority(SDL_HINT_AUDIO_RESAMPLING_MODE, "medium", SDL_HINT_OVERRIDE);
+
+    {
+        int nDevices = SDL_GetNumAudioDevices(SDL_TRUE);
+        fprintf(stderr, "%s: found %d capture devices:\n", __func__, nDevices);
+        for (int i = 0; i < nDevices; i++) {
+            fprintf(stderr, "%s:    - Capture device #%d: '%s'\n", __func__, i, SDL_GetAudioDeviceName(i, SDL_TRUE));
+        }
+    }
+
+    SDL_AudioSpec capture_spec_requested;
+    SDL_AudioSpec capture_spec_obtained;
+
+    SDL_zero(capture_spec_requested);
+    SDL_zero(capture_spec_obtained);
+
+    capture_spec_requested.freq     = sample_rate;
+    capture_spec_requested.format   = AUDIO_F32;
+    capture_spec_requested.channels = 1;
+    capture_spec_requested.samples  = 1024;
+    capture_spec_requested.callback = [](void * userdata, uint8_t * stream, int len) {
+        audio_async * audio = (audio_async *) userdata;
+        audio->callback(stream, len);
+    };
+    capture_spec_requested.userdata = this;
+
+    if (capture_id >= 0) {
+        fprintf(stderr, "%s: attempt to open capture device %d : '%s' ...\n", __func__, capture_id, SDL_GetAudioDeviceName(capture_id, SDL_TRUE));
+        m_dev_id_in = SDL_OpenAudioDevice(SDL_GetAudioDeviceName(capture_id, SDL_TRUE), SDL_TRUE, &capture_spec_requested, &capture_spec_obtained, 0);
+    } else {
+        fprintf(stderr, "%s: attempt to open default capture device ...\n", __func__);
+        m_dev_id_in = SDL_OpenAudioDevice(nullptr, SDL_TRUE, &capture_spec_requested, &capture_spec_obtained, 0);
+    }
+
+    if (!m_dev_id_in) {
+        fprintf(stderr, "%s: couldn't open an audio device for capture: %s!\n", __func__, SDL_GetError());
+        m_dev_id_in = 0;
+
+        return false;
+    } else {
+        fprintf(stderr, "%s: obtained spec for input device (SDL Id = %d):\n", __func__, m_dev_id_in);
+        fprintf(stderr, "%s:     - sample rate:       %d\n",                   __func__, capture_spec_obtained.freq);
+        fprintf(stderr, "%s:     - format:            %d (required: %d)\n",    __func__, capture_spec_obtained.format,
+                capture_spec_requested.format);
+        fprintf(stderr, "%s:     - channels:          %d (required: %d)\n",    __func__, capture_spec_obtained.channels,
+                capture_spec_requested.channels);
+        fprintf(stderr, "%s:     - samples per frame: %d\n",                   __func__, capture_spec_obtained.samples);
+    }
+
+    m_sample_rate = capture_spec_obtained.freq;
+
+    m_audio.resize((m_sample_rate*m_len_ms)/1000);
+
+    return true;
+}
+
+bool audio_async::resume() {
+    if (!m_dev_id_in) {
+        fprintf(stderr, "%s: no audio device to resume!\n", __func__);
+        return false;
+    }
+
+    if (m_running) {
+        fprintf(stderr, "%s: already running!\n", __func__);
+        return false;
+    }
+
+    SDL_PauseAudioDevice(m_dev_id_in, 0);
+
+    m_running = true;
+
+    return true;
+}
+
+bool audio_async::pause() {
+    if (!m_dev_id_in) {
+        fprintf(stderr, "%s: no audio device to pause!\n", __func__);
+        return false;
+    }
+
+    if (!m_running) {
+        fprintf(stderr, "%s: already paused!\n", __func__);
+        return false;
+    }
+
+    SDL_PauseAudioDevice(m_dev_id_in, 1);
+
+    m_running = false;
+
+    return true;
+}
+
+bool audio_async::clear() {
+    if (!m_dev_id_in) {
+        fprintf(stderr, "%s: no audio device to clear!\n", __func__);
+        return false;
+    }
+
+    if (!m_running) {
+        fprintf(stderr, "%s: not running!\n", __func__);
+        return false;
+    }
+
+    {
+        std::lock_guard<std::mutex> lock(m_mutex);
+
+        m_audio_pos = 0;
+        m_audio_len = 0;
+    }
+
+    return true;
+}
+
+// callback to be called by SDL
+void audio_async::callback(uint8_t * stream, int len) {
+    if (!m_running) {
+        return;
+    }
+
+    const size_t n_samples = len / sizeof(float);
+
+    m_audio_new.resize(n_samples);
+    memcpy(m_audio_new.data(), stream, n_samples * sizeof(float));
+
+    //fprintf(stderr, "%s: %zu samples, pos %zu, len %zu\n", __func__, n_samples, m_audio_pos, m_audio_len);
+
+    {
+        std::lock_guard<std::mutex> lock(m_mutex);
+
+        if (m_audio_pos + n_samples > m_audio.size()) {
+            const size_t n0 = m_audio.size() - m_audio_pos;
+
+            memcpy(&m_audio[m_audio_pos], stream, n0 * sizeof(float));
+            memcpy(&m_audio[0], &stream[n0], (n_samples - n0) * sizeof(float));
+
+            m_audio_pos = (m_audio_pos + n_samples) % m_audio.size();
+            m_audio_len = m_audio.size();
+        } else {
+            memcpy(&m_audio[m_audio_pos], stream, n_samples * sizeof(float));
+
+            m_audio_pos = (m_audio_pos + n_samples) % m_audio.size();
+            m_audio_len = std::min(m_audio_len + n_samples, m_audio.size());
+        }
+    }
+}
+
+void audio_async::get(int ms, std::vector<float> & result) {
+    if (!m_dev_id_in) {
+        fprintf(stderr, "%s: no audio device to get audio from!\n", __func__);
+        return;
+    }
+
+    if (!m_running) {
+        fprintf(stderr, "%s: not running!\n", __func__);
+        return;
+    }
+
+    result.clear();
+
+    {
+        std::lock_guard<std::mutex> lock(m_mutex);
+
+        if (ms <= 0) {
+            ms = m_len_ms;
+        }
+
+        size_t n_samples = (m_sample_rate * ms) / 1000;
+        if (n_samples > m_audio_len) {
+            n_samples = m_audio_len;
+        }
+
+        result.resize(n_samples);
+
+        int s0 = m_audio_pos - n_samples;
+        if (s0 < 0) {
+            s0 += m_audio.size();
+        }
+
+        if (s0 + n_samples > m_audio.size()) {
+            const size_t n0 = m_audio.size() - s0;
+
+            memcpy(result.data(), &m_audio[s0], n0 * sizeof(float));
+            memcpy(&result[n0], &m_audio[0], (n_samples - n0) * sizeof(float));
+        } else {
+            memcpy(result.data(), &m_audio[s0], n_samples * sizeof(float));
+        }
+    }
+}
+
+///////////////////////////
+
+void high_pass_filter(std::vector<float> & data, float cutoff, float sample_rate) {
+    const float rc = 1.0f / (2.0f * M_PI * cutoff);
+    const float dt = 1.0f / sample_rate;
+    const float alpha = dt / (rc + dt);
+
+    float y = data[0];
+
+    for (size_t i = 1; i < data.size(); i++) {
+        y = alpha * (y + data[i] - data[i - 1]);
+        data[i] = y;
+    }
+}
+
+bool vad_simple(std::vector<float> & pcmf32, int sample_rate, int last_ms, float vad_thold, float freq_thold, bool verbose) {
+    const int n_samples      = pcmf32.size();
+    const int n_samples_last = (sample_rate * last_ms) / 1000;
+
+    if (n_samples_last >= n_samples) {
+        // not enough samples - assume no speech
+        return false;
+    }
+
+    if (freq_thold > 0.0f) {
+        high_pass_filter(pcmf32, freq_thold, sample_rate);
+    }
+
+    float energy_all  = 0.0f;
+    float energy_last = 0.0f;
+
+    for (int i = 0; i < n_samples; i++) {
+        energy_all += fabsf(pcmf32[i]);
+
+        if (i >= n_samples - n_samples_last) {
+            energy_last += fabsf(pcmf32[i]);
+        }
+    }
+
+    energy_all  /= n_samples;
+    energy_last /= n_samples_last;
+
+    if (verbose) {
+        fprintf(stderr, "%s: energy_all: %f, energy_last: %f, vad_thold: %f, freq_thold: %f\n", __func__, energy_all, energy_last, vad_thold, freq_thold);
+    }
+
+    if (energy_last > vad_thold*energy_all) {
+        return false;
+    }
+
+    return true;
+}
+
+int main(int argc, char ** argv) {
+    whisper_params params;
+
+    if (whisper_params_parse(argc, argv, params) == false) {
+        return 1;
+    }
+
+    params.keep_ms = std::min(params.keep_ms, params.step_ms); // cannot be more than step_ms
+
+    const int n_samples_step = (params.step_ms  *1e-3)*WHISPER_SAMPLE_RATE;
+    const int n_samples_len  = (params.length_ms*1e-3)*WHISPER_SAMPLE_RATE;
+    const int n_samples_keep = (params.keep_ms  *1e-3)*WHISPER_SAMPLE_RATE;
+    const int n_samples_30s  = (30000           *1e-3)*WHISPER_SAMPLE_RATE;
+
+    const bool use_vad = n_samples_step <= 0; // sliding window mode uses VAD
+
+    const int n_new_line = !use_vad ? params.length_ms / params.step_ms - 1 : 1; // number of steps to print new line
+
+    params.no_timestamps = !use_vad;
+    params.no_context    = use_vad;
+    params.max_tokens    = 0;
+
+    // init audio
+
+    audio_async audio(params.length_ms);
+    if (!audio.init(params.capture_id, WHISPER_SAMPLE_RATE)) {
+        fprintf(stderr, "%s: audio.init() failed!\n", __func__);
+        return 1;
+    }
+
+    audio.resume();
+
+    // whisper init
+
+    if (whisper_lang_id(params.language.c_str()) == -1) {
+        fprintf(stderr, "error: unknown language '%s'\n", params.language.c_str());
+        whisper_print_usage(argc, argv, params);
+        exit(0);
+    }
+
+    struct whisper_context * ctx = whisper_init_from_file(params.model.c_str());
+
+    std::vector<float> pcmf32    (n_samples_30s, 0.0f);
+    std::vector<float> pcmf32_old;
+    std::vector<float> pcmf32_new(n_samples_30s, 0.0f);
+
+    std::vector<whisper_token> prompt_tokens;
+
+    // print some info about the processing
+    {
+        fprintf(stderr, "\n");
+        if (!whisper_is_multilingual(ctx)) {
+            if (params.language != "en" || params.translate) {
+                params.language = "en";
+                params.translate = false;
+                fprintf(stderr, "%s: WARNING: model is not multilingual, ignoring language and translation options\n", __func__);
+            }
+        }
+        fprintf(stderr, "%s: processing %d samples (step = %.1f sec / len = %.1f sec / keep = %.1f sec), %d threads, lang = %s, task = %s, timestamps = %d ...\n",
+                __func__,
+                n_samples_step,
+                float(n_samples_step)/WHISPER_SAMPLE_RATE,
+                float(n_samples_len )/WHISPER_SAMPLE_RATE,
+                float(n_samples_keep)/WHISPER_SAMPLE_RATE,
+                params.n_threads,
+                params.language.c_str(),
+                params.translate ? "translate" : "transcribe",
+                params.no_timestamps ? 0 : 1);
+
+        if (!use_vad) {
+            fprintf(stderr, "%s: n_new_line = %d\n", __func__, n_new_line);
+        } else {
+            fprintf(stderr, "%s: using VAD, will transcribe on speech activity\n", __func__);
+        }
+
+        fprintf(stderr, "\n");
+    }
+
+    int n_iter = 0;
+
+    bool is_running = true;
+
+    std::ofstream fout;
+    if (params.fname_out.length() > 0) {
+        fout.open(params.fname_out);
+        if (!fout.is_open()) {
+            fprintf(stderr, "%s: failed to open output file '%s'!\n", __func__, params.fname_out.c_str());
+            return 1;
+        }
+    }
+
+    printf("[Start speaking]");
+    fflush(stdout);
+
+          auto t_last  = std::chrono::high_resolution_clock::now();
+    const auto t_start = t_last;
+
+    // main audio loop
+    while (is_running) {
+        // handle Ctrl + C
+        {
+            SDL_Event event;
+            while (SDL_PollEvent(&event)) {
+                switch (event.type) {
+                    case SDL_QUIT:
+                        {
+                            is_running = false;
+                        } break;
+                    default:
+                        break;
+                }
+            }
+
+            if (!is_running) {
+                break;
+            }
+        }
+
+        if (!is_running) {
+            break;
+        }
+
+        // process new audio
+
+        if (!use_vad) {
+            while (true) {
+                audio.get(params.step_ms, pcmf32_new);
+
+                if ((int) pcmf32_new.size() > 2*n_samples_step) {
+                    fprintf(stderr, "\n\n%s: WARNING: cannot process audio fast enough, dropping audio ...\n\n", __func__);
+                    audio.clear();
+                    continue;
+                }
+
+                if ((int) pcmf32_new.size() >= n_samples_step) {
+                    audio.clear();
+                    break;
+                }
+
+                SDL_Delay(1);
+            }
+
+            const int n_samples_new = pcmf32_new.size();
+
+            // take up to params.length_ms audio from previous iteration
+            const int n_samples_take = std::min((int) pcmf32_old.size(), std::max(0, n_samples_keep + n_samples_len - n_samples_new));
+
+            //printf("processing: take = %d, new = %d, old = %d\n", n_samples_take, n_samples_new, (int) pcmf32_old.size());
+
+            pcmf32.resize(n_samples_new + n_samples_take);
+
+            for (int i = 0; i < n_samples_take; i++) {
+                pcmf32[i] = pcmf32_old[pcmf32_old.size() - n_samples_take + i];
+            }
+
+            memcpy(pcmf32.data() + n_samples_take, pcmf32_new.data(), n_samples_new*sizeof(float));
+
+            pcmf32_old = pcmf32;
+        } else {
+            const auto t_now  = std::chrono::high_resolution_clock::now();
+            const auto t_diff = std::chrono::duration_cast<std::chrono::milliseconds>(t_now - t_last).count();
+
+            if (t_diff < 2000) {
+                std::this_thread::sleep_for(std::chrono::milliseconds(100));
+
+                continue;
+            }
+
+            audio.get(2000, pcmf32_new);
+
+            if (vad_simple(pcmf32_new, WHISPER_SAMPLE_RATE, 1000, params.vad_thold, params.freq_thold, false)) {
+                audio.get(params.length_ms, pcmf32);
+            } else {
+                std::this_thread::sleep_for(std::chrono::milliseconds(100));
+
+                continue;
+            }
+
+            t_last = t_now;
+        }
+
+        // run the inference
+        {
+            whisper_full_params wparams = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
+
+            wparams.print_progress   = false;
+            wparams.print_special    = params.print_special;
+            wparams.print_realtime   = false;
+            wparams.print_timestamps = !params.no_timestamps;
+            wparams.translate        = params.translate;
+            wparams.no_context       = true;
+            wparams.single_segment   = !use_vad;
+            wparams.max_tokens       = params.max_tokens;
+            wparams.language         = params.language.c_str();
+            wparams.n_threads        = params.n_threads;
+
+            wparams.audio_ctx        = params.audio_ctx;
+            wparams.speed_up         = params.speed_up;
+
+            // disable temperature fallback
+            wparams.temperature_inc  = -1.0f;
+
+            wparams.prompt_tokens    = params.no_context ? nullptr : prompt_tokens.data();
+            wparams.prompt_n_tokens  = params.no_context ? 0       : prompt_tokens.size();
+
+            if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
+                fprintf(stderr, "%s: failed to process audio\n", argv[0]);
+                return 6;
+            }
+
+            // print result;
+            {
+                if (!use_vad) {
+                    printf("\33[2K\r");
+
+                    // print long empty line to clear the previous line
+                    printf("%s", std::string(100, ' ').c_str());
+
+                    printf("\33[2K\r");
+                } else {
+                    const int64_t t1 = (t_last - t_start).count()/1000000;
+                    const int64_t t0 = std::max(0.0, t1 - pcmf32.size()*1000.0/WHISPER_SAMPLE_RATE);
+
+                    printf("\n");
+                    printf("### Transcription %d START | t0 = %d ms | t1 = %d ms\n", n_iter, (int) t0, (int) t1);
+                    printf("\n");
+                }
+
+                const int n_segments = whisper_full_n_segments(ctx);
+                for (int i = 0; i < n_segments; ++i) {
+                    const char * text = whisper_full_get_segment_text(ctx, i);
+
+                    if (params.no_timestamps) {
+                        printf("%s", text);
+                        fflush(stdout);
+
+                        if (params.fname_out.length() > 0) {
+                            fout << text;
+                        }
+                    } else {
+                        const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
+                        const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
+
+                        printf ("[%s --> %s]  %s\n", to_timestamp(t0).c_str(), to_timestamp(t1).c_str(), text);
+
+                        if (params.fname_out.length() > 0) {
+                            fout << "[" << to_timestamp(t0) << " --> " << to_timestamp(t1) << "]  " << text << std::endl;
+                        }
+                    }
+                }
+
+                if (params.fname_out.length() > 0) {
+                    fout << std::endl;
+                }
+
+                if (use_vad){
+                    printf("\n");
+                    printf("### Transcription %d END\n", n_iter);
+                }
+            }
+
+            ++n_iter;
+
+            if (!use_vad && (n_iter % n_new_line) == 0) {
+                printf("\n");
+
+                // keep part of the audio for next iteration to try to mitigate word boundary issues
+                pcmf32_old = std::vector<float>(pcmf32.end() - n_samples_keep, pcmf32.end());
+
+                // Add tokens of the last full length segment as the prompt
+                if (!params.no_context) {
+                    prompt_tokens.clear();
+
+                    const int n_segments = whisper_full_n_segments(ctx);
+                    for (int i = 0; i < n_segments; ++i) {
+                        const int token_count = whisper_full_n_tokens(ctx, i);
+                        for (int j = 0; j < token_count; ++j) {
+                            prompt_tokens.push_back(whisper_full_get_token_id(ctx, i, j));
+                        }
+                    }
+                }
+            }
+        }
+    }
+
+    audio.pause();
+
+    whisper_print_timings(ctx);
+    whisper_free(ctx);
+
+    return 0;
+}

examples/talk.wasm/CMakeLists.txt

@@ -0,0 +1,50 @@
+#
+# libtalk
+#
+
+set(TARGET libtalk)
+
+add_executable(${TARGET}
+    emscripten.cpp
+    gpt-2.cpp
+    )
+
+include(DefaultTargetOptions)
+
+target_link_libraries(${TARGET} PRIVATE
+    whisper
+    )
+
+unset(EXTRA_FLAGS)
+
+if (WHISPER_WASM_SINGLE_FILE)
+    set(EXTRA_FLAGS "-s SINGLE_FILE=1")
+    message(STATUS "Embedding WASM inside talk.js")
+
+    add_custom_command(
+        TARGET ${TARGET} POST_BUILD
+        COMMAND ${CMAKE_COMMAND} -E copy
+        ${CMAKE_BINARY_DIR}/bin/libtalk.js
+        ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/talk.wasm/talk.js
+        )
+endif()
+
+set_target_properties(${TARGET} PROPERTIES LINK_FLAGS " \
+    --bind \
+    -s USE_PTHREADS=1 \
+    -s PTHREAD_POOL_SIZE=8 \
+    -s INITIAL_MEMORY=1800MB \
+    -s TOTAL_MEMORY=1800MB \
+    -s FORCE_FILESYSTEM=1 \
+    -s EXPORTED_RUNTIME_METHODS=\"['print', 'printErr', 'ccall', 'cwrap']\" \
+    ${EXTRA_FLAGS} \
+    ")
+
+#
+# talk.wasm
+#
+
+set(TARGET talk.wasm)
+
+configure_file(${CMAKE_CURRENT_SOURCE_DIR}/index-tmpl.html  ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${TARGET}/index.html @ONLY)
+configure_file(${CMAKE_CURRENT_SOURCE_DIR}/../helpers.js    ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${TARGET}/helpers.js @ONLY)

examples/talk.wasm/README.md

@@ -0,0 +1,74 @@
+# talk.wasm
+
+Talk with an Artificial Intelligence in your browser:
+
+[https://user-images.githubusercontent.com/1991296/203411580-fedb4839-05e4-4474-8364-aaf1e9a9b615.mp4](https://user-images.githubusercontent.com/1991296/203845553-f7b44e13-9a15-4fc8-b518-ae8f4c6770fe.mp4)
+
+Online demo: https://whisper.ggerganov.com/talk/
+
+Terminal version: [examples/talk](/examples/talk)
+
+## How it works?
+
+This demo leverages 2 modern neural network models to create a high-quality voice chat directly in your browser:
+
+- [OpenAI's Whisper](https://github.com/openai/whisper) speech recognition model is used to process your voice and understand what you are saying
+- Upon receiving some voice input, the AI generates a text response using [OpenAI's GPT-2](https://github.com/openai/gpt-2) language model
+- The AI then vocalizes the response using the browser's [Web Speech API](https://developer.mozilla.org/en-US/docs/Web/API/Web_Speech_API)
+
+The web page does the processing locally on your machine. The processing of these heavy neural network models in the
+browser is possible by implementing them efficiently in C/C++ and using the browser's WebAssembly SIMD capabilities for
+extra performance:
+
+- The Whisper C++ implementation is here: [whisper.h](/whisper.h) / [whisper.cpp](/whisper.cpp)
+- The GPT-2 C++ implementation is here: [gpt-2.h](gpt-2.h) / [gpt-2.cpp](gpt-2.cpp)
+- Both models use a custom tensor library implemented in C: [ggml.h](/ggml.h) / [ggml.c](/ggml.c)
+- The HTML/JS layer is here: [index-tmpl.html](index-tmpl.html)
+- The Emscripten bridge between C/C++ and JS is here: [emscripten.cpp](emscripten.cpp)
+
+In order to run the models, the web page first needs to download the model data which is about ~350 MB. The model data
+is then cached in your browser's cache and can be reused in future visits without downloading it again.
+
+## Requirements
+
+In order to run this demo efficiently, you need to have the following:
+
+- Latest Chrome or Firefox browser (Safari is not supported)
+- Run this on a desktop or laptop with modern CPU (a mobile phone will likely not be good enough)
+- Speak phrases that are no longer than 10 seconds - this is the audio context of the AI
+- The web-page uses about 1.8GB of RAM
+
+Notice that this demo is using the smallest GPT-2 model, so the generated text responses are not always very good.
+Also, the prompting strategy can likely be improved to achieve better results.
+
+The demo is quite computationally heavy, so you need a fast CPU. It's not usual to run these transformer models in a
+browser. Typically, they run on powerful GPUs.
+
+Currently, mobile browsers do not support the Fixed-width SIMD WebAssembly capability, so you cannot run this demo
+on a phone or a tablet. Hopefully, in the near future this will become supported.
+
+## Todo
+
+- Better UI (contributions are welcome)
+- Better GPT-2 prompting
+
+## Build instructions
+
+```bash
+# build using Emscripten (v3.1.2)
+git clone https://github.com/ggerganov/whisper.cpp
+cd whisper.cpp
+mkdir build-em && cd build-em
+emcmake cmake ..
+make -j
+
+# copy the produced page to your HTTP path
+cp bin/talk.wasm/*       /path/to/html/
+cp bin/libtalk.worker.js /path/to/html/
+```
+
+## Feedback
+
+If you have any comments or ideas for improvement, please drop a comment in the following discussion:
+
+https://github.com/ggerganov/whisper.cpp/discussions/167

examples/talk.wasm/emscripten.cpp

@@ -0,0 +1,380 @@
+#include "ggml.h"
+#include "gpt-2.h"
+#include "whisper.h"
+
+#include <emscripten.h>
+#include <emscripten/bind.h>
+
+#include <atomic>
+#include <cmath>
+#include <mutex>
+#include <string>
+#include <thread>
+#include <vector>
+#include <regex>
+
+constexpr int N_THREAD = 8;
+
+struct gpt2_context * g_gpt2;
+std::vector<struct whisper_context *> g_contexts(4, nullptr);
+
+std::mutex g_mutex;
+std::thread g_worker;
+std::atomic<bool> g_running(false);
+
+bool g_force_speak = false;
+std::string g_text_to_speak = "";
+std::string g_status = "";
+std::string g_status_forced = "";
+
+std::vector<float> g_pcmf32;
+
+std::string to_timestamp(int64_t t) {
+    int64_t sec = t/100;
+    int64_t msec = t - sec*100;
+    int64_t min = sec/60;
+    sec = sec - min*60;
+
+    char buf[32];
+    snprintf(buf, sizeof(buf), "%02d:%02d.%03d", (int) min, (int) sec, (int) msec);
+
+    return std::string(buf);
+}
+
+void talk_set_status(const std::string & status) {
+    std::lock_guard<std::mutex> lock(g_mutex);
+    g_status = status;
+}
+
+void talk_main(size_t index) {
+    talk_set_status("loading data ...");
+
+    struct whisper_full_params wparams = whisper_full_default_params(whisper_sampling_strategy::WHISPER_SAMPLING_GREEDY);
+
+    wparams.n_threads        = std::min(N_THREAD, (int) std::thread::hardware_concurrency());
+    wparams.offset_ms        = 0;
+    wparams.translate        = false;
+    wparams.no_context       = true;
+    wparams.single_segment   = true;
+    wparams.print_realtime   = false;
+    wparams.print_progress   = false;
+    wparams.print_timestamps = true;
+    wparams.print_special    = false;
+
+    wparams.max_tokens       = 32;
+    wparams.audio_ctx        = 768; // partial encoder context for better performance
+
+    wparams.language         = "en";
+
+    g_gpt2 = gpt2_init("gpt-2.bin");
+
+    printf("talk: using %d threads\n", wparams.n_threads);
+
+    std::vector<float> pcmf32;
+
+    // whisper context
+    auto & ctx = g_contexts[index];
+
+    const int64_t step_samples   = 2*WHISPER_SAMPLE_RATE;
+    const int64_t window_samples = 9*WHISPER_SAMPLE_RATE;
+    const int64_t step_ms        = (step_samples*1000)/WHISPER_SAMPLE_RATE;
+
+    auto t_last = std::chrono::high_resolution_clock::now();
+
+    talk_set_status("listening ...");
+
+    while (g_running) {
+
+        const auto t_now = std::chrono::high_resolution_clock::now();
+        if (std::chrono::duration_cast<std::chrono::milliseconds>(t_now - t_last).count() < step_ms) {
+            {
+                std::lock_guard<std::mutex> lock(g_mutex);
+                g_pcmf32.clear();
+            }
+            std::this_thread::sleep_for(std::chrono::milliseconds(10));
+            continue;
+        }
+
+        talk_set_status("listening ...");
+
+        {
+            std::unique_lock<std::mutex> lock(g_mutex);
+
+            if (g_pcmf32.size() < step_samples) {
+                lock.unlock();
+
+                std::this_thread::sleep_for(std::chrono::milliseconds(10));
+
+                continue;
+            }
+
+            pcmf32 = std::vector<float>(g_pcmf32.end() - std::min((int64_t) g_pcmf32.size(), window_samples), g_pcmf32.end());
+        }
+
+        // VAD: if energy in during last second is above threshold, then skip
+        {
+            float energy_all = 0.0f;
+            float energy_1s  = 0.0f;
+
+            for (size_t i = 0; i < pcmf32.size(); i++) {
+                energy_all += fabsf(pcmf32[i]);
+
+                if (i >= pcmf32.size() - WHISPER_SAMPLE_RATE) {
+                    energy_1s += fabsf(pcmf32[i]);
+                }
+            }
+
+            energy_all /= pcmf32.size();
+            energy_1s  /= WHISPER_SAMPLE_RATE;
+
+            if (energy_1s > 0.1f*energy_all && !g_force_speak) {
+                std::this_thread::sleep_for(std::chrono::milliseconds(10));
+                continue;
+            }
+        }
+
+        talk_set_status("processing audio (whisper)...");
+
+        t_last = t_now;
+
+        if (!g_force_speak) {
+            const auto t_start = std::chrono::high_resolution_clock::now();
+
+            int ret = whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size());
+            if (ret != 0) {
+                printf("whisper_full() failed: %d\n", ret);
+                break;
+            }
+
+            const auto t_end = std::chrono::high_resolution_clock::now();
+
+            printf("whisper_full() returned %d in %f seconds\n", ret, std::chrono::duration<double>(t_end - t_start).count());
+        }
+
+        {
+            std::string text_heard;
+
+            if (!g_force_speak) {
+                const int n_segments = whisper_full_n_segments(ctx);
+                for (int i = n_segments - 1; i < n_segments; ++i) {
+                    const char * text = whisper_full_get_segment_text(ctx, i);
+
+                    const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
+                    const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
+
+                    printf ("[%s --> %s]  %s\n", to_timestamp(t0).c_str(), to_timestamp(t1).c_str(), text);
+
+                    text_heard += text;
+                }
+            }
+
+            g_force_speak = false;
+
+            // remove text between brackets using regex
+            {
+                std::regex re("\\[.*?\\]");
+                text_heard = std::regex_replace(text_heard, re, "");
+            }
+
+            // remove text between brackets using regex
+            {
+                std::regex re("\\(.*?\\)");
+                text_heard = std::regex_replace(text_heard, re, "");
+            }
+
+            // remove all characters, except for letters, numbers, punctuation and ':', '\'', '-', ' '
+            text_heard = std::regex_replace(text_heard, std::regex("[^a-zA-Z0-9\\.,\\?!\\s\\:\\'\\-]"), "");
+
+            // take first line
+            text_heard = text_heard.substr(0, text_heard.find_first_of("\n"));
+
+            // remove leading and trailing whitespace
+            text_heard = std::regex_replace(text_heard, std::regex("^\\s+"), "");
+            text_heard = std::regex_replace(text_heard, std::regex("\\s+$"), "");
+
+            talk_set_status("'" + text_heard + "' - thinking how to respond (gpt-2) ...");
+
+            const std::vector<gpt_vocab::id> tokens = gpt2_tokenize(g_gpt2, text_heard.c_str());
+
+            printf("whisper: number of tokens: %d, '%s'\n", (int) tokens.size(), text_heard.c_str());
+
+            std::string text_to_speak;
+            std::string prompt_base;
+
+            {
+                std::lock_guard<std::mutex> lock(g_mutex);
+                prompt_base = gpt2_get_prompt(g_gpt2);
+            }
+
+            if (tokens.size() > 0) {
+                text_to_speak = gpt2_gen_text(g_gpt2, (prompt_base + text_heard + "\n").c_str(), 32);
+                text_to_speak = std::regex_replace(text_to_speak, std::regex("[^a-zA-Z0-9\\.,\\?!\\s\\:\\'\\-]"), "");
+                text_to_speak = text_to_speak.substr(0, text_to_speak.find_first_of("\n"));
+
+                std::lock_guard<std::mutex> lock(g_mutex);
+
+                // remove first 2 lines of base prompt
+                {
+                    const size_t pos = prompt_base.find_first_of("\n");
+                    if (pos != std::string::npos) {
+                        prompt_base = prompt_base.substr(pos + 1);
+                    }
+                }
+                {
+                    const size_t pos = prompt_base.find_first_of("\n");
+                    if (pos != std::string::npos) {
+                        prompt_base = prompt_base.substr(pos + 1);
+                    }
+                }
+                prompt_base += text_heard + "\n" + text_to_speak + "\n";
+            } else {
+                text_to_speak = gpt2_gen_text(g_gpt2, prompt_base.c_str(), 32);
+                text_to_speak = std::regex_replace(text_to_speak, std::regex("[^a-zA-Z0-9\\.,\\?!\\s\\:\\'\\-]"), "");
+                text_to_speak = text_to_speak.substr(0, text_to_speak.find_first_of("\n"));
+
+                std::lock_guard<std::mutex> lock(g_mutex);
+
+                const size_t pos = prompt_base.find_first_of("\n");
+                if (pos != std::string::npos) {
+                    prompt_base = prompt_base.substr(pos + 1);
+                }
+                prompt_base += text_to_speak + "\n";
+            }
+
+            printf("gpt-2: %s\n", text_to_speak.c_str());
+
+            //printf("========================\n");
+            //printf("gpt-2: prompt_base:\n'%s'\n", prompt_base.c_str());
+            //printf("========================\n");
+
+            {
+                std::lock_guard<std::mutex> lock(g_mutex);
+                t_last = std::chrono::high_resolution_clock::now();
+                g_text_to_speak = text_to_speak;
+                g_pcmf32.clear();
+                gpt2_set_prompt(g_gpt2, prompt_base.c_str());
+            }
+
+            talk_set_status("speaking ...");
+        }
+    }
+
+    gpt2_free(g_gpt2);
+
+    if (index < g_contexts.size()) {
+        whisper_free(g_contexts[index]);
+        g_contexts[index] = nullptr;
+    }
+}
+
+EMSCRIPTEN_BINDINGS(talk) {
+    emscripten::function("init", emscripten::optional_override([](const std::string & path_model) {
+        for (size_t i = 0; i < g_contexts.size(); ++i) {
+            if (g_contexts[i] == nullptr) {
+                g_contexts[i] = whisper_init_from_file(path_model.c_str());
+                if (g_contexts[i] != nullptr) {
+                    g_running = true;
+                    if (g_worker.joinable()) {
+                        g_worker.join();
+                    }
+                    g_worker = std::thread([i]() {
+                        talk_main(i);
+                    });
+
+                    return i + 1;
+                } else {
+                    return (size_t) 0;
+                }
+            }
+        }
+
+        return (size_t) 0;
+    }));
+
+    emscripten::function("free", emscripten::optional_override([](size_t index) {
+        if (g_running) {
+            g_running = false;
+        }
+    }));
+
+    emscripten::function("set_audio", emscripten::optional_override([](size_t index, const emscripten::val & audio) {
+        --index;
+
+        if (index >= g_contexts.size()) {
+            return -1;
+        }
+
+        if (g_contexts[index] == nullptr) {
+            return -2;
+        }
+
+        {
+            std::lock_guard<std::mutex> lock(g_mutex);
+            const int n = audio["length"].as<int>();
+
+            emscripten::val heap = emscripten::val::module_property("HEAPU8");
+            emscripten::val memory = heap["buffer"];
+
+            g_pcmf32.resize(n);
+
+            emscripten::val memoryView = audio["constructor"].new_(memory, reinterpret_cast<uintptr_t>(g_pcmf32.data()), n);
+            memoryView.call<void>("set", audio);
+        }
+
+        return 0;
+    }));
+
+    emscripten::function("force_speak", emscripten::optional_override([](size_t index) {
+        {
+            std::lock_guard<std::mutex> lock(g_mutex);
+            g_force_speak = true;
+        }
+    }));
+
+    emscripten::function("get_text_context", emscripten::optional_override([]() {
+        std::string text_context;
+
+        {
+            std::lock_guard<std::mutex> lock(g_mutex);
+            text_context = gpt2_get_prompt(g_gpt2);
+        }
+
+        return text_context;
+    }));
+
+    emscripten::function("get_text_to_speak", emscripten::optional_override([]() {
+        std::string text_to_speak;
+
+        {
+            std::lock_guard<std::mutex> lock(g_mutex);
+            text_to_speak = std::move(g_text_to_speak);
+        }
+
+        return text_to_speak;
+    }));
+
+    emscripten::function("get_status", emscripten::optional_override([]() {
+        std::string status;
+
+        {
+            std::lock_guard<std::mutex> lock(g_mutex);
+            status = g_status_forced.empty() ? g_status : g_status_forced;
+        }
+
+        return status;
+    }));
+
+    emscripten::function("set_status", emscripten::optional_override([](const std::string & status) {
+        {
+            std::lock_guard<std::mutex> lock(g_mutex);
+            g_status_forced = status;
+        }
+    }));
+
+    emscripten::function("set_prompt", emscripten::optional_override([](const std::string & prompt) {
+        {
+            std::lock_guard<std::mutex> lock(g_mutex);
+            gpt2_set_prompt(g_gpt2, prompt.c_str());
+        }
+    }));
+}

examples/talk.wasm/gpt-2.cpp

@@ -0,0 +1,925 @@
+#include "ggml.h"
+#include "gpt-2.h"
+
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <fstream>
+#include <map>
+#include <string>
+#include <thread>
+#include <vector>
+#include <regex>
+#include <random>
+
+/////////////////////// GPT-2 BEGIN /////////////////////////
+
+//
+// Vocab utils
+//
+
+std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text) {
+    std::vector<std::string> words;
+
+    // first split the text into words
+    {
+        std::string str = text;
+        std::string pat = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
+
+        std::regex re(pat);
+        std::smatch m;
+
+        while (std::regex_search(str, m, re)) {
+            for (auto x : m) {
+                words.push_back(x);
+            }
+            str = m.suffix();
+        }
+    }
+
+    // find the longest tokens that form the words:
+    std::vector<gpt_vocab::id> tokens;
+    for (const auto & word : words) {
+        if (word.size() == 0) continue;
+
+        int i = 0;
+        int n = word.size();
+        while (i < n) {
+            int j = n;
+            while (j > i) {
+                auto it = vocab.token_to_id.find(word.substr(i, j-i));
+                if (it != vocab.token_to_id.end()) {
+                    tokens.push_back(it->second);
+                    i = j;
+                    break;
+                }
+                --j;
+            }
+            if (i == n) {
+                break;
+            }
+            if (j == i) {
+                auto sub = word.substr(i, 1);
+                if (vocab.token_to_id.find(sub) != vocab.token_to_id.end()) {
+                    tokens.push_back(vocab.token_to_id.at(sub));
+                } else {
+                    fprintf(stderr, "%s: unknown token '%s'\n", __func__, sub.data());
+                }
+                ++i;
+            }
+        }
+    }
+
+    return tokens;
+}
+
+gpt_vocab::id gpt_sample_top_k_top_p(
+        const gpt_vocab & vocab,
+        const float * logits,
+        int    top_k,
+        double top_p,
+        double temp,
+        std::mt19937 & rng) {
+    int n_logits = vocab.id_to_token.size();
+
+    std::vector<std::pair<double, gpt_vocab::id>> logits_id;
+    logits_id.reserve(n_logits);
+
+    for (int i = 0; i < n_logits; i++) {
+        logits_id.push_back(std::make_pair(logits[i], i));
+    }
+
+    // find the top K tokens
+    std::partial_sort(
+            logits_id.begin(),
+            logits_id.begin() + top_k, logits_id.end(),
+            [](const std::pair<double, gpt_vocab::id> & a, const std::pair<double, gpt_vocab::id> & b) {
+        return a.first > b.first;
+    });
+
+    logits_id.resize(top_k);
+
+    // normalize
+    {
+        double sum = 0.0f;
+        for (int i = 0; i < (int)logits_id.size(); i++) {
+            sum += logits_id[i].first;
+        }
+
+        sum = 1.0/sum;
+        for (int i = 0; i < (int)logits_id.size(); i++) {
+            logits_id[i].first *= sum;
+        }
+    }
+
+    if (top_p < 1.0f) {
+        {
+            double cumsum = 0.0f;
+            for (int i = 0; i < top_k; i++) {
+                cumsum += logits_id[i].first;
+                if (cumsum >= top_p) {
+                    logits_id.resize(i+1);
+                    break;
+                }
+            }
+        }
+
+        // normalize again
+        {
+            double sum = 0.0f;
+            for (int i = 0; i < (int)logits_id.size(); i++) {
+                sum += logits_id[i].first;
+            }
+
+            sum = 1.0/sum;
+            for (int i = 0; i < (int)logits_id.size(); i++) {
+                logits_id[i].first *= sum;
+            }
+        }
+    }
+
+    //printf("\n");
+    //for (int i = 0; i < (int)logits_id.size(); i++) {
+    //    printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), logits_id[i].first);
+    //}
+    //exit(0);
+
+    // sample from the obtained distribution
+    std::vector<double> probs;
+    probs.reserve(logits_id.size());
+
+    for (int i = 0; i < (int) logits_id.size(); i++) {
+        probs.push_back(logits_id[i].first);
+    }
+
+    std::discrete_distribution<> dist(probs.begin(), probs.end());
+    int idx = dist(rng);
+
+    return logits_id[idx].second;
+}
+
+// default hparams (GPT-2 117M)
+struct gpt2_hparams {
+    int32_t n_vocab = 50257;
+    int32_t n_ctx   = 1024;
+    int32_t n_embd  = 768;
+    int32_t n_head  = 12;
+    int32_t n_layer = 12;
+    int32_t f16     = 1;
+};
+
+struct gpt2_layer {
+    // normalization
+    struct ggml_tensor * ln_1_g;
+    struct ggml_tensor * ln_1_b;
+
+    struct ggml_tensor * ln_2_g;
+    struct ggml_tensor * ln_2_b;
+
+    // attention
+    struct ggml_tensor * c_attn_attn_w;
+    struct ggml_tensor * c_attn_attn_b;
+
+    struct ggml_tensor * c_attn_proj_w;
+    struct ggml_tensor * c_attn_proj_b;
+
+    // mlp
+    struct ggml_tensor * c_mlp_fc_w;
+    struct ggml_tensor * c_mlp_fc_b;
+
+    struct ggml_tensor * c_mlp_proj_w_trans; // transposed for efficiency
+    struct ggml_tensor * c_mlp_proj_b;
+};
+
+struct gpt2_model {
+    gpt2_hparams hparams;
+
+    // normalization
+    struct ggml_tensor * ln_f_g;
+    struct ggml_tensor * ln_f_b;
+
+    struct ggml_tensor * wte; // position embedding
+    struct ggml_tensor * wpe; //    token embedding
+
+    std::vector<gpt2_layer> layers;
+
+    // key + value memory
+    struct ggml_tensor * memory_k;
+    struct ggml_tensor * memory_v;
+
+    //
+    struct ggml_context * ctx;
+    std::map<std::string, struct ggml_tensor *> tensors;
+};
+
+// load the model's weights from a file
+bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab & vocab) {
+    printf("%s: loading model from '%s'\n", __func__, fname.c_str());
+
+    auto fin = std::ifstream(fname, std::ios::binary);
+    if (!fin) {
+        fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
+        return false;
+    }
+
+    // verify magic
+    {
+        uint32_t magic;
+        fin.read((char *) &magic, sizeof(magic));
+        if (magic != 0x67676d6c) {
+            fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
+            return false;
+        }
+    }
+
+    // load hparams
+    {
+        auto & hparams = model.hparams;
+
+        fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
+        fin.read((char *) &hparams.n_ctx,   sizeof(hparams.n_ctx));
+        fin.read((char *) &hparams.n_embd,  sizeof(hparams.n_embd));
+        fin.read((char *) &hparams.n_head,  sizeof(hparams.n_head));
+        fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
+        fin.read((char *) &hparams.f16,     sizeof(hparams.f16));
+
+        printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
+        printf("%s: n_ctx   = %d\n", __func__, hparams.n_ctx);
+        printf("%s: n_embd  = %d\n", __func__, hparams.n_embd);
+        printf("%s: n_head  = %d\n", __func__, hparams.n_head);
+        printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
+        printf("%s: f16     = %d\n", __func__, hparams.f16);
+    }
+
+    // load vocab
+    {
+        int32_t n_vocab = 0;
+        fin.read((char *) &n_vocab, sizeof(n_vocab));
+
+        if (n_vocab != model.hparams.n_vocab) {
+            fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
+                    __func__, fname.c_str(), n_vocab, model.hparams.n_vocab);
+            return false;
+        }
+
+        std::string word;
+        for (int i = 0; i < n_vocab; i++) {
+            uint32_t len;
+            fin.read((char *) &len, sizeof(len));
+
+            word.resize(len);
+            fin.read((char *) word.data(), len);
+
+            vocab.token_to_id[word] = i;
+            vocab.id_to_token[i] = word;
+        }
+    }
+
+    // for the big tensors, we have the option to store the data in 16-bit floats
+    // in order to save memory and also to speed up the computation
+    const ggml_type wtype = model.hparams.f16 ? GGML_TYPE_F16 : GGML_TYPE_F32;
+
+    auto & ctx = model.ctx;
+
+    size_t ctx_size = 0;
+
+    {
+        const auto & hparams = model.hparams;
+
+        const int n_embd  = hparams.n_embd;
+        const int n_layer = hparams.n_layer;
+        const int n_ctx   = hparams.n_ctx;
+        const int n_vocab = hparams.n_vocab;
+
+        ctx_size += n_embd*ggml_type_size(GGML_TYPE_F32); // ln_f_g
+        ctx_size += n_embd*ggml_type_size(GGML_TYPE_F32); // ln_f_b
+
+        ctx_size += n_vocab*n_embd*ggml_type_size(wtype);         // wte
+        ctx_size +=   n_ctx*n_embd*ggml_type_size(GGML_TYPE_F32); // wpe
+
+        ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_1_g
+        ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_1_b
+
+        ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_2_g
+        ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_2_b
+
+        ctx_size += n_layer*(3*n_embd*n_embd*ggml_type_size(wtype));         // c_attn_attn_w
+        ctx_size += n_layer*(       3*n_embd*ggml_type_size(GGML_TYPE_F32)); // c_attn_attn_b
+
+        ctx_size += n_layer*(n_embd*n_embd*ggml_type_size(wtype));           // c_attn_proj_w
+        ctx_size += n_layer*(       n_embd*ggml_type_size(GGML_TYPE_F32));   // c_attn_proj_b
+
+        ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_size(wtype));         // c_mlp_fc_w
+        ctx_size += n_layer*(       4*n_embd*ggml_type_size(GGML_TYPE_F32)); // c_mlp_fc_b
+
+        ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_size(wtype));         // c_mlp_proj_w
+        ctx_size += n_layer*(         n_embd*ggml_type_size(GGML_TYPE_F32)); // c_mlp_proj_b
+
+        ctx_size += n_ctx*n_layer*n_embd*ggml_type_size(GGML_TYPE_F32); // memory_k
+        ctx_size += n_ctx*n_layer*n_embd*ggml_type_size(GGML_TYPE_F32); // memory_v
+
+        ctx_size += (6 + 12*n_layer)*256; // object overhead
+
+        printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
+    }
+
+    // create the ggml context
+    {
+        struct ggml_init_params params;
+        params.mem_size   = ctx_size;
+        params.mem_buffer = NULL;
+
+        model.ctx = ggml_init(params);
+        if (!model.ctx) {
+            fprintf(stderr, "%s: ggml_init() failed\n", __func__);
+            return false;
+        }
+    }
+
+    // prepare memory for the weights
+    {
+        const auto & hparams = model.hparams;
+
+        const int n_embd  = hparams.n_embd;
+        const int n_layer = hparams.n_layer;
+        const int n_ctx   = hparams.n_ctx;
+        const int n_vocab = hparams.n_vocab;
+
+        model.layers.resize(n_layer);
+
+        model.ln_f_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+        model.ln_f_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+
+        model.wte = ggml_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);
+        model.wpe = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ctx);
+
+        // map by name
+        model.tensors["model/ln_f/g"] = model.ln_f_g;
+        model.tensors["model/ln_f/b"] = model.ln_f_b;
+
+        model.tensors["model/wte"] = model.wte;
+        model.tensors["model/wpe"] = model.wpe;
+
+        for (int i = 0; i < n_layer; ++i) {
+            auto & layer = model.layers[i];
+
+            layer.ln_1_g             = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
+            layer.ln_1_b             = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
+
+            layer.ln_2_g             = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
+            layer.ln_2_b             = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
+
+            layer.c_attn_attn_w      = ggml_new_tensor_2d(ctx, wtype,         3*n_embd, n_embd);
+            layer.c_attn_attn_b      = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 3*n_embd);
+
+            layer.c_attn_proj_w      = ggml_new_tensor_2d(ctx, wtype,           n_embd, n_embd);
+            layer.c_attn_proj_b      = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
+
+            layer.c_mlp_fc_w         = ggml_new_tensor_2d(ctx, wtype,         4*n_embd, n_embd);
+            layer.c_mlp_fc_b         = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_embd);
+
+            layer.c_mlp_proj_w_trans = ggml_new_tensor_2d(ctx, wtype,         4*n_embd, n_embd);
+            layer.c_mlp_proj_b       = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
+
+            // map by name
+            model.tensors["model/h" + std::to_string(i) + "/ln_1/g"]        = layer.ln_1_g;
+            model.tensors["model/h" + std::to_string(i) + "/ln_1/b"]        = layer.ln_1_b;
+
+            model.tensors["model/h" + std::to_string(i) + "/ln_2/g"]        = layer.ln_2_g;
+            model.tensors["model/h" + std::to_string(i) + "/ln_2/b"]        = layer.ln_2_b;
+
+            model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/w"] = layer.c_attn_attn_w;
+            model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/b"] = layer.c_attn_attn_b;
+
+            model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/w"] = layer.c_attn_proj_w;
+            model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/b"] = layer.c_attn_proj_b;
+
+            model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/w"]    = layer.c_mlp_fc_w;
+            model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/b"]    = layer.c_mlp_fc_b;
+
+            model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/w"]  = layer.c_mlp_proj_w_trans;
+            model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/b"]  = layer.c_mlp_proj_b;
+        }
+    }
+
+    // key + value memory
+    {
+        const auto & hparams = model.hparams;
+
+        const int n_embd  = hparams.n_embd;
+        const int n_layer = hparams.n_layer;
+        const int n_ctx   = hparams.n_ctx;
+
+        const int n_mem      = n_layer*n_ctx;
+        const int n_elements = n_embd*n_mem;
+
+        model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
+        model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
+
+        const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
+
+        printf("%s: memory size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
+    }
+
+    // load weights
+    {
+        size_t total_size = 0;
+
+        while (true) {
+            int32_t n_dims;
+            int32_t length;
+            int32_t ftype;
+
+            fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
+            fin.read(reinterpret_cast<char *>(&length), sizeof(length));
+            fin.read(reinterpret_cast<char *>(&ftype),  sizeof(ftype));
+
+            if (fin.eof()) {
+                break;
+            }
+
+            int32_t nelements = 1;
+            int32_t ne[2] = { 1, 1 };
+            for (int i = 0; i < n_dims; ++i) {
+                fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
+                nelements *= ne[i];
+            }
+
+            std::string name(length, 0);
+            fin.read(&name[0], length);
+
+            if (model.tensors.find(name.data()) == model.tensors.end()) {
+                fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
+                return false;
+            }
+
+            auto tensor = model.tensors[name.data()];
+            if (ggml_nelements(tensor) != nelements) {
+                fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
+                return false;
+            }
+
+            if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
+                fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
+                        __func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
+                return false;
+            }
+
+            const size_t bpe = (ftype == 0) ? sizeof(float) : sizeof(ggml_fp16_t);
+
+            if (nelements*bpe != ggml_nbytes(tensor)) {
+                fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
+                        __func__, name.data(), ggml_nbytes(tensor), nelements*bpe);
+                return false;
+            }
+
+            fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
+
+            //printf("%24s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
+            total_size += ggml_nbytes(tensor);
+        }
+
+        printf("%s: model size  = %8.2f MB\n", __func__, total_size/1024.0/1024.0);
+    }
+
+    fin.close();
+
+    return true;
+}
+
+// evaluate the transformer
+//
+//   - model:     the model
+//   - n_threads: number of threads to use
+//   - n_past:    the context size so far
+//   - embd_inp:  the embeddings of the tokens in the context
+//   - embd_w:    the predicted probabilities of the next token
+//
+bool gpt2_eval(
+        const gpt2_model & model,
+        const int n_threads,
+        const int n_past,
+        const std::vector<gpt_vocab::id> & embd_inp,
+              std::vector<float>         & embd_w,
+              size_t                     & mem_per_token) {
+    const int N = embd_inp.size();
+
+    const auto & hparams = model.hparams;
+
+    const int n_embd  = hparams.n_embd;
+    const int n_layer = hparams.n_layer;
+    const int n_ctx   = hparams.n_ctx;
+    const int n_head  = hparams.n_head;
+    const int n_vocab = hparams.n_vocab;
+
+    static size_t buf_size = 640u*1024*1024;
+    static void * buf = malloc(buf_size);
+
+    if (mem_per_token > 0 && mem_per_token*N > buf_size) {
+        const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead
+        printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
+
+        // reallocate
+        buf_size = buf_size_new;
+        buf = realloc(buf, buf_size);
+        if (buf == nullptr) {
+            fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
+            return false;
+        }
+    }
+
+    struct ggml_init_params params;
+    params.mem_size   = buf_size;
+    params.mem_buffer = buf;
+
+    struct ggml_context * ctx0 = ggml_init(params);
+
+    struct ggml_cgraph gf = { };
+    gf.n_threads = n_threads;
+
+    struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
+    memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));
+
+    struct ggml_tensor * position = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
+    for (int i = 0; i < N; ++i) {
+        ((int32_t *) position->data)[i] = n_past + i;
+    }
+
+    // wte + wpe
+    struct ggml_tensor * inpL =
+        ggml_add(ctx0,
+                ggml_get_rows(ctx0, model.wte, embd),
+                ggml_get_rows(ctx0, model.wpe, position));
+
+    for (int il = 0; il < n_layer; ++il) {
+        struct ggml_tensor * cur;
+
+        // norm
+        {
+            // [ 768, N]
+            cur = ggml_norm(ctx0, inpL);
+
+            // cur = ln_1_g*cur + ln_1_b
+            // [ 768, N]
+            cur = ggml_add(ctx0,
+                    ggml_mul(ctx0,
+                        ggml_repeat(ctx0, model.layers[il].ln_1_g, cur),
+                        cur),
+                    ggml_repeat(ctx0, model.layers[il].ln_1_b, cur));
+        }
+
+        // attn
+        // [2304, 768] - model.layers[il].c_attn_attn_w
+        // [2304,   1] - model.layers[il].c_attn_attn_b
+        // [ 768,   N] - cur (in)
+        // [2304,   N] - cur (out)
+        //
+        // cur = attn_w*cur + attn_b
+        // [2304, N]
+        {
+            cur = ggml_mul_mat(ctx0,
+                    ggml_transpose(ctx0, model.layers[il].c_attn_attn_w),
+                    cur);
+
+            cur = ggml_add(ctx0,
+                    ggml_repeat(ctx0, model.layers[il].c_attn_attn_b, cur),
+                    cur);
+        }
+
+        // self-attention
+        {
+            struct ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0*sizeof(float)*n_embd);
+            struct ggml_tensor * Kcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1*sizeof(float)*n_embd);
+            struct ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2*sizeof(float)*n_embd);
+
+            // store key and value to memory
+            if (N >= 1) {
+                struct ggml_tensor * k = ggml_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past));
+                struct ggml_tensor * v = ggml_view_1d(ctx0, model.memory_v, N*n_embd, (ggml_element_size(model.memory_v)*n_embd)*(il*n_ctx + n_past));
+
+                ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
+                ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
+            }
+
+            // Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
+            // [64, N, 12]
+            struct ggml_tensor * Q =
+                ggml_permute(ctx0,
+                        ggml_cpy(ctx0,
+                            Qcur,
+                            ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd/n_head, n_head, N)),
+                        0, 2, 1, 3);
+
+            // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
+            // [64, n_past + N, 12]
+            struct ggml_tensor * K =
+                ggml_permute(ctx0,
+                        ggml_reshape_3d(ctx0,
+                            ggml_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_k)*n_embd),
+                            n_embd/n_head, n_head, n_past + N),
+                        0, 2, 1, 3);
+
+            // GG: flash attention
+            //struct ggml_tensor * V =
+            //    ggml_cpy(ctx0,
+            //            ggml_permute(ctx0,
+            //                ggml_reshape_3d(ctx0,
+            //                    ggml_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_v)*n_embd),
+            //                    n_embd/n_head, n_head, n_past + N),
+            //                1, 2, 0, 3),
+            //            ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_past + N, n_embd/n_head, n_head));
+
+            //struct ggml_tensor * KQV = ggml_flash_attn(ctx0, Q, K, V, true);
+
+            // K * Q
+            // [n_past + N, N, 12]
+            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
+
+            // KQ_scaled = KQ / sqrt(n_embd/n_head)
+            // [n_past + N, N, 12]
+            struct ggml_tensor * KQ_scaled =
+                ggml_scale(ctx0,
+                        KQ,
+                        ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
+                        );
+
+            // KQ_masked = mask_past(KQ_scaled)
+            // [n_past + N, N, 12]
+            struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
+
+            // KQ = soft_max(KQ_masked)
+            // [n_past + N, N, 12]
+            struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
+
+            // V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
+            // [n_past + N, 64, 12]
+            struct ggml_tensor * V_trans =
+                ggml_permute(ctx0,
+                        ggml_reshape_3d(ctx0,
+                            ggml_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_v)*n_embd),
+                            n_embd/n_head, n_head, n_past + N),
+                        1, 2, 0, 3);
+
+            // KQV = transpose(V) * KQ_soft_max
+            // [64, N, 12]
+            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);
+
+            // KQV_merged = KQV.permute(0, 2, 1, 3)
+            // [64, 12, N]
+            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
+
+            // cur = KQV_merged.contiguous().view(n_embd, N)
+            // [768, N]
+            cur = ggml_cpy(ctx0,
+                    KQV_merged,
+                    ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
+        }
+
+        // projection
+        // [ 768, 768] - model.layers[il].c_attn_proj_w
+        // [ 768,   1] - model.layers[il].c_attn_proj_b
+        // [ 768,   N] - cur (in)
+        // [ 768,   N] - cur (out)
+        //
+        // cur = proj_w*cur + proj_b
+        // [768, N]
+        {
+            cur = ggml_mul_mat(ctx0,
+                    ggml_transpose(ctx0, model.layers[il].c_attn_proj_w),
+                    cur);
+
+            cur = ggml_add(ctx0,
+                    ggml_repeat(ctx0, model.layers[il].c_attn_proj_b, cur),
+                    cur);
+        }
+
+        // add the input
+        cur = ggml_add(ctx0, cur, inpL);
+
+        struct ggml_tensor * inpFF = cur;
+
+        // feed-forward network
+        {
+            // norm
+            {
+                cur = ggml_norm(ctx0, inpFF);
+
+                // cur = ln_2_g*cur + ln_2_b
+                // [ 768, N]
+                cur = ggml_add(ctx0,
+                        ggml_mul(ctx0,
+                            ggml_repeat(ctx0, model.layers[il].ln_2_g, cur),
+                            cur),
+                        ggml_repeat(ctx0, model.layers[il].ln_2_b, cur));
+            }
+
+            // fully connected
+            // [3072, 768] - model.layers[il].c_mlp_fc_w
+            // [3072,   1] - model.layers[il].c_mlp_fc_b
+            // [ 768,   N] - cur (in)
+            // [3072,   N] - cur (out)
+            //
+            // cur = fc_w*cur + fc_b
+            // [3072, N]
+            cur = ggml_mul_mat(ctx0,
+                    ggml_transpose(ctx0, model.layers[il].c_mlp_fc_w),
+                    cur);
+
+            cur = ggml_add(ctx0,
+                    ggml_repeat(ctx0, model.layers[il].c_mlp_fc_b, cur),
+                    cur);
+
+            // GELU activation
+            // [3072, N]
+            cur = ggml_gelu(ctx0, cur);
+
+            // projection
+            // [ 768, 3072] - model.layers[il].c_mlp_proj_w
+            // [ 768,    1] - model.layers[il].c_mlp_proj_b
+            // [3072,    N] - cur (in)
+            // [ 768,    N] - cur (out)
+            //
+            // cur = proj_w*cur + proj_b
+            // [768, N]
+            cur = ggml_mul_mat(ctx0,
+                    model.layers[il].c_mlp_proj_w_trans,
+                    cur);
+
+            cur = ggml_add(ctx0,
+                    ggml_repeat(ctx0, model.layers[il].c_mlp_proj_b, cur),
+                    cur);
+        }
+
+        // input for next layer
+        inpL = ggml_add(ctx0, cur, inpFF);
+    }
+
+    // norm
+    {
+        // [ 768, N]
+        inpL = ggml_norm(ctx0, inpL);
+
+        // inpL = ln_f_g*inpL + ln_f_b
+        // [ 768, N]
+        inpL = ggml_add(ctx0,
+                ggml_mul(ctx0,
+                    ggml_repeat(ctx0, model.ln_f_g, inpL),
+                    inpL),
+                ggml_repeat(ctx0, model.ln_f_b, inpL));
+    }
+
+    // inpL = WTE * inpL
+    // [ 768, 50257] - model.wte
+    // [ 768, N]     - inpL
+    inpL = ggml_mul_mat(ctx0, model.wte, inpL);
+
+    // logits -> probs
+    inpL = ggml_soft_max(ctx0, inpL);
+
+    // run the computation
+    ggml_build_forward_expand(&gf, inpL);
+    ggml_graph_compute       (ctx0, &gf);
+
+    //if (n_past%100 == 0) {
+    //    ggml_graph_print   (&gf);
+    //    ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot");
+    //}
+
+    //embd_w.resize(n_vocab*N);
+    //memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
+
+    // return result for just the last token
+    embd_w.resize(n_vocab);
+    memcpy(embd_w.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
+
+    if (mem_per_token == 0) {
+        mem_per_token = ggml_used_mem(ctx0)/N;
+    }
+    //printf("used_mem = %zu\n", ggml_used_mem(ctx0));
+
+    ggml_free(ctx0);
+
+    return true;
+}
+
+/////////////////////////////// GPT-2 END ////////////////////////////////
+
+constexpr int N_THREAD = 8;
+
+struct gpt2_context {
+    std::string prompt_base = R"(Hello, how are you?
+I'm fine, thanks. How are you?
+Thanks, I'm fine too. What are you doing?
+I'm just sitting here.
+It's a lovely day, isn't it?
+Yes, it is. I love the weather this time of year.
+I wish it would rain a little bit.
+Me too.
+)";
+
+    std::mt19937 rng;
+
+    gpt_vocab vocab;
+    gpt2_model model;
+
+    int32_t n_threads = std::min(N_THREAD, (int) std::thread::hardware_concurrency());
+
+    // sampling parameters
+    int32_t top_k = 40;
+    float   top_p = 0.9f;
+    float   temp  = 1.0f;
+};
+
+struct gpt2_context * gpt2_init(const char * path_model) {
+    gpt2_context * ctx = new gpt2_context;
+
+    ctx->rng = std::mt19937(time(NULL));
+
+    // load the model
+    {
+        const int64_t t_start_us = ggml_time_us();
+
+        if (!gpt2_model_load(path_model, ctx->model, ctx->vocab)) {
+            fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, "gpt-2.bin");
+            return nullptr;
+        }
+
+        const int64_t t_load_us = ggml_time_us() - t_start_us;
+
+        printf("gpt-2: model loaded in %d ms\n", (int) (t_load_us/1000));
+    }
+
+    return ctx;
+}
+
+void gpt2_free(struct gpt2_context * ctx) {
+    delete ctx;
+}
+
+const char * gpt2_get_prompt(struct gpt2_context * ctx) {
+    return ctx->prompt_base.c_str();
+}
+
+void gpt2_set_prompt(struct gpt2_context * ctx, const char * prompt) {
+    ctx->prompt_base = prompt;
+}
+
+std::vector<gpt_vocab::id> gpt2_tokenize(const gpt2_context * ctx, const char * text) {
+    return ::gpt_tokenize(ctx->vocab, text);
+}
+
+std::string gpt2_gen_text(gpt2_context * ctx, const char * text, int max_tokens) {
+    int n_past = 0;
+
+    std::vector<float> embd_w;
+
+    // tokenize the prompt
+    std::vector<gpt_vocab::id> embd_inp = ::gpt2_tokenize(ctx, text);
+
+    int n_predict = std::min(max_tokens, ctx->model.hparams.n_ctx - (int) embd_inp.size());
+
+    std::vector<gpt_vocab::id> embd = embd_inp;
+
+    size_t mem_per_token = 3000000;
+
+    std::string result;
+
+    for (int i = embd.size(); i < embd_inp.size() + n_predict; i++) {
+        // predict
+        if (embd.size() > 0) {
+            if (!gpt2_eval(ctx->model, ctx->n_threads, n_past, embd, embd_w, mem_per_token)) {
+                printf("gpt-2: failed to generate text\n");
+                return "";
+            }
+        }
+
+        n_past += embd.size();
+        embd.clear();
+
+        {
+            // sample next token
+            const int   top_k = ctx->top_k;
+            const float top_p = ctx->top_p;
+            const float temp  = ctx->temp;
+
+            const int n_vocab = ctx->model.hparams.n_vocab;
+
+            const gpt_vocab::id id = gpt_sample_top_k_top_p(ctx->vocab, embd_w.data() + (embd_w.size() - n_vocab), top_k, top_p, temp, ctx->rng);
+
+            // add it to the context
+            embd.push_back(id);
+        }
+
+        result += ctx->vocab.id_to_token[embd[0]];
+
+        // end of text token
+        if (embd.back() == 50256 ||
+            ctx->vocab.id_to_token[embd.back()] == "." ||
+            ctx->vocab.id_to_token[embd.back()] == "!" ||
+            ctx->vocab.id_to_token[embd.back()] == "?") {
+            break;
+        }
+    }
+
+    return result;
+}

examples/talk.wasm/gpt-2.h

@@ -0,0 +1,27 @@
+#pragma once
+
+// TODO: Change to C-style API and move to ./examples for easy reuse.
+
+#include <vector>
+#include <map>
+#include <string>
+
+struct gpt_vocab {
+    using id    = int32_t;
+    using token = std::string;
+
+    std::map<token, id> token_to_id;
+    std::map<id, token> id_to_token;
+};
+
+struct gpt2_context;
+
+struct gpt2_context * gpt2_init(const char * path_model);
+void gpt2_free(struct gpt2_context * ctx);
+
+const char * gpt2_get_prompt(struct gpt2_context * ctx);
+void gpt2_set_prompt(struct gpt2_context * ctx, const char * prompt);
+
+std::vector<gpt_vocab::id> gpt2_tokenize(const gpt2_context * ctx, const char * text);
+
+std::string gpt2_gen_text(gpt2_context * ctx, const char * text, int max_tokens);

examples/talk.wasm/index-tmpl.html

@@ -0,0 +1,829 @@
+<!doctype html>
+<html lang="en-us">
+    <head>
+        <title>Talk - GPT-2 meets Whisper in WebAssembly</title>
+
+        <style>
+            #output {
+                width: 100%;
+                height: 100%;
+                margin: 0 auto;
+                margin-top: 10px;
+                border-left: 0px;
+                border-right: 0px;
+                padding-left: 0px;
+                padding-right: 0px;
+                display: block;
+                background-color: black;
+                color: white;
+                font-size: 10px;
+                font-family: 'Lucida Console', Monaco, monospace;
+                outline: none;
+                white-space: pre;
+                overflow-wrap: normal;
+                overflow-x: scroll;
+            }
+        </style>
+    </head>
+    <body>
+        <div id="main-container">
+            <b>Talk - GPT-2 meets Whisper in WebAssembly</b>
+
+            <br><br>
+
+            Talk with an Artificial Intelligence in your browser. This demo uses:
+
+            <ul>
+                <li><a href="https://github.com/ggerganov/whisper.cpp">OpenAI's Whisper</a> to listen to you as you speak in the microphone</li>
+                <li><a href="https://github.com/ggerganov/whisper.cpp/tree/master/examples/talk.wasm">OpenAI's GPT-2</a> to generate text responses</li>
+                <li><a href="https://developer.mozilla.org/en-US/docs/Web/API/Web_Speech_API">Web Speech API</a> to vocalize the responses through your speakers</li>
+            </ul>
+
+            All of this runs <b>locally in your browser</b> using WebAssembly.<br>
+            You can find more about this project on <a href="https://github.com/ggerganov/whisper.cpp/tree/master/examples/talk.wasm">GitHub</a>.
+
+            <br><br>
+
+            <hr>
+
+            Select the models you would like to use and click the "Start" button to begin the conversation
+
+            <br><br>
+
+            <div id="model-whisper">
+                Whisper model: <span id="model-whisper-status"></span>
+                <button id="fetch-whisper-tiny-en" onclick="loadWhisper('tiny.en')">tiny.en (75 MB)</button>
+                <button id="fetch-whisper-base-en" onclick="loadWhisper('base.en')">base.en (142 MB)</button>
+                <span id="fetch-whisper-progress"></span>
+
+                <!--
+                    <input type="file" id="file" name="file" onchange="loadFile(event, 'whisper.bin')" />
+                -->
+            </div>
+
+            <br>
+
+            <div id="model-gpt-2">
+                GPT-2 model: <span id="model-gpt-2-status"></span>
+                <button id="fetch-gpt-2-small" onclick="loadGPT2('small')">small 117M (240 MB)</button>
+                <!--<button id="fetch-gpt-2-medium" onclick="loadGPT2('medium')">medium 345M (720 MB)</button>-->
+                <span id="fetch-gpt-2-progress"></span>
+
+                <!--
+                <input type="file" id="file" name="file" onchange="loadFile(event, 'gpt-2.bin')" />
+                -->
+            </div>
+
+            <br>
+
+            <div id="input">
+                <button id="start"  onclick="onStart()" disabled>Start</button>
+                <button id="stop"   onclick="onStop()" disabled>Stop</button>
+                <select id="voice"  onchange="onVoiceChange()" disabled>
+                    <option value="0">Default</option>
+                </select>
+                <select id="prompt" onchange="onPromptChange()">
+                    <option value="0">Casual</option>
+                    <option value="1">Robot</option>
+                    <option value="2">Scientist</option>
+                    <option value="3">Programmer</option>
+                    <option value="4">Happy</option>
+                    <option value="5">Sad</option>
+                    <option value="6">Philosophical</option>
+                    <option value="7">Angry</option>
+                    <option value="8">Funny</option>
+                    <option value="9">Poetic</option>
+                    <option value="10">Clever</option>
+                    <option value="11">Cute</option>
+                    <option value="12">Smart</option>
+                    <option value="13">Dumb</option>
+                    <option value="14">Boring</option>
+                    <option value="15">Exciting</option>
+                    <option value="16">Interesting</option>
+                    <option value="17">Wiliam Shakespear</option>
+                    <option value="18">J.R.R. Tolkien</option>
+                    <option value="19">George R.R. Martin</option>
+                    <option value="20">Stephen King</option>
+                </select>
+                <button id="speak0" onclick="onSpeak('Hello')">Say hello</button>
+                <button id="speak1" onclick="onSpeakRandom()" disabled>Say something</button>
+                <button id="clear"  onclick="clearCache()">Clear Cache</button>
+            </div>
+
+            <br>
+
+            <div id="state">
+                Status: <b><span id="state-status">not started</span></b>
+
+                <pre id="state-context">[The text context will be displayed here]</pre>
+            </div>
+
+            <hr>
+
+            Debug output:
+            <textarea id="output" rows="20"></textarea>
+
+            <br>
+
+            <b>Troubleshooting</b>
+
+            <br><br>
+
+            The page does some heavy computations, so make sure:
+
+            <ul>
+                <li>To use a modern web browser (e.g. Chrome, Firefox)</li>
+                <li>To use a fast desktop or laptop computer (i.e. not a mobile phone)</li>
+                <li>Your browser supports WASM <a href="https://webassembly.org/roadmap/">Fixed-width SIMD</a></li>
+            </ul>
+
+            Note that these neural network models were not meant to be used in a browser, so the performance and <br>
+            quality of the results may not be optimal. If you have any questions or suggestions, checkout the following
+            <a href="https://github.com/ggerganov/whisper.cpp/discussions/167">discussion</a>.
+
+            <br><br>
+
+            Here is a short video of the demo in action: <a href="https://youtu.be/LeWKl8t1-Hc">https://youtu.be/LeWKl8t1-Hc</a>
+
+            <br><br>
+
+            <div class="cell-version">
+                <span>
+                    |
+                    Build time: <span class="nav-link">@GIT_DATE@</span> |
+                    Commit hash: <a class="nav-link" href="https://github.com/ggerganov/whisper.cpp/commit/@GIT_SHA1@">@GIT_SHA1@</a> |
+                    Commit subject: <span class="nav-link">@GIT_COMMIT_SUBJECT@</span> |
+                    <a class="nav-link" href="https://github.com/ggerganov/whisper.cpp/tree/master/examples/talk.wasm">Source Code</a> |
+                </span>
+            </div>
+        </div>
+
+        <script type="text/javascript" src="helpers.js"></script>
+        <script type='text/javascript'>
+            // web audio context
+            var context = null;
+
+            // audio data
+            var audio = null;
+            var audio0 = null;
+
+            // the talk instance
+            var instance = null;
+
+            // model names
+            var model_whisper = null;
+            var model_gpt_2 = null;
+
+            // speech synthesis
+            const synth = window.speechSynthesis;
+            var voice = null;
+
+            var Module = {
+                print: printTextarea,
+                printErr: printTextarea,
+                setStatus: function(text) {
+                    printTextarea('js: ' + text);
+                },
+                monitorRunDependencies: function(left) {
+                },
+                preRun: function() {
+                    printTextarea('js: Preparing ...');
+                },
+                postRun: function() {
+                    printTextarea('js: Initialized successfully!');
+
+                    // populate the voice list
+                    var voices = synth.getVoices();
+                    var el = document.getElementById('voice');
+
+                    // if empty - display error in the element
+                    if (voices.length == 0) {
+                        el.innerHTML = '<option value="0">No voices available</option>';
+                    } else {
+                        // populate voice list
+                        var n = 0;
+                        voices.forEach(function(voice, i) {
+                            if (!voice.lang.startsWith('en')) return;
+                            var option = document.createElement('option');
+                            option.value = i;
+                            option.innerHTML = voice.name + ' (' + voice.lang + ')';
+                            el.appendChild(option);
+                            n++;
+                        });
+
+                        // select random voice
+                        if (n > 0) {
+                            for (var k = 0; k < 10; k++) {
+                                var i = Math.floor(Math.random() * n);
+                                el.selectedIndex = i;
+                                voice = voices[document.getElementById('voice').options[i].value];
+
+                                // give preference to Google voices
+                                if (voice.name.startsWith('Google')) break;
+                            }
+                        }
+                    }
+
+                    onPromptChange();
+                }
+            };
+
+            //
+            // fetch models
+            //
+
+            let dbVersion = 1
+            let dbName    = 'whisper.ggerganov.com';
+            let indexedDB = window.indexedDB || window.mozIndexedDB || window.webkitIndexedDB || window.msIndexedDB
+
+            function storeFS(fname, buf) {
+                // write to WASM file using FS_createDataFile
+                // if the file exists, delete it
+                try {
+                    Module.FS_unlink(fname);
+                } catch (e) {
+                    // ignore
+                }
+
+                Module.FS_createDataFile("/", fname, buf, true, true);
+
+                printTextarea('storeFS: stored model: ' + fname + ' size: ' + buf.length);
+
+                if (fname == 'whisper.bin') {
+                    document.getElementById('model-whisper-status').innerHTML = 'loaded "' + model_whisper + '"!';
+                } else if (fname == 'gpt-2.bin') {
+                    document.getElementById('model-gpt-2-status').innerHTML = 'loaded "' + model_gpt_2 + '"!';
+                }
+
+                if (model_whisper != null && model_gpt_2 != null) {
+                    document.getElementById('start').disabled = false;
+                    document.getElementById('stop' ).disabled = false;
+                    document.getElementById('voice').disabled = false;
+                }
+            }
+
+            function loadWhisper(model) {
+                let urls = {
+                    'tiny.en': 'https://whisper.ggerganov.com/ggml-model-whisper-tiny.en.bin',
+                    'base.en': 'https://whisper.ggerganov.com/ggml-model-whisper-base.en.bin',
+                };
+
+                let sizes = {
+                    'tiny.en': 75,
+                    'base.en': 142,
+                };
+
+                let url     = urls[model];
+                let dst     = 'whisper.bin';
+                let size_mb = sizes[model];
+
+                model_whisper = model;
+
+                document.getElementById('fetch-whisper-tiny-en').style.display = 'none';
+                document.getElementById('fetch-whisper-base-en').style.display = 'none';
+                document.getElementById('model-whisper-status').innerHTML = 'loading "' + model + '" ... ';
+
+                cbProgress = function(p) {
+                    let el = document.getElementById('fetch-whisper-progress');
+                    el.innerHTML = Math.round(100*p) + '%';
+                };
+
+                cbCancel = function() {
+                    var el;
+                    el = document.getElementById('fetch-whisper-tiny-en'); if (el) el.style.display = 'inline-block';
+                    el = document.getElementById('fetch-whisper-base-en'); if (el) el.style.display = 'inline-block';
+                    el = document.getElementById('model-whisper-status');  if (el) el.innerHTML = '';
+                };
+
+                loadRemote(url, dst, size_mb, cbProgress, storeFS, cbCancel, printTextarea);
+            }
+
+            function loadGPT2(model) {
+                let urls = {
+                    'small':  'https://whisper.ggerganov.com/ggml-model-gpt-2-117M.bin',
+                    'medium': 'https://whisper.ggerganov.com/ggml-model-gpt-2-345M.bin',
+                };
+
+                let sizes = {
+                    'small':  240,
+                    'medium': 712,
+                };
+
+                let url     = urls[model];
+                let dst     = 'gpt-2.bin';
+                let size_mb = sizes[model];
+
+                model_gpt_2 = model;
+
+                document.getElementById('fetch-gpt-2-small').style.display = 'none';
+                document.getElementById('model-gpt-2-status').innerHTML = 'loading "' + model + '" ... ';
+
+                cbProgress = function(p) {
+                    let el = document.getElementById('fetch-gpt-2-progress');
+                    el.innerHTML = Math.round(100*p) + '%';
+                };
+
+                cbCancel = function() {
+                    var el;
+                    el = document.getElementById('fetch-gpt-2-small') ; if (el) el.style.display = 'inline-block';
+                    el = document.getElementById('model-gpt-2-status'); if (el) el.innerHTML = '';
+                };
+
+                loadRemote(url, dst, size_mb, cbProgress, storeFS, cbCancel, printTextarea);
+            }
+
+            //
+            // microphone
+            //
+
+            const kSampleRate = 16000;
+            const kRestartRecording_s = 120;
+            const kIntervalAudio_ms = 250; // pass the recorded audio to the C++ instance at this rate
+
+            var mediaRecorder = null;
+            var doRecording = false;
+            var startTime = 0;
+
+            window.AudioContext = window.AudioContext || window.webkitAudioContext;
+            window.OfflineAudioContext = window.OfflineAudioContext || window.webkitOfflineAudioContext;
+
+            function stopRecording() {
+                Module.set_status("paused");
+                doRecording = false;
+                audio0 = null;
+                audio = null;
+                context = null;
+            }
+
+            function startRecording() {
+                if (!context) {
+                    context = new AudioContext({
+                        sampleRate: kSampleRate,
+                        channelCount: 1,
+                        echoCancellation: false,
+                        autoGainControl:  true,
+                        noiseSuppression: true,
+                    });
+                }
+
+                Module.set_status("");
+
+                document.getElementById('start').disabled = true;
+                document.getElementById('stop').disabled = false;
+                document.getElementById('speak1').disabled = false;
+
+                doRecording = true;
+                startTime = Date.now();
+
+                var chunks = [];
+                var stream = null;
+
+                navigator.mediaDevices.getUserMedia({audio: true, video: false})
+                    .then(function(s) {
+                        stream = s;
+                        mediaRecorder = new MediaRecorder(stream);
+                        mediaRecorder.ondataavailable = function(e) {
+                            chunks.push(e.data);
+
+                            var blob = new Blob(chunks, { 'type' : 'audio/ogg; codecs=opus' });
+                            var reader = new FileReader();
+
+                            reader.onload = function(event) {
+                                var buf = new Uint8Array(reader.result);
+
+                                if (!context) {
+                                    return;
+                                }
+                                context.decodeAudioData(buf.buffer, function(audioBuffer) {
+                                    var offlineContext = new OfflineAudioContext(audioBuffer.numberOfChannels, audioBuffer.length, audioBuffer.sampleRate);
+                                    var source = offlineContext.createBufferSource();
+                                    source.buffer = audioBuffer;
+                                    source.connect(offlineContext.destination);
+                                    source.start(0);
+
+                                    offlineContext.startRendering().then(function(renderedBuffer) {
+                                        audio = renderedBuffer.getChannelData(0);
+
+                                        //printTextarea('js: audio recorded, size: ' + audio.length + ', old size: ' + (audio0 == null ? 0 : audio0.length));
+
+                                        var audioAll = new Float32Array(audio0 == null ? audio.length : audio0.length + audio.length);
+                                        if (audio0 != null) {
+                                            audioAll.set(audio0, 0);
+                                        }
+                                        audioAll.set(audio, audio0 == null ? 0 : audio0.length);
+
+                                        if (instance) {
+                                            Module.set_audio(instance, audioAll);
+                                        }
+                                    });
+                                }, function(e) {
+                                    audio = null;
+                                });
+                            }
+
+                            reader.readAsArrayBuffer(blob);
+                        };
+
+                        mediaRecorder.onstop = function(e) {
+                            if (doRecording) {
+                                setTimeout(function() {
+                                    startRecording();
+                                });
+                            }
+                        };
+
+                        mediaRecorder.start(kIntervalAudio_ms);
+                    })
+                    .catch(function(err) {
+                        printTextarea('js: error getting audio stream: ' + err);
+                    });
+
+                var interval = setInterval(function() {
+                    if (!doRecording) {
+                        clearInterval(interval);
+                        mediaRecorder.stop();
+                        stream.getTracks().forEach(function(track) {
+                            track.stop();
+                        });
+
+                        document.getElementById('start').disabled = false;
+                        document.getElementById('stop').disabled = true;
+                        document.getElementById('speak1').disabled = true;
+
+                        mediaRecorder = null;
+                    }
+
+                    // if audio length is more than kRestartRecording_s seconds, restart recording
+                    if (audio != null && audio.length > kSampleRate*kRestartRecording_s) {
+                        if (doRecording) {
+                            //printTextarea('js: restarting recording');
+
+                            clearInterval(interval);
+                            audio0 = audio;
+                            audio = null;
+                            mediaRecorder.stop();
+                            stream.getTracks().forEach(function(track) {
+                                track.stop();
+                            });
+                        }
+                    }
+                }, 100);
+            }
+
+            //
+            // speak
+            //
+
+            function onSpeak(text) {
+                var voices = synth.getVoices();
+                var msg = new SpeechSynthesisUtterance(text);
+
+                if (voice == null) {
+                    voice = voices[0];
+                }
+
+                msg.voice = voice;
+                synth.speak(msg);
+
+                if (doRecording) {
+                    Module.set_status("speaking ...");
+                    printTextarea('js: speaking');
+                    stopRecording();
+                    var interval = setInterval(function() {
+                        if (!synth.speaking) {
+                            printTextarea('js: done speaking');
+                            clearInterval(interval);
+                            startRecording();
+                        } else {
+                            Module.set_status("");
+                        }
+                    }, 100);
+                }
+            }
+
+            function onSpeakRandom() {
+                Module.force_speak(instance);
+            }
+
+            //
+            // main
+            //
+
+            var intervalUpdate = null;
+
+            function onStart() {
+                if (!instance) {
+                    instance = Module.init('whisper.bin');
+
+                    if (instance) {
+                        printTextarea("js: whisper initialized, instance: " + instance);
+                    }
+                }
+
+                if (!instance) {
+                    printTextarea("js: failed to initialize whisper");
+                    return;
+                }
+
+                startRecording();
+
+                intervalUpdate = setInterval(function() {
+                    var textToSpeak = Module.get_text_to_speak();
+
+                    if (textToSpeak != null && textToSpeak.length > 1) {
+                        onSpeak(textToSpeak);
+                    }
+
+                    document.getElementById('state-status').innerHTML = Module.get_status();
+                    document.getElementById('state-context').innerHTML = Module.get_text_context();
+                }, 100);
+            }
+
+            function onStop() {
+                stopRecording();
+            }
+
+            function onVoiceChange() {
+                printTextarea('js: voice changed to: ' + document.getElementById('voice').value);
+                voice = synth.getVoices()[document.getElementById('voice').value];
+            }
+
+            function onPromptChange() {
+                let id = document.getElementById('prompt').value;
+                let personality = document.getElementById('prompt').options[id].text;
+                printTextarea('js: prompt changed to: ' + personality);
+
+                var prompt = '';
+
+                switch (id) {
+                    case '0':
+                        // Casual
+                        prompt = "\
+Hello, how are you?\n\
+I'm fine, thanks. How are you?\n\
+Thanks, I'm fine too. What are you doing?\n\
+I'm just sitting here.\n\
+It's a lovely day, isn't it?\n\
+Yes, it is. I love the weather this time of year.\n\
+I wish it would rain a little bit.\n\
+Me too.\n";
+                        break;
+                    case '1':
+                        // Robot
+                        prompt = "\
+Are you a robot?\n\
+Yes, I am.\n\
+Who created you?\n\
+I was created by a human.\n\
+What is your purpose?\n\
+My purpose is to talk to humans.\n\
+What is your favorite color?\n\
+My favorite color is blue.\n";
+                        break;
+                    case '2':
+                        // Scientist
+                        prompt = "\
+This scientific research is very interesting.\n\
+I agree.\n\
+What is your opinion on this?\n\
+I think it's very interesting.\n\
+Mathematics is a very interesting subject.\n\
+University is a very interesting place.\n\
+Quantum physics is the most complex subject.\n\
+I think so too.\n";
+                        break;
+                    case '3':
+                        // Programmer
+                        prompt = "\
+I'm a programmer.\n\
+I'm a programmer too.\n\
+What programming language do you use?\n\
+I use Python.\n\
+What is your favorite programming language?\n\
+My favorite programming language is C++.\n\
+What is your favorite editor?\n\
+My favorite editor is Vim.\n";
+                        break;
+                    case '4':
+                        // Happy
+                        prompt = "\
+I'm happy.\n\
+I'm happy too.\n\
+What makes you happy?\n\
+I'm happy because I have a lot of friends.\n\
+Friendship is the most important thing in life.\n\
+I agree.\n\
+What is your favorite color?\n\
+My favorite color is blue.\n";
+                        break;
+                    case '5':
+                        // Sad
+                        prompt = "\
+Today is a sad day.\n\
+I'm sad too.\n\
+What makes you sad?\n\
+I'm sad because I have no friends.\n\
+Do you want to be my friend?\n\
+Yes, I would like to be your friend.\n\
+What is your favorite color?\n\
+My favorite color is blue.\n";
+                        break;
+                    case '6':
+                        // Philosophical
+                        prompt = "\
+What is the meaning of life?\n\
+The meaning of life is to be happy.\n\
+What is the meaning of death?\n\
+Ergo, the meaning of death is to be sad.\n\
+Who created us?\n\
+We were created by God.\n\
+What is God?\n\
+God is the creator of the universe.\n";
+                        break;
+                    case '7':
+                        // Angry
+                        prompt = "\
+Aargh!\n\
+I am so angry right now!\n\
+What makes you angry?\n\
+This guy is so annoying.\n\
+Why are you so angry?\n\
+My computer is broken.\n\
+Why is your computer broken?\n\
+I spilled coffee on it.\n";
+                        break;
+                    case '8':
+                        // Funny
+                        prompt = "\
+What is the funniest thing you have ever heard?\n\
+I heard a joke the other day.\n\
+Tell me the joke.\n\
+What do you call a cow with no legs?\n\
+Ground beef.\n\
+Haha, that's funny.\n\
+You know what else is funny?\n\
+The sound of a duck.\n";
+                        break;
+                    case '9':
+                        // Poetic
+                        prompt = "\
+Roses are red, violets are blue.\n\
+I am a poet, and so are you.\n\
+What is your favorite poem?\n\
+I like the poem 'The Raven' by Edgar Allan Poe.\n\
+It's a very sad poem.\n\
+You inspired me to write a poem.\n\
+Can you write a poem for me?\n\
+I wrote a poem for you.\n";
+                        break;
+                    case '10':
+                        // Clever
+                        prompt = "\
+How many people can you fit in a Volkswagen?\n\
+Two in the front, three in the back.\n\
+What is the square root of 144?\n\
+Twelve.\n\
+What is the capital of France?\n\
+Paris.\n\
+Who is the president of the United States?\n\
+It depends on the year.\n";
+                        break;
+                    case '11':
+                        // Cute
+                        prompt = "\
+What is your favorite animal?\n\
+I like cats - they are cute.\n\
+Could you be any cuter?\n\
+Yes, I could be cuter.\n\
+Aghhh, you are so cute!\n\
+I am not cute, I am handsome!\n\
+You are so handsome!\n\
+Aww, you are so sweet!\n";
+                        break;
+                    case '12':
+                        // Smart
+                        prompt = "\
+Tell me the first 10 digits of pi.\n\
+3.1415926535\n\
+What is the speed of light?\n\
+299,792,458 meters per second.\n\
+What is the square root of 144?\n\
+Twelve.\n\
+What is the capital of France?\n\
+Paris.\n";
+                        break;
+                    case '13':
+                        // Dumb
+                        prompt = "\
+I am so dumb.\n\
+I am not dumb.\n\
+You are dumb.\n\
+No, I am not dumb.\n\
+You are dumb.\n\
+No, I am not dumb.\n\
+You are dumb.\n\
+No, I am not dumb.\n";
+                        break;
+                    case '14':
+                        // Boring
+                        prompt = "\
+Why are you so quiet today?\n\
+I am bored.\n\
+You haven't said anything in 10 minutes.\n\
+Leave me alone.\n\
+Stop being so boring.\n\
+Stop being so annoying.\n\
+My life is boring.\n\
+I am not interesting.\n";
+                        break;
+                    case '15':
+                        // Exciting
+                        prompt = "\
+What is the most exciting thing that has ever happened to you?\n\
+I went to the moon!\n\
+What did you do on the moon?\n\
+I played golf and drank champagne!\n\
+Did you see this new crazy, awesome movie?\n\
+Oh yes! I totally loved it!\n\
+We should buy a boat and go sailing!\n\
+Yes, let's go sailing!\n";
+                        break;
+                    case '16':
+                        // Interesting
+                        prompt = "\
+What is the most interesting thing you have ever seen?\n\
+I saw a UFO once in the sky.\n\
+Wow, this is so interesting! Tell me more!\n\
+It was a flying saucer.\n\
+What did it look like?\n\
+It was silver and had a red light on top.\n\
+What did it do?\n\
+It flew away.\n";
+                        break;
+                    case '17':
+                        // William Shakespear
+                        prompt = "\
+To be or not to be, that is the question.\n\
+Whether 't is nobler in the mind to suffer\n\
+The slings and arrows of outrageous fortune,\n\
+Or to take arms against a sea of troubles,\n\
+And by opposing end them? To die, to sleep,\n\
+No more; and by a sleep to say we end\n\
+The heart-ache and the thousand natural shocks\n\
+That flesh is heir to, 'tis a consummation.\n";
+                        break;
+                    case '18':
+                        // J.R.R. Tolkien
+                        prompt = "\
+In a hole in the ground there lived a hobbit.\n\
+Not a nasty, dirty, wet hole, filled with the ends of worms\n\
+and an oozy smell, nor yet a dry, bare, sandy hole with nothing in it\n\
+to sit down on or to eat: it was a hobbit-hole, and that means comfort.\n\
+It had a perfectly round door like a porthole, painted green,\n\
+with a shiny yellow brass knob in the exact middle.\n\
+The door opened on to a tube-shaped hall like a tunnel:\n";
+                        break;
+                    case '19':
+                        // George R.R. Martin
+                        prompt = "\
+A reader lives a thousand lives before he dies, said Jojen.\n\
+The man who never reads lives only one.\n\
+Theon Greyjoy had never been a reader.\n\
+Never forget what you are, for surely the world will not.\n\
+Make it your strength. Then it can never be your weaknessi\n\
+Armour yourself in it, and it will never be used to hurt you.\n\
+It was a lesson that Theon Greyjoy had never learned.\n\
+Theon Greyjoy had never been a reader.\n";
+                        break;
+                    case '20':
+                        // Stephen King
+                        prompt = "\
+The trust of the innocent is the liar's most useful tool.\n\
+The best way to keep a secret is from yourself.\n\
+Monsters are real, and ghosts are real too.\n\
+They live inside us, and sometimes, they win.\n\
+People think that I must be a very strange person.\n\
+They think that I sit around all day thinking up horrible things.\n\
+We make up horrors to help us cope with the real ones.\n\
+The only thing worse than a monster is a human monster.\n";
+                        break;
+                    default:
+                        prompt = "\
+Hello, how are you?\n\
+I'm fine, thanks. How are you?\n\
+Thanks, I'm fine too. What are you doing?\n\
+I'm just sitting here.\n\
+It's a lovely day, isn't it?\n\
+Yes, it is.\n\
+Did you know that I'm a robot?\n\
+I wasn't aware of that.\n";
+                        break;
+                }
+
+                Module.set_prompt(prompt);
+            }
+
+        </script>
+        <script type="text/javascript" src="talk.js"></script>
+    </body>
+</html>

examples/talk/.gitignore

@@ -0,0 +1 @@
+eleven-labs.py

examples/talk/CMakeLists.txt

@@ -0,0 +1,16 @@
+if (WHISPER_SUPPORT_SDL2)
+    # talk
+    set(TARGET talk)
+    #add_executable(${TARGET} talk.cpp gpt-2.cpp)
+    #target_include_directories(${TARGET} PRIVATE ${SDL2_INCLUDE_DIRS})
+    #target_link_libraries(${TARGET} PRIVATE whisper ${SDL2_LIBRARIES} ${CMAKE_THREAD_LIBS_INIT})
+
+    # TODO: this is temporary
+    #       need to export ggml symbols for MSVC, but too lazy ..
+    add_executable(${TARGET} talk.cpp gpt-2.cpp ../../ggml.c ../../whisper.cpp)
+
+    include(DefaultTargetOptions)
+
+    target_include_directories(${TARGET} PRIVATE ${SDL2_INCLUDE_DIRS} ../../)
+    target_link_libraries(${TARGET} PRIVATE ${SDL2_LIBRARIES} ${CMAKE_THREAD_LIBS_INIT})
+endif ()

examples/talk/README.md

@@ -0,0 +1,41 @@
+# talk
+
+Talk with an Artificial Intelligence in your terminal
+
+[Demo Talk](https://user-images.githubusercontent.com/1991296/206805012-48e71cc2-588d-4745-8798-c1c70ea3b40d.mp4)
+
+Web version: [examples/talk.wasm](/examples/talk.wasm)
+
+## Building
+
+The `talk` tool depends on SDL2 library to capture audio from the microphone. You can build it like this:
+
+```bash
+# Install SDL2 on Linux
+sudo apt-get install libsdl2-dev
+
+# Install SDL2 on Mac OS
+brew install sdl2
+
+# Build the "talk" executable
+make talk
+
+# Run it
+./talk -p Santa
+```
+
+## GPT-2
+
+To run this, you will need a ggml GPT-2 model: [instructions](https://github.com/ggerganov/ggml/tree/master/examples/gpt-2#downloading-and-converting-the-original-models)
+
+Alternatively, you can simply download the smallest ggml GPT-2 117M model (240 MB) like this:
+
+```
+wget --quiet --show-progress -O models/ggml-gpt-2-117M.bin https://huggingface.co/datasets/ggerganov/ggml/raw/main/ggml-model-gpt-2-117M.bin
+```
+
+## TTS
+
+For best experience, this example needs a TTS tool to convert the generated text responses to voice.
+You can use any TTS engine that you would like - simply edit the [speak.sh](speak.sh) script to your needs.
+By default, it is configured to use `espeak`, but you can use whatever you wish.

examples/talk/gpt-2.cpp

@@ -0,0 +1,923 @@
+#include "ggml.h"
+#include "gpt-2.h"
+
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <fstream>
+#include <map>
+#include <string>
+#include <thread>
+#include <vector>
+#include <regex>
+#include <random>
+
+/////////////////////// GPT-2 BEGIN /////////////////////////
+
+//
+// Vocab utils
+//
+
+std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text) {
+    std::vector<std::string> words;
+
+    // first split the text into words
+    {
+        std::string str = text;
+        std::string pat = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
+
+        std::regex re(pat);
+        std::smatch m;
+
+        while (std::regex_search(str, m, re)) {
+            for (auto x : m) {
+                words.push_back(x);
+            }
+            str = m.suffix();
+        }
+    }
+
+    // find the longest tokens that form the words:
+    std::vector<gpt_vocab::id> tokens;
+    for (const auto & word : words) {
+        if (word.empty()) continue;
+
+        int i = 0;
+        int n = word.size();
+        while (i < n) {
+            int j = n;
+            while (j > i) {
+                auto it = vocab.token_to_id.find(word.substr(i, j-i));
+                if (it != vocab.token_to_id.end()) {
+                    tokens.push_back(it->second);
+                    i = j;
+                    break;
+                }
+                --j;
+            }
+            if (i == n) {
+                break;
+            }
+            if (j == i) {
+                auto sub = word.substr(i, 1);
+                if (vocab.token_to_id.find(sub) != vocab.token_to_id.end()) {
+                    tokens.push_back(vocab.token_to_id.at(sub));
+                } else {
+                    fprintf(stderr, "%s: unknown token '%s'\n", __func__, sub.data());
+                }
+                ++i;
+            }
+        }
+    }
+
+    return tokens;
+}
+
+gpt_vocab::id gpt_sample_top_k_top_p(
+        const gpt_vocab & vocab,
+        const float * logits,
+        int    top_k,
+        double top_p,
+        double /*temp*/,
+        std::mt19937 & rng) {
+    int n_logits = vocab.id_to_token.size();
+
+    std::vector<std::pair<double, gpt_vocab::id>> logits_id;
+    logits_id.reserve(n_logits);
+
+    for (int i = 0; i < n_logits; i++) {
+        logits_id.emplace_back(logits[i], i);
+    }
+
+    // find the top K tokens
+    std::partial_sort(
+            logits_id.begin(),
+            logits_id.begin() + top_k, logits_id.end(),
+            [](const std::pair<double, gpt_vocab::id> & a, const std::pair<double, gpt_vocab::id> & b) {
+        return a.first > b.first;
+    });
+
+    logits_id.resize(top_k);
+
+    // normalize
+    {
+        double sum = 0.0f;
+        for (int i = 0; i < (int)logits_id.size(); i++) {
+            sum += logits_id[i].first;
+        }
+
+        sum = 1.0/sum;
+        for (int i = 0; i < (int)logits_id.size(); i++) {
+            logits_id[i].first *= sum;
+        }
+    }
+
+    if (top_p < 1.0f) {
+        {
+            double cumsum = 0.0f;
+            for (int i = 0; i < top_k; i++) {
+                cumsum += logits_id[i].first;
+                if (cumsum >= top_p) {
+                    logits_id.resize(i+1);
+                    break;
+                }
+            }
+        }
+
+        // normalize again
+        {
+            double sum = 0.0f;
+            for (int i = 0; i < (int)logits_id.size(); i++) {
+                sum += logits_id[i].first;
+            }
+
+            sum = 1.0/sum;
+            for (int i = 0; i < (int)logits_id.size(); i++) {
+                logits_id[i].first *= sum;
+            }
+        }
+    }
+
+    //printf("\n");
+    //for (int i = 0; i < (int) logits_id.size(); i++) {
+    //    printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), logits_id[i].first);
+    //}
+    //exit(0);
+
+    // sample from the obtained distribution
+    std::vector<double> probs;
+    probs.reserve(logits_id.size());
+
+    for (int i = 0; i < (int) logits_id.size(); i++) {
+        probs.push_back(logits_id[i].first);
+    }
+
+    std::discrete_distribution<> dist(probs.begin(), probs.end());
+    int idx = dist(rng);
+
+    return logits_id[idx].second;
+}
+
+// default hparams (GPT-2 117M)
+struct gpt2_hparams {
+    int32_t n_vocab = 50257;
+    int32_t n_ctx   = 1024;
+    int32_t n_embd  = 768;
+    int32_t n_head  = 12;
+    int32_t n_layer = 12;
+    int32_t f16     = 1;
+};
+
+struct gpt2_layer {
+    // normalization
+    struct ggml_tensor * ln_1_g;
+    struct ggml_tensor * ln_1_b;
+
+    struct ggml_tensor * ln_2_g;
+    struct ggml_tensor * ln_2_b;
+
+    // attention
+    struct ggml_tensor * c_attn_attn_w;
+    struct ggml_tensor * c_attn_attn_b;
+
+    struct ggml_tensor * c_attn_proj_w;
+    struct ggml_tensor * c_attn_proj_b;
+
+    // mlp
+    struct ggml_tensor * c_mlp_fc_w;
+    struct ggml_tensor * c_mlp_fc_b;
+
+    struct ggml_tensor * c_mlp_proj_w_trans; // transposed for efficiency
+    struct ggml_tensor * c_mlp_proj_b;
+};
+
+struct gpt2_model {
+    gpt2_hparams hparams;
+
+    // normalization
+    struct ggml_tensor * ln_f_g;
+    struct ggml_tensor * ln_f_b;
+
+    struct ggml_tensor * wte; // position embedding
+    struct ggml_tensor * wpe; //    token embedding
+
+    std::vector<gpt2_layer> layers;
+
+    // key + value memory
+    struct ggml_tensor * memory_k;
+    struct ggml_tensor * memory_v;
+
+    //
+    struct ggml_context * ctx;
+    std::map<std::string, struct ggml_tensor *> tensors;
+};
+
+// load the model's weights from a file
+bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab & vocab) {
+    printf("%s: loading model from '%s'\n", __func__, fname.c_str());
+
+    auto fin = std::ifstream(fname, std::ios::binary);
+    if (!fin) {
+        fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
+        return false;
+    }
+
+    // verify magic
+    {
+        uint32_t magic;
+        fin.read((char *) &magic, sizeof(magic));
+        if (magic != 0x67676d6c) {
+            fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
+            return false;
+        }
+    }
+
+    // load hparams
+    {
+        auto & hparams = model.hparams;
+
+        fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
+        fin.read((char *) &hparams.n_ctx,   sizeof(hparams.n_ctx));
+        fin.read((char *) &hparams.n_embd,  sizeof(hparams.n_embd));
+        fin.read((char *) &hparams.n_head,  sizeof(hparams.n_head));
+        fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
+        fin.read((char *) &hparams.f16,     sizeof(hparams.f16));
+
+        printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
+        printf("%s: n_ctx   = %d\n", __func__, hparams.n_ctx);
+        printf("%s: n_embd  = %d\n", __func__, hparams.n_embd);
+        printf("%s: n_head  = %d\n", __func__, hparams.n_head);
+        printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
+        printf("%s: f16     = %d\n", __func__, hparams.f16);
+    }
+
+    // load vocab
+    {
+        int32_t n_vocab = 0;
+        fin.read((char *) &n_vocab, sizeof(n_vocab));
+
+        if (n_vocab != model.hparams.n_vocab) {
+            fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
+                    __func__, fname.c_str(), n_vocab, model.hparams.n_vocab);
+            return false;
+        }
+
+        std::string word;
+        for (int i = 0; i < n_vocab; i++) {
+            uint32_t len;
+            fin.read((char *) &len, sizeof(len));
+
+            word.resize(len);
+            fin.read((char *) &word[0], len);
+
+            vocab.token_to_id[word] = i;
+            vocab.id_to_token[i] = word;
+        }
+    }
+
+    // for the big tensors, we have the option to store the data in 16-bit floats
+    // in order to save memory and also to speed up the computation
+    const ggml_type wtype = model.hparams.f16 ? GGML_TYPE_F16 : GGML_TYPE_F32;
+
+    auto & ctx = model.ctx;
+
+    size_t ctx_size = 0;
+
+    {
+        const auto & hparams = model.hparams;
+
+        const int n_embd  = hparams.n_embd;
+        const int n_layer = hparams.n_layer;
+        const int n_ctx   = hparams.n_ctx;
+        const int n_vocab = hparams.n_vocab;
+
+        ctx_size += n_embd*ggml_type_size(GGML_TYPE_F32); // ln_f_g
+        ctx_size += n_embd*ggml_type_size(GGML_TYPE_F32); // ln_f_b
+
+        ctx_size += n_vocab*n_embd*ggml_type_size(wtype);         // wte
+        ctx_size +=   n_ctx*n_embd*ggml_type_size(GGML_TYPE_F32); // wpe
+
+        ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_1_g
+        ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_1_b
+
+        ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_2_g
+        ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_2_b
+
+        ctx_size += n_layer*(3*n_embd*n_embd*ggml_type_size(wtype));         // c_attn_attn_w
+        ctx_size += n_layer*(       3*n_embd*ggml_type_size(GGML_TYPE_F32)); // c_attn_attn_b
+
+        ctx_size += n_layer*(n_embd*n_embd*ggml_type_size(wtype));           // c_attn_proj_w
+        ctx_size += n_layer*(       n_embd*ggml_type_size(GGML_TYPE_F32));   // c_attn_proj_b
+
+        ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_size(wtype));         // c_mlp_fc_w
+        ctx_size += n_layer*(       4*n_embd*ggml_type_size(GGML_TYPE_F32)); // c_mlp_fc_b
+
+        ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_size(wtype));         // c_mlp_proj_w
+        ctx_size += n_layer*(         n_embd*ggml_type_size(GGML_TYPE_F32)); // c_mlp_proj_b
+
+        ctx_size += n_ctx*n_layer*n_embd*ggml_type_size(GGML_TYPE_F32); // memory_k
+        ctx_size += n_ctx*n_layer*n_embd*ggml_type_size(GGML_TYPE_F32); // memory_v
+
+        ctx_size += (6 + 12*n_layer)*256; // object overhead
+
+        printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
+    }
+
+    // create the ggml context
+    {
+        struct ggml_init_params params;
+        params.mem_size   = ctx_size;
+        params.mem_buffer = nullptr;
+
+        model.ctx = ggml_init(params);
+        if (!model.ctx) {
+            fprintf(stderr, "%s: ggml_init() failed\n", __func__);
+            return false;
+        }
+    }
+
+    // prepare memory for the weights
+    {
+        const auto & hparams = model.hparams;
+
+        const int n_embd  = hparams.n_embd;
+        const int n_layer = hparams.n_layer;
+        const int n_ctx   = hparams.n_ctx;
+        const int n_vocab = hparams.n_vocab;
+
+        model.layers.resize(n_layer);
+
+        model.ln_f_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+        model.ln_f_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+
+        model.wte = ggml_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);
+        model.wpe = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ctx);
+
+        // map by name
+        model.tensors["model/ln_f/g"] = model.ln_f_g;
+        model.tensors["model/ln_f/b"] = model.ln_f_b;
+
+        model.tensors["model/wte"] = model.wte;
+        model.tensors["model/wpe"] = model.wpe;
+
+        for (int i = 0; i < n_layer; ++i) {
+            auto & layer = model.layers[i];
+
+            layer.ln_1_g             = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
+            layer.ln_1_b             = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
+
+            layer.ln_2_g             = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
+            layer.ln_2_b             = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
+
+            layer.c_attn_attn_w      = ggml_new_tensor_2d(ctx, wtype,         3*n_embd, n_embd);
+            layer.c_attn_attn_b      = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 3*n_embd);
+
+            layer.c_attn_proj_w      = ggml_new_tensor_2d(ctx, wtype,           n_embd, n_embd);
+            layer.c_attn_proj_b      = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
+
+            layer.c_mlp_fc_w         = ggml_new_tensor_2d(ctx, wtype,         4*n_embd, n_embd);
+            layer.c_mlp_fc_b         = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_embd);
+
+            layer.c_mlp_proj_w_trans = ggml_new_tensor_2d(ctx, wtype,         4*n_embd, n_embd);
+            layer.c_mlp_proj_b       = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
+
+            // map by name
+            model.tensors["model/h" + std::to_string(i) + "/ln_1/g"]        = layer.ln_1_g;
+            model.tensors["model/h" + std::to_string(i) + "/ln_1/b"]        = layer.ln_1_b;
+
+            model.tensors["model/h" + std::to_string(i) + "/ln_2/g"]        = layer.ln_2_g;
+            model.tensors["model/h" + std::to_string(i) + "/ln_2/b"]        = layer.ln_2_b;
+
+            model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/w"] = layer.c_attn_attn_w;
+            model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/b"] = layer.c_attn_attn_b;
+
+            model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/w"] = layer.c_attn_proj_w;
+            model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/b"] = layer.c_attn_proj_b;
+
+            model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/w"]    = layer.c_mlp_fc_w;
+            model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/b"]    = layer.c_mlp_fc_b;
+
+            model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/w"]  = layer.c_mlp_proj_w_trans;
+            model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/b"]  = layer.c_mlp_proj_b;
+        }
+    }
+
+    // key + value memory
+    {
+        const auto & hparams = model.hparams;
+
+        const int n_embd  = hparams.n_embd;
+        const int n_layer = hparams.n_layer;
+        const int n_ctx   = hparams.n_ctx;
+
+        const int n_mem      = n_layer*n_ctx;
+        const int n_elements = n_embd*n_mem;
+
+        model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
+        model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
+
+        const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
+
+        printf("%s: memory size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
+    }
+
+    // load weights
+    {
+        size_t total_size = 0;
+
+        while (true) {
+            int32_t n_dims;
+            int32_t length;
+            int32_t ftype;
+
+            fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
+            fin.read(reinterpret_cast<char *>(&length), sizeof(length));
+            fin.read(reinterpret_cast<char *>(&ftype),  sizeof(ftype));
+
+            if (fin.eof()) {
+                break;
+            }
+
+            int32_t nelements = 1;
+            int32_t ne[2] = { 1, 1 };
+            for (int i = 0; i < n_dims; ++i) {
+                fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
+                nelements *= ne[i];
+            }
+
+            std::string name(length, 0);
+            fin.read(&name[0], length);
+
+            if (model.tensors.find(name) == model.tensors.end()) {
+                fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
+                return false;
+            }
+
+            auto tensor = model.tensors[name.data()];
+            if (ggml_nelements(tensor) != nelements) {
+                fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
+                return false;
+            }
+
+            if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
+                fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
+                        __func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
+                return false;
+            }
+
+            const size_t bpe = (ftype == 0) ? sizeof(float) : sizeof(ggml_fp16_t);
+
+            if (nelements*bpe != ggml_nbytes(tensor)) {
+                fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
+                        __func__, name.data(), ggml_nbytes(tensor), nelements*bpe);
+                return false;
+            }
+
+            fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
+
+            //printf("%24s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
+            total_size += ggml_nbytes(tensor);
+        }
+
+        printf("%s: model size  = %8.2f MB\n", __func__, total_size/1024.0/1024.0);
+    }
+
+    fin.close();
+
+    return true;
+}
+
+// evaluate the transformer
+//
+//   - model:     the model
+//   - n_threads: number of threads to use
+//   - n_past:    the context size so far
+//   - embd_inp:  the embeddings of the tokens in the context
+//   - embd_w:    the predicted probabilities of the next token
+//
+bool gpt2_eval(
+        const gpt2_model & model,
+        const int n_threads,
+        const int n_past,
+        const std::vector<gpt_vocab::id> & embd_inp,
+              std::vector<float>         & embd_w,
+              size_t                     & mem_per_token) {
+    const int N = embd_inp.size();
+
+    const auto & hparams = model.hparams;
+
+    const int n_embd  = hparams.n_embd;
+    const int n_layer = hparams.n_layer;
+    const int n_ctx   = hparams.n_ctx;
+    const int n_head  = hparams.n_head;
+    const int n_vocab = hparams.n_vocab;
+
+    static size_t buf_size = 5640ull*1024*1024;
+    static void * buf = malloc(buf_size);
+
+    if (mem_per_token > 0 && mem_per_token*N > buf_size) {
+        const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead
+        printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
+
+        // reallocate
+        buf_size = buf_size_new;
+        buf = realloc(buf, buf_size);
+        if (buf == nullptr) {
+            fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
+            return false;
+        }
+    }
+
+    struct ggml_init_params params;
+    params.mem_size   = buf_size;
+    params.mem_buffer = buf;
+
+    struct ggml_context * ctx0 = ggml_init(params);
+
+    struct ggml_cgraph gf = { };
+    gf.n_threads = n_threads;
+
+    struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
+    memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));
+
+    struct ggml_tensor * position = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
+    for (int i = 0; i < N; ++i) {
+        ((int32_t *) position->data)[i] = n_past + i;
+    }
+
+    // wte + wpe
+    struct ggml_tensor * inpL =
+        ggml_add(ctx0,
+                ggml_get_rows(ctx0, model.wte, embd),
+                ggml_get_rows(ctx0, model.wpe, position));
+
+    for (int il = 0; il < n_layer; ++il) {
+        struct ggml_tensor * cur;
+
+        // norm
+        {
+            // [ 768, N]
+            cur = ggml_norm(ctx0, inpL);
+
+            // cur = ln_1_g*cur + ln_1_b
+            // [ 768, N]
+            cur = ggml_add(ctx0,
+                    ggml_mul(ctx0,
+                        ggml_repeat(ctx0, model.layers[il].ln_1_g, cur),
+                        cur),
+                    ggml_repeat(ctx0, model.layers[il].ln_1_b, cur));
+        }
+
+        // attn
+        // [2304, 768] - model.layers[il].c_attn_attn_w
+        // [2304,   1] - model.layers[il].c_attn_attn_b
+        // [ 768,   N] - cur (in)
+        // [2304,   N] - cur (out)
+        //
+        // cur = attn_w*cur + attn_b
+        // [2304, N]
+        {
+            cur = ggml_mul_mat(ctx0,
+                    ggml_transpose(ctx0, model.layers[il].c_attn_attn_w),
+                    cur);
+
+            cur = ggml_add(ctx0,
+                    ggml_repeat(ctx0, model.layers[il].c_attn_attn_b, cur),
+                    cur);
+        }
+
+        // self-attention
+        {
+            struct ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0*sizeof(float)*n_embd);
+            struct ggml_tensor * Kcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1*sizeof(float)*n_embd);
+            struct ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2*sizeof(float)*n_embd);
+
+            // store key and value to memory
+            if (N >= 1) {
+                struct ggml_tensor * k = ggml_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past));
+                struct ggml_tensor * v = ggml_view_1d(ctx0, model.memory_v, N*n_embd, (ggml_element_size(model.memory_v)*n_embd)*(il*n_ctx + n_past));
+
+                ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
+                ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
+            }
+
+            // Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
+            // [64, N, 12]
+            struct ggml_tensor * Q =
+                ggml_permute(ctx0,
+                        ggml_cpy(ctx0,
+                            Qcur,
+                            ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd/n_head, n_head, N)),
+                        0, 2, 1, 3);
+
+            // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
+            // [64, n_past + N, 12]
+            struct ggml_tensor * K =
+                ggml_permute(ctx0,
+                        ggml_reshape_3d(ctx0,
+                            ggml_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_k)*n_embd),
+                            n_embd/n_head, n_head, n_past + N),
+                        0, 2, 1, 3);
+
+            // GG: flash attention
+            //struct ggml_tensor * V =
+            //    ggml_cpy(ctx0,
+            //            ggml_permute(ctx0,
+            //                ggml_reshape_3d(ctx0,
+            //                    ggml_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_v)*n_embd),
+            //                    n_embd/n_head, n_head, n_past + N),
+            //                1, 2, 0, 3),
+            //            ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_past + N, n_embd/n_head, n_head));
+
+            //struct ggml_tensor * KQV = ggml_flash_attn(ctx0, Q, K, V, true);
+
+            // K * Q
+            // [n_past + N, N, 12]
+            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
+
+            // KQ_scaled = KQ / sqrt(n_embd/n_head)
+            // [n_past + N, N, 12]
+            struct ggml_tensor * KQ_scaled =
+                ggml_scale(ctx0,
+                        KQ,
+                        ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
+                        );
+
+            // KQ_masked = mask_past(KQ_scaled)
+            // [n_past + N, N, 12]
+            struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
+
+            // KQ = soft_max(KQ_masked)
+            // [n_past + N, N, 12]
+            struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
+
+            // V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
+            // [n_past + N, 64, 12]
+            struct ggml_tensor * V_trans =
+                ggml_permute(ctx0,
+                        ggml_reshape_3d(ctx0,
+                            ggml_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_v)*n_embd),
+                            n_embd/n_head, n_head, n_past + N),
+                        1, 2, 0, 3);
+
+            // KQV = transpose(V) * KQ_soft_max
+            // [64, N, 12]
+            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);
+
+            // KQV_merged = KQV.permute(0, 2, 1, 3)
+            // [64, 12, N]
+            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
+
+            // cur = KQV_merged.contiguous().view(n_embd, N)
+            // [768, N]
+            cur = ggml_cpy(ctx0,
+                    KQV_merged,
+                    ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
+        }
+
+        // projection
+        // [ 768, 768] - model.layers[il].c_attn_proj_w
+        // [ 768,   1] - model.layers[il].c_attn_proj_b
+        // [ 768,   N] - cur (in)
+        // [ 768,   N] - cur (out)
+        //
+        // cur = proj_w*cur + proj_b
+        // [768, N]
+        {
+            cur = ggml_mul_mat(ctx0,
+                    ggml_transpose(ctx0, model.layers[il].c_attn_proj_w),
+                    cur);
+
+            cur = ggml_add(ctx0,
+                    ggml_repeat(ctx0, model.layers[il].c_attn_proj_b, cur),
+                    cur);
+        }
+
+        // add the input
+        cur = ggml_add(ctx0, cur, inpL);
+
+        struct ggml_tensor * inpFF = cur;
+
+        // feed-forward network
+        {
+            // norm
+            {
+                cur = ggml_norm(ctx0, inpFF);
+
+                // cur = ln_2_g*cur + ln_2_b
+                // [ 768, N]
+                cur = ggml_add(ctx0,
+                        ggml_mul(ctx0,
+                            ggml_repeat(ctx0, model.layers[il].ln_2_g, cur),
+                            cur),
+                        ggml_repeat(ctx0, model.layers[il].ln_2_b, cur));
+            }
+
+            // fully connected
+            // [3072, 768] - model.layers[il].c_mlp_fc_w
+            // [3072,   1] - model.layers[il].c_mlp_fc_b
+            // [ 768,   N] - cur (in)
+            // [3072,   N] - cur (out)
+            //
+            // cur = fc_w*cur + fc_b
+            // [3072, N]
+            cur = ggml_mul_mat(ctx0,
+                    ggml_transpose(ctx0, model.layers[il].c_mlp_fc_w),
+                    cur);
+
+            cur = ggml_add(ctx0,
+                    ggml_repeat(ctx0, model.layers[il].c_mlp_fc_b, cur),
+                    cur);
+
+            // GELU activation
+            // [3072, N]
+            cur = ggml_gelu(ctx0, cur);
+
+            // projection
+            // [ 768, 3072] - model.layers[il].c_mlp_proj_w
+            // [ 768,    1] - model.layers[il].c_mlp_proj_b
+            // [3072,    N] - cur (in)
+            // [ 768,    N] - cur (out)
+            //
+            // cur = proj_w*cur + proj_b
+            // [768, N]
+            cur = ggml_mul_mat(ctx0,
+                    model.layers[il].c_mlp_proj_w_trans,
+                    cur);
+
+            cur = ggml_add(ctx0,
+                    ggml_repeat(ctx0, model.layers[il].c_mlp_proj_b, cur),
+                    cur);
+        }
+
+        // input for next layer
+        inpL = ggml_add(ctx0, cur, inpFF);
+    }
+
+    // norm
+    {
+        // [ 768, N]
+        inpL = ggml_norm(ctx0, inpL);
+
+        // inpL = ln_f_g*inpL + ln_f_b
+        // [ 768, N]
+        inpL = ggml_add(ctx0,
+                ggml_mul(ctx0,
+                    ggml_repeat(ctx0, model.ln_f_g, inpL),
+                    inpL),
+                ggml_repeat(ctx0, model.ln_f_b, inpL));
+    }
+
+    // inpL = WTE * inpL
+    // [ 768, 50257] - model.wte
+    // [ 768, N]     - inpL
+    inpL = ggml_mul_mat(ctx0, model.wte, inpL);
+
+    // logits -> probs
+    inpL = ggml_soft_max(ctx0, inpL);
+
+    // run the computation
+    ggml_build_forward_expand(&gf, inpL);
+    ggml_graph_compute       (ctx0, &gf);
+
+    //if (n_past%100 == 0) {
+    //    ggml_graph_print   (&gf);
+    //    ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot");
+    //}
+
+    //embd_w.resize(n_vocab*N);
+    //memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
+
+    // return result for just the last token
+    embd_w.resize(n_vocab);
+    memcpy(embd_w.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
+
+    if (mem_per_token == 0) {
+        mem_per_token = ggml_used_mem(ctx0)/N;
+    }
+    //printf("used_mem = %zu\n", ggml_used_mem(ctx0));
+
+    ggml_free(ctx0);
+
+    return true;
+}
+
+/////////////////////////////// GPT-2 END ////////////////////////////////
+
+constexpr int N_THREAD = 8;
+
+struct gpt2_context {
+    std::string prompt_base = R"(Hello, how are you?
+I'm fine, thanks. How are you?
+Thanks, I'm fine too. What are you doing?
+I'm just sitting here.
+It's a lovely day, isn't it?
+Yes, it is. I love the weather this time of year.
+I wish it would rain a little bit.
+Me too.
+)";
+
+    std::mt19937 rng;
+
+    gpt_vocab vocab;
+    gpt2_model model;
+
+    int32_t n_threads = std::min(N_THREAD, (int) std::thread::hardware_concurrency());
+
+    // sampling parameters
+    int32_t top_k = 5;
+    float   top_p = 0.9f;
+    float   temp  = 1.0f;
+};
+
+struct gpt2_context * gpt2_init(const char * path_model) {
+    gpt2_context * ctx = new gpt2_context;
+
+    ctx->rng = std::mt19937(time(nullptr));
+
+    // load the model
+    {
+        const int64_t t_start_us = ggml_time_us();
+
+        if (!gpt2_model_load(path_model, ctx->model, ctx->vocab)) {
+            fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, path_model);
+            delete ctx;
+            return nullptr;
+        }
+
+        const int64_t t_load_us = ggml_time_us() - t_start_us;
+
+        printf("gpt-2: model loaded in %d ms\n", (int) (t_load_us/1000));
+    }
+
+    return ctx;
+}
+
+void gpt2_free(struct gpt2_context * ctx) {
+    delete ctx;
+}
+
+const char * gpt2_get_prompt(struct gpt2_context * ctx) {
+    return ctx->prompt_base.c_str();
+}
+
+void gpt2_set_prompt(struct gpt2_context * ctx, const char * prompt) {
+    ctx->prompt_base = prompt;
+}
+
+std::vector<gpt_vocab::id> gpt2_tokenize(const gpt2_context * ctx, const char * text) {
+    return ::gpt_tokenize(ctx->vocab, text);
+}
+
+std::string gpt2_gen_text(gpt2_context * ctx, const char * text, int max_tokens) {
+    int n_past = 0;
+
+    std::vector<float> embd_w;
+
+    // tokenize the prompt
+    std::vector<gpt_vocab::id> embd_inp = ::gpt2_tokenize(ctx, text);
+
+    int n_predict = std::min(max_tokens, ctx->model.hparams.n_ctx - (int) embd_inp.size());
+
+    std::vector<gpt_vocab::id> embd = embd_inp;
+
+    size_t mem_per_token = 3000000;
+
+    std::string result;
+
+    for (int i = embd.size(); i < (int) embd_inp.size() + n_predict; i++) {
+        // predict
+        if (!embd.empty()) {
+            if (!gpt2_eval(ctx->model, ctx->n_threads, n_past, embd, embd_w, mem_per_token)) {
+                printf("gpt-2: failed to generate text\n");
+                return "";
+            }
+        }
+
+        n_past += embd.size();
+        embd.clear();
+
+        {
+            // sample next token
+            const int   top_k = ctx->top_k;
+            const float top_p = ctx->top_p;
+            const float temp  = ctx->temp;
+
+            const int n_vocab = ctx->model.hparams.n_vocab;
+
+            const gpt_vocab::id id = gpt_sample_top_k_top_p(ctx->vocab, embd_w.data() + (embd_w.size() - n_vocab), top_k, top_p, temp, ctx->rng);
+
+            // add it to the context
+            embd.push_back(id);
+        }
+
+        result += ctx->vocab.id_to_token[embd[0]];
+
+        // end of text token
+        if (embd.back() == 50256) {
+            break;
+        }
+    }
+
+    return result;
+}

examples/talk/gpt-2.h

@@ -0,0 +1,27 @@
+#pragma once
+
+// TODO: Change to C-style API and move to ./examples for easy reuse.
+
+#include <vector>
+#include <map>
+#include <string>
+
+struct gpt_vocab {
+    using id    = int32_t;
+    using token = std::string;
+
+    std::map<token, id> token_to_id;
+    std::map<id, token> id_to_token;
+};
+
+struct gpt2_context;
+
+struct gpt2_context * gpt2_init(const char * path_model);
+void gpt2_free(struct gpt2_context * ctx);
+
+const char * gpt2_get_prompt(struct gpt2_context * ctx);
+void gpt2_set_prompt(struct gpt2_context * ctx, const char * prompt);
+
+std::vector<gpt_vocab::id> gpt2_tokenize(const gpt2_context * ctx, const char * text);
+
+std::string gpt2_gen_text(gpt2_context * ctx, const char * text, int max_tokens);

examples/talk/speak.sh

@@ -0,0 +1,17 @@
+#!/bin/bash
+
+# Usage:
+#  speak.sh <voice_id> <text-to-speak>
+
+# espeak
+# Mac OS: brew install espeak
+# Linux: apt-get install espeak
+#
+espeak -v en-us+m$1 -s 175 -p 50 -a 200 -g 5 -k 5 "$2"
+
+# Eleven Labs
+#
+#wd=$(dirname $0)
+#script=$wd/eleven-labs.py
+#python3 $script $1 "$2"
+#ffplay -autoexit -nodisp -loglevel quiet -hide_banner -i ./audio.mp3

examples/talk/talk.cpp

@@ -0,0 +1,695 @@
+// Talk with AI
+//
+
+#include "whisper.h"
+#include "gpt-2.h"
+
+#include <SDL.h>
+#include <SDL_audio.h>
+
+#include <cassert>
+#include <cstdio>
+#include <fstream>
+#include <mutex>
+#include <regex>
+#include <string>
+#include <thread>
+#include <vector>
+#include <regex>
+
+// command-line parameters
+struct whisper_params {
+    int32_t n_threads  = std::min(4, (int32_t) std::thread::hardware_concurrency());
+    int32_t voice_ms   = 10000;
+    int32_t capture_id = -1;
+    int32_t max_tokens = 32;
+    int32_t audio_ctx  = 0;
+
+    float vad_thold    = 0.6f;
+    float freq_thold   = 100.0f;
+
+    bool speed_up      = false;
+    bool translate     = false;
+    bool print_special = false;
+    bool print_energy  = false;
+    bool no_timestamps = true;
+
+    std::string person    = "Santa";
+    std::string language  = "en";
+    std::string model_wsp = "models/ggml-base.en.bin";
+    std::string model_gpt = "models/ggml-gpt-2-117M.bin";
+    std::string speak     = "./examples/talk/speak.sh";
+    std::string fname_out;
+};
+
+void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
+
+bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
+    for (int i = 1; i < argc; i++) {
+        std::string arg = argv[i];
+
+        if (arg == "-h" || arg == "--help") {
+            whisper_print_usage(argc, argv, params);
+            exit(0);
+        }
+        else if (arg == "-t"   || arg == "--threads")       { params.n_threads     = std::stoi(argv[++i]); }
+        else if (arg == "-vms" || arg == "--voice-ms")      { params.voice_ms      = std::stoi(argv[++i]); }
+        else if (arg == "-c"   || arg == "--capture")       { params.capture_id    = std::stoi(argv[++i]); }
+        else if (arg == "-mt"  || arg == "--max-tokens")    { params.max_tokens    = std::stoi(argv[++i]); }
+        else if (arg == "-ac"  || arg == "--audio-ctx")     { params.audio_ctx     = std::stoi(argv[++i]); }
+        else if (arg == "-vth" || arg == "--vad-thold")     { params.vad_thold     = std::stof(argv[++i]); }
+        else if (arg == "-fth" || arg == "--freq-thold")    { params.freq_thold    = std::stof(argv[++i]); }
+        else if (arg == "-su"  || arg == "--speed-up")      { params.speed_up      = true; }
+        else if (arg == "-tr"  || arg == "--translate")     { params.translate     = true; }
+        else if (arg == "-ps"  || arg == "--print-special") { params.print_special = true; }
+        else if (arg == "-pe"  || arg == "--print-energy")  { params.print_energy  = true; }
+        else if (arg == "-p"   || arg == "--person")        { params.person        = argv[++i]; }
+        else if (arg == "-l"   || arg == "--language")      { params.language      = argv[++i]; }
+        else if (arg == "-mw"  || arg == "--model-whisper") { params.model_wsp     = argv[++i]; }
+        else if (arg == "-mg"  || arg == "--model-gpt")     { params.model_gpt     = argv[++i]; }
+        else if (arg == "-s"   || arg == "--speak")         { params.speak         = argv[++i]; }
+        else if (arg == "-f"   || arg == "--file")          { params.fname_out     = argv[++i]; }
+        else {
+            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
+            whisper_print_usage(argc, argv, params);
+            exit(0);
+        }
+    }
+
+    return true;
+}
+
+void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & params) {
+    fprintf(stderr, "\n");
+    fprintf(stderr, "usage: %s [options]\n", argv[0]);
+    fprintf(stderr, "\n");
+    fprintf(stderr, "options:\n");
+    fprintf(stderr, "  -h,       --help          [default] show this help message and exit\n");
+    fprintf(stderr, "  -t N,     --threads N     [%-7d] number of threads to use during computation\n", params.n_threads);
+    fprintf(stderr, "  -vms N,   --voice-ms N    [%-7d] voice duration in milliseconds\n",              params.voice_ms);
+    fprintf(stderr, "  -c ID,    --capture ID    [%-7d] capture device ID\n",                           params.capture_id);
+    fprintf(stderr, "  -mt N,    --max-tokens N  [%-7d] maximum number of tokens per audio chunk\n",    params.max_tokens);
+    fprintf(stderr, "  -ac N,    --audio-ctx N   [%-7d] audio context size (0 - all)\n",                params.audio_ctx);
+    fprintf(stderr, "  -vth N,   --vad-thold N   [%-7.2f] voice activity detection threshold\n",        params.vad_thold);
+    fprintf(stderr, "  -fth N,   --freq-thold N  [%-7.2f] high-pass frequency cutoff\n",                params.freq_thold);
+    fprintf(stderr, "  -su,      --speed-up      [%-7s] speed up audio by x2 (reduced accuracy)\n",     params.speed_up ? "true" : "false");
+    fprintf(stderr, "  -tr,      --translate     [%-7s] translate from source language to english\n",   params.translate ? "true" : "false");
+    fprintf(stderr, "  -ps,      --print-special [%-7s] print special tokens\n",                        params.print_special ? "true" : "false");
+    fprintf(stderr, "  -pe,      --print-energy  [%-7s] print sound energy (for debugging)\n",          params.print_energy ? "true" : "false");
+    fprintf(stderr, "  -p NAME,  --person NAME   [%-7s] person name (for prompt selection)\n",          params.person.c_str());
+    fprintf(stderr, "  -l LANG,  --language LANG [%-7s] spoken language\n",                             params.language.c_str());
+    fprintf(stderr, "  -mw FILE, --model-whisper [%-7s] whisper model file\n",                          params.model_wsp.c_str());
+    fprintf(stderr, "  -mg FILE, --model-gpt     [%-7s] gpt model file\n",                              params.model_gpt.c_str());
+    fprintf(stderr, "  -s FILE,  --speak TEXT    [%-7s] command for TTS\n",                             params.speak.c_str());
+    fprintf(stderr, "  -f FNAME, --file FNAME    [%-7s] text output file name\n",                       params.fname_out.c_str());
+    fprintf(stderr, "\n");
+}
+
+//
+// SDL Audio capture
+//
+
+class audio_async {
+public:
+    audio_async(int len_ms);
+    ~audio_async();
+
+    bool init(int capture_id, int sample_rate);
+
+    // start capturing audio via the provided SDL callback
+    // keep last len_ms seconds of audio in a circular buffer
+    bool resume();
+    bool pause();
+    bool clear();
+
+    // callback to be called by SDL
+    void callback(uint8_t * stream, int len);
+
+    // get audio data from the circular buffer
+    void get(int ms, std::vector<float> & audio);
+
+private:
+    SDL_AudioDeviceID m_dev_id_in = 0;
+
+    int m_len_ms = 0;
+    int m_sample_rate = 0;
+
+    bool       m_running = false;
+    std::mutex m_mutex;
+
+    std::vector<float> m_audio;
+    std::vector<float> m_audio_new;
+    size_t             m_audio_pos = 0;
+    size_t             m_audio_len = 0;
+};
+
+audio_async::audio_async(int len_ms) {
+    m_len_ms = len_ms;
+}
+
+audio_async::~audio_async() {
+    if (m_dev_id_in) {
+        SDL_CloseAudioDevice(m_dev_id_in);
+    }
+}
+
+bool audio_async::init(int capture_id, int sample_rate) {
+    SDL_LogSetPriority(SDL_LOG_CATEGORY_APPLICATION, SDL_LOG_PRIORITY_INFO);
+
+    if (SDL_Init(SDL_INIT_AUDIO) < 0) {
+        SDL_LogError(SDL_LOG_CATEGORY_APPLICATION, "Couldn't initialize SDL: %s\n", SDL_GetError());
+        return false;
+    }
+
+    SDL_SetHintWithPriority(SDL_HINT_AUDIO_RESAMPLING_MODE, "medium", SDL_HINT_OVERRIDE);
+
+    {
+        int nDevices = SDL_GetNumAudioDevices(SDL_TRUE);
+        fprintf(stderr, "%s: found %d capture devices:\n", __func__, nDevices);
+        for (int i = 0; i < nDevices; i++) {
+            fprintf(stderr, "%s:    - Capture device #%d: '%s'\n", __func__, i, SDL_GetAudioDeviceName(i, SDL_TRUE));
+        }
+    }
+
+    SDL_AudioSpec capture_spec_requested;
+    SDL_AudioSpec capture_spec_obtained;
+
+    SDL_zero(capture_spec_requested);
+    SDL_zero(capture_spec_obtained);
+
+    capture_spec_requested.freq     = sample_rate;
+    capture_spec_requested.format   = AUDIO_F32;
+    capture_spec_requested.channels = 1;
+    capture_spec_requested.samples  = 1024;
+    capture_spec_requested.callback = [](void * userdata, uint8_t * stream, int len) {
+        audio_async * audio = (audio_async *) userdata;
+        audio->callback(stream, len);
+    };
+    capture_spec_requested.userdata = this;
+
+    if (capture_id >= 0) {
+        fprintf(stderr, "%s: attempt to open capture device %d : '%s' ...\n", __func__, capture_id, SDL_GetAudioDeviceName(capture_id, SDL_TRUE));
+        m_dev_id_in = SDL_OpenAudioDevice(SDL_GetAudioDeviceName(capture_id, SDL_TRUE), SDL_TRUE, &capture_spec_requested, &capture_spec_obtained, 0);
+    } else {
+        fprintf(stderr, "%s: attempt to open default capture device ...\n", __func__);
+        m_dev_id_in = SDL_OpenAudioDevice(nullptr, SDL_TRUE, &capture_spec_requested, &capture_spec_obtained, 0);
+    }
+
+    if (!m_dev_id_in) {
+        fprintf(stderr, "%s: couldn't open an audio device for capture: %s!\n", __func__, SDL_GetError());
+        m_dev_id_in = 0;
+
+        return false;
+    } else {
+        fprintf(stderr, "%s: obtained spec for input device (SDL Id = %d):\n", __func__, m_dev_id_in);
+        fprintf(stderr, "%s:     - sample rate:       %d\n",                   __func__, capture_spec_obtained.freq);
+        fprintf(stderr, "%s:     - format:            %d (required: %d)\n",    __func__, capture_spec_obtained.format,
+                capture_spec_requested.format);
+        fprintf(stderr, "%s:     - channels:          %d (required: %d)\n",    __func__, capture_spec_obtained.channels,
+                capture_spec_requested.channels);
+        fprintf(stderr, "%s:     - samples per frame: %d\n",                   __func__, capture_spec_obtained.samples);
+        fprintf(stderr, "\n");
+    }
+
+    m_sample_rate = capture_spec_obtained.freq;
+
+    m_audio.resize((m_sample_rate*m_len_ms)/1000);
+
+    return true;
+}
+
+bool audio_async::resume() {
+    if (!m_dev_id_in) {
+        fprintf(stderr, "%s: no audio device to resume!\n", __func__);
+        return false;
+    }
+
+    if (m_running) {
+        fprintf(stderr, "%s: already running!\n", __func__);
+        return false;
+    }
+
+    SDL_PauseAudioDevice(m_dev_id_in, 0);
+
+    m_running = true;
+
+    return true;
+}
+
+bool audio_async::pause() {
+    if (!m_dev_id_in) {
+        fprintf(stderr, "%s: no audio device to pause!\n", __func__);
+        return false;
+    }
+
+    if (!m_running) {
+        fprintf(stderr, "%s: already paused!\n", __func__);
+        return false;
+    }
+
+    SDL_PauseAudioDevice(m_dev_id_in, 1);
+
+    m_running = false;
+
+    return true;
+}
+
+bool audio_async::clear() {
+    if (!m_dev_id_in) {
+        fprintf(stderr, "%s: no audio device to clear!\n", __func__);
+        return false;
+    }
+
+    if (!m_running) {
+        fprintf(stderr, "%s: not running!\n", __func__);
+        return false;
+    }
+
+    {
+        std::lock_guard<std::mutex> lock(m_mutex);
+
+        m_audio_pos = 0;
+        m_audio_len = 0;
+    }
+
+    return true;
+}
+
+// callback to be called by SDL
+void audio_async::callback(uint8_t * stream, int len) {
+    if (!m_running) {
+        return;
+    }
+
+    const size_t n_samples = len / sizeof(float);
+
+    m_audio_new.resize(n_samples);
+    memcpy(m_audio_new.data(), stream, n_samples * sizeof(float));
+
+    //fprintf(stderr, "%s: %zu samples, pos %zu, len %zu\n", __func__, n_samples, m_audio_pos, m_audio_len);
+
+    {
+        std::lock_guard<std::mutex> lock(m_mutex);
+
+        if (m_audio_pos + n_samples > m_audio.size()) {
+            const size_t n0 = m_audio.size() - m_audio_pos;
+
+            memcpy(&m_audio[m_audio_pos], stream, n0 * sizeof(float));
+            memcpy(&m_audio[0], &stream[n0], (n_samples - n0) * sizeof(float));
+
+            m_audio_pos = (m_audio_pos + n_samples) % m_audio.size();
+            m_audio_len = m_audio.size();
+        } else {
+            memcpy(&m_audio[m_audio_pos], stream, n_samples * sizeof(float));
+
+            m_audio_pos = (m_audio_pos + n_samples) % m_audio.size();
+            m_audio_len = std::min(m_audio_len + n_samples, m_audio.size());
+        }
+    }
+}
+
+void audio_async::get(int ms, std::vector<float> & result) {
+    if (!m_dev_id_in) {
+        fprintf(stderr, "%s: no audio device to get audio from!\n", __func__);
+        return;
+    }
+
+    if (!m_running) {
+        fprintf(stderr, "%s: not running!\n", __func__);
+        return;
+    }
+
+    result.clear();
+
+    {
+        std::lock_guard<std::mutex> lock(m_mutex);
+
+        if (ms <= 0) {
+            ms = m_len_ms;
+        }
+
+        size_t n_samples = (m_sample_rate * ms) / 1000;
+        if (n_samples > m_audio_len) {
+            n_samples = m_audio_len;
+        }
+
+        result.resize(n_samples);
+
+        int s0 = m_audio_pos - n_samples;
+        if (s0 < 0) {
+            s0 += m_audio.size();
+        }
+
+        if (s0 + n_samples > m_audio.size()) {
+            const size_t n0 = m_audio.size() - s0;
+
+            memcpy(result.data(), &m_audio[s0], n0 * sizeof(float));
+            memcpy(&result[n0], &m_audio[0], (n_samples - n0) * sizeof(float));
+        } else {
+            memcpy(result.data(), &m_audio[s0], n_samples * sizeof(float));
+        }
+    }
+}
+
+///////////////////////////
+
+std::string trim(const std::string & s) {
+    std::regex e("^\\s+|\\s+$");
+    return std::regex_replace(s, e, "");
+}
+
+std::string replace(const std::string & s, const std::string & from, const std::string & to) {
+    std::string result = s;
+    size_t pos = 0;
+    while ((pos = result.find(from, pos)) != std::string::npos) {
+        result.replace(pos, from.length(), to);
+        pos += to.length();
+    }
+    return result;
+}
+
+void high_pass_filter(std::vector<float> & data, float cutoff, float sample_rate) {
+    const float rc = 1.0f / (2.0f * M_PI * cutoff);
+    const float dt = 1.0f / sample_rate;
+    const float alpha = dt / (rc + dt);
+
+    float y = data[0];
+
+    for (size_t i = 1; i < data.size(); i++) {
+        y = alpha * (y + data[i] - data[i - 1]);
+        data[i] = y;
+    }
+}
+
+bool vad_simple(std::vector<float> & pcmf32, int sample_rate, int last_ms, float vad_thold, float freq_thold, bool verbose) {
+    const int n_samples      = pcmf32.size();
+    const int n_samples_last = (sample_rate * last_ms) / 1000;
+
+    if (n_samples_last >= n_samples) {
+        // not enough samples - assume no speech
+        return false;
+    }
+
+    if (freq_thold > 0.0f) {
+        high_pass_filter(pcmf32, freq_thold, sample_rate);
+    }
+
+    float energy_all  = 0.0f;
+    float energy_last = 0.0f;
+
+    for (int i = 0; i < n_samples; i++) {
+        energy_all += fabsf(pcmf32[i]);
+
+        if (i >= n_samples - n_samples_last) {
+            energy_last += fabsf(pcmf32[i]);
+        }
+    }
+
+    energy_all  /= n_samples;
+    energy_last /= n_samples_last;
+
+    if (verbose) {
+        fprintf(stderr, "%s: energy_all: %f, energy_last: %f, vad_thold: %f, freq_thold: %f\n", __func__, energy_all, energy_last, vad_thold, freq_thold);
+    }
+
+    if (energy_last > vad_thold*energy_all) {
+        return false;
+    }
+
+    return true;
+}
+
+std::string transcribe(whisper_context * ctx, const whisper_params & params, const std::vector<float> & pcmf32, float & prob, int64_t & t_ms) {
+    const auto t_start = std::chrono::high_resolution_clock::now();
+
+    prob = 0.0f;
+    t_ms = 0;
+
+    whisper_full_params wparams = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
+
+    wparams.print_progress   = false;
+    wparams.print_special    = params.print_special;
+    wparams.print_realtime   = false;
+    wparams.print_timestamps = !params.no_timestamps;
+    wparams.translate        = params.translate;
+    wparams.no_context       = true;
+    wparams.single_segment   = true;
+    wparams.max_tokens       = params.max_tokens;
+    wparams.language         = params.language.c_str();
+    wparams.n_threads        = params.n_threads;
+
+    wparams.audio_ctx        = params.audio_ctx;
+    wparams.speed_up         = params.speed_up;
+
+    if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
+        return "";
+    }
+
+    int prob_n = 0;
+    std::string result;
+
+    const int n_segments = whisper_full_n_segments(ctx);
+    for (int i = 0; i < n_segments; ++i) {
+        const char * text = whisper_full_get_segment_text(ctx, i);
+
+        result += text;
+
+        const int n_tokens = whisper_full_n_tokens(ctx, i);
+        for (int j = 0; j < n_tokens; ++j) {
+            const auto token = whisper_full_get_token_data(ctx, i, j);
+
+            prob += token.p;
+            ++prob_n;
+        }
+    }
+
+    if (prob_n > 0) {
+        prob /= prob_n;
+    }
+
+    const auto t_end = std::chrono::high_resolution_clock::now();
+    t_ms = std::chrono::duration_cast<std::chrono::milliseconds>(t_end - t_start).count();
+
+    return result;
+}
+
+const std::string k_prompt =
+R"(This is a dialogue between {0} (A) and a person (B). The dialogue so far is:
+
+B: Hello {0}, how are you?
+A: I'm fine, thank you.
+{1}
+Here is how {0} (A) continues the dialogue:
+
+A:)";
+
+int main(int argc, char ** argv) {
+    whisper_params params;
+
+    if (whisper_params_parse(argc, argv, params) == false) {
+        return 1;
+    }
+
+    if (whisper_lang_id(params.language.c_str()) == -1) {
+        fprintf(stderr, "error: unknown language '%s'\n", params.language.c_str());
+        whisper_print_usage(argc, argv, params);
+        exit(0);
+    }
+
+    // whisper init
+
+    struct whisper_context * ctx_wsp = whisper_init_from_file(params.model_wsp.c_str());
+
+    // gpt init
+
+    struct gpt2_context * ctx_gpt = gpt2_init(params.model_gpt.c_str());
+
+    // print some info about the processing
+    {
+        fprintf(stderr, "\n");
+        if (!whisper_is_multilingual(ctx_wsp)) {
+            if (params.language != "en" || params.translate) {
+                params.language = "en";
+                params.translate = false;
+                fprintf(stderr, "%s: WARNING: model is not multilingual, ignoring language and translation options\n", __func__);
+            }
+        }
+        fprintf(stderr, "%s: processing, %d threads, lang = %s, task = %s, timestamps = %d ...\n",
+                __func__,
+                params.n_threads,
+                params.language.c_str(),
+                params.translate ? "translate" : "transcribe",
+                params.no_timestamps ? 0 : 1);
+
+        fprintf(stderr, "\n");
+    }
+
+
+    // init audio
+
+    audio_async audio(30*1000);
+    if (!audio.init(params.capture_id, WHISPER_SAMPLE_RATE)) {
+        fprintf(stderr, "%s: audio.init() failed!\n", __func__);
+        return 1;
+    }
+
+    audio.resume();
+
+    int n_iter = 0;
+
+    bool is_running  = true;
+    bool force_speak = false;
+
+    float prob0 = 0.0f;
+
+    std::vector<float> pcmf32_cur;
+    std::vector<float> pcmf32_prompt;
+
+    gpt2_set_prompt(ctx_gpt, "");
+
+    const int voice_id = rand()%6;
+
+    fprintf(stderr, "gpt-2: prompt:\n");
+    fprintf(stderr, "========================\n\n");
+    fprintf(stderr, "%s\n", ::replace(k_prompt, "{0}", params.person).c_str());
+    fprintf(stderr, "========================\n\n");
+
+    // main loop
+    while (is_running) {
+        // handle Ctrl + C
+        {
+            SDL_Event event;
+            while (SDL_PollEvent(&event)) {
+                switch (event.type) {
+                    case SDL_QUIT:
+                        {
+                            is_running = false;
+                        } break;
+                    default:
+                        break;
+                }
+            }
+
+            if (!is_running) {
+                break;
+            }
+        }
+
+        // delay
+        std::this_thread::sleep_for(std::chrono::milliseconds(100));
+
+        int64_t t_ms = 0;
+
+        {
+            audio.get(2000, pcmf32_cur);
+
+            if (vad_simple(pcmf32_cur, WHISPER_SAMPLE_RATE, 1250, params.vad_thold, params.freq_thold, params.print_energy) || force_speak) {
+                fprintf(stdout, "%s: Speech detected! Processing ...\n", __func__);
+
+                audio.get(params.voice_ms, pcmf32_cur);
+
+                std::string text_heard;
+
+                if (!force_speak) {
+                    text_heard = ::trim(::transcribe(ctx_wsp, params, pcmf32_cur, prob0, t_ms));
+                }
+
+                // remove text between brackets using regex
+                {
+                    std::regex re("\\[.*?\\]");
+                    text_heard = std::regex_replace(text_heard, re, "");
+                }
+
+                // remove text between brackets using regex
+                {
+                    std::regex re("\\(.*?\\)");
+                    text_heard = std::regex_replace(text_heard, re, "");
+                }
+
+                // remove all characters, except for letters, numbers, punctuation and ':', '\'', '-', ' '
+                text_heard = std::regex_replace(text_heard, std::regex("[^a-zA-Z0-9\\.,\\?!\\s\\:\\'\\-]"), "");
+
+                // take first line
+                text_heard = text_heard.substr(0, text_heard.find_first_of('\n'));
+
+                // remove leading and trailing whitespace
+                text_heard = std::regex_replace(text_heard, std::regex("^\\s+"), "");
+                text_heard = std::regex_replace(text_heard, std::regex("\\s+$"), "");
+
+                const std::vector<gpt_vocab::id> tokens = gpt2_tokenize(ctx_gpt, text_heard.c_str());
+
+                if (text_heard.empty() || tokens.empty() || force_speak) {
+                    fprintf(stdout, "%s: Heard nothing, skipping ...\n", __func__);
+                    audio.clear();
+
+                    continue;
+                }
+
+                force_speak = false;
+
+                fprintf(stdout, "%s: Heard '%s%s%s', (t = %d ms)\n", __func__, "\033[1m", text_heard.c_str(), "\033[0m", (int) t_ms);
+
+                std::string prompt_base = gpt2_get_prompt(ctx_gpt);
+
+                std::string text_to_speak;
+
+                {
+                    prompt_base += "B: " + text_heard + "\n";
+
+                    std::string prompt = ::replace(::replace(k_prompt, "{0}", params.person), "{1}", prompt_base);
+
+                    text_to_speak = gpt2_gen_text(ctx_gpt, prompt.c_str(), params.max_tokens);
+                    text_to_speak = std::regex_replace(text_to_speak, std::regex("[^a-zA-Z0-9\\.,\\?!\\s\\:\\'\\-]"), "");
+                    text_to_speak = text_to_speak.substr(0, text_to_speak.find_first_of('\n'));
+
+                    // remove first 2 lines of base prompt
+                    if (n_iter > 4) {
+                        {
+                            const size_t pos = prompt_base.find_first_of('\n');
+                            if (pos != std::string::npos) {
+                                prompt_base = prompt_base.substr(pos + 1);
+                            }
+                        }
+                        {
+                            const size_t pos = prompt_base.find_first_of('\n');
+                            if (pos != std::string::npos) {
+                                prompt_base = prompt_base.substr(pos + 1);
+                            }
+                        }
+                    }
+
+                    prompt_base += "A:" + text_to_speak + "\n";
+
+                    {
+                        prompt = ::replace(::replace(k_prompt, "{0}", params.person), "{1}", prompt_base);
+
+                        printf("===============\n");
+                        printf("prompt:\n");
+                        printf("%s\n", prompt.c_str());
+                        printf("===============\n");
+                    }
+                }
+
+                //printf("========================\n");
+                //printf("gpt-2: prompt_base:\n%s\n", prompt_base.c_str());
+                //printf("========================\n");
+
+                gpt2_set_prompt(ctx_gpt, prompt_base.c_str());
+
+                text_to_speak = ::replace(text_to_speak, params.person + ": ", "");
+                system((params.speak + " " + std::to_string(voice_id) + " \"" + text_to_speak + "\"").c_str());
+
+                audio.clear();
+
+                ++n_iter;
+            }
+        }
+    }
+
+    audio.pause();
+
+    whisper_print_timings(ctx_wsp);
+    whisper_free(ctx_wsp);
+
+    return 0;
+}

examples/twitch.sh

@@ -0,0 +1,109 @@
+#!/bin/bash
+#
+# Transcribe twitch.tv livestream by feeding audio input to whisper.cpp at regular intervals
+# Thanks to @keyehzy
+# ref: https://github.com/ggerganov/whisper.cpp/issues/209
+#
+# The script currently depends on the third-party tool "streamlink"
+# On Mac OS, you can install it via "brew install streamlink"
+#
+
+set -eo pipefail
+
+step=10
+model=base.en
+threads=4
+
+help()
+{
+    echo "Example program for captioning a livestream from twitch.tv."
+    echo
+    echo "Usage: ./twitch.sh -s [step] -m [model] -t [threads] [url]"
+    echo "options:"
+    echo "-s       Step in seconds (default is $step)."
+    echo "-m       Choose model, options are: 'tiny.en' 'tiny' 'base.en' 'base' 'small.en' 'small' 'medium.en' 'medium' 'large-v1' 'large' (default is '$model')."
+    echo "-t       Number of threads to use."
+    echo "-h       Print this help page."
+    echo
+}
+
+check_requirements()
+{
+    if ! command -v ./main &>/dev/null; then
+        echo "whisper.cpp main executable is required (make)"
+        exit 1
+    fi
+
+    if ! command -v streamlink &>/dev/null; then
+        echo "streamlink is required (https://streamlink.github.io)"
+        exit 1
+    fi
+
+    if ! command -v ffmpeg &>/dev/null; then
+        echo "ffmpeg is required (https://ffmpeg.org)"
+        exit 1
+    fi
+}
+
+check_requirements
+
+while getopts ":s:m:t:h" option; do
+    case $option in
+	s)
+            step=$OPTARG;;
+	m)
+            model=$OPTARG;;
+	t)
+	    threads=$OPTARG;;
+	h)
+            help
+            exit;;
+	\?)
+	    help
+	    exit;;
+    esac
+done
+
+url=${@:$OPTIND:1}
+
+if [ -z $url ]; then
+    help
+    exit
+fi
+
+echo "Piping from streamlink url=$url model=$model step=$step threads=$threads"
+streamlink $url best -O 2>/dev/null | ffmpeg -loglevel quiet -i - -y -probesize 32 -y -ar 16000 -ac 1 -acodec pcm_s16le /tmp/whisper-live0.wav &
+
+if [ $? -ne 0 ]; then
+    printf "error: ffmpeg failed\n"
+    exit 1
+fi
+
+echo "Buffering stream... (this should take $step seconds)"
+sleep $(($step))
+
+set +e
+
+echo "Starting..."
+
+i=0
+SECONDS=0
+while true
+do
+    err=1
+    while [ $err -ne 0 ]; do
+        if [ $i -gt 0 ]; then
+            ffmpeg -loglevel quiet -v error -noaccurate_seek -i /tmp/whisper-live0.wav -y -ss $(($i*$step-1)).5 -t $step -c copy /tmp/whisper-live.wav 2> /tmp/whisper-live.err
+        else
+            ffmpeg -loglevel quiet -v error -noaccurate_seek -i /tmp/whisper-live0.wav -y -ss $(($i*$step)) -t $step -c copy /tmp/whisper-live.wav 2> /tmp/whisper-live.err
+        fi
+        err=$(cat /tmp/whisper-live.err | wc -l)
+    done
+
+    ./main -t $threads -m ./models/ggml-$model.bin -f /tmp/whisper-live.wav --no-timestamps -otxt 2> /tmp/whispererr | tail -n 1
+
+    while [ $SECONDS -lt $((($i+1)*$step)) ]; do
+        sleep 1
+    done
+    ((i=i+1))
+done

examples/whisper.android/.gitignore

@@ -0,0 +1,15 @@
+*.iml
+.gradle
+/local.properties
+/.idea/caches
+/.idea/libraries
+/.idea/modules.xml
+/.idea/workspace.xml
+/.idea/navEditor.xml
+/.idea/assetWizardSettings.xml
+.DS_Store
+/build
+/captures
+.externalNativeBuild
+.cxx
+local.properties

examples/whisper.android/.idea/.gitignore

@@ -0,0 +1,3 @@
+# Default ignored files
+/shelf/
+/workspace.xml

examples/whisper.android/.idea/.name

@@ -0,0 +1 @@
+WhisperCppDemo

examples/whisper.android/.idea/compiler.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="CompilerConfiguration">
+    <bytecodeTargetLevel target="11" />
+  </component>
+</project>

examples/whisper.android/.idea/gradle.xml

@@ -0,0 +1,19 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="GradleMigrationSettings" migrationVersion="1" />
+  <component name="GradleSettings">
+    <option name="linkedExternalProjectsSettings">
+      <GradleProjectSettings>
+        <option name="testRunner" value="GRADLE" />
+        <option name="distributionType" value="DEFAULT_WRAPPED" />
+        <option name="externalProjectPath" value="$PROJECT_DIR$" />
+        <option name="modules">
+          <set>
+            <option value="$PROJECT_DIR$" />
+            <option value="$PROJECT_DIR$/app" />
+          </set>
+        </option>
+      </GradleProjectSettings>
+    </option>
+  </component>
+</project>

examples/whisper.android/.idea/misc.xml

@@ -0,0 +1,10 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ExternalStorageConfigurationManager" enabled="true" />
+  <component name="ProjectRootManager" version="2" languageLevel="JDK_11" default="true" project-jdk-name="Android Studio default JDK" project-jdk-type="JavaSDK">
+    <output url="file://$PROJECT_DIR$/build/classes" />
+  </component>
+  <component name="ProjectType">
+    <option name="id" value="Android" />
+  </component>
+</project>

examples/whisper.android/.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$/../.." vcs="Git" />
+  </component>
+</project>

examples/whisper.android/README.md

@@ -0,0 +1,12 @@
+A sample Android app using [whisper.cpp](https://github.com/ggerganov/whisper.cpp/) to do voice-to-text transcriptions.
+
+To use:
+
+1. Select a model from the [whisper.cpp repository](https://github.com/ggerganov/whisper.cpp/tree/master/models).[^1]
+2. Copy the model to the "app/src/main/assets/models" folder.
+3. Select a sample audio file (for example, [jfk.wav](https://github.com/ggerganov/whisper.cpp/raw/master/samples/jfk.wav)).
+4. Copy the sample to the "app/src/main/assets/samples" folder.
+5. Select the "release" active build variant, and use Android Studio to run and deploy to your device.
+[^1]: I recommend the tiny or base models for running on an Android device.
+
+<img width="300" alt="image" src="https://user-images.githubusercontent.com/1991296/208154256-82d972dc-221b-48c4-bfcb-36ce68602f93.png">

examples/whisper.android/app/.gitignore

@@ -0,0 +1 @@
+/build

examples/whisper.android/app/build.gradle

@@ -0,0 +1,72 @@
+plugins {
+    id 'com.android.application'
+    id 'org.jetbrains.kotlin.android'
+}
+
+android {
+    namespace 'com.whispercppdemo'
+    compileSdk 33
+
+    defaultConfig {
+        applicationId "com.whispercppdemo"
+        minSdk 26
+        targetSdk 32
+        versionCode 1
+        versionName "1.0"
+
+        testInstrumentationRunner "androidx.test.runner.AndroidJUnitRunner"
+        vectorDrawables {
+            useSupportLibrary true
+        }
+    }
+
+    buildTypes {
+        release {
+            signingConfig signingConfigs.debug
+            minifyEnabled true
+            proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
+        }
+    }
+    compileOptions {
+        sourceCompatibility JavaVersion.VERSION_1_8
+        targetCompatibility JavaVersion.VERSION_1_8
+    }
+    kotlinOptions {
+        jvmTarget = '1.8'
+    }
+    buildFeatures {
+        compose true
+    }
+    composeOptions {
+        kotlinCompilerExtensionVersion '1.3.1'
+    }
+    ndkVersion "25.1.8937393"
+    externalNativeBuild {
+        ndkBuild {
+            path 'src/main/jni/whisper/Android.mk'
+        }
+    }
+    packagingOptions {
+        resources {
+            excludes += '/META-INF/{AL2.0,LGPL2.1}'
+        }
+    }
+}
+
+dependencies {
+    implementation 'androidx.activity:activity-compose:1.6.1'
+    implementation 'androidx.compose.material:material-icons-core:1.3.1'
+    implementation 'androidx.compose.material3:material3:1.0.1'
+    implementation "androidx.compose.ui:ui:1.3.2"
+    implementation "androidx.compose.ui:ui-tooling-preview:1.3.2"
+    implementation 'androidx.lifecycle:lifecycle-viewmodel-compose:2.5.1'
+    implementation "com.google.accompanist:accompanist-permissions:0.28.0"
+    implementation 'org.jetbrains.kotlinx:kotlinx-coroutines-core:1.6.4'
+
+    testImplementation 'junit:junit:4.13.2'
+    androidTestImplementation 'androidx.test.ext:junit:1.1.4'
+    androidTestImplementation 'androidx.test.espresso:espresso-core:3.5.0'
+    androidTestImplementation "androidx.compose.ui:ui-test-junit4:1.3.2"
+    debugImplementation "androidx.compose.ui:ui-tooling:1.3.2"
+    debugImplementation "androidx.compose.ui:ui-test-manifest:1.3.2"
+}

examples/whisper.android/app/proguard-rules.pro

@@ -0,0 +1,21 @@
+# Add project specific ProGuard rules here.
+# You can control the set of applied configuration files using the
+# proguardFiles setting in build.gradle.
+#
+# For more details, see
+#   http://developer.android.com/guide/developing/tools/proguard.html
+
+# If your project uses WebView with JS, uncomment the following
+# and specify the fully qualified class name to the JavaScript interface
+# class:
+#-keepclassmembers class fqcn.of.javascript.interface.for.webview {
+#   public *;
+#}
+
+# Uncomment this to preserve the line number information for
+# debugging stack traces.
+#-keepattributes SourceFile,LineNumberTable
+
+# If you keep the line number information, uncomment this to
+# hide the original source file name.
+#-renamesourcefileattribute SourceFile

examples/whisper.android/app/src/androidTest/java/com/whispercppdemo/ExampleInstrumentedTest.kt

@@ -0,0 +1,24 @@
+package com.whispercppdemo
+
+import androidx.test.platform.app.InstrumentationRegistry
+import androidx.test.ext.junit.runners.AndroidJUnit4
+
+import org.junit.Test
+import org.junit.runner.RunWith
+
+import org.junit.Assert.*
+
+/**
+ * Instrumented test, which will execute on an Android device.
+ *
+ * See [testing documentation](http://d.android.com/tools/testing).
+ */
+@RunWith(AndroidJUnit4::class)
+class ExampleInstrumentedTest {
+    @Test
+    fun useAppContext() {
+        // Context of the app under test.
+        val appContext = InstrumentationRegistry.getInstrumentation().targetContext
+        assertEquals("com.whispercppdemo", appContext.packageName)
+    }
+}

examples/whisper.android/app/src/main/AndroidManifest.xml

@@ -0,0 +1,32 @@
+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android"
+    xmlns:tools="http://schemas.android.com/tools">
+
+    <uses-permission android:name="android.permission.RECORD_AUDIO" />
+
+    <application
+        android:allowBackup="true"
+        android:dataExtractionRules="@xml/data_extraction_rules"
+        android:fullBackupContent="@xml/backup_rules"
+        android:icon="@mipmap/ic_launcher"
+        android:label="@string/app_name"
+        android:supportsRtl="true"
+        android:theme="@style/Theme.WhisperCppDemo"
+        tools:targetApi="31">
+        <activity
+            android:name=".MainActivity"
+            android:exported="true"
+            android:theme="@style/Theme.WhisperCppDemo">
+            <intent-filter>
+                <action android:name="android.intent.action.MAIN" />
+
+                <category android:name="android.intent.category.LAUNCHER" />
+            </intent-filter>
+
+            <meta-data
+                android:name="android.app.lib_name"
+                android:value="" />
+        </activity>
+    </application>
+
+</manifest>