coreml : attempt to fix ANE-optimized models

Hopefully I didn't break something - haven't tested

.github/workflows/build.yml

@@ -125,8 +125,10 @@ jobs:
         include:
           - arch: Win32
             s2arc: x86
+            jnaPath: win32-x86
           - arch: x64
             s2arc: x64
+            jnaPath: win32-x86-64
           - sdl2: ON
             s2ver: 2.26.0
 
@@ -159,6 +161,12 @@ jobs:
         if: matrix.sdl2 == 'ON'
         run: copy "$env:SDL2_DIR/../lib/${{ matrix.s2arc }}/SDL2.dll" build/bin/${{ matrix.build }}
 
+      - name: Upload dll
+        uses: actions/upload-artifact@v3
+        with:
+          name: ${{ matrix.jnaPath }}_whisper.dll
+          path: build/bin/${{ matrix.build }}/whisper.dll
+
       - name: Upload binaries
         if: matrix.sdl2 == 'ON'
         uses: actions/upload-artifact@v1
@@ -235,6 +243,61 @@ jobs:
         with:
           name: whisper-blas-bin-${{ matrix.arch }}
           path: build/bin/${{ matrix.build }}
+          
+  windows-cublas:
+    runs-on: windows-latest
+    
+    strategy:
+      matrix:
+        build: [Release]
+        arch: [x64]
+        cublas: [ON]
+        sdl2: [ON]
+        include:
+          - 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: Install CUDA Toolkit
+        id: cuda-toolkit
+        uses: Jimver/cuda-toolkit@v0.2.10
+        
+      - 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_CUBLAS=1
+      
+      - 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/${{ matrix.build }}
+          
+      - name: Upload binaries
+        if: matrix.sdl2 == 'ON'
+        uses: actions/upload-artifact@v1
+        with:
+          name: whisper-cublas-bin-${{ matrix.arch }}
+          path: build/bin/${{ matrix.build }}
 
   emscripten:
     runs-on: ubuntu-latest
@@ -265,3 +328,85 @@ jobs:
           popd
           emcmake cmake . -DCMAKE_BUILD_TYPE=${{ matrix.build }}
           make
+
+  ios:
+    runs-on: macos-latest
+
+    strategy:
+      matrix:
+        build: [Release]
+
+    steps:
+      - name: Clone
+        uses: actions/checkout@v1
+
+      - name: Configure
+        run: |
+          cp models/for-tests-ggml-base.en.bin models/ggml-base.en.bin
+          mkdir models/ggml-base.en-encoder.mlmodelc
+
+      - name: Build objc example
+        run: xcodebuild -project examples/whisper.objc/whisper.objc.xcodeproj -scheme whisper.objc -configuration ${{ matrix.build }} -sdk iphonesimulator build
+      
+      - name: Build swiftui example
+        run: xcodebuild -project examples/whisper.swiftui/whisper.swiftui.xcodeproj -scheme WhisperCppDemo -configuration ${{ matrix.build }} -sdk iphonesimulator build
+
+  android:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Clone
+        uses: actions/checkout@v1
+
+      - name: Install Java
+        uses: actions/setup-java@v3
+        with:
+          distribution: zulu
+          java-version: 17
+      
+      - name: Setup Android SDK
+        uses: android-actions/setup-android@v2
+
+      - name: Build
+        run: |
+          cd examples/whisper.android
+          ./gradlew assembleRelease --no-daemon
+
+  java:
+    needs: [ 'windows' ]
+    runs-on: windows-latest
+    steps:
+      - uses: actions/checkout@v1
+
+      - name: Install Java
+        uses: actions/setup-java@v1
+        with:
+          java-version: 17
+
+      - name: Download Windows lib
+        uses: actions/download-artifact@v3
+        with:
+          name: win32-x86-64_whisper.dll
+          path: bindings/java/build/generated/resources/main/win32-x86-64
+
+      - name: Build
+        run: |
+          models\download-ggml-model.cmd tiny.en
+          cd bindings/java
+          chmod +x ./gradlew
+          ./gradlew build
+
+      - name: Upload jar
+        uses: actions/upload-artifact@v3
+        with:
+          name: whispercpp.jar
+          path: bindings/java/build/libs/whispercpp-*.jar
+
+#      - name: Publish package
+#        if: ${{ github.ref == 'refs/heads/master' }}
+#        uses: gradle/gradle-build-action@v2
+#        with:
+#          arguments: publish
+#        env:
+#          MAVEN_USERNAME: ${{ secrets.OSSRH_USERNAME }}
+#          MAVEN_PASSWORD: ${{ secrets.OSSRH_TOKEN }}

.gitignore

@@ -1,6 +1,8 @@
 *.o
 *.a
 .cache/
+.coreml/
+.test/
 .vs/
 .vscode/
 .DS_Store
@@ -10,6 +12,7 @@ build-em/
 build-debug/
 build-release/
 build-static/
+build-cublas/
 build-no-accel/
 build-sanitize-addr/
 build-sanitize-thread/
@@ -18,7 +21,9 @@ build-sanitize-thread/
 /stream
 /command
 /talk
+/talk-llama
 /bench
+/quantize
 
 arm_neon.h
 sync.sh
@@ -32,3 +37,10 @@ examples/whisper.objc/whisper.objc.xcodeproj/xcuserdata/
 examples/whisper.objc/whisper.objc.xcodeproj/project.xcworkspace/xcuserdata
 
 extra/bench-gg.txt
+
+models/*.mlmodel
+models/*.mlmodelc
+models/*.mlpackage
+bindings/java/.gradle/
+bindings/java/.idea/
+.idea/

CMakeLists.txt

@@ -1,6 +1,6 @@
 cmake_minimum_required (VERSION 3.0)
 
-project(whisper.cpp VERSION 1.2.1)
+project(whisper.cpp VERSION 1.4.2)
 
 # Add path to modules
 list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/")
@@ -35,30 +35,40 @@ endif()
 
 # options
 
-option(BUILD_SHARED_LIBS               "whisper: build shared libs" ${BUILD_SHARED_LIBS_DEFAULT})
+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_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_UNDEFINED      "whisper: enable undefined 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_EXAMPLES          "whisper: build examples" ${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)
+option(WHISPER_SDL2                   "whisper: support for libSDL2" 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)
+option(WHISPER_NO_F16C                "whisper: disable F16c" OFF)
+
+option(WHISPER_OPENVINO               "whisper: support for OpenVINO" 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)
+    option(WHISPER_NO_ACCELERATE         "whisper: disable Accelerate framework" OFF)
+    option(WHISPER_COREML                "whisper: enable Core ML framework"     OFF)
+    option(WHISPER_COREML_ALLOW_FALLBACK "whisper: allow non-CoreML fallback"    OFF)
 else()
-    option(WHISPER_SUPPORT_OPENBLAS    "whisper: support for OpenBLAS" OFF)
+    option(WHISPER_BLAS                  "whisper: use BLAS libraries"  OFF)
+    option(WHISPER_BLAS_VENDOR           "whisper: BLAS library vendor" Generic)
+    option(WHISPER_OPENBLAS              "whisper: prefer OpenBLAS"     OFF)
+    option(WHISPER_CUBLAS                "whisper: support for cuBLAS"  OFF)
+    option(WHISPER_CLBLAST               "whisper: use CLBlast"         OFF)
 endif()
 
-option(WHISPER_PERF                    "whisper: enable perf timings" OFF)
+option(WHISPER_PERF "whisper: enable perf timings" OFF)
 
 # sanitizers
 
@@ -86,31 +96,106 @@ endif()
 
 find_package(Threads 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")
+# on APPLE
+if (APPLE)
+    # include Accelerate framework
+    if (NOT WHISPER_NO_ACCELERATE)
+        find_library(ACCELERATE_FRAMEWORK Accelerate)
 
-        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")
+        if (ACCELERATE_FRAMEWORK)
+            message(STATUS "Accelerate framework found")
+
+            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()
+
+    if (WHISPER_COREML)
+        find_library(FOUNDATION_FRAMEWORK Foundation)
+        find_library(COREML_FRAMEWORK CoreML)
+
+        if (COREML_FRAMEWORK)
+            message(STATUS "CoreML framework found")
+
+            set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DWHISPER_USE_COREML)
+        else()
+            message(WARNING "CoreML framework not found")
+        endif()
+
+        if (WHISPER_COREML_ALLOW_FALLBACK)
+            set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DWHISPER_COREML_ALLOW_FALLBACK)
+        endif()
     endif()
 endif()
 
-if (WHISPER_SUPPORT_OPENBLAS)
-    find_library(OPENBLAS_LIB
-        NAMES openblas libopenblas
-        )
-    if (OPENBLAS_LIB)
-        message(STATUS "OpenBLAS found")
+if (WHISPER_OPENBLAS)
+    set(WHISPER_BLAS_VENDOR "OpenBLAS")
+    set(WHISPER_BLAS ON)
+endif()
 
-        set(WHISPER_EXTRA_LIBS  ${WHISPER_EXTRA_LIBS}  ${OPENBLAS_LIB})
+if (WHISPER_BLAS)
+    set(BLA_STATIC 1)
+    set(BLA_VENDOR ${WHISPER_BLAS_VENDOR})
+#    set(BLA_PREFER_PKGCONFIG 1)
+    set(BLA_SIZEOF_INTEGER 8)
+    find_package(BLAS)
+
+    if(BLAS_FOUND)
+        message(STATUS "BLAS compatible library found")
+        message(STATUS "Libraries ${BLAS_LIBRARIES}")
         set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DGGML_USE_OPENBLAS)
+
+        include_directories(${BLAS_INCLUDE_DIRS})
+        set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} ${BLAS_LIBRARIES})
     else()
-        message(WARNING "OpenBLAS not found")
+        message(WARNING "BLAS library was not found")
     endif()
+endif ()
+
+if (WHISPER_CUBLAS)
+    cmake_minimum_required(VERSION 3.17)
+
+    find_package(CUDAToolkit)
+
+    if (CUDAToolkit_FOUND)
+        message(STATUS "cuBLAS found")
+
+        enable_language(CUDA)
+
+        set(GGML_CUDA_SOURCES ggml-cuda.cu ggml-cuda.h)
+
+        add_compile_definitions(GGML_USE_CUBLAS)
+
+        if (WHISPER_STATIC)
+            set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)
+        else()
+            set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} CUDA::cudart CUDA::cublas CUDA::cublasLt)
+        endif()
+
+    else()
+        message(WARNING "cuBLAS not found")
+    endif()
+endif()
+
+if (WHISPER_CLBLAST)
+    find_package(CLBlast)
+    if (CLBlast_FOUND)
+        message(STATUS "CLBlast found")
+
+        set(GGML_OPENCL_SOURCES ggml-opencl.cpp ggml-opencl.h)
+
+        add_compile_definitions(GGML_USE_CLBLAST)
+
+        set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} clblast)
+    else()
+        message(WARNING "CLBlast not found")
+    endif()
+endif()
+
+if( WHISPER_OPENVINO )
+    find_package(OpenVINO REQUIRED COMPONENTS Runtime)
 endif()
 
 # compiler flags
@@ -155,9 +240,17 @@ if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm" OR ${CMAKE_SYSTEM_PROCESSOR} MATCHES
 else()
     message(STATUS "x86 detected")
     if (MSVC)
-        set(CMAKE_CXX_FLAGS "${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")
+            if(NOT WHISPER_NO_AVX2)
+                set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /arch:AVX2")
+                set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /arch:AVX2")
+                set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /arch:AVX2")
+            else()
+                if(NOT WHISPER_NO_AVX)
+                    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /arch:AVX")
+                    set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /arch:AVX")
+                    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /arch:AVX")
+                endif()
+            endif()
     else()
         if (EMSCRIPTEN)
             set(CMAKE_C_FLAGS   "${CMAKE_C_FLAGS}   -pthread")
@@ -183,6 +276,51 @@ if (WHISPER_PERF)
     set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DGGML_PERF)
 endif()
 
+#
+# whisper.coreml - Core ML support
+#
+
+if (WHISPER_COREML)
+    set(TARGET whisper.coreml)
+
+    add_library(${TARGET}
+        coreml/whisper-encoder.h
+        coreml/whisper-encoder.mm
+        coreml/whisper-encoder-impl.h
+        coreml/whisper-encoder-impl.m
+        )
+
+    include(DefaultTargetOptions)
+
+    target_include_directories(${TARGET} PUBLIC
+        .
+        )
+
+    target_link_libraries(${TARGET} PRIVATE ${FOUNDATION_FRAMEWORK} ${COREML_FRAMEWORK})
+
+    set_target_properties(${TARGET} PROPERTIES
+        COMPILE_FLAGS "-fobjc-arc"
+        )
+endif()
+
+if (WHISPER_OPENVINO)
+    set(TARGET whisper.openvino)
+
+    add_library(${TARGET} OBJECT
+        openvino/whisper-openvino-encoder.h
+        openvino/whisper-openvino-encoder.cpp
+        )
+
+    target_include_directories(${TARGET} PUBLIC
+        .
+        )
+
+    set_property(TARGET ${TARGET} PROPERTY POSITION_INDEPENDENT_CODE ON)
+    set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DWHISPER_USE_OPENVINO)
+
+    target_link_libraries(${TARGET} PRIVATE openvino::runtime)
+endif()
+
 #
 # whisper - this is the main library of the project
 #
@@ -192,6 +330,8 @@ set(TARGET whisper)
 add_library(${TARGET}
     ggml.h
     ggml.c
+    ${GGML_CUDA_SOURCES}
+    ${GGML_OPENCL_SOURCES}
     whisper.h
     whisper.cpp
     )
@@ -202,6 +342,14 @@ target_include_directories(${TARGET} PUBLIC
     .
     )
 
+if (WHISPER_COREML)
+    target_link_libraries(${TARGET} PRIVATE whisper.coreml)
+endif()
+
+if (WHISPER_OPENVINO)
+    target_link_libraries(${TARGET} PRIVATE whisper.openvino)
+endif()
+
 if (MSVC)
     target_link_libraries(${TARGET} PRIVATE ${WHISPER_EXTRA_LIBS} ${CMAKE_THREAD_LIBS_INIT})
 
@@ -217,7 +365,19 @@ if (BUILD_SHARED_LIBS)
 
     target_compile_definitions(${TARGET} PUBLIC
         WHISPER_SHARED
+        GGML_SHARED
         )
+
+    target_compile_definitions(${TARGET} PRIVATE
+        WHISPER_BUILD
+        GGML_BUILD
+        )
+endif()
+
+if (GGML_CUDA_SOURCES)
+    message(STATUS "GGML CUDA sources found, configuring CUDA architecture")
+    set_property(TARGET whisper PROPERTY CUDA_ARCHITECTURES OFF)
+    set_property(TARGET whisper PROPERTY CUDA_SELECT_NVCC_ARCH_FLAGS "Auto")
 endif()
 
 if (EMSCRIPTEN)

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2022 Georgi Gerganov
+Copyright (c) 2023 Georgi Gerganov
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

Makefile

@@ -1,3 +1,5 @@
+default: main bench quantize
+
 ifndef UNAME_S
 UNAME_S := $(shell uname -s)
 endif
@@ -36,10 +38,17 @@ LDFLAGS  =
 
 # ref: https://github.com/ggerganov/whisper.cpp/issues/37
 ifneq ($(wildcard /usr/include/musl/*),)
-	CFLAGS   += -D_POSIX_SOURCE -D_GNU_SOURCE
+	CFLAGS += -D_POSIX_SOURCE -D_GNU_SOURCE
 	CXXFLAGS += -D_POSIX_SOURCE -D_GNU_SOURCE
 endif
 
+# RLIMIT_MEMLOCK came in BSD, is not specified in POSIX.1,
+# and on macOS its availability depends on enabling Darwin extensions
+ifeq ($(UNAME_S),Darwin)
+	CFLAGS   += -D_DARWIN_C_SOURCE
+	CXXFLAGS += -D_DARWIN_C_SOURCE
+endif
+
 # OS specific
 # TODO: support Windows
 ifeq ($(UNAME_S),Linux)
@@ -77,10 +86,6 @@ ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686))
 			CFLAGS += -mavx2
 		endif
 	else ifeq ($(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,$(AVX2_M)))
 			CFLAGS += -mavx2
@@ -92,16 +97,17 @@ ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686))
 		F16C_M := $(shell grep "f16c " /proc/cpuinfo)
 		ifneq (,$(findstring f16c,$(F16C_M)))
 			CFLAGS += -mf16c
+
+			AVX1_M := $(shell grep "avx " /proc/cpuinfo)
+			ifneq (,$(findstring avx,$(AVX1_M)))
+				CFLAGS += -mavx
+			endif
 		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,$(AVX2_M)))
 			CFLAGS += -mavx2
@@ -113,6 +119,11 @@ ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686))
 		F16C_M := $(shell sysinfo -cpu | grep "F16C ")
 		ifneq (,$(findstring f16c,$(F16C_M)))
 			CFLAGS += -mf16c
+
+			AVX1_M := $(shell sysinfo -cpu | grep "AVX ")
+			ifneq (,$(findstring avx,$(AVX1_M)))
+				CFLAGS += -mavx
+			endif
 		endif
 	else
 		CFLAGS += -mfma -mf16c -mavx -mavx2
@@ -121,6 +132,7 @@ endif
 ifeq ($(UNAME_M),amd64)
 	CFLAGS += -mavx -mavx2 -mfma -mf16c
 endif
+
 ifneq ($(filter ppc64%,$(UNAME_M)),)
 	POWER9_M := $(shell grep "POWER9" /proc/cpuinfo)
 	ifneq (,$(findstring POWER9,$(POWER9_M)))
@@ -131,6 +143,7 @@ ifneq ($(filter ppc64%,$(UNAME_M)),)
 		CXXFLAGS += -std=c++23 -DGGML_BIG_ENDIAN
 	endif
 endif
+
 ifndef WHISPER_NO_ACCELERATE
 	# Mac M1 - include Accelerate framework
 	ifeq ($(UNAME_S),Darwin)
@@ -138,29 +151,71 @@ ifndef WHISPER_NO_ACCELERATE
 		LDFLAGS += -framework Accelerate
 	endif
 endif
+
+ifdef WHISPER_COREML
+	CXXFLAGS += -DWHISPER_USE_COREML
+	LDFLAGS  += -framework Foundation -framework CoreML
+
+ifdef WHISPER_COREML_ALLOW_FALLBACK
+	CXXFLAGS += -DWHISPER_COREML_ALLOW_FALLBACK
+endif
+endif
+
 ifdef WHISPER_OPENBLAS
 	CFLAGS  += -DGGML_USE_OPENBLAS -I/usr/local/include/openblas
 	LDFLAGS += -lopenblas
 endif
+
+ifdef WHISPER_CUBLAS
+	CFLAGS      += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/$(UNAME_M)-linux/include
+	CXXFLAGS    += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/$(UNAME_M)-linux/include
+	LDFLAGS     += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib
+	WHISPER_OBJ += ggml-cuda.o
+	NVCC        = nvcc
+	NVCCFLAGS   = --forward-unknown-to-host-compiler -arch=any
+
+ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
+	$(NVCC) $(NVCCFLAGS) $(CXXFLAGS) -Wno-pedantic -c $< -o $@
+endif
+
+ifdef WHISPER_CLBLAST
+	CFLAGS 		+= -DGGML_USE_CLBLAST
+	LDFLAGS	 	+= -lclblast -lOpenCL
+	WHISPER_OBJ	+= ggml-opencl.o
+
+ggml-opencl.o: ggml-opencl.cpp ggml-opencl.h
+	$(CC) $(CFLAGS) -c $< -o $@
+endif
+
 ifdef WHISPER_GPROF
 	CFLAGS   += -pg
 	CXXFLAGS += -pg
 endif
+
 ifneq ($(filter aarch64%,$(UNAME_M)),)
-	CFLAGS += -mcpu=native
+	CFLAGS   += -mcpu=native
 	CXXFLAGS += -mcpu=native
 endif
+
 ifneq ($(filter armv6%,$(UNAME_M)),)
-	# Raspberry Pi 1, 2, 3
-	CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access
+	# 32-bit Raspberry Pi 1, 2, 3
+	CFLAGS += -mfpu=neon -mfp16-format=ieee -mno-unaligned-access
 endif
+
 ifneq ($(filter armv7%,$(UNAME_M)),)
-	# Raspberry Pi 4
-	CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations
+	# 32-bit ARM, for example on Armbian or possibly raspbian
+	#CFLAGS   += -mfpu=neon -mfp16-format=ieee -funsafe-math-optimizations -mno-unaligned-access
+	#CXXFLAGS += -mfpu=neon -mfp16-format=ieee -funsafe-math-optimizations -mno-unaligned-access
+
+	# 64-bit ARM on 32-bit OS, use these (TODO: auto-detect 64-bit)
+	CFLAGS   += -mfpu=neon-fp-armv8 -mfp16-format=ieee -funsafe-math-optimizations -mno-unaligned-access
+	CXXFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -funsafe-math-optimizations -mno-unaligned-access
 endif
+
 ifneq ($(filter armv8%,$(UNAME_M)),)
 	# Raspberry Pi 4
-	CFLAGS += -mfp16-format=ieee -mno-unaligned-access
+	CFLAGS   += -mfpu=neon-fp-armv8 -mfp16-format=ieee -funsafe-math-optimizations -mno-unaligned-access
+	CXXFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -funsafe-math-optimizations -mno-unaligned-access
 endif
 
 #
@@ -178,26 +233,36 @@ $(info I CC:       $(CCV))
 $(info I CXX:      $(CXXV))
 $(info )
 
-default: main
-
 #
 # Build library
 #
 
-ggml.o: ggml.c ggml.h
-	$(CC)  $(CFLAGS)   -c ggml.c -o ggml.o
+ggml.o: ggml.c ggml.h ggml-cuda.h
+	$(CC)  $(CFLAGS)   -c $< -o $@
 
-whisper.o: whisper.cpp whisper.h
-	$(CXX) $(CXXFLAGS) -c whisper.cpp -o whisper.o
+whisper.o: whisper.cpp whisper.h ggml.h ggml-cuda.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
 
-libwhisper.a: ggml.o whisper.o
-	$(AR) rcs libwhisper.a ggml.o whisper.o
+ifndef WHISPER_COREML
+WHISPER_OBJ += whisper.o
+else
+whisper-encoder.o: coreml/whisper-encoder.mm coreml/whisper-encoder.h
+	$(CXX) -O3 -I . -fobjc-arc -c coreml/whisper-encoder.mm -o whisper-encoder.o
 
-libwhisper.so: ggml.o whisper.o
-	$(CXX) $(CXXFLAGS) -shared -o libwhisper.so ggml.o whisper.o $(LDFLAGS)
+whisper-encoder-impl.o: coreml/whisper-encoder-impl.m coreml/whisper-encoder-impl.h
+	$(CXX) -O3 -I . -fobjc-arc -c coreml/whisper-encoder-impl.m -o whisper-encoder-impl.o
+
+WHISPER_OBJ += whisper.o whisper-encoder.o whisper-encoder-impl.o
+endif
+
+libwhisper.a: ggml.o $(WHISPER_OBJ)
+	$(AR) rcs libwhisper.a ggml.o $(WHISPER_OBJ)
+
+libwhisper.so: ggml.o $(WHISPER_OBJ)
+	$(CXX) $(CXXFLAGS) -shared -o libwhisper.so ggml.o $(WHISPER_OBJ) $(LDFLAGS)
 
 clean:
-	rm -f *.o main stream command talk bench libwhisper.a libwhisper.so
+	rm -f *.o main stream command talk talk-llama bench quantize libwhisper.a libwhisper.so
 
 #
 # Examples
@@ -205,24 +270,30 @@ clean:
 
 CC_SDL=`sdl2-config --cflags --libs`
 
-SRC_COMMON = examples/common.cpp
+SRC_COMMON     = examples/common.cpp examples/common-ggml.cpp
 SRC_COMMON_SDL = examples/common-sdl.cpp
 
-main: examples/main/main.cpp $(SRC_COMMON) ggml.o whisper.o
-	$(CXX) $(CXXFLAGS) examples/main/main.cpp $(SRC_COMMON) ggml.o whisper.o -o main $(LDFLAGS)
+main: examples/main/main.cpp $(SRC_COMMON) ggml.o $(WHISPER_OBJ)
+	$(CXX) $(CXXFLAGS) examples/main/main.cpp $(SRC_COMMON) ggml.o $(WHISPER_OBJ) -o main $(LDFLAGS)
 	./main -h
 
-stream: examples/stream/stream.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o whisper.o
-	$(CXX) $(CXXFLAGS) examples/stream/stream.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o whisper.o -o stream $(CC_SDL) $(LDFLAGS)
+bench: examples/bench/bench.cpp ggml.o $(WHISPER_OBJ)
+	$(CXX) $(CXXFLAGS) examples/bench/bench.cpp ggml.o $(WHISPER_OBJ) -o bench $(LDFLAGS)
 
-command: examples/command/command.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o whisper.o
-	$(CXX) $(CXXFLAGS) examples/command/command.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o whisper.o -o command $(CC_SDL) $(LDFLAGS)
+quantize: examples/quantize/quantize.cpp ggml.o $(WHISPER_OBJ) $(SRC_COMMON)
+	$(CXX) $(CXXFLAGS) examples/quantize/quantize.cpp $(SRC_COMMON) ggml.o $(WHISPER_OBJ) -o quantize $(LDFLAGS)
 
-talk: examples/talk/talk.cpp examples/talk/gpt-2.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o whisper.o
-	$(CXX) $(CXXFLAGS) examples/talk/talk.cpp examples/talk/gpt-2.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o whisper.o -o talk $(CC_SDL) $(LDFLAGS)
+stream: examples/stream/stream.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ)
+	$(CXX) $(CXXFLAGS) examples/stream/stream.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ) -o stream $(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)
+command: examples/command/command.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ)
+	$(CXX) $(CXXFLAGS) examples/command/command.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ) -o command $(CC_SDL) $(LDFLAGS)
+
+talk: examples/talk/talk.cpp examples/talk/gpt-2.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ)
+	$(CXX) $(CXXFLAGS) examples/talk/talk.cpp examples/talk/gpt-2.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ) -o talk $(CC_SDL) $(LDFLAGS)
+
+talk-llama: examples/talk-llama/talk-llama.cpp examples/talk-llama/llama.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ)
+	$(CXX) $(CXXFLAGS) examples/talk-llama/talk-llama.cpp examples/talk-llama/llama.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ) -o talk-llama $(CC_SDL) $(LDFLAGS)
 
 #
 # Audio samples
@@ -237,12 +308,16 @@ samples:
 	@wget --quiet --show-progress -O samples/gb1.ogg https://upload.wikimedia.org/wikipedia/commons/1/1f/George_W_Bush_Columbia_FINAL.ogg
 	@wget --quiet --show-progress -O samples/hp0.ogg https://upload.wikimedia.org/wikipedia/en/d/d4/En.henryfphillips.ogg
 	@wget --quiet --show-progress -O samples/mm1.wav https://cdn.openai.com/whisper/draft-20220913a/micro-machines.wav
+	@wget --quiet --show-progress -O samples/a13.mp3 https://upload.wikimedia.org/wikipedia/commons/transcoded/6/6f/Apollo13-wehaveaproblem.ogg/Apollo13-wehaveaproblem.ogg.mp3
 	@echo "Converting to 16-bit WAV ..."
 	@ffmpeg -loglevel -0 -y -i samples/gb0.ogg -ar 16000 -ac 1 -c:a pcm_s16le samples/gb0.wav
 	@ffmpeg -loglevel -0 -y -i samples/gb1.ogg -ar 16000 -ac 1 -c:a pcm_s16le samples/gb1.wav
 	@ffmpeg -loglevel -0 -y -i samples/hp0.ogg -ar 16000 -ac 1 -c:a pcm_s16le samples/hp0.wav
+	@rm samples/*.ogg
 	@ffmpeg -loglevel -0 -y -i samples/mm1.wav -ar 16000 -ac 1 -c:a pcm_s16le samples/mm0.wav
 	@rm samples/mm1.wav
+	@ffmpeg -loglevel -0 -y -i samples/a13.mp3 -ar 16000 -ac 1 -c:a pcm_s16le -ss 00:00:00 -to 00:00:30 samples/a13.wav
+	@rm samples/a13.mp3
 
 #
 # Models

README.md

@@ -1,21 +1,27 @@
 # whisper.cpp
 
+![whisper.cpp](https://user-images.githubusercontent.com/1991296/235238348-05d0f6a4-da44-4900-a1de-d0707e75b763.jpeg)
+
 [![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: [v1.2.1](https://github.com/ggerganov/whisper.cpp/releases/tag/v1.2.1) / [Roadmap | F.A.Q.](https://github.com/ggerganov/whisper.cpp/discussions/126)
+Beta: [v1.4.2](https://github.com/ggerganov/whisper.cpp/releases/tag/v1.4.2) / Stable: [v1.2.1](https://github.com/ggerganov/whisper.cpp/releases/tag/v1.2.1) / [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
-- Apple silicon first-class citizen - optimized via Arm Neon and Accelerate framework
+- Apple silicon first-class citizen - optimized via ARM NEON, Accelerate framework and [Core ML](https://github.com/ggerganov/whisper.cpp#core-ml-support)
 - AVX intrinsics support for x86 architectures
 - VSX intrinsics support for POWER architectures
 - Mixed F16 / F32 precision
+- [4-bit and 5-bit integer quantization support](https://github.com/ggerganov/whisper.cpp#quantization)
 - Low memory usage (Flash Attention)
 - Zero memory allocations at runtime
 - Runs on the CPU
+- [Partial GPU support for NVIDIA via cuBLAS](https://github.com/ggerganov/whisper.cpp#nvidia-gpu-support-via-cublas)
+- [Partial OpenCL GPU support via CLBlast](https://github.com/ggerganov/whisper.cpp#opencl-gpu-support-via-clblast)
+- [BLAS CPU support via OpenBLAS](https://github.com/ggerganov/whisper.cpp#blas-cpu-support-via-openblas)
 - [C-style API](https://github.com/ggerganov/whisper.cpp/blob/master/whisper.h)
 
 Supported platforms:
@@ -23,6 +29,7 @@ Supported platforms:
 - [x] Mac OS (Intel and Arm)
 - [x] [iOS](examples/whisper.objc)
 - [x] [Android](examples/whisper.android)
+- [x] [Java](bindings/java/README.md)
 - [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)]
@@ -58,12 +65,16 @@ the Accelerate framework utilizes the special-purpose AMX coprocessor available
 
 ## Quick start
 
-First, download one of the Whisper models converted in [ggml format](models). For example:
+First clone the repository.
+
+Then, download one of the Whisper models converted in [ggml format](models). For example:
 
 ```bash
 bash ./models/download-ggml-model.sh base.en
 ```
 
+If you wish to convert the Whisper models to ggml format yourself, instructions are in [models/README.md](models/README.md).
+
 Now build the [main](examples/main) example and transcribe an audio file like this:
 
 ```bash
@@ -104,6 +115,7 @@ options:
   -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
+  -tdrz,     --tinydiarize       [false  ] enable tinydiarize (requires a tdrz model)
   -di,       --diarize           [false  ] stereo audio diarization
   -nf,       --no-fallback       [false  ] do not use temperature fallback while decoding
   -otxt,     --output-txt        [false  ] output result in a text file
@@ -223,10 +235,134 @@ make large
 | medium | 1.5 GB | ~1.7 GB | `fd9727b6e1217c2f614f9b698455c4ffd82463b4` |
 | large  | 2.9 GB | ~3.3 GB | `0f4c8e34f21cf1a914c59d8b3ce882345ad349d6` |
 
+## Quantization
+
+`whisper.cpp` supports integer quantization of the Whisper `ggml` models.
+Quantized models require less memory and disk space and depending on the hardware can be processed more efficiently.
+
+Here are the steps for creating and using a quantized model:
+
+```bash
+# quantize a model with Q5_0 method
+make quantize
+./quantize models/ggml-base.en.bin models/ggml-base.en-q5_0.bin q5_0
+
+# run the examples as usual, specifying the quantized model file
+./main -m models/ggml-base.en-q5_0.bin ./samples/gb0.wav
+```
+
+## Core ML support
+
+On Apple Silicon devices, the Encoder inference can be executed on the Apple Neural Engine (ANE) via Core ML. This can result in significant
+speed-up - more than x3 faster compared with CPU-only execution. Here are the instructions for generating a Core ML model and using it with `whisper.cpp`:
+
+- Install Python dependencies needed for the creation of the Core ML model:
+
+  ```bash
+  pip install ane_transformers
+  pip install openai-whisper
+  pip install coremltools
+  ```
+
+  - To ensure `coremltools` operates correctly, please confirm that [Xcode](https://developer.apple.com/xcode/) is installed and execute `xcode-select --install` to install the command-line tools.
+  - Python 3.10 is recommended.
+  - [OPTIONAL] It is recommended to utilize a Python version management system, such as [Miniconda](https://docs.conda.io/en/latest/miniconda.html)  for this step:
+    - To create an environment, use: `conda create -n py310-whisper python=3.10 -y`
+    - To activate the environment, use: `conda activate py310-whisper`
+
+- Generate a Core ML model. For example, to generate a `base.en` model, use:
+
+  ```bash
+  ./models/generate-coreml-model.sh base.en
+  ```
+
+  This will generate the folder `models/ggml-base.en-encoder.mlmodelc`
+
+- Build `whisper.cpp` with Core ML support:
+
+  ```bash
+  # using Makefile
+  make clean
+  WHISPER_COREML=1 make -j
+
+  # using CMake
+  cd build
+  cmake -DWHISPER_COREML=1 ..
+  ```
+
+- Run the examples as usual. For example:
+
+  ```bash
+  ./main -m models/ggml-base.en.bin -f samples/jfk.wav
+
+  ...
+
+  whisper_init_state: loading Core ML model from 'models/ggml-base.en-encoder.mlmodelc'
+  whisper_init_state: first run on a device may take a while ...
+  whisper_init_state: Core ML model loaded
+
+  system_info: n_threads = 4 / 10 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 | COREML = 1 |
+
+  ...
+  ```
+
+  The first run on a device is slow, since the ANE service compiles the Core ML model to some device-specific format.
+  Next runs are faster.
+
+For more information about the Core ML implementation please refer to PR [#566](https://github.com/ggerganov/whisper.cpp/pull/566).
+
+## NVIDIA GPU support via cuBLAS
+
+With NVIDIA cards the Encoder processing can to a large extent be offloaded to the GPU through cuBLAS.
+First, make sure you have installed `cuda`: https://developer.nvidia.com/cuda-downloads
+
+Now build `whisper.cpp` with cuBLAS support:
+
+```
+make clean
+WHISPER_CUBLAS=1 make -j
+```
+
+## OpenCL GPU support via CLBlast
+
+For cards and integrated GPUs that support OpenCL, the Encoder processing can be largely offloaded to the GPU through CLBlast. This is especially useful for users with AMD APUs or low end devices for up to ~2x speedup.
+
+First, make sure you have installed `CLBlast` for your OS or Distribution: https://github.com/CNugteren/CLBlast
+
+Now build `whisper.cpp` with CLBlast support:
+
+```
+Makefile:
+cd whisper.cpp
+make clean
+WHISPER_CLBLAST=1 make -j
+
+CMake:
+cd whisper.cpp ; mkdir build ; cd build
+cmake -DWHISPER_CLBLAST=ON  ..
+make clean
+make -j
+cp bin/* ../ 
+```
+
+
+Run all the examples as usual.
+
+## BLAS CPU support via OpenBLAS
+
+Encoder processing can be accelerated on the CPU via OpenBLAS.
+First, make sure you have installed `openblas`: https://www.openblas.net/
+
+Now build `whisper.cpp` with OpenBLAS support:
+
+```
+make clean
+WHISPER_OPENBLAS=1 make -j
+```
+
 ## Limitations
 
 - Inference only
-- No GPU support (yet)
 
 ## Another example
 
@@ -313,7 +449,7 @@ whisper_print_timings:    total time = 32733.52 ms
 ## Real-time audio input example
 
 This is a naive example of performing real-time inference on audio from your microphone.
-The [stream](examples/stream) tool samples the audio every half a second and runs the transcription continously.
+The [stream](examples/stream) tool samples the audio every half a second and runs the transcription continuously.
 More info is available in [issue #10](https://github.com/ggerganov/whisper.cpp/issues/10).
 
 ```java
@@ -328,6 +464,10 @@ https://user-images.githubusercontent.com/1991296/194935793-76afede7-cfa8-48d8-a
 Adding the `--print-colors` argument will print the transcribed text using an experimental color coding strategy
 to highlight words with high or low confidence:
 
+```java
+./main -m models/ggml-base.en.bin -f samples/gb0.wav --print-colors
+```
+
 <img width="965" alt="image" src="https://user-images.githubusercontent.com/1991296/197356445-311c8643-9397-4e5e-b46e-0b4b4daa2530.png">
 
 ## Controlling the length of the generated text segments (experimental)
@@ -354,7 +494,7 @@ main: processing './samples/jfk.wav' (176000 samples, 11.0 sec), 4 threads, 1 pr
 [00:00:10.020 --> 00:00:11.000]   country.
 ```
 
-## Word-level timestamp
+## Word-level timestamp (experimental)
 
 The `--max-len` argument can be used to obtain word-level timestamps. Simply use `-ml 1`:
 
@@ -367,7 +507,7 @@ system_info: n_threads = 4 / 10 | AVX2 = 0 | AVX512 = 0 | NEON = 1 | FP16_VA = 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]  
+[00:00:00.000 --> 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
@@ -395,6 +535,32 @@ main: processing './samples/jfk.wav' (176000 samples, 11.0 sec), 4 threads, 1 pr
 [00:00:10.510 --> 00:00:11.000]  .
 ```
 
+## Speaker segmentation via tinydiarize (experimental)
+
+More information about this approach is available here: https://github.com/ggerganov/whisper.cpp/pull/1058
+
+Sample usage:
+
+```py
+# download a tinydiarize compatible model
+./models/download-ggml-model.sh small.en-tdrz
+
+# run as usual, adding the "-tdrz" command-line argument
+./main -f ./samples/a13.wav -m ./models/ggml-small.en-tdrz.bin -tdrz
+...
+main: processing './samples/a13.wav' (480000 samples, 30.0 sec), 4 threads, 1 processors, lang = en, task = transcribe, tdrz = 1, timestamps = 1 ...
+...
+[00:00:00.000 --> 00:00:03.800]   Okay Houston, we've had a problem here. [SPEAKER_TURN]
+[00:00:03.800 --> 00:00:06.200]   This is Houston. Say again please. [SPEAKER_TURN]
+[00:00:06.200 --> 00:00:08.260]   Uh Houston we've had a problem.
+[00:00:08.260 --> 00:00:11.320]   We've had a main beam up on a volt. [SPEAKER_TURN]
+[00:00:11.320 --> 00:00:13.820]   Roger main beam interval. [SPEAKER_TURN]
+[00:00:13.820 --> 00:00:15.100]   Uh uh [SPEAKER_TURN]
+[00:00:15.100 --> 00:00:18.020]   So okay stand, by thirteen we're looking at it. [SPEAKER_TURN]
+[00:00:18.020 --> 00:00:25.740]   Okay uh right now uh Houston the uh voltage is uh is looking good um.
+[00:00:27.620 --> 00:00:29.940]   And we had a a pretty large bank or so.
+```
+
 ## Karaoke-style movie generation (experimental)
 
 The [main](examples/main) example provides support for output of karaoke-style movies, where the
@@ -478,8 +644,11 @@ in [models](models).
 - [X] Javascript: [bindings/javascript](bindings/javascript) | [#309](https://github.com/ggerganov/whisper.cpp/discussions/309)
   - React Native (iOS / Android): [whisper.rn](https://github.com/mybigday/whisper.rn)
 - [X] Go: [bindings/go](bindings/go) | [#312](https://github.com/ggerganov/whisper.cpp/discussions/312)
+- [X] Java:
+  - [GiviMAD/whisper-jni](https://github.com/GiviMAD/whisper-jni)
 - [X] Ruby: [bindings/ruby](bindings/ruby) | [#507](https://github.com/ggerganov/whisper.cpp/discussions/507)
 - [X] Objective-C / Swift: [ggerganov/whisper.spm](https://github.com/ggerganov/whisper.spm) | [#313](https://github.com/ggerganov/whisper.cpp/discussions/313)
+  - [exPHAT/SwiftWhisper](https://github.com/exPHAT/SwiftWhisper)
 - [X] .NET: | [#422](https://github.com/ggerganov/whisper.cpp/discussions/422)
   - [sandrohanea/whisper.net](https://github.com/sandrohanea/whisper.net)
   - [NickDarvey/whisper](https://github.com/NickDarvey/whisper)
@@ -487,6 +656,7 @@ in [models](models).
   - [stlukey/whispercpp.py](https://github.com/stlukey/whispercpp.py) (Cython)
   - [aarnphm/whispercpp](https://github.com/aarnphm/whispercpp) (Pybind11)
 - [X] R: [bnosac/audio.whisper](https://github.com/bnosac/audio.whisper)
+- [X] Unity: [macoron/whisper.unity](https://github.com/Macoron/whisper.unity)
 
 ## Examples
 
@@ -500,6 +670,7 @@ Some of the examples are even ported to run in the browser using WebAssembly. Ch
 | [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 |
+| [talk-llama](examples/talk-llama) | | Talk with a LLaMA 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 |

bindings/go/Makefile

@@ -32,7 +32,7 @@ mkdir:
 modtidy:
 	@go mod tidy
 
-clean: 
+clean:
 	@echo Clean
 	@rm -fr $(BUILD_DIR)
 	@go clean

bindings/go/README.md

@@ -31,7 +31,7 @@ func main() {
 	if err != nil {
 		panic(err)
 	}
-	if err := context.Process(samples, nil); err != nil {
+	if err := context.Process(samples, nil, nil); err != nil {
 		return err
 	}
 
@@ -71,7 +71,7 @@ The examples are placed in the `build` directory. Once built, you can download a
 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 
+./build/go-whisper -model models/ggml-tiny.en.bin samples/jfk.wav
 ```
 
 ## Using the bindings

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

@@ -67,7 +67,7 @@ func Process(model whisper.Model, path string, flags *Flags) error {
 	// Process the data
 	fmt.Fprintf(flags.Output(), "  ...processing %q\n", path)
 	context.ResetTimings()
-	if err := context.Process(data, cb); err != nil {
+	if err := context.Process(data, cb, nil); err != nil {
 		return err
 	}
 

bindings/go/params.go

@@ -105,6 +105,10 @@ func (p *Params) SetMaxSegmentLength(n int) {
 	p.max_len = C.int(n)
 }
 
+func (p *Params) SetTokenTimestamps(b bool) {
+	p.token_timestamps = toBool(b)
+}
+
 // Set max tokens per segment (0 = no limit)
 func (p *Params) SetMaxTokensPerSegment(n int) {
 	p.max_tokens = C.int(n)

bindings/go/pkg/whisper/context.go

@@ -93,7 +93,7 @@ func (context *context) SetOffset(v time.Duration) {
 
 // Set duration of audio to process
 func (context *context) SetDuration(v time.Duration) {
-	context.params.SetOffset(int(v.Milliseconds()))
+	context.params.SetDuration(int(v.Milliseconds()))
 }
 
 // Set timestamp token probability threshold (~0.01)
@@ -111,6 +111,11 @@ func (context *context) SetMaxSegmentLength(n uint) {
 	context.params.SetMaxSegmentLength(int(n))
 }
 
+// Set token timestamps flag
+func (context *context) SetTokenTimestamps(b bool) {
+	context.params.SetTokenTimestamps(b)
+}
+
 // Set max tokens per segment (0 = no limit)
 func (context *context) SetMaxTokensPerSegment(n uint) {
 	context.params.SetMaxTokensPerSegment(int(n))
@@ -147,12 +152,16 @@ func (context *context) WhisperLangAutoDetect(offset_ms int, n_threads int) ([]f
 }
 
 // Process new sample data and return any errors
-func (context *context) Process(data []float32, cb SegmentCallback) error {
+func (context *context) Process(
+	data []float32,
+	callNewSegment SegmentCallback,
+	callProgress ProgressCallback,
+) error {
 	if context.model.ctx == nil {
 		return ErrInternalAppError
 	}
 	// If the callback is defined then we force on single_segment mode
-	if cb != nil {
+	if callNewSegment != nil {
 		context.params.SetSingleSegment(true)
 	}
 
@@ -160,24 +169,28 @@ func (context *context) Process(data []float32, cb SegmentCallback) error {
 	processors := 0
 	if processors > 1 {
 		if err := context.model.ctx.Whisper_full_parallel(context.params, data, processors, nil, func(new int) {
-			if cb != nil {
+			if callNewSegment != 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))
+					callNewSegment(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 {
+		if callNewSegment != 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))
+				callNewSegment(toSegment(context.model.ctx, i))
 			}
 		}
+	}, func(progress int) {
+		if callProgress != nil {
+			callProgress(progress)
+		}
 	}); err != nil {
 		return err
 	}
@@ -280,10 +293,14 @@ func toSegment(ctx *whisper.Context, n int) Segment {
 func toTokens(ctx *whisper.Context, n int) []Token {
 	result := make([]Token, ctx.Whisper_full_n_tokens(n))
 	for i := 0; i < len(result); i++ {
+		data := ctx.Whisper_full_get_token_data(n, 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),
+			Id:    int(ctx.Whisper_full_get_token_id(n, i)),
+			Text:  ctx.Whisper_full_get_token_text(n, i),
+			P:     ctx.Whisper_full_get_token_p(n, i),
+			Start: time.Duration(data.T0()) * time.Millisecond * 10,
+			End:   time.Duration(data.T1()) * time.Millisecond * 10,
 		}
 	}
 	return result

bindings/go/pkg/whisper/interface.go

@@ -12,6 +12,10 @@ import (
 // time. It is called during the Process function
 type SegmentCallback func(Segment)
 
+// ProgressCallback is the callback function for reporting progress during
+// processing. It is called during the Process function
+type ProgressCallback func(int)
+
 // Model is the interface to a whisper model. Create a new model with the
 // function whisper.New(string)
 type Model interface {
@@ -41,12 +45,13 @@ type Context interface {
 	SetTokenThreshold(float32)    // Set timestamp token probability threshold
 	SetTokenSumThreshold(float32) // Set timestamp token sum probability threshold
 	SetMaxSegmentLength(uint)     // Set max segment length in characters
+	SetTokenTimestamps(bool)      // Set token timestamps flag
 	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
+	Process([]float32, SegmentCallback, ProgressCallback) error
 
 	// After process is called, return segments until the end of the stream
 	// is reached, when io.EOF is returned.
@@ -85,7 +90,8 @@ type Segment struct {
 
 // Token is a text or special token
 type Token struct {
-	Id   int
-	Text string
-	P    float32
+	Id         int
+	Text       string
+	P          float32
+	Start, End time.Duration
 }

bindings/go/whisper.go

@@ -15,6 +15,7 @@ import (
 #include <stdlib.h>
 
 extern void callNewSegment(void* user_data, int new);
+extern void callProgress(void* user_data, int progress);
 extern bool callEncoderBegin(void* user_data);
 
 // Text segment callback
@@ -26,6 +27,15 @@ static void whisper_new_segment_cb(struct whisper_context* ctx, struct whisper_s
     }
 }
 
+// Progress callback
+// Called on every newly generated text segment
+// Use the whisper_full_...() functions to obtain the text segments
+static void whisper_progress_cb(struct whisper_context* ctx, struct whisper_state* state, int progress, void* user_data) {
+    if(user_data != NULL && ctx != NULL) {
+        callProgress(user_data, progress);
+    }
+}
+
 // Encoder begin callback
 // If not NULL, called before the encoder starts
 // If it returns false, the computation is aborted
@@ -43,6 +53,8 @@ static struct whisper_full_params whisper_full_default_params_cb(struct whisper_
 	params.new_segment_callback_user_data = (void*)(ctx);
 	params.encoder_begin_callback = whisper_encoder_begin_cb;
 	params.encoder_begin_callback_user_data = (void*)(ctx);
+	params.progress_callback = whisper_progress_cb;
+	params.progress_callback_user_data = (void*)(ctx);
 	return params;
 }
 */
@@ -258,13 +270,13 @@ func (ctx *Context) Whisper_token_lang(lang_id int) Token {
 }
 
 // Task tokens
-func Whisper_token_translate() Token {
-	return Token(C.whisper_token_translate())
+func (ctx *Context) Whisper_token_translate() Token {
+	return Token(C.whisper_token_translate((*C.struct_whisper_context)(ctx)))
 }
 
 // Task tokens
-func Whisper_token_transcribe() Token {
-	return Token(C.whisper_token_transcribe())
+func (ctx *Context) Whisper_token_transcribe() Token {
+	return Token(C.whisper_token_transcribe((*C.struct_whisper_context)(ctx)))
 }
 
 // Performance information
@@ -290,11 +302,19 @@ func (ctx *Context) Whisper_full_default_params(strategy SamplingStrategy) Param
 
 // 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 {
+func (ctx *Context) Whisper_full(
+	params Params,
+	samples []float32,
+	encoderBeginCallback func() bool,
+	newSegmentCallback func(int),
+	progressCallback func(int),
+) error {
 	registerEncoderBeginCallback(ctx, encoderBeginCallback)
 	registerNewSegmentCallback(ctx, newSegmentCallback)
+	registerProgressCallback(ctx, progressCallback)
 	defer registerEncoderBeginCallback(ctx, nil)
 	defer registerNewSegmentCallback(ctx, nil)
+	defer registerProgressCallback(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 {
@@ -318,6 +338,18 @@ func (ctx *Context) Whisper_full_parallel(params Params, samples []float32, proc
 	}
 }
 
+// Return the id of the autodetected language, returns -1 if not found
+// Added to whisper.cpp in
+// https://github.com/ggerganov/whisper.cpp/commit/a1c1583cc7cd8b75222857afc936f0638c5683d6
+//
+// Examples:
+//
+//	"de" -> 2
+//	"german" -> 2
+func (ctx *Context) Whisper_full_lang_id() int {
+	return int(C.whisper_full_lang_id((*C.struct_whisper_context)(ctx)))
+}
+
 // 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 {
@@ -356,7 +388,7 @@ func (ctx *Context) Whisper_full_get_token_id(segment int, token 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 {
+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)))
 }
 
@@ -370,6 +402,7 @@ func (ctx *Context) Whisper_full_get_token_p(segment int, token int) float32 {
 
 var (
 	cbNewSegment   = make(map[unsafe.Pointer]func(int))
+	cbProgress     = make(map[unsafe.Pointer]func(int))
 	cbEncoderBegin = make(map[unsafe.Pointer]func() bool)
 )
 
@@ -381,6 +414,14 @@ func registerNewSegmentCallback(ctx *Context, fn func(int)) {
 	}
 }
 
+func registerProgressCallback(ctx *Context, fn func(int)) {
+	if fn == nil {
+		delete(cbProgress, unsafe.Pointer(ctx))
+	} else {
+		cbProgress[unsafe.Pointer(ctx)] = fn
+	}
+}
+
 func registerEncoderBeginCallback(ctx *Context, fn func() bool) {
 	if fn == nil {
 		delete(cbEncoderBegin, unsafe.Pointer(ctx))
@@ -396,6 +437,13 @@ func callNewSegment(user_data unsafe.Pointer, new C.int) {
 	}
 }
 
+//export callProgress
+func callProgress(user_data unsafe.Pointer, progress C.int) {
+	if fn, ok := cbProgress[user_data]; ok {
+		fn(int(progress))
+	}
+}
+
 //export callEncoderBegin
 func callEncoderBegin(user_data unsafe.Pointer) C.bool {
 	if fn, ok := cbEncoderBegin[user_data]; ok {
@@ -407,3 +455,15 @@ func callEncoderBegin(user_data unsafe.Pointer) C.bool {
 	}
 	return true
 }
+
+func (t TokenData) T0() int64 {
+	return int64(t.t0)
+}
+
+func (t TokenData) T1() int64 {
+	return int64(t.t1)
+}
+
+func (t TokenData) Id() Token {
+	return Token(t.id)
+}

bindings/go/whisper_test.go

@@ -52,7 +52,7 @@ func Test_Whisper_001(t *testing.T) {
 	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)
+	err = ctx.Whisper_full(params, data, nil, nil, nil)
 	assert.NoError(err)
 
 	// Print out tokens

bindings/java/.idea/uiDesigner.xml

@@ -0,0 +1,124 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Palette2">
+    <group name="Swing">
+      <item class="com.intellij.uiDesigner.HSpacer" tooltip-text="Horizontal Spacer" icon="/com/intellij/uiDesigner/icons/hspacer.svg" removable="false" auto-create-binding="false" can-attach-label="false">
+        <default-constraints vsize-policy="1" hsize-policy="6" anchor="0" fill="1" />
+      </item>
+      <item class="com.intellij.uiDesigner.VSpacer" tooltip-text="Vertical Spacer" icon="/com/intellij/uiDesigner/icons/vspacer.svg" removable="false" auto-create-binding="false" can-attach-label="false">
+        <default-constraints vsize-policy="6" hsize-policy="1" anchor="0" fill="2" />
+      </item>
+      <item class="javax.swing.JPanel" icon="/com/intellij/uiDesigner/icons/panel.svg" removable="false" auto-create-binding="false" can-attach-label="false">
+        <default-constraints vsize-policy="3" hsize-policy="3" anchor="0" fill="3" />
+      </item>
+      <item class="javax.swing.JScrollPane" icon="/com/intellij/uiDesigner/icons/scrollPane.svg" removable="false" auto-create-binding="false" can-attach-label="true">
+        <default-constraints vsize-policy="7" hsize-policy="7" anchor="0" fill="3" />
+      </item>
+      <item class="javax.swing.JButton" icon="/com/intellij/uiDesigner/icons/button.svg" removable="false" auto-create-binding="true" can-attach-label="false">
+        <default-constraints vsize-policy="0" hsize-policy="3" anchor="0" fill="1" />
+        <initial-values>
+          <property name="text" value="Button" />
+        </initial-values>
+      </item>
+      <item class="javax.swing.JRadioButton" icon="/com/intellij/uiDesigner/icons/radioButton.svg" removable="false" auto-create-binding="true" can-attach-label="false">
+        <default-constraints vsize-policy="0" hsize-policy="3" anchor="8" fill="0" />
+        <initial-values>
+          <property name="text" value="RadioButton" />
+        </initial-values>
+      </item>
+      <item class="javax.swing.JCheckBox" icon="/com/intellij/uiDesigner/icons/checkBox.svg" removable="false" auto-create-binding="true" can-attach-label="false">
+        <default-constraints vsize-policy="0" hsize-policy="3" anchor="8" fill="0" />
+        <initial-values>
+          <property name="text" value="CheckBox" />
+        </initial-values>
+      </item>
+      <item class="javax.swing.JLabel" icon="/com/intellij/uiDesigner/icons/label.svg" removable="false" auto-create-binding="false" can-attach-label="false">
+        <default-constraints vsize-policy="0" hsize-policy="0" anchor="8" fill="0" />
+        <initial-values>
+          <property name="text" value="Label" />
+        </initial-values>
+      </item>
+      <item class="javax.swing.JTextField" icon="/com/intellij/uiDesigner/icons/textField.svg" removable="false" auto-create-binding="true" can-attach-label="true">
+        <default-constraints vsize-policy="0" hsize-policy="6" anchor="8" fill="1">
+          <preferred-size width="150" height="-1" />
+        </default-constraints>
+      </item>
+      <item class="javax.swing.JPasswordField" icon="/com/intellij/uiDesigner/icons/passwordField.svg" removable="false" auto-create-binding="true" can-attach-label="true">
+        <default-constraints vsize-policy="0" hsize-policy="6" anchor="8" fill="1">
+          <preferred-size width="150" height="-1" />
+        </default-constraints>
+      </item>
+      <item class="javax.swing.JFormattedTextField" icon="/com/intellij/uiDesigner/icons/formattedTextField.svg" removable="false" auto-create-binding="true" can-attach-label="true">
+        <default-constraints vsize-policy="0" hsize-policy="6" anchor="8" fill="1">
+          <preferred-size width="150" height="-1" />
+        </default-constraints>
+      </item>
+      <item class="javax.swing.JTextArea" icon="/com/intellij/uiDesigner/icons/textArea.svg" removable="false" auto-create-binding="true" can-attach-label="true">
+        <default-constraints vsize-policy="6" hsize-policy="6" anchor="0" fill="3">
+          <preferred-size width="150" height="50" />
+        </default-constraints>
+      </item>
+      <item class="javax.swing.JTextPane" icon="/com/intellij/uiDesigner/icons/textPane.svg" removable="false" auto-create-binding="true" can-attach-label="true">
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+          <preferred-size width="150" height="50" />
+        </default-constraints>
+      </item>
+      <item class="javax.swing.JEditorPane" icon="/com/intellij/uiDesigner/icons/editorPane.svg" removable="false" auto-create-binding="true" can-attach-label="true">
+        <default-constraints vsize-policy="6" hsize-policy="6" anchor="0" fill="3">
+          <preferred-size width="150" height="50" />
+        </default-constraints>
+      </item>
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+        <default-constraints vsize-policy="0" hsize-policy="2" anchor="8" fill="1" />
+      </item>
+      <item class="javax.swing.JTable" icon="/com/intellij/uiDesigner/icons/table.svg" removable="false" auto-create-binding="true" can-attach-label="false">
+        <default-constraints vsize-policy="6" hsize-policy="6" anchor="0" fill="3">
+          <preferred-size width="150" height="50" />
+        </default-constraints>
+      </item>
+      <item class="javax.swing.JList" icon="/com/intellij/uiDesigner/icons/list.svg" removable="false" auto-create-binding="true" can-attach-label="false">
+        <default-constraints vsize-policy="6" hsize-policy="2" anchor="0" fill="3">
+          <preferred-size width="150" height="50" />
+        </default-constraints>
+      </item>
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+          <preferred-size width="200" height="200" />
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+      <item class="javax.swing.JProgressBar" icon="/com/intellij/uiDesigner/icons/progressbar.svg" removable="false" auto-create-binding="true" can-attach-label="false">
+        <default-constraints vsize-policy="0" hsize-policy="6" anchor="0" fill="1" />
+      </item>
+      <item class="javax.swing.JToolBar" icon="/com/intellij/uiDesigner/icons/toolbar.svg" removable="false" auto-create-binding="false" can-attach-label="false">
+        <default-constraints vsize-policy="0" hsize-policy="6" anchor="0" fill="1">
+          <preferred-size width="-1" height="20" />
+        </default-constraints>
+      </item>
+      <item class="javax.swing.JToolBar$Separator" icon="/com/intellij/uiDesigner/icons/toolbarSeparator.svg" removable="false" auto-create-binding="false" can-attach-label="false">
+        <default-constraints vsize-policy="0" hsize-policy="0" anchor="0" fill="1" />
+      </item>
+      <item class="javax.swing.JScrollBar" icon="/com/intellij/uiDesigner/icons/scrollbar.svg" removable="false" auto-create-binding="true" can-attach-label="false">
+        <default-constraints vsize-policy="6" hsize-policy="0" anchor="0" fill="2" />
+      </item>
+    </group>
+  </component>
+</project>

bindings/java/README.md

@@ -0,0 +1,71 @@
+# Java JNI bindings for Whisper
+
+This package provides Java JNI bindings for whisper.cpp. They have been tested on:
+
+  * <strike>Darwin (OS X) 12.6 on x64_64</strike>
+  * Ubuntu on x86_64
+  * Windows on x86_64
+
+The "low level" bindings are in `WhisperCppJnaLibrary`. The most simple usage is as follows:
+
+JNA will attempt to load the `whispercpp` shared library from:
+
+- jna.library.path
+- jna.platform.library
+- ~/Library/Frameworks
+- /Library/Frameworks
+- /System/Library/Frameworks
+- classpath
+
+```java
+import io.github.ggerganov.whispercpp.WhisperCpp;
+
+public class Example {
+
+    public static void main(String[] args) {
+        WhisperCpp whisper = new WhisperCpp();
+        // By default, models are loaded from ~/.cache/whisper/ and are usually named "ggml-${name}.bin"
+        // or you can provide the absolute path to the model file.
+        long context = whisper.initContext("base.en");
+        try {
+            var whisperParams = whisper.getFullDefaultParams(WhisperSamplingStrategy.WHISPER_SAMPLING_GREEDY);
+            // custom configuration if required
+            whisperParams.temperature_inc = 0f;
+            
+            var samples = readAudio(); // divide each value by 32767.0f
+            whisper.fullTranscribe(whisperParams, samples);
+            
+            int segmentCount = whisper.getTextSegmentCount(context);
+            for (int i = 0; i < segmentCount; i++) {
+                String text = whisper.getTextSegment(context, i);
+                System.out.println(segment.getText());
+            }
+        } finally {
+             whisper.freeContext(context);
+        }
+     }
+}
+```
+
+## Building & Testing
+
+In order to build, you need to have the JDK 8 or higher installed. Run the tests with:
+
+```bash
+git clone https://github.com/ggerganov/whisper.cpp.git
+cd whisper.cpp/bindings/java
+
+./gradlew build
+```
+
+You need to have the `whisper` library in your [JNA library path](https://java-native-access.github.io/jna/4.2.1/com/sun/jna/NativeLibrary.html). On Windows the dll is included in the jar and you can update it:
+
+```bash
+copy /y ..\..\build\bin\Release\whisper.dll build\generated\resources\main\win32-x86-64\whisper.dll
+```
+
+
+## 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/java/build.gradle

@@ -0,0 +1,112 @@
+plugins {
+    id 'java'
+    id 'java-library'
+    id 'maven-publish'
+}
+
+archivesBaseName = 'whispercpp'
+group = 'io.github.ggerganov'
+version = '1.4.0'
+
+sourceCompatibility = 1.8
+targetCompatibility = 1.8
+
+sourceSets {
+    main {
+        resources {
+            srcDirs = ['src/main/resources', 'build/generated/resources/main']
+        }
+    }
+    test {
+        runtimeClasspath += files('build/generated/resources/main')
+    }
+}
+
+tasks.register('copyLibwhisperDynlib', Copy) {
+    from '../../build'
+    include 'libwhisper.dynlib'
+    into 'build/generated/resources/main/darwin'
+}
+
+tasks.register('copyLibwhisperSo', Copy) {
+    from '../../build'
+    include 'libwhisper.so'
+    into 'build/generated/resources/main/linux-x86-64'
+}
+
+tasks.register('copyWhisperDll', Copy) {
+    from '../../build/Release'
+    include 'whisper.dll'
+    into 'build/generated/resources/main/windows-x86-64'
+}
+
+tasks.register('copyLibs') {
+    dependsOn copyLibwhisperDynlib, copyLibwhisperSo, copyWhisperDll
+}
+
+test {
+    systemProperty 'jna.library.path', project.file('build/generated/resources/main').absolutePath
+}
+
+java {
+    withSourcesJar()
+    withJavadocJar()
+}
+
+jar {
+    exclude '**/whisper_java.exp', '**/whisper_java.lib'
+}
+
+javadoc {
+    options.addStringOption('Xdoclint:none', '-quiet')
+}
+
+tasks.withType(Test) {
+    useJUnitPlatform()
+}
+
+dependencies {
+    implementation "net.java.dev.jna:jna:5.13.0"
+    testImplementation "org.junit.jupiter:junit-jupiter:5.9.2"
+    testImplementation "org.assertj:assertj-core:3.24.2"
+}
+
+repositories {
+    mavenCentral()
+}
+
+publishing {
+    publications {
+        mavenJava(MavenPublication) {
+            artifactId = 'whispercpp'
+            from components.java
+            pom {
+                name = 'whispercpp'
+                description = "Java JNA bindings for OpenAI's Whisper model, implemented in C/C++"
+                url = 'https://github.com/ggerganov/whisper.cpp'
+                licenses {
+                    license {
+                        name = 'MIT licence'
+                        url = 'https://raw.githubusercontent.com/ggerganov/whisper.cpp/master/LICENSE'
+                    }
+                }
+                developers {
+                    developer {
+                        id = 'ggerganov'
+                        name = 'Georgi Gerganov'
+                        email = 'ggerganov@gmail.com'
+                    }
+                    developer {
+                        id = 'nalbion'
+                        name = 'Nicholas Albion'
+                        email = 'nalbion@yahoo.com'
+                    }
+                }
+                scm {
+                    connection = 'scm:git:git://github.com/ggerganov/whisper.cpp.git'
+                    url = 'https://github.com/ggerganov/whisper.cpp'
+                }
+            }
+        }
+    }
+}

bindings/java/gradle.properties

@@ -0,0 +1,6 @@
+org.gradle.jvmargs=-Xms256m -Xmx1024m
+system.include.dir=/usr/include
+#system.local.include.dir=../../include
+system.local.include.dir=./build/generated/sources/headers/java/main
+jni.include.dir=/usr/lib/jvm/java-8-openjdk-amd64/include/
+jni.lib.dir=/usr/lib/jvm/java-8-openjdk-amd64/lib/

bindings/java/gradle/wrapper/gradle-wrapper.properties

@@ -0,0 +1,6 @@
+distributionBase=GRADLE_USER_HOME
+distributionPath=wrapper/dists
+distributionUrl=https\://services.gradle.org/distributions/gradle-8.1-bin.zip
+networkTimeout=10000
+zipStoreBase=GRADLE_USER_HOME
+zipStorePath=wrapper/dists

bindings/java/gradlew

@@ -0,0 +1,244 @@
+#!/bin/sh
+
+#
+# Copyright © 2015-2021 the original authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+##############################################################################
+#
+#   Gradle start up script for POSIX generated by Gradle.
+#
+#   Important for running:
+#
+#   (1) You need a POSIX-compliant shell to run this script. If your /bin/sh is
+#       noncompliant, but you have some other compliant shell such as ksh or
+#       bash, then to run this script, type that shell name before the whole
+#       command line, like:
+#
+#           ksh Gradle
+#
+#       Busybox and similar reduced shells will NOT work, because this script
+#       requires all of these POSIX shell features:
+#         * functions;
+#         * expansions «$var», «${var}», «${var:-default}», «${var+SET}»,
+#           «${var#prefix}», «${var%suffix}», and «$( cmd )»;
+#         * compound commands having a testable exit status, especially «case»;
+#         * various built-in commands including «command», «set», and «ulimit».
+#
+#   Important for patching:
+#
+#   (2) This script targets any POSIX shell, so it avoids extensions provided
+#       by Bash, Ksh, etc; in particular arrays are avoided.
+#
+#       The "traditional" practice of packing multiple parameters into a
+#       space-separated string is a well documented source of bugs and security
+#       problems, so this is (mostly) avoided, by progressively accumulating
+#       options in "$@", and eventually passing that to Java.
+#
+#       Where the inherited environment variables (DEFAULT_JVM_OPTS, JAVA_OPTS,
+#       and GRADLE_OPTS) rely on word-splitting, this is performed explicitly;
+#       see the in-line comments for details.
+#
+#       There are tweaks for specific operating systems such as AIX, CygWin,
+#       Darwin, MinGW, and NonStop.
+#
+#   (3) This script is generated from the Groovy template
+#       https://github.com/gradle/gradle/blob/HEAD/subprojects/plugins/src/main/resources/org/gradle/api/internal/plugins/unixStartScript.txt
+#       within the Gradle project.
+#
+#       You can find Gradle at https://github.com/gradle/gradle/.
+#
+##############################################################################
+
+# Attempt to set APP_HOME
+
+# Resolve links: $0 may be a link
+app_path=$0
+
+# Need this for daisy-chained symlinks.
+while
+    APP_HOME=${app_path%"${app_path##*/}"}  # leaves a trailing /; empty if no leading path
+    [ -h "$app_path" ]
+do
+    ls=$( ls -ld "$app_path" )
+    link=${ls#*' -> '}
+    case $link in             #(
+      /*)   app_path=$link ;; #(
+      *)    app_path=$APP_HOME$link ;;
+    esac
+done
+
+# This is normally unused
+# shellcheck disable=SC2034
+APP_BASE_NAME=${0##*/}
+APP_HOME=$( cd "${APP_HOME:-./}" && pwd -P ) || exit
+
+# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+DEFAULT_JVM_OPTS='"-Xmx64m" "-Xms64m"'
+
+# Use the maximum available, or set MAX_FD != -1 to use that value.
+MAX_FD=maximum
+
+warn () {
+    echo "$*"
+} >&2
+
+die () {
+    echo
+    echo "$*"
+    echo
+    exit 1
+} >&2
+
+# OS specific support (must be 'true' or 'false').
+cygwin=false
+msys=false
+darwin=false
+nonstop=false
+case "$( uname )" in                #(
+  CYGWIN* )         cygwin=true  ;; #(
+  Darwin* )         darwin=true  ;; #(
+  MSYS* | MINGW* )  msys=true    ;; #(
+  NONSTOP* )        nonstop=true ;;
+esac
+
+CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar
+
+
+# Determine the Java command to use to start the JVM.
+if [ -n "$JAVA_HOME" ] ; then
+    if [ -x "$JAVA_HOME/jre/sh/java" ] ; then
+        # IBM's JDK on AIX uses strange locations for the executables
+        JAVACMD=$JAVA_HOME/jre/sh/java
+    else
+        JAVACMD=$JAVA_HOME/bin/java
+    fi
+    if [ ! -x "$JAVACMD" ] ; then
+        die "ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+    fi
+else
+    JAVACMD=java
+    which java >/dev/null 2>&1 || die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+fi
+
+# Increase the maximum file descriptors if we can.
+if ! "$cygwin" && ! "$darwin" && ! "$nonstop" ; then
+    case $MAX_FD in #(
+      max*)
+        # In POSIX sh, ulimit -H is undefined. That's why the result is checked to see if it worked.
+        # shellcheck disable=SC3045
+        MAX_FD=$( ulimit -H -n ) ||
+            warn "Could not query maximum file descriptor limit"
+    esac
+    case $MAX_FD in  #(
+      '' | soft) :;; #(
+      *)
+        # In POSIX sh, ulimit -n is undefined. That's why the result is checked to see if it worked.
+        # shellcheck disable=SC3045
+        ulimit -n "$MAX_FD" ||
+            warn "Could not set maximum file descriptor limit to $MAX_FD"
+    esac
+fi
+
+# Collect all arguments for the java command, stacking in reverse order:
+#   * args from the command line
+#   * the main class name
+#   * -classpath
+#   * -D...appname settings
+#   * --module-path (only if needed)
+#   * DEFAULT_JVM_OPTS, JAVA_OPTS, and GRADLE_OPTS environment variables.
+
+# For Cygwin or MSYS, switch paths to Windows format before running java
+if "$cygwin" || "$msys" ; then
+    APP_HOME=$( cygpath --path --mixed "$APP_HOME" )
+    CLASSPATH=$( cygpath --path --mixed "$CLASSPATH" )
+
+    JAVACMD=$( cygpath --unix "$JAVACMD" )
+
+    # Now convert the arguments - kludge to limit ourselves to /bin/sh
+    for arg do
+        if
+            case $arg in                                #(
+              -*)   false ;;                            # don't mess with options #(
+              /?*)  t=${arg#/} t=/${t%%/*}              # looks like a POSIX filepath
+                    [ -e "$t" ] ;;                      #(
+              *)    false ;;
+            esac
+        then
+            arg=$( cygpath --path --ignore --mixed "$arg" )
+        fi
+        # Roll the args list around exactly as many times as the number of
+        # args, so each arg winds up back in the position where it started, but
+        # possibly modified.
+        #
+        # NB: a `for` loop captures its iteration list before it begins, so
+        # changing the positional parameters here affects neither the number of
+        # iterations, nor the values presented in `arg`.
+        shift                   # remove old arg
+        set -- "$@" "$arg"      # push replacement arg
+    done
+fi
+
+# Collect all arguments for the java command;
+#   * $DEFAULT_JVM_OPTS, $JAVA_OPTS, and $GRADLE_OPTS can contain fragments of
+#     shell script including quotes and variable substitutions, so put them in
+#     double quotes to make sure that they get re-expanded; and
+#   * put everything else in single quotes, so that it's not re-expanded.
+
+set -- \
+        "-Dorg.gradle.appname=$APP_BASE_NAME" \
+        -classpath "$CLASSPATH" \
+        org.gradle.wrapper.GradleWrapperMain \
+        "$@"
+
+# Stop when "xargs" is not available.
+if ! command -v xargs >/dev/null 2>&1
+then
+    die "xargs is not available"
+fi
+
+# Use "xargs" to parse quoted args.
+#
+# With -n1 it outputs one arg per line, with the quotes and backslashes removed.
+#
+# In Bash we could simply go:
+#
+#   readarray ARGS < <( xargs -n1 <<<"$var" ) &&
+#   set -- "${ARGS[@]}" "$@"
+#
+# but POSIX shell has neither arrays nor command substitution, so instead we
+# post-process each arg (as a line of input to sed) to backslash-escape any
+# character that might be a shell metacharacter, then use eval to reverse
+# that process (while maintaining the separation between arguments), and wrap
+# the whole thing up as a single "set" statement.
+#
+# This will of course break if any of these variables contains a newline or
+# an unmatched quote.
+#
+
+eval "set -- $(
+        printf '%s\n' "$DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS" |
+        xargs -n1 |
+        sed ' s~[^-[:alnum:]+,./:=@_]~\\&~g; ' |
+        tr '\n' ' '
+    )" '"$@"'
+
+exec "$JAVACMD" "$@"

bindings/java/gradlew.bat

@@ -0,0 +1,92 @@
+@rem
+@rem Copyright 2015 the original author or authors.
+@rem
+@rem Licensed under the Apache License, Version 2.0 (the "License");
+@rem you may not use this file except in compliance with the License.
+@rem You may obtain a copy of the License at
+@rem
+@rem      https://www.apache.org/licenses/LICENSE-2.0
+@rem
+@rem Unless required by applicable law or agreed to in writing, software
+@rem distributed under the License is distributed on an "AS IS" BASIS,
+@rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+@rem See the License for the specific language governing permissions and
+@rem limitations under the License.
+@rem
+
+@if "%DEBUG%"=="" @echo off
+@rem ##########################################################################
+@rem
+@rem  Gradle startup script for Windows
+@rem
+@rem ##########################################################################
+
+@rem Set local scope for the variables with windows NT shell
+if "%OS%"=="Windows_NT" setlocal
+
+set DIRNAME=%~dp0
+if "%DIRNAME%"=="" set DIRNAME=.
+@rem This is normally unused
+set APP_BASE_NAME=%~n0
+set APP_HOME=%DIRNAME%
+
+@rem Resolve any "." and ".." in APP_HOME to make it shorter.
+for %%i in ("%APP_HOME%") do set APP_HOME=%%~fi
+
+@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+set DEFAULT_JVM_OPTS="-Xmx64m" "-Xms64m"
+
+@rem Find java.exe
+if defined JAVA_HOME goto findJavaFromJavaHome
+
+set JAVA_EXE=java.exe
+%JAVA_EXE% -version >NUL 2>&1
+if %ERRORLEVEL% equ 0 goto execute
+
+echo.
+echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+echo.
+echo Please set the JAVA_HOME variable in your environment to match the
+echo location of your Java installation.
+
+goto fail
+
+:findJavaFromJavaHome
+set JAVA_HOME=%JAVA_HOME:"=%
+set JAVA_EXE=%JAVA_HOME%/bin/java.exe
+
+if exist "%JAVA_EXE%" goto execute
+
+echo.
+echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%
+echo.
+echo Please set the JAVA_HOME variable in your environment to match the
+echo location of your Java installation.
+
+goto fail
+
+:execute
+@rem Setup the command line
+
+set CLASSPATH=%APP_HOME%\gradle\wrapper\gradle-wrapper.jar
+
+
+@rem Execute Gradle
+"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %*
+
+:end
+@rem End local scope for the variables with windows NT shell
+if %ERRORLEVEL% equ 0 goto mainEnd
+
+:fail
+rem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of
+rem the _cmd.exe /c_ return code!
+set EXIT_CODE=%ERRORLEVEL%
+if %EXIT_CODE% equ 0 set EXIT_CODE=1
+if not ""=="%GRADLE_EXIT_CONSOLE%" exit %EXIT_CODE%
+exit /b %EXIT_CODE%
+
+:mainEnd
+if "%OS%"=="Windows_NT" endlocal
+
+:omega

bindings/java/settings.gradle

@@ -0,0 +1 @@
+rootProject.name = "whispercpp"

bindings/java/src/main/java/io/github/ggerganov/whispercpp/WhisperContext.java

@@ -0,0 +1,39 @@
+package io.github.ggerganov.whispercpp;
+
+import com.sun.jna.Structure;
+import com.sun.jna.ptr.PointerByReference;
+import io.github.ggerganov.whispercpp.ggml.GgmlType;
+import io.github.ggerganov.whispercpp.WhisperModel;
+
+import java.util.List;
+
+public class WhisperContext extends Structure {
+    int t_load_us = 0;
+    int t_start_us = 0;
+
+    /** weight type (FP32 / FP16 / QX) */
+    GgmlType wtype = GgmlType.GGML_TYPE_F16;
+    /** intermediate type (FP32 or FP16) */
+    GgmlType itype = GgmlType.GGML_TYPE_F16;
+
+//    WhisperModel model;
+    public PointerByReference model;
+//    whisper_vocab vocab;
+//    whisper_state * state = nullptr;
+    public PointerByReference vocab;
+    public PointerByReference state;
+
+    /** populated by whisper_init_from_file() */
+    String path_model;
+
+//    public static class ByReference extends WhisperContext implements Structure.ByReference {
+//    }
+//
+//    public static class ByValue extends WhisperContext implements Structure.ByValue {
+//    }
+//
+//    @Override
+//    protected List<String> getFieldOrder() {
+//        return List.of("t_load_us", "t_start_us", "wtype", "itype", "model", "vocab", "state", "path_model");
+//    }
+}

bindings/java/src/main/java/io/github/ggerganov/whispercpp/WhisperCpp.java

@@ -0,0 +1,151 @@
+package io.github.ggerganov.whispercpp;
+
+import com.sun.jna.Native;
+import com.sun.jna.Pointer;
+import io.github.ggerganov.whispercpp.params.WhisperFullParams;
+import io.github.ggerganov.whispercpp.params.WhisperSamplingStrategy;
+
+import java.io.File;
+import java.io.FileNotFoundException;
+import java.io.IOException;
+
+/**
+ * Before calling most methods, you must call `initContext(modelPath)` to initialise the `ctx` Pointer.
+ */
+public class WhisperCpp implements AutoCloseable {
+    private WhisperCppJnaLibrary lib = WhisperCppJnaLibrary.instance;
+    private Pointer ctx = null;
+    private Pointer greedyPointer = null;
+    private Pointer beamPointer = null;
+
+    public File modelDir() {
+        String modelDirPath = System.getenv("XDG_CACHE_HOME");
+        if (modelDirPath == null) {
+            modelDirPath = System.getProperty("user.home") + "/.cache";
+        }
+
+        return new File(modelDirPath, "whisper");
+    }
+
+    /**
+     * @param modelPath - absolute path, or just the name (eg: "base", "base-en" or "base.en")
+     */
+    public void initContext(String modelPath) throws FileNotFoundException {
+        if (ctx != null) {
+            lib.whisper_free(ctx);
+        }
+
+        if (!modelPath.contains("/") && !modelPath.contains("\\")) {
+            if (!modelPath.endsWith(".bin")) {
+                modelPath = "ggml-" + modelPath.replace("-", ".") + ".bin";
+            }
+
+            modelPath = new File(modelDir(), modelPath).getAbsolutePath();
+        }
+
+        ctx = lib.whisper_init_from_file(modelPath);
+
+        if (ctx == null) {
+            throw new FileNotFoundException(modelPath);
+        }
+    }
+
+    /**
+     * Provides default params which can be used with `whisper_full()` etc.
+     * Because this function allocates memory for the params, the caller must call either:
+     * - call `whisper_free_params()`
+     * - `Native.free(Pointer.nativeValue(pointer));`
+     *
+     * @param strategy - GREEDY
+     */
+    public WhisperFullParams getFullDefaultParams(WhisperSamplingStrategy strategy) {
+        Pointer pointer;
+
+        // whisper_full_default_params_by_ref allocates memory which we need to delete, so only create max 1 pointer for each strategy.
+        if (strategy == WhisperSamplingStrategy.WHISPER_SAMPLING_GREEDY) {
+            if (greedyPointer == null) {
+                greedyPointer = lib.whisper_full_default_params_by_ref(strategy.ordinal());
+            }
+            pointer = greedyPointer;
+        } else {
+            if (beamPointer == null) {
+                beamPointer = lib.whisper_full_default_params_by_ref(strategy.ordinal());
+            }
+            pointer = beamPointer;
+        }
+
+        WhisperFullParams params = new WhisperFullParams(pointer);
+        params.read();
+        return params;
+    }
+
+    @Override
+    public void close() {
+        freeContext();
+        freeParams();
+        System.out.println("Whisper closed");
+    }
+
+    private void freeContext() {
+        if (ctx != null) {
+            lib.whisper_free(ctx);
+        }
+    }
+
+    private void freeParams() {
+        if (greedyPointer != null) {
+            Native.free(Pointer.nativeValue(greedyPointer));
+            greedyPointer = null;
+        }
+        if (beamPointer != null) {
+            Native.free(Pointer.nativeValue(beamPointer));
+            beamPointer = null;
+        }
+    }
+
+    /**
+     * Run the entire model: PCM -> log mel spectrogram -> encoder -> decoder -> text.
+     * Not thread safe for same context
+     * Uses the specified decoding strategy to obtain the text.
+     */
+    public String fullTranscribe(WhisperFullParams whisperParams, float[] audioData) throws IOException {
+        if (ctx == null) {
+            throw new IllegalStateException("Model not initialised");
+        }
+
+        if (lib.whisper_full(ctx, whisperParams, audioData, audioData.length) != 0) {
+            throw new IOException("Failed to process audio");
+        }
+
+        int nSegments = lib.whisper_full_n_segments(ctx);
+
+        StringBuilder str = new StringBuilder();
+
+        for (int i = 0; i < nSegments; i++) {
+            String text = lib.whisper_full_get_segment_text(ctx, i);
+            System.out.println("Segment:" + text);
+            str.append(text);
+        }
+
+        return str.toString().trim();
+    }
+
+//    public int getTextSegmentCount(Pointer ctx) {
+//        return lib.whisper_full_n_segments(ctx);
+//    }
+//    public String getTextSegment(Pointer ctx, int index) {
+//        return lib.whisper_full_get_segment_text(ctx, index);
+//    }
+
+    public String getSystemInfo() {
+        return lib.whisper_print_system_info();
+    }
+
+    public int benchMemcpy(int nthread) {
+        return lib.whisper_bench_memcpy(nthread);
+    }
+
+    public int benchGgmlMulMat(int nthread) {
+        return lib.whisper_bench_ggml_mul_mat(nthread);
+    }
+}

bindings/java/src/main/java/io/github/ggerganov/whispercpp/WhisperCppJnaLibrary.java

@@ -0,0 +1,376 @@
+package io.github.ggerganov.whispercpp;
+
+import com.sun.jna.Library;
+import com.sun.jna.Native;
+import com.sun.jna.Pointer;
+import io.github.ggerganov.whispercpp.model.WhisperModelLoader;
+import io.github.ggerganov.whispercpp.model.WhisperTokenData;
+import io.github.ggerganov.whispercpp.params.WhisperFullParams;
+
+public interface WhisperCppJnaLibrary extends Library {
+    WhisperCppJnaLibrary instance = Native.load("whisper", WhisperCppJnaLibrary.class);
+
+    String whisper_print_system_info();
+
+    /**
+     * Allocate (almost) all memory needed for the model by loading from a file.
+     *
+     * @param path_model Path to the model file
+     * @return Whisper context on success, null on failure
+     */
+    Pointer whisper_init_from_file(String path_model);
+
+    /**
+     * Allocate (almost) all memory needed for the model by loading from a buffer.
+     *
+     * @param buffer       Model buffer
+     * @param buffer_size  Size of the model buffer
+     * @return Whisper context on success, null on failure
+     */
+    Pointer whisper_init_from_buffer(Pointer buffer, int buffer_size);
+
+    /**
+     * Allocate (almost) all memory needed for the model using a model loader.
+     *
+     * @param loader Model loader
+     * @return Whisper context on success, null on failure
+     */
+    Pointer whisper_init(WhisperModelLoader loader);
+
+    /**
+     * Allocate (almost) all memory needed for the model by loading from a file without allocating the state.
+     *
+     * @param path_model Path to the model file
+     * @return Whisper context on success, null on failure
+     */
+    Pointer whisper_init_from_file_no_state(String path_model);
+
+    /**
+     * Allocate (almost) all memory needed for the model by loading from a buffer without allocating the state.
+     *
+     * @param buffer       Model buffer
+     * @param buffer_size  Size of the model buffer
+     * @return Whisper context on success, null on failure
+     */
+    Pointer whisper_init_from_buffer_no_state(Pointer buffer, int buffer_size);
+
+//    Pointer whisper_init_from_buffer_no_state(Pointer buffer, long buffer_size);
+
+    /**
+     * Allocate (almost) all memory needed for the model using a model loader without allocating the state.
+     *
+     * @param loader Model loader
+     * @return Whisper context on success, null on failure
+     */
+    Pointer whisper_init_no_state(WhisperModelLoader loader);
+
+    /**
+     * Allocate memory for the Whisper state.
+     *
+     * @param ctx Whisper context
+     * @return Whisper state on success, null on failure
+     */
+    Pointer whisper_init_state(Pointer ctx);
+
+    /**
+     * Free all allocated memory associated with the Whisper context.
+     *
+     * @param ctx Whisper context
+     */
+    void whisper_free(Pointer ctx);
+
+    /**
+     * Free all allocated memory associated with the Whisper state.
+     *
+     * @param state Whisper state
+     */
+    void whisper_free_state(Pointer state);
+
+
+    /**
+     * Convert RAW PCM audio to log mel spectrogram.
+     * The resulting spectrogram is stored inside the default state of the provided whisper context.
+     *
+     * @param ctx - Pointer to a WhisperContext
+     * @return 0 on success
+     */
+    int whisper_pcm_to_mel(Pointer ctx, final float[] samples, int n_samples, int n_threads);
+
+    /**
+     * @param ctx Pointer to a WhisperContext
+     * @param state Pointer to WhisperState
+     * @param n_samples
+     * @param n_threads
+     * @return 0 on success
+     */
+    int whisper_pcm_to_mel_with_state(Pointer ctx, Pointer state, final float[] samples, int n_samples, int n_threads);
+
+    /**
+     * This can be used to set a custom log mel spectrogram inside the default state of 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
+     * @return 0 on success
+     */
+    int whisper_set_mel(Pointer ctx, final float[] data, int n_len, int n_mel);
+    int whisper_set_mel_with_state(Pointer ctx, Pointer state, final float[] data, int n_len, int n_mel);
+
+    /**
+     * Run the Whisper encoder on the log mel spectrogram stored inside the default state in 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.
+     * @return 0 on success
+     */
+    int whisper_encode(Pointer ctx, int offset, int n_threads);
+
+    int whisper_encode_with_state(Pointer ctx, Pointer state, int offset, int n_threads);
+
+    /**
+     * 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.
+     * Returns 0 on success
+     * TODO: add support for multiple decoders
+     */
+    int whisper_decode(Pointer ctx, Pointer tokens, int n_tokens, int n_past, int n_threads);
+
+    /**
+     * @param ctx
+     * @param state
+     * @param tokens Pointer to int tokens
+     * @param n_tokens
+     * @param n_past
+     * @param n_threads
+     * @return
+     */
+    int whisper_decode_with_state(Pointer ctx, Pointer state, Pointer tokens, int n_tokens, int n_past, int n_threads);
+
+    /**
+     * 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, no more than n_max_tokens
+     * Returns -1 on failure
+     * TODO: not sure if correct
+     */
+    int whisper_tokenize(Pointer ctx, String text, Pointer tokens, int n_max_tokens);
+
+    /** Largest language id (i.e. number of available languages - 1) */
+    int whisper_lang_max_id();
+
+    /**
+     * @return the id of the specified language, returns -1 if not found.
+     * Examples:
+     *   "de" -> 2
+     *   "german" -> 2
+     */
+    int whisper_lang_id(String lang);
+
+    /** @return the short string of the specified language id (e.g. 2 -> "de"), returns nullptr if not found */
+    String whisper_lang_str(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 top language id or negative on failure
+     * If not null, fills the lang_probs array with the probabilities of all languages
+     * The array must be whisper_lang_max_id() + 1 in size
+     *
+     * ref: https://github.com/openai/whisper/blob/main/whisper/decoding.py#L18-L69
+     */
+    int whisper_lang_auto_detect(Pointer ctx, int offset_ms, int n_threads, float[] lang_probs);
+
+    int whisper_lang_auto_detect_with_state(Pointer ctx, Pointer state, int offset_ms, int n_threads, float[] lang_probs);
+
+    int whisper_n_len           (Pointer ctx); // mel length
+    int whisper_n_len_from_state(Pointer state); // mel length
+    int whisper_n_vocab         (Pointer ctx);
+    int whisper_n_text_ctx      (Pointer ctx);
+    int whisper_n_audio_ctx     (Pointer ctx);
+    int whisper_is_multilingual (Pointer ctx);
+
+    int whisper_model_n_vocab      (Pointer ctx);
+    int whisper_model_n_audio_ctx  (Pointer ctx);
+    int whisper_model_n_audio_state(Pointer ctx);
+    int whisper_model_n_audio_head (Pointer ctx);
+    int whisper_model_n_audio_layer(Pointer ctx);
+    int whisper_model_n_text_ctx   (Pointer ctx);
+    int whisper_model_n_text_state (Pointer ctx);
+    int whisper_model_n_text_head  (Pointer ctx);
+    int whisper_model_n_text_layer (Pointer ctx);
+    int whisper_model_n_mels       (Pointer ctx);
+    int whisper_model_ftype        (Pointer ctx);
+    int whisper_model_type         (Pointer ctx);
+
+    /**
+     * Token logits obtained from the last call to whisper_decode().
+     * The logits for the last token are stored in the last row
+     * Rows: n_tokens
+     * Cols: n_vocab
+     */
+    float[] whisper_get_logits           (Pointer ctx);
+    float[] whisper_get_logits_from_state(Pointer state);
+
+    // Token Id -> String. Uses the vocabulary in the provided context
+    String whisper_token_to_str(Pointer ctx, int token);
+    String whisper_model_type_readable(Pointer ctx);
+
+    // Special tokens
+    int whisper_token_eot (Pointer ctx);
+    int whisper_token_sot (Pointer ctx);
+    int whisper_token_prev(Pointer ctx);
+    int whisper_token_solm(Pointer ctx);
+    int whisper_token_not (Pointer ctx);
+    int whisper_token_beg (Pointer ctx);
+    int whisper_token_lang(Pointer ctx, int lang_id);
+
+    // Task tokens
+    int whisper_token_translate (Pointer ctx);
+    int whisper_token_transcribe(Pointer ctx);
+
+    // Performance information from the default state.
+    void whisper_print_timings(Pointer ctx);
+    void whisper_reset_timings(Pointer ctx);
+
+    // Note: Even if `whisper_full_params is stripped back to just 4 ints, JNA throws "Invalid memory access"
+    //       when `whisper_full_default_params()` tries to return a struct.
+    // WhisperFullParams whisper_full_default_params(int strategy);
+
+    /**
+     * Provides default params which can be used with `whisper_full()` etc.
+     * Because this function allocates memory for the params, the caller must call either:
+     * - call `whisper_free_params()`
+     * - `Native.free(Pointer.nativeValue(pointer));`
+     *
+     * @param strategy - WhisperSamplingStrategy.value
+     */
+    Pointer whisper_full_default_params_by_ref(int strategy);
+
+    void whisper_free_params(Pointer params);
+
+    /**
+     * Run the entire model: PCM -> log mel spectrogram -> encoder -> decoder -> text
+     * Not thread safe for same context
+     * Uses the specified decoding strategy to obtain the text.
+     */
+    int whisper_full(Pointer ctx, WhisperFullParams params, final float[] samples, int n_samples);
+
+    int whisper_full_with_state(Pointer ctx, Pointer state, WhisperFullParams params, final float[] samples, int n_samples);
+
+    // Split the input audio in chunks and process each chunk separately using whisper_full_with_state()
+    // Result is stored in the default state of the context
+    // Not thread safe if executed in parallel on the same context.
+    // 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.
+    int whisper_full_parallel(Pointer ctx, WhisperFullParams params, final float[] samples, int n_samples, int n_processors);
+
+    /**
+     * Number of generated text segments.
+     * A segment can be a few words, a sentence, or even a paragraph.
+     * @param ctx Pointer to WhisperContext
+     */
+    int whisper_full_n_segments (Pointer ctx);
+
+    /**
+     * @param state Pointer to WhisperState
+     */
+    int whisper_full_n_segments_from_state(Pointer state);
+
+    /**
+     * Language id associated with the context's default state.
+     * @param ctx Pointer to WhisperContext
+     */
+    int whisper_full_lang_id(Pointer ctx);
+
+    /** Language id associated with the provided state */
+    int whisper_full_lang_id_from_state(Pointer state);
+
+    /**
+     * Convert RAW PCM audio to log mel spectrogram but applies a Phase Vocoder to speed up the audio x2.
+     * The resulting spectrogram is stored inside the default state of the provided whisper context.
+     * @return 0 on success
+     */
+    int whisper_pcm_to_mel_phase_vocoder(Pointer ctx, final float[] samples, int n_samples, int n_threads);
+
+    int whisper_pcm_to_mel_phase_vocoder_with_state(Pointer ctx, Pointer state, final float[] samples, int n_samples, int n_threads);
+
+    /** Get the start time of the specified segment. */
+    long whisper_full_get_segment_t0(Pointer ctx, int i_segment);
+
+    /** Get the start time of the specified segment from the state. */
+    long whisper_full_get_segment_t0_from_state(Pointer state, int i_segment);
+
+    /** Get the end time of the specified segment. */
+    long whisper_full_get_segment_t1(Pointer ctx, int i_segment);
+
+    /** Get the end time of the specified segment from the state. */
+    long whisper_full_get_segment_t1_from_state(Pointer state, int i_segment);
+
+    /** Get the text of the specified segment. */
+    String whisper_full_get_segment_text(Pointer ctx, int i_segment);
+
+    /** Get the text of the specified segment from the state. */
+    String whisper_full_get_segment_text_from_state(Pointer state, int i_segment);
+
+    /** Get the number of tokens in the specified segment. */
+    int whisper_full_n_tokens(Pointer ctx, int i_segment);
+
+    /** Get the number of tokens in the specified segment from the state. */
+    int whisper_full_n_tokens_from_state(Pointer state, int i_segment);
+
+    /** Get the token text of the specified token in the specified segment. */
+    String whisper_full_get_token_text(Pointer ctx, int i_segment, int i_token);
+
+
+    /** Get the token text of the specified token in the specified segment from the state. */
+    String whisper_full_get_token_text_from_state(Pointer ctx, Pointer state, int i_segment, int i_token);
+
+    /** Get the token ID of the specified token in the specified segment. */
+    int whisper_full_get_token_id(Pointer ctx, int i_segment, int i_token);
+
+    /** Get the token ID of the specified token in the specified segment from the state. */
+    int whisper_full_get_token_id_from_state(Pointer state, int i_segment, int i_token);
+
+    /** Get token data for the specified token in the specified segment. */
+    WhisperTokenData whisper_full_get_token_data(Pointer ctx, int i_segment, int i_token);
+
+    /** Get token data for the specified token in the specified segment from the state. */
+    WhisperTokenData whisper_full_get_token_data_from_state(Pointer state, int i_segment, int i_token);
+
+    /** Get the probability of the specified token in the specified segment. */
+    float whisper_full_get_token_p(Pointer ctx, int i_segment, int i_token);
+
+    /** Get the probability of the specified token in the specified segment from the state. */
+    float whisper_full_get_token_p_from_state(Pointer state, int i_segment, int i_token);
+
+    /**
+     * Benchmark function for memcpy.
+     *
+     * @param nThreads Number of threads to use for the benchmark.
+     * @return The result of the benchmark.
+     */
+    int whisper_bench_memcpy(int nThreads);
+
+    /**
+     * Benchmark function for memcpy as a string.
+     *
+     * @param nThreads Number of threads to use for the benchmark.
+     * @return The result of the benchmark as a string.
+     */
+    String whisper_bench_memcpy_str(int nThreads);
+
+    /**
+     * Benchmark function for ggml_mul_mat.
+     *
+     * @param nThreads Number of threads to use for the benchmark.
+     * @return The result of the benchmark.
+     */
+    int whisper_bench_ggml_mul_mat(int nThreads);
+
+    /**
+     * Benchmark function for ggml_mul_mat as a string.
+     *
+     * @param nThreads Number of threads to use for the benchmark.
+     * @return The result of the benchmark as a string.
+     */
+    String whisper_bench_ggml_mul_mat_str(int nThreads);
+}

bindings/java/src/main/java/io/github/ggerganov/whispercpp/callbacks/WhisperEncoderBeginCallback.java

@@ -0,0 +1,24 @@
+package io.github.ggerganov.whispercpp.callbacks;
+
+import com.sun.jna.Callback;
+import com.sun.jna.Pointer;
+import io.github.ggerganov.whispercpp.WhisperContext;
+import io.github.ggerganov.whispercpp.model.WhisperState;
+
+/**
+ * Callback before the encoder starts.
+ * If not null, called before the encoder starts.
+ * If it returns false, the computation is aborted.
+ */
+public interface WhisperEncoderBeginCallback extends Callback {
+
+    /**
+     * Callback method before the encoder starts.
+     *
+     * @param ctx        The whisper context.
+     * @param state      The whisper state.
+     * @param user_data  User data.
+     * @return True if the computation should proceed, false otherwise.
+     */
+    boolean callback(Pointer ctx, Pointer state, Pointer user_data);
+}

bindings/java/src/main/java/io/github/ggerganov/whispercpp/callbacks/WhisperLogitsFilterCallback.java

@@ -0,0 +1,25 @@
+package io.github.ggerganov.whispercpp.callbacks;
+
+import com.sun.jna.Callback;
+import com.sun.jna.Pointer;
+import io.github.ggerganov.whispercpp.model.WhisperTokenData;
+
+/**
+ * Callback to filter logits.
+ * Can be used to modify the logits before sampling.
+ * If not null, called after applying temperature to logits.
+ */
+public interface WhisperLogitsFilterCallback extends Callback {
+
+    /**
+     * Callback method to filter logits.
+     *
+     * @param ctx        The whisper context.
+     * @param state      The whisper state.
+     * @param tokens     The array of whisper_token_data.
+     * @param n_tokens   The number of tokens.
+     * @param logits     The array of logits.
+     * @param user_data  User data.
+     */
+    void callback(Pointer ctx, Pointer state, WhisperTokenData[] tokens, int n_tokens, float[] logits, Pointer user_data);
+}

bindings/java/src/main/java/io/github/ggerganov/whispercpp/callbacks/WhisperNewSegmentCallback.java

@@ -0,0 +1,24 @@
+package io.github.ggerganov.whispercpp.callbacks;
+
+import com.sun.jna.Callback;
+import com.sun.jna.Pointer;
+import io.github.ggerganov.whispercpp.WhisperContext;
+import io.github.ggerganov.whispercpp.model.WhisperState;
+
+/**
+ * Callback for the text segment.
+ * Called on every newly generated text segment.
+ * Use the whisper_full_...() functions to obtain the text segments.
+ */
+public interface WhisperNewSegmentCallback extends Callback {
+
+    /**
+     * Callback method for the text segment.
+     *
+     * @param ctx        The whisper context.
+     * @param state      The whisper state.
+     * @param n_new      The number of newly generated text segments.
+     * @param user_data  User data.
+     */
+    void callback(Pointer ctx, Pointer state, int n_new, Pointer user_data);
+}

bindings/java/src/main/java/io/github/ggerganov/whispercpp/callbacks/WhisperProgressCallback.java

@@ -0,0 +1,22 @@
+package io.github.ggerganov.whispercpp.callbacks;
+
+import com.sun.jna.Callback;
+import com.sun.jna.Pointer;
+import io.github.ggerganov.whispercpp.WhisperContext;
+import io.github.ggerganov.whispercpp.model.WhisperState;
+
+/**
+ * Callback for progress updates.
+ */
+public interface WhisperProgressCallback extends Callback {
+
+    /**
+     * Callback method for progress updates.
+     *
+     * @param ctx        The whisper context.
+     * @param state      The whisper state.
+     * @param progress   The progress value.
+     * @param user_data  User data.
+     */
+    void callback(Pointer ctx, Pointer state, int progress, Pointer user_data);
+}

bindings/java/src/main/java/io/github/ggerganov/whispercpp/ggml/GgmlTensor.java

@@ -0,0 +1,4 @@
+package io.github.ggerganov.whispercpp.ggml;
+
+public class GgmlTensor {
+}

bindings/java/src/main/java/io/github/ggerganov/whispercpp/ggml/GgmlType.java

@@ -0,0 +1,18 @@
+package io.github.ggerganov.whispercpp.ggml;
+
+public enum GgmlType {
+    GGML_TYPE_F32,
+    GGML_TYPE_F16,
+    GGML_TYPE_Q4_0,
+    GGML_TYPE_Q4_1,
+    REMOVED_GGML_TYPE_Q4_2,  // support has been removed
+    REMOVED_GGML_TYPE_Q4_3, // support has been removed
+    GGML_TYPE_Q5_0,
+    GGML_TYPE_Q5_1,
+    GGML_TYPE_Q8_0,
+    GGML_TYPE_Q8_1,
+    GGML_TYPE_I8,
+    GGML_TYPE_I16,
+    GGML_TYPE_I32,
+    GGML_TYPE_COUNT,
+}

bindings/java/src/main/java/io/github/ggerganov/whispercpp/model/EModel.java

@@ -0,0 +1,10 @@
+package io.github.ggerganov.whispercpp.model;
+
+public enum EModel {
+    MODEL_UNKNOWN,
+    MODEL_TINY,
+    MODEL_BASE,
+    MODEL_SMALL,
+    MODEL_MEDIUM,
+    MODEL_LARGE,
+}

bindings/java/src/main/java/io/github/ggerganov/whispercpp/model/WhisperModel.java

@@ -0,0 +1,49 @@
+package io.github.ggerganov.whispercpp;
+
+import io.github.ggerganov.whispercpp.ggml.GgmlTensor;
+import io.github.ggerganov.whispercpp.model.EModel;
+
+public class WhisperModel {
+//    EModel type = EModel.MODEL_UNKNOWN;
+//
+//    WhisperHParams hparams;
+//    WhisperFilters filters;
+//
+//    // encoder.positional_embedding
+//    GgmlTensor e_pe;
+//
+//    // encoder.conv1
+//    GgmlTensor e_conv_1_w;
+//    GgmlTensor e_conv_1_b;
+//
+//    // encoder.conv2
+//    GgmlTensor e_conv_2_w;
+//    GgmlTensor e_conv_2_b;
+//
+//    // encoder.ln_post
+//    GgmlTensor e_ln_w;
+//    GgmlTensor e_ln_b;
+//
+//    // decoder.positional_embedding
+//    GgmlTensor d_pe;
+//
+//    // decoder.token_embedding
+//    GgmlTensor d_te;
+//
+//    // decoder.ln
+//    GgmlTensor d_ln_w;
+//    GgmlTensor d_ln_b;
+//
+//    std::vector<whisper_layer_encoder> layers_encoder;
+//    std::vector<whisper_layer_decoder> layers_decoder;
+//
+//    // context
+//    struct ggml_context * ctx;
+//
+//    // the model memory buffer is read-only and can be shared between processors
+//    std::vector<uint8_t> * buf;
+//
+//    // tensors
+//    int n_loaded;
+//    Map<String, GgmlTensor> tensors;
+}

bindings/java/src/main/java/io/github/ggerganov/whispercpp/model/WhisperModelLoader.java

@@ -0,0 +1,62 @@
+package io.github.ggerganov.whispercpp.model;
+
+import com.sun.jna.Callback;
+import com.sun.jna.Pointer;
+import com.sun.jna.Structure;
+
+
+public class WhisperModelLoader extends Structure {
+    public Pointer context;
+    public ReadFunction read;
+    public EOFFunction eof;
+    public CloseFunction close;
+
+    public static class ReadFunction implements Callback {
+        public Pointer invoke(Pointer ctx, Pointer output, int readSize) {
+            // TODO
+            return ctx;
+        }
+    }
+
+    public static class EOFFunction implements Callback {
+        public boolean invoke(Pointer ctx) {
+            // TODO
+            return false;
+        }
+    }
+
+    public static class CloseFunction implements Callback {
+        public void invoke(Pointer ctx) {
+            // TODO
+        }
+    }
+
+//    public WhisperModelLoader(Pointer p) {
+//        super(p);
+//        read = new ReadFunction();
+//        eof = new EOFFunction();
+//        close = new CloseFunction();
+//        read.setCallback(this);
+//        eof.setCallback(this);
+//        close.setCallback(this);
+//        read.write();
+//        eof.write();
+//        close.write();
+//    }
+
+    public WhisperModelLoader() {
+        super();
+    }
+
+    public interface ReadCallback extends Callback {
+        Pointer invoke(Pointer ctx, Pointer output, int readSize);
+    }
+
+    public interface EOFCallback extends Callback {
+        boolean invoke(Pointer ctx);
+    }
+
+    public interface CloseCallback extends Callback {
+        void invoke(Pointer ctx);
+    }
+}

bindings/java/src/main/java/io/github/ggerganov/whispercpp/model/WhisperState.java

@@ -0,0 +1,4 @@
+package io.github.ggerganov.whispercpp.model;
+
+public class WhisperState {
+}

bindings/java/src/main/java/io/github/ggerganov/whispercpp/model/WhisperTokenData.java

@@ -0,0 +1,50 @@
+package io.github.ggerganov.whispercpp.model;
+
+import com.sun.jna.Structure;
+
+import java.util.Arrays;
+import java.util.List;
+
+/**
+ * Structure representing token data.
+ */
+public class WhisperTokenData extends Structure {
+
+    /** Token ID. */
+    public int id;
+
+    /** Forced timestamp token ID. */
+    public int tid;
+
+    /** Probability of the token. */
+    public float p;
+
+    /** Log probability of the token. */
+    public float plog;
+
+    /** Probability of the timestamp token. */
+    public float pt;
+
+    /** Sum of probabilities of all timestamp tokens. */
+    public float ptsum;
+
+    /**
+     * Start time of the token (token-level timestamp data).
+     * Do not use if you haven't computed token-level timestamps.
+     */
+    public long t0;
+
+    /**
+     * End time of the token (token-level timestamp data).
+     * Do not use if you haven't computed token-level timestamps.
+     */
+    public long t1;
+
+    /** Voice length of the token. */
+    public float vlen;
+
+    @Override
+    protected List<String> getFieldOrder() {
+        return Arrays.asList("id", "tid", "p", "plog", "pt", "ptsum", "t0", "t1", "vlen");
+    }
+}

bindings/java/src/main/java/io/github/ggerganov/whispercpp/params/BeamSearchParams.java

@@ -0,0 +1,19 @@
+package io.github.ggerganov.whispercpp.params;
+
+import com.sun.jna.Structure;
+
+import java.util.Arrays;
+import java.util.List;
+
+public class BeamSearchParams extends Structure {
+    /** ref: <a href="https://github.com/openai/whisper/blob/f82bc59f5ea234d4b97fb2860842ed38519f7e65/whisper/transcribe.py#L265">...</a> */
+    public int beam_size;
+
+    /** ref: <a href="https://arxiv.org/pdf/2204.05424.pdf">...</a> */
+    public float patience;
+
+    @Override
+    protected List<String> getFieldOrder() {
+        return Arrays.asList("beam_size", "patience");
+    }
+}

bindings/java/src/main/java/io/github/ggerganov/whispercpp/params/CBool.java

@@ -0,0 +1,30 @@
+package io.github.ggerganov.whispercpp.params;
+
+import com.sun.jna.IntegerType;
+
+import java.util.function.BooleanSupplier;
+
+public class CBool extends IntegerType implements BooleanSupplier {
+    public static final int SIZE = 1;
+    public static final CBool FALSE = new CBool(0);
+    public static final CBool TRUE = new CBool(1);
+
+
+    public CBool() {
+        this(0);
+    }
+
+    public CBool(long value) {
+        super(SIZE, value, true);
+    }
+
+    @Override
+    public boolean getAsBoolean() {
+        return intValue() == 1;
+    }
+
+    @Override
+    public String toString() {
+        return intValue() == 1 ? "true" : "false";
+    }
+}

bindings/java/src/main/java/io/github/ggerganov/whispercpp/params/GreedyParams.java

@@ -0,0 +1,16 @@
+package io.github.ggerganov.whispercpp.params;
+
+import com.sun.jna.Structure;
+
+import java.util.Collections;
+import java.util.List;
+
+public class GreedyParams extends Structure {
+    /** <a href="https://github.com/openai/whisper/blob/f82bc59f5ea234d4b97fb2860842ed38519f7e65/whisper/transcribe.py#L264">...</a> */
+    public int best_of;
+
+    @Override
+    protected List<String> getFieldOrder() {
+        return Collections.singletonList("best_of");
+    }
+}

bindings/java/src/main/java/io/github/ggerganov/whispercpp/params/WhisperFilters.java

@@ -0,0 +1,10 @@
+package io.github.ggerganov.whispercpp.params;
+
+import java.util.List;
+
+public class WhisperFilters {
+    int n_mel;
+    int n_fft;
+
+    List<Float> data;
+}

bindings/java/src/main/java/io/github/ggerganov/whispercpp/params/WhisperFullParams.java

@@ -0,0 +1,321 @@
+package io.github.ggerganov.whispercpp.params;
+
+import com.sun.jna.*;
+import io.github.ggerganov.whispercpp.callbacks.WhisperEncoderBeginCallback;
+import io.github.ggerganov.whispercpp.callbacks.WhisperLogitsFilterCallback;
+import io.github.ggerganov.whispercpp.callbacks.WhisperNewSegmentCallback;
+import io.github.ggerganov.whispercpp.callbacks.WhisperProgressCallback;
+
+import java.util.Arrays;
+import java.util.List;
+
+/**
+ * Parameters for the whisper_full() function.
+ * If you change the order or add new parameters, make sure to update the default values in whisper.cpp:
+ * whisper_full_default_params()
+ */
+public class WhisperFullParams extends Structure {
+
+    public WhisperFullParams(Pointer p) {
+        super(p);
+//        super(p, ALIGN_MSVC);
+//        super(p, ALIGN_GNUC);
+    }
+
+    /** Sampling strategy for whisper_full() function. */
+    public int strategy;
+
+    /** Number of threads. (default = 4) */
+    public int n_threads;
+
+    /** Maximum tokens to use from past text as a prompt for the decoder. (default = 16384) */
+    public int n_max_text_ctx;
+
+    /** Start offset in milliseconds. (default = 0) */
+    public int offset_ms;
+
+    /** Audio duration to process in milliseconds. (default = 0) */
+    public int duration_ms;
+
+    /** Translate flag. (default = false) */
+    public CBool translate;
+
+    /** The compliment of translateMode() */
+    public void transcribeMode() {
+        translate = CBool.FALSE;
+    }
+
+    /** The compliment of transcribeMode() */
+    public void translateMode() {
+        translate = CBool.TRUE;
+    }
+
+    /** Flag to indicate whether to use past transcription (if any) as an initial prompt for the decoder. (default = true) */
+    public CBool no_context;
+
+    /** Flag to indicate whether to use past transcription (if any) as an initial prompt for the decoder. (default = true) */
+    public void enableContext(boolean enable) {
+        no_context = enable ? CBool.FALSE : CBool.TRUE;
+    }
+
+    /** Flag to force single segment output (useful for streaming). (default = false) */
+    public CBool single_segment;
+
+    /** Flag to force single segment output (useful for streaming). (default = false) */
+    public void singleSegment(boolean single) {
+        single_segment = single ? CBool.TRUE : CBool.FALSE;
+    }
+
+    /** Flag to print special tokens (e.g., <SOT>, <EOT>, <BEG>, etc.). (default = false) */
+    public CBool print_special;
+
+    /** Flag to print special tokens (e.g., <SOT>, <EOT>, <BEG>, etc.). (default = false) */
+    public void printSpecial(boolean enable) {
+        print_special = enable ? CBool.TRUE : CBool.FALSE;
+    }
+
+    /** Flag to print progress information. (default = true) */
+    public CBool print_progress;
+
+    /** Flag to print progress information. (default = true) */
+    public void printProgress(boolean enable) {
+        print_progress = enable ? CBool.TRUE : CBool.FALSE;
+    }
+
+    /** Flag to print results from within whisper.cpp (avoid it, use callback instead). (default = true) */
+    public CBool print_realtime;
+
+    /** Flag to print results from within whisper.cpp (avoid it, use callback instead). (default = true) */
+    public void printRealtime(boolean enable) {
+        print_realtime = enable ? CBool.TRUE : CBool.FALSE;
+    }
+
+    /** Flag to print timestamps for each text segment when printing realtime. (default = true) */
+    public CBool print_timestamps;
+
+    /** Flag to print timestamps for each text segment when printing realtime. (default = true) */
+    public void printTimestamps(boolean enable) {
+        print_timestamps = enable ? CBool.TRUE : CBool.FALSE;
+    }
+
+    /** [EXPERIMENTAL] Flag to enable token-level timestamps. (default = false) */
+    public CBool token_timestamps;
+
+    /** [EXPERIMENTAL] Flag to enable token-level timestamps. (default = false) */
+    public void tokenTimestamps(boolean enable) {
+        token_timestamps = enable ? CBool.TRUE : CBool.FALSE;
+    }
+
+    /** [EXPERIMENTAL] Timestamp token probability threshold (~0.01). (default = 0.01) */
+    public float thold_pt;
+
+    /** [EXPERIMENTAL] Timestamp token sum probability threshold (~0.01). */
+    public float thold_ptsum;
+
+    /** Maximum segment length in characters. (default = 0) */
+    public int max_len;
+
+    /** Flag to split on word rather than on token (when used with max_len). (default = false) */
+    public CBool split_on_word;
+
+    /** Flag to split on word rather than on token (when used with max_len). (default = false) */
+    public void splitOnWord(boolean enable) {
+        split_on_word = enable ? CBool.TRUE : CBool.FALSE;
+    }
+
+    /** Maximum tokens per segment (0, default = no limit) */
+    public int max_tokens;
+
+    /** Flag to speed up the audio by 2x using Phase Vocoder. (default = false) */
+    public CBool speed_up;
+
+    /** Flag to speed up the audio by 2x using Phase Vocoder. (default = false) */
+    public void speedUp(boolean enable) {
+        speed_up = enable ? CBool.TRUE : CBool.FALSE;
+    }
+
+    /** Overwrite the audio context size (0 = use default). */
+    public int audio_ctx;
+
+    /** Enable tinydiarize (default = false) */
+    public CBool tdrz_enable;
+
+    /** Enable tinydiarize (default = false) */
+    public void tdrzEnable(boolean enable) {
+        tdrz_enable = enable ? CBool.TRUE : CBool.FALSE;
+    }
+
+    /** Tokens to provide to the whisper decoder as an initial prompt.
+     * These are prepended to any existing text context from a previous call. */
+    public String initial_prompt;
+
+    /** Prompt tokens. (int*) */
+    public Pointer prompt_tokens;
+
+    public void setPromptTokens(int[] tokens) {
+        Memory mem = new Memory(tokens.length * 4L);
+        mem.write(0, tokens, 0, tokens.length);
+        prompt_tokens = mem;
+    }
+
+    /** Number of prompt tokens. */
+    public int prompt_n_tokens;
+
+    /** Language for auto-detection.
+     * For auto-detection, set to `null`, `""`, or "auto". */
+    public String language;
+
+    /** Flag to indicate whether to detect language automatically. */
+    public CBool detect_language;
+
+    /** Flag to indicate whether to detect language automatically. */
+    public void detectLanguage(boolean enable) {
+        detect_language = enable ? CBool.TRUE : CBool.FALSE;
+    }
+
+    // Common decoding parameters.
+
+    /** Flag to suppress blank tokens. */
+    public CBool suppress_blank;
+
+    public void suppressBlanks(boolean enable) {
+        suppress_blank = enable ? CBool.TRUE : CBool.FALSE;
+    }
+
+    /** Flag to suppress non-speech tokens. */
+    public CBool suppress_non_speech_tokens;
+
+    /** Flag to suppress non-speech tokens. */
+    public void suppressNonSpeechTokens(boolean enable) {
+        suppress_non_speech_tokens = enable ? CBool.TRUE : CBool.FALSE;
+    }
+
+    /** Initial decoding temperature. */
+    public float temperature;
+
+    /** Maximum initial timestamp. */
+    public float max_initial_ts;
+
+    /** Length penalty. */
+    public float length_penalty;
+
+    // Fallback parameters.
+
+    /** Temperature increment. */
+    public float temperature_inc;
+
+    /** Entropy threshold (similar to OpenAI's "compression_ratio_threshold"). */
+    public float entropy_thold;
+
+    /** Log probability threshold. */
+    public float logprob_thold;
+
+    /** No speech threshold. */
+    public float no_speech_thold;
+
+    /** Greedy decoding parameters. */
+    public GreedyParams greedy;
+
+    /**
+     * Beam search decoding parameters.
+     */
+    public BeamSearchParams beam_search;
+
+    public void setBestOf(int bestOf) {
+        if (greedy == null) {
+            greedy = new GreedyParams();
+        }
+        greedy.best_of = bestOf;
+    }
+
+    public void setBeamSize(int beamSize) {
+        if (beam_search == null) {
+            beam_search = new BeamSearchParams();
+        }
+        beam_search.beam_size = beamSize;
+    }
+
+    public void setBeamSizeAndPatience(int beamSize, float patience) {
+        if (beam_search == null) {
+            beam_search = new BeamSearchParams();
+        }
+        beam_search.beam_size = beamSize;
+        beam_search.patience = patience;
+    }
+
+    /**
+     * Callback for every newly generated text segment.
+     * WhisperNewSegmentCallback
+     */
+    public Pointer new_segment_callback;
+
+    /**
+     * User data for the new_segment_callback.
+     */
+    public Pointer new_segment_callback_user_data;
+
+    /**
+     * Callback on each progress update.
+     * WhisperProgressCallback
+     */
+    public Pointer progress_callback;
+
+    /**
+     * User data for the progress_callback.
+     */
+    public Pointer progress_callback_user_data;
+
+    /**
+     * Callback each time before the encoder starts.
+     * WhisperEncoderBeginCallback
+     */
+    public Pointer encoder_begin_callback;
+
+    /**
+     * User data for the encoder_begin_callback.
+     */
+    public Pointer encoder_begin_callback_user_data;
+
+    /**
+     * Callback by each decoder to filter obtained logits.
+     * WhisperLogitsFilterCallback
+     */
+    public Pointer logits_filter_callback;
+
+    /**
+     * User data for the logits_filter_callback.
+     */
+    public Pointer logits_filter_callback_user_data;
+
+
+    public void setNewSegmentCallback(WhisperNewSegmentCallback callback) {
+        new_segment_callback = CallbackReference.getFunctionPointer(callback);
+    }
+
+    public void setProgressCallback(WhisperProgressCallback callback) {
+        progress_callback = CallbackReference.getFunctionPointer(callback);
+    }
+
+    public void setEncoderBeginCallbackeginCallbackCallback(WhisperEncoderBeginCallback callback) {
+        encoder_begin_callback = CallbackReference.getFunctionPointer(callback);
+    }
+
+    public void setLogitsFilterCallback(WhisperLogitsFilterCallback callback) {
+        logits_filter_callback = CallbackReference.getFunctionPointer(callback);
+    }
+
+    @Override
+    protected List<String> getFieldOrder() {
+        return Arrays.asList("strategy", "n_threads", "n_max_text_ctx", "offset_ms", "duration_ms", "translate",
+                "no_context", "single_segment",
+                "print_special", "print_progress", "print_realtime", "print_timestamps",  "token_timestamps",
+                "thold_pt", "thold_ptsum", "max_len", "split_on_word", "max_tokens", "speed_up", "audio_ctx",
+                "tdrz_enable", "initial_prompt", "prompt_tokens", "prompt_n_tokens", "language", "detect_language",
+                "suppress_blank", "suppress_non_speech_tokens", "temperature", "max_initial_ts", "length_penalty",
+                "temperature_inc", "entropy_thold", "logprob_thold", "no_speech_thold", "greedy", "beam_search",
+                "new_segment_callback", "new_segment_callback_user_data",
+                "progress_callback", "progress_callback_user_data",
+                "encoder_begin_callback", "encoder_begin_callback_user_data",
+                "logits_filter_callback", "logits_filter_callback_user_data");
+    }
+}

bindings/java/src/main/java/io/github/ggerganov/whispercpp/params/WhisperHParams.java

@@ -0,0 +1,15 @@
+package io.github.ggerganov.whispercpp.params;
+
+public class WhisperHParams {
+    int n_vocab       = 51864;
+    int n_audio_ctx   = 1500;
+    int n_audio_state = 384;
+    int n_audio_head  = 6;
+    int n_audio_layer = 4;
+    int n_text_ctx    = 448;
+    int n_text_state  = 384;
+    int n_text_head   = 6;
+    int n_text_layer  = 4;
+    int n_mels        = 80;
+    int ftype         = 1;
+}

bindings/java/src/main/java/io/github/ggerganov/whispercpp/params/WhisperSamplingStrategy.java

@@ -0,0 +1,10 @@
+package io.github.ggerganov.whispercpp.params;
+
+/** Available sampling strategies */
+public enum WhisperSamplingStrategy {
+    /** similar to OpenAI's GreedyDecoder */
+    WHISPER_SAMPLING_GREEDY,
+
+    /** similar to OpenAI's BeamSearchDecoder */
+    WHISPER_SAMPLING_BEAM_SEARCH
+}

bindings/java/src/test/java/io/github/ggerganov/whispercpp/WhisperCppTest.java

@@ -0,0 +1,102 @@
+package io.github.ggerganov.whispercpp;
+
+import static org.junit.jupiter.api.Assertions.*;
+
+import io.github.ggerganov.whispercpp.params.CBool;
+import io.github.ggerganov.whispercpp.params.WhisperFullParams;
+import io.github.ggerganov.whispercpp.params.WhisperSamplingStrategy;
+import org.junit.jupiter.api.BeforeAll;
+import org.junit.jupiter.api.Test;
+import javax.sound.sampled.AudioInputStream;
+import javax.sound.sampled.AudioSystem;
+import java.io.File;
+import java.io.FileNotFoundException;
+
+class WhisperCppTest {
+    private static WhisperCpp whisper = new WhisperCpp();
+    private static boolean modelInitialised = false;
+
+    @BeforeAll
+    static void init() throws FileNotFoundException {
+        // By default, models are loaded from ~/.cache/whisper/ and are usually named "ggml-${name}.bin"
+        // or you can provide the absolute path to the model file.
+        String modelName = "../../models/ggml-tiny.en.bin";
+        try {
+            whisper.initContext(modelName);
+//            whisper.getFullDefaultParams(WhisperSamplingStrategy.WHISPER_SAMPLING_GREEDY);
+//            whisper.getJavaDefaultParams(WhisperSamplingStrategy.WHISPER_SAMPLING_BEAM_SEARCH);
+            modelInitialised = true;
+        } catch (FileNotFoundException ex) {
+            System.out.println("Model " + modelName + " not found");
+        }
+    }
+
+    @Test
+    void testGetDefaultFullParams_BeamSearch() {
+        // When
+        WhisperFullParams params = whisper.getFullDefaultParams(WhisperSamplingStrategy.WHISPER_SAMPLING_BEAM_SEARCH);
+
+        // Then
+        assertEquals(WhisperSamplingStrategy.WHISPER_SAMPLING_BEAM_SEARCH.ordinal(), params.strategy);
+        assertNotEquals(0, params.n_threads);
+        assertEquals(16384, params.n_max_text_ctx);
+        assertFalse(params.translate);
+        assertEquals(0.01f, params.thold_pt);
+        assertEquals(2, params.beam_search.beam_size);
+        assertEquals(-1.0f, params.beam_search.patience);
+    }
+
+    @Test
+    void testGetDefaultFullParams_Greedy() {
+        // When
+        WhisperFullParams params = whisper.getFullDefaultParams(WhisperSamplingStrategy.WHISPER_SAMPLING_GREEDY);
+
+        // Then
+        assertEquals(WhisperSamplingStrategy.WHISPER_SAMPLING_GREEDY.ordinal(), params.strategy);
+        assertNotEquals(0, params.n_threads);
+        assertEquals(16384, params.n_max_text_ctx);
+        assertEquals(2, params.greedy.best_of);
+    }
+
+    @Test
+    void testFullTranscribe() throws Exception {
+        if (!modelInitialised) {
+            System.out.println("Model not initialised, skipping test");
+            return;
+        }
+
+        // Given
+        File file = new File(System.getProperty("user.dir"), "../../samples/jfk.wav");
+        AudioInputStream audioInputStream = AudioSystem.getAudioInputStream(file);
+
+        byte[] b = new byte[audioInputStream.available()];
+        float[] floats = new float[b.length / 2];
+
+//        WhisperFullParams params = whisper.getFullDefaultParams(WhisperSamplingStrategy.WHISPER_SAMPLING_GREEDY);
+        WhisperFullParams params = whisper.getFullDefaultParams(WhisperSamplingStrategy.WHISPER_SAMPLING_BEAM_SEARCH);
+        params.setProgressCallback((ctx, state, progress, user_data) -> System.out.println("progress: " + progress));
+        params.print_progress = CBool.FALSE;
+//        params.initial_prompt = "and so my fellow Americans um, like";
+
+
+        try {
+            audioInputStream.read(b);
+
+            for (int i = 0, j = 0; i < b.length; i += 2, j++) {
+                int intSample = (int) (b[i + 1]) << 8 | (int) (b[i]) & 0xFF;
+                floats[j] = intSample / 32767.0f;
+            }
+
+            // When
+            String result = whisper.fullTranscribe(params, floats);
+
+            // Then
+            System.err.println(result);
+            assertEquals("And so my fellow Americans ask not what your country can do for you " +
+                    "ask what you can do for your country.",
+                    result.replace(",", ""));
+        } finally {
+            audioInputStream.close();
+        }
+    }
+}

bindings/java/src/test/java/io/github/ggerganov/whispercpp/WhisperJnaLibraryTest.java

@@ -0,0 +1,17 @@
+package io.github.ggerganov.whispercpp;
+
+import static org.junit.jupiter.api.Assertions.*;
+
+import org.junit.jupiter.api.Test;
+
+class WhisperJnaLibraryTest {
+
+    @Test
+    void testWhisperPrint_system_info() {
+        String systemInfo = WhisperCppJnaLibrary.instance.whisper_print_system_info();
+        // eg: "AVX = 1 | AVX2 = 1 | AVX512 = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0
+        //    | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | VSX = 0 | COREML = 0 | "
+        System.out.println("System info: " + systemInfo);
+        assertTrue(systemInfo.length() > 10);
+    }
+}

bindings/javascript/package.json

@@ -1,6 +1,6 @@
 {
   "name": "whisper.cpp",
-  "version": "1.2.1",
+  "version": "1.4.2",
   "description": "Whisper speech recognition",
   "main": "whisper.js",
   "scripts": {

coreml/whisper-decoder-impl.h

@@ -0,0 +1,146 @@
+//
+// whisper-decoder-impl.h
+//
+// This file was automatically generated and should not be edited.
+//
+
+#import <Foundation/Foundation.h>
+#import <CoreML/CoreML.h>
+#include <stdint.h>
+#include <os/log.h>
+
+NS_ASSUME_NONNULL_BEGIN
+
+
+/// Model Prediction Input Type
+API_AVAILABLE(macos(12.0), ios(15.0), watchos(8.0), tvos(15.0)) __attribute__((visibility("hidden")))
+@interface whisper_decoder_implInput : NSObject<MLFeatureProvider>
+
+/// token_data as 1 by 1 matrix of 32-bit integers
+@property (readwrite, nonatomic, strong) MLMultiArray * token_data;
+
+/// audio_data as 1 × 384 × 1 × 1500 4-dimensional array of floats
+@property (readwrite, nonatomic, strong) MLMultiArray * audio_data;
+- (instancetype)init NS_UNAVAILABLE;
+- (instancetype)initWithToken_data:(MLMultiArray *)token_data audio_data:(MLMultiArray *)audio_data NS_DESIGNATED_INITIALIZER;
+
+@end
+
+
+/// Model Prediction Output Type
+API_AVAILABLE(macos(12.0), ios(15.0), watchos(8.0), tvos(15.0)) __attribute__((visibility("hidden")))
+@interface whisper_decoder_implOutput : NSObject<MLFeatureProvider>
+
+/// var_1195 as multidimensional array of floats
+@property (readwrite, nonatomic, strong) MLMultiArray * var_1195;
+- (instancetype)init NS_UNAVAILABLE;
+- (instancetype)initWithVar_1195:(MLMultiArray *)var_1195 NS_DESIGNATED_INITIALIZER;
+
+@end
+
+
+/// Class for model loading and prediction
+API_AVAILABLE(macos(12.0), ios(15.0), watchos(8.0), tvos(15.0)) __attribute__((visibility("hidden")))
+@interface whisper_decoder_impl : NSObject
+@property (readonly, nonatomic, nullable) MLModel * model;
+
+/**
+    URL of the underlying .mlmodelc directory.
+*/
++ (nullable NSURL *)URLOfModelInThisBundle;
+
+/**
+    Initialize whisper_decoder_impl instance from an existing MLModel object.
+
+    Usually the application does not use this initializer unless it makes a subclass of whisper_decoder_impl.
+    Such application may want to use `-[MLModel initWithContentsOfURL:configuration:error:]` and `+URLOfModelInThisBundle` to create a MLModel object to pass-in.
+*/
+- (instancetype)initWithMLModel:(MLModel *)model NS_DESIGNATED_INITIALIZER;
+
+/**
+    Initialize whisper_decoder_impl instance with the model in this bundle.
+*/
+- (nullable instancetype)init;
+
+/**
+    Initialize whisper_decoder_impl instance with the model in this bundle.
+
+    @param configuration The model configuration object
+    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
+*/
+- (nullable instancetype)initWithConfiguration:(MLModelConfiguration *)configuration error:(NSError * _Nullable __autoreleasing * _Nullable)error;
+
+/**
+    Initialize whisper_decoder_impl instance from the model URL.
+
+    @param modelURL URL to the .mlmodelc directory for whisper_decoder_impl.
+    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
+*/
+- (nullable instancetype)initWithContentsOfURL:(NSURL *)modelURL error:(NSError * _Nullable __autoreleasing * _Nullable)error;
+
+/**
+    Initialize whisper_decoder_impl instance from the model URL.
+
+    @param modelURL URL to the .mlmodelc directory for whisper_decoder_impl.
+    @param configuration The model configuration object
+    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
+*/
+- (nullable instancetype)initWithContentsOfURL:(NSURL *)modelURL configuration:(MLModelConfiguration *)configuration error:(NSError * _Nullable __autoreleasing * _Nullable)error;
+
+/**
+    Construct whisper_decoder_impl instance asynchronously with configuration.
+    Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
+
+    @param configuration The model configuration
+    @param handler When the model load completes successfully or unsuccessfully, the completion handler is invoked with a valid whisper_decoder_impl instance or NSError object.
+*/
++ (void)loadWithConfiguration:(MLModelConfiguration *)configuration completionHandler:(void (^)(whisper_decoder_impl * _Nullable model, NSError * _Nullable error))handler;
+
+/**
+    Construct whisper_decoder_impl instance asynchronously with URL of .mlmodelc directory and optional configuration.
+
+    Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
+
+    @param modelURL The model URL.
+    @param configuration The model configuration
+    @param handler When the model load completes successfully or unsuccessfully, the completion handler is invoked with a valid whisper_decoder_impl instance or NSError object.
+*/
++ (void)loadContentsOfURL:(NSURL *)modelURL configuration:(MLModelConfiguration *)configuration completionHandler:(void (^)(whisper_decoder_impl * _Nullable model, NSError * _Nullable error))handler;
+
+/**
+    Make a prediction using the standard interface
+    @param input an instance of whisper_decoder_implInput to predict from
+    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
+    @return the prediction as whisper_decoder_implOutput
+*/
+- (nullable whisper_decoder_implOutput *)predictionFromFeatures:(whisper_decoder_implInput *)input error:(NSError * _Nullable __autoreleasing * _Nullable)error;
+
+/**
+    Make a prediction using the standard interface
+    @param input an instance of whisper_decoder_implInput to predict from
+    @param options prediction options
+    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
+    @return the prediction as whisper_decoder_implOutput
+*/
+- (nullable whisper_decoder_implOutput *)predictionFromFeatures:(whisper_decoder_implInput *)input options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error;
+
+/**
+    Make a prediction using the convenience interface
+    @param token_data as 1 by 1 matrix of 32-bit integers:
+    @param audio_data as 1 × 384 × 1 × 1500 4-dimensional array of floats:
+    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
+    @return the prediction as whisper_decoder_implOutput
+*/
+- (nullable whisper_decoder_implOutput *)predictionFromToken_data:(MLMultiArray *)token_data audio_data:(MLMultiArray *)audio_data error:(NSError * _Nullable __autoreleasing * _Nullable)error;
+
+/**
+    Batch prediction
+    @param inputArray array of whisper_decoder_implInput instances to obtain predictions from
+    @param options prediction options
+    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
+    @return the predictions as NSArray<whisper_decoder_implOutput *>
+*/
+- (nullable NSArray<whisper_decoder_implOutput *> *)predictionsFromInputs:(NSArray<whisper_decoder_implInput*> *)inputArray options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error;
+@end
+
+NS_ASSUME_NONNULL_END

coreml/whisper-decoder-impl.m

@@ -0,0 +1,201 @@
+//
+// whisper-decoder-impl.m
+//
+// This file was automatically generated and should not be edited.
+//
+
+#if !__has_feature(objc_arc)
+#error This file must be compiled with automatic reference counting enabled (-fobjc-arc)
+#endif
+
+#import "whisper-decoder-impl.h"
+
+@implementation whisper_decoder_implInput
+
+- (instancetype)initWithToken_data:(MLMultiArray *)token_data audio_data:(MLMultiArray *)audio_data {
+    self = [super init];
+    if (self) {
+        _token_data = token_data;
+        _audio_data = audio_data;
+    }
+    return self;
+}
+
+- (NSSet<NSString *> *)featureNames {
+    return [NSSet setWithArray:@[@"token_data", @"audio_data"]];
+}
+
+- (nullable MLFeatureValue *)featureValueForName:(NSString *)featureName {
+    if ([featureName isEqualToString:@"token_data"]) {
+        return [MLFeatureValue featureValueWithMultiArray:self.token_data];
+    }
+    if ([featureName isEqualToString:@"audio_data"]) {
+        return [MLFeatureValue featureValueWithMultiArray:self.audio_data];
+    }
+    return nil;
+}
+
+@end
+
+@implementation whisper_decoder_implOutput
+
+- (instancetype)initWithVar_1195:(MLMultiArray *)var_1195 {
+    self = [super init];
+    if (self) {
+        _var_1195 = var_1195;
+    }
+    return self;
+}
+
+- (NSSet<NSString *> *)featureNames {
+    return [NSSet setWithArray:@[@"var_1195"]];
+}
+
+- (nullable MLFeatureValue *)featureValueForName:(NSString *)featureName {
+    if ([featureName isEqualToString:@"var_1195"]) {
+        return [MLFeatureValue featureValueWithMultiArray:self.var_1195];
+    }
+    return nil;
+}
+
+@end
+
+@implementation whisper_decoder_impl
+
+
+/**
+    URL of the underlying .mlmodelc directory.
+*/
++ (nullable NSURL *)URLOfModelInThisBundle {
+    NSString *assetPath = [[NSBundle bundleForClass:[self class]] pathForResource:@"whisper_decoder_impl" ofType:@"mlmodelc"];
+    if (nil == assetPath) { os_log_error(OS_LOG_DEFAULT, "Could not load whisper-decoder-impl.mlmodelc in the bundle resource"); return nil; }
+    return [NSURL fileURLWithPath:assetPath];
+}
+
+
+/**
+    Initialize whisper_decoder_impl instance from an existing MLModel object.
+
+    Usually the application does not use this initializer unless it makes a subclass of whisper_decoder_impl.
+    Such application may want to use `-[MLModel initWithContentsOfURL:configuration:error:]` and `+URLOfModelInThisBundle` to create a MLModel object to pass-in.
+*/
+- (instancetype)initWithMLModel:(MLModel *)model {
+    self = [super init];
+    if (!self) { return nil; }
+    _model = model;
+    if (_model == nil) { return nil; }
+    return self;
+}
+
+
+/**
+    Initialize whisper_decoder_impl instance with the model in this bundle.
+*/
+- (nullable instancetype)init {
+    return [self initWithContentsOfURL:(NSURL * _Nonnull)self.class.URLOfModelInThisBundle error:nil];
+}
+
+
+/**
+    Initialize whisper_decoder_impl instance with the model in this bundle.
+
+    @param configuration The model configuration object
+    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
+*/
+- (nullable instancetype)initWithConfiguration:(MLModelConfiguration *)configuration error:(NSError * _Nullable __autoreleasing * _Nullable)error {
+    return [self initWithContentsOfURL:(NSURL * _Nonnull)self.class.URLOfModelInThisBundle configuration:configuration error:error];
+}
+
+
+/**
+    Initialize whisper_decoder_impl instance from the model URL.
+
+    @param modelURL URL to the .mlmodelc directory for whisper_decoder_impl.
+    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
+*/
+- (nullable instancetype)initWithContentsOfURL:(NSURL *)modelURL error:(NSError * _Nullable __autoreleasing * _Nullable)error {
+    MLModel *model = [MLModel modelWithContentsOfURL:modelURL error:error];
+    if (model == nil) { return nil; }
+    return [self initWithMLModel:model];
+}
+
+
+/**
+    Initialize whisper_decoder_impl instance from the model URL.
+
+    @param modelURL URL to the .mlmodelc directory for whisper_decoder_impl.
+    @param configuration The model configuration object
+    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
+*/
+- (nullable instancetype)initWithContentsOfURL:(NSURL *)modelURL configuration:(MLModelConfiguration *)configuration error:(NSError * _Nullable __autoreleasing * _Nullable)error {
+    MLModel *model = [MLModel modelWithContentsOfURL:modelURL configuration:configuration error:error];
+    if (model == nil) { return nil; }
+    return [self initWithMLModel:model];
+}
+
+
+/**
+    Construct whisper_decoder_impl instance asynchronously with configuration.
+    Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
+
+    @param configuration The model configuration
+    @param handler When the model load completes successfully or unsuccessfully, the completion handler is invoked with a valid whisper_decoder_impl instance or NSError object.
+*/
++ (void)loadWithConfiguration:(MLModelConfiguration *)configuration completionHandler:(void (^)(whisper_decoder_impl * _Nullable model, NSError * _Nullable error))handler {
+    [self loadContentsOfURL:(NSURL * _Nonnull)[self URLOfModelInThisBundle]
+              configuration:configuration
+          completionHandler:handler];
+}
+
+
+/**
+    Construct whisper_decoder_impl instance asynchronously with URL of .mlmodelc directory and optional configuration.
+
+    Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
+
+    @param modelURL The model URL.
+    @param configuration The model configuration
+    @param handler When the model load completes successfully or unsuccessfully, the completion handler is invoked with a valid whisper_decoder_impl instance or NSError object.
+*/
++ (void)loadContentsOfURL:(NSURL *)modelURL configuration:(MLModelConfiguration *)configuration completionHandler:(void (^)(whisper_decoder_impl * _Nullable model, NSError * _Nullable error))handler {
+    [MLModel loadContentsOfURL:modelURL
+                 configuration:configuration
+             completionHandler:^(MLModel *model, NSError *error) {
+        if (model != nil) {
+            whisper_decoder_impl *typedModel = [[whisper_decoder_impl alloc] initWithMLModel:model];
+            handler(typedModel, nil);
+        } else {
+            handler(nil, error);
+        }
+    }];
+}
+
+- (nullable whisper_decoder_implOutput *)predictionFromFeatures:(whisper_decoder_implInput *)input error:(NSError * _Nullable __autoreleasing * _Nullable)error {
+    return [self predictionFromFeatures:input options:[[MLPredictionOptions alloc] init] error:error];
+}
+
+- (nullable whisper_decoder_implOutput *)predictionFromFeatures:(whisper_decoder_implInput *)input options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error {
+    id<MLFeatureProvider> outFeatures = [self.model predictionFromFeatures:input options:options error:error];
+    if (!outFeatures) { return nil; }
+    return [[whisper_decoder_implOutput alloc] initWithVar_1195:(MLMultiArray *)[outFeatures featureValueForName:@"var_1195"].multiArrayValue];
+}
+
+- (nullable whisper_decoder_implOutput *)predictionFromToken_data:(MLMultiArray *)token_data audio_data:(MLMultiArray *)audio_data error:(NSError * _Nullable __autoreleasing * _Nullable)error {
+    whisper_decoder_implInput *input_ = [[whisper_decoder_implInput alloc] initWithToken_data:token_data audio_data:audio_data];
+    return [self predictionFromFeatures:input_ error:error];
+}
+
+- (nullable NSArray<whisper_decoder_implOutput *> *)predictionsFromInputs:(NSArray<whisper_decoder_implInput*> *)inputArray options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error {
+    id<MLBatchProvider> inBatch = [[MLArrayBatchProvider alloc] initWithFeatureProviderArray:inputArray];
+    id<MLBatchProvider> outBatch = [self.model predictionsFromBatch:inBatch options:options error:error];
+    if (!outBatch) { return nil; }
+    NSMutableArray<whisper_decoder_implOutput*> *results = [NSMutableArray arrayWithCapacity:(NSUInteger)outBatch.count];
+    for (NSInteger i = 0; i < outBatch.count; i++) {
+        id<MLFeatureProvider> resultProvider = [outBatch featuresAtIndex:i];
+        whisper_decoder_implOutput * result = [[whisper_decoder_implOutput alloc] initWithVar_1195:(MLMultiArray *)[resultProvider featureValueForName:@"var_1195"].multiArrayValue];
+        [results addObject:result];
+    }
+    return results;
+}
+
+@end

coreml/whisper-encoder-impl.h

@@ -0,0 +1,142 @@
+//
+// whisper-encoder-impl.h
+//
+// This file was automatically generated and should not be edited.
+//
+
+#import <Foundation/Foundation.h>
+#import <CoreML/CoreML.h>
+#include <stdint.h>
+#include <os/log.h>
+
+NS_ASSUME_NONNULL_BEGIN
+
+
+/// Model Prediction Input Type
+API_AVAILABLE(macos(12.0), ios(15.0), watchos(8.0), tvos(15.0)) __attribute__((visibility("hidden")))
+@interface whisper_encoder_implInput : NSObject<MLFeatureProvider>
+
+/// logmel_data as 1 × 80 × 3000 3-dimensional array of floats
+@property (readwrite, nonatomic, strong) MLMultiArray * logmel_data;
+- (instancetype)init NS_UNAVAILABLE;
+- (instancetype)initWithLogmel_data:(MLMultiArray *)logmel_data NS_DESIGNATED_INITIALIZER;
+
+@end
+
+
+/// Model Prediction Output Type
+API_AVAILABLE(macos(12.0), ios(15.0), watchos(8.0), tvos(15.0)) __attribute__((visibility("hidden")))
+@interface whisper_encoder_implOutput : NSObject<MLFeatureProvider>
+
+/// output as multidimensional array of floats
+@property (readwrite, nonatomic, strong) MLMultiArray * output;
+- (instancetype)init NS_UNAVAILABLE;
+- (instancetype)initWithOutput:(MLMultiArray *)output NS_DESIGNATED_INITIALIZER;
+
+@end
+
+
+/// Class for model loading and prediction
+API_AVAILABLE(macos(12.0), ios(15.0), watchos(8.0), tvos(15.0)) __attribute__((visibility("hidden")))
+@interface whisper_encoder_impl : NSObject
+@property (readonly, nonatomic, nullable) MLModel * model;
+
+/**
+    URL of the underlying .mlmodelc directory.
+*/
++ (nullable NSURL *)URLOfModelInThisBundle;
+
+/**
+    Initialize whisper_encoder_impl instance from an existing MLModel object.
+
+    Usually the application does not use this initializer unless it makes a subclass of whisper_encoder_impl.
+    Such application may want to use `-[MLModel initWithContentsOfURL:configuration:error:]` and `+URLOfModelInThisBundle` to create a MLModel object to pass-in.
+*/
+- (instancetype)initWithMLModel:(MLModel *)model NS_DESIGNATED_INITIALIZER;
+
+/**
+    Initialize whisper_encoder_impl instance with the model in this bundle.
+*/
+- (nullable instancetype)init;
+
+/**
+    Initialize whisper_encoder_impl instance with the model in this bundle.
+
+    @param configuration The model configuration object
+    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
+*/
+- (nullable instancetype)initWithConfiguration:(MLModelConfiguration *)configuration error:(NSError * _Nullable __autoreleasing * _Nullable)error;
+
+/**
+    Initialize whisper_encoder_impl instance from the model URL.
+
+    @param modelURL URL to the .mlmodelc directory for whisper_encoder_impl.
+    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
+*/
+- (nullable instancetype)initWithContentsOfURL:(NSURL *)modelURL error:(NSError * _Nullable __autoreleasing * _Nullable)error;
+
+/**
+    Initialize whisper_encoder_impl instance from the model URL.
+
+    @param modelURL URL to the .mlmodelc directory for whisper_encoder_impl.
+    @param configuration The model configuration object
+    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
+*/
+- (nullable instancetype)initWithContentsOfURL:(NSURL *)modelURL configuration:(MLModelConfiguration *)configuration error:(NSError * _Nullable __autoreleasing * _Nullable)error;
+
+/**
+    Construct whisper_encoder_impl instance asynchronously with configuration.
+    Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
+
+    @param configuration The model configuration
+    @param handler When the model load completes successfully or unsuccessfully, the completion handler is invoked with a valid whisper_encoder_impl instance or NSError object.
+*/
++ (void)loadWithConfiguration:(MLModelConfiguration *)configuration completionHandler:(void (^)(whisper_encoder_impl * _Nullable model, NSError * _Nullable error))handler;
+
+/**
+    Construct whisper_encoder_impl instance asynchronously with URL of .mlmodelc directory and optional configuration.
+
+    Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
+
+    @param modelURL The model URL.
+    @param configuration The model configuration
+    @param handler When the model load completes successfully or unsuccessfully, the completion handler is invoked with a valid whisper_encoder_impl instance or NSError object.
+*/
++ (void)loadContentsOfURL:(NSURL *)modelURL configuration:(MLModelConfiguration *)configuration completionHandler:(void (^)(whisper_encoder_impl * _Nullable model, NSError * _Nullable error))handler;
+
+/**
+    Make a prediction using the standard interface
+    @param input an instance of whisper_encoder_implInput to predict from
+    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
+    @return the prediction as whisper_encoder_implOutput
+*/
+- (nullable whisper_encoder_implOutput *)predictionFromFeatures:(whisper_encoder_implInput *)input error:(NSError * _Nullable __autoreleasing * _Nullable)error;
+
+/**
+    Make a prediction using the standard interface
+    @param input an instance of whisper_encoder_implInput to predict from
+    @param options prediction options
+    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
+    @return the prediction as whisper_encoder_implOutput
+*/
+- (nullable whisper_encoder_implOutput *)predictionFromFeatures:(whisper_encoder_implInput *)input options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error;
+
+/**
+    Make a prediction using the convenience interface
+    @param logmel_data as 1 × 80 × 3000 3-dimensional array of floats:
+    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
+    @return the prediction as whisper_encoder_implOutput
+*/
+- (nullable whisper_encoder_implOutput *)predictionFromLogmel_data:(MLMultiArray *)logmel_data error:(NSError * _Nullable __autoreleasing * _Nullable)error;
+
+/**
+    Batch prediction
+    @param inputArray array of whisper_encoder_implInput instances to obtain predictions from
+    @param options prediction options
+    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
+    @return the predictions as NSArray<whisper_encoder_implOutput *>
+*/
+- (nullable NSArray<whisper_encoder_implOutput *> *)predictionsFromInputs:(NSArray<whisper_encoder_implInput*> *)inputArray options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error;
+@end
+
+NS_ASSUME_NONNULL_END

coreml/whisper-encoder-impl.m

@@ -0,0 +1,197 @@
+//
+// whisper-encoder-impl.m
+//
+// This file was automatically generated and should not be edited.
+//
+
+#if !__has_feature(objc_arc)
+#error This file must be compiled with automatic reference counting enabled (-fobjc-arc)
+#endif
+
+#import "whisper-encoder-impl.h"
+
+@implementation whisper_encoder_implInput
+
+- (instancetype)initWithLogmel_data:(MLMultiArray *)logmel_data {
+    self = [super init];
+    if (self) {
+        _logmel_data = logmel_data;
+    }
+    return self;
+}
+
+- (NSSet<NSString *> *)featureNames {
+    return [NSSet setWithArray:@[@"logmel_data"]];
+}
+
+- (nullable MLFeatureValue *)featureValueForName:(NSString *)featureName {
+    if ([featureName isEqualToString:@"logmel_data"]) {
+        return [MLFeatureValue featureValueWithMultiArray:self.logmel_data];
+    }
+    return nil;
+}
+
+@end
+
+@implementation whisper_encoder_implOutput
+
+- (instancetype)initWithOutput:(MLMultiArray *)output {
+    self = [super init];
+    if (self) {
+        _output = output;
+    }
+    return self;
+}
+
+- (NSSet<NSString *> *)featureNames {
+    return [NSSet setWithArray:@[@"output"]];
+}
+
+- (nullable MLFeatureValue *)featureValueForName:(NSString *)featureName {
+    if ([featureName isEqualToString:@"output"]) {
+        return [MLFeatureValue featureValueWithMultiArray:self.output];
+    }
+    return nil;
+}
+
+@end
+
+@implementation whisper_encoder_impl
+
+
+/**
+    URL of the underlying .mlmodelc directory.
+*/
++ (nullable NSURL *)URLOfModelInThisBundle {
+    NSString *assetPath = [[NSBundle bundleForClass:[self class]] pathForResource:@"whisper_encoder_impl" ofType:@"mlmodelc"];
+    if (nil == assetPath) { os_log_error(OS_LOG_DEFAULT, "Could not load whisper-encoder-impl.mlmodelc in the bundle resource"); return nil; }
+    return [NSURL fileURLWithPath:assetPath];
+}
+
+
+/**
+    Initialize whisper_encoder_impl instance from an existing MLModel object.
+
+    Usually the application does not use this initializer unless it makes a subclass of whisper_encoder_impl.
+    Such application may want to use `-[MLModel initWithContentsOfURL:configuration:error:]` and `+URLOfModelInThisBundle` to create a MLModel object to pass-in.
+*/
+- (instancetype)initWithMLModel:(MLModel *)model {
+    self = [super init];
+    if (!self) { return nil; }
+    _model = model;
+    if (_model == nil) { return nil; }
+    return self;
+}
+
+
+/**
+    Initialize whisper_encoder_impl instance with the model in this bundle.
+*/
+- (nullable instancetype)init {
+    return [self initWithContentsOfURL:(NSURL * _Nonnull)self.class.URLOfModelInThisBundle error:nil];
+}
+
+
+/**
+    Initialize whisper_encoder_impl instance with the model in this bundle.
+
+    @param configuration The model configuration object
+    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
+*/
+- (nullable instancetype)initWithConfiguration:(MLModelConfiguration *)configuration error:(NSError * _Nullable __autoreleasing * _Nullable)error {
+    return [self initWithContentsOfURL:(NSURL * _Nonnull)self.class.URLOfModelInThisBundle configuration:configuration error:error];
+}
+
+
+/**
+    Initialize whisper_encoder_impl instance from the model URL.
+
+    @param modelURL URL to the .mlmodelc directory for whisper_encoder_impl.
+    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
+*/
+- (nullable instancetype)initWithContentsOfURL:(NSURL *)modelURL error:(NSError * _Nullable __autoreleasing * _Nullable)error {
+    MLModel *model = [MLModel modelWithContentsOfURL:modelURL error:error];
+    if (model == nil) { return nil; }
+    return [self initWithMLModel:model];
+}
+
+
+/**
+    Initialize whisper_encoder_impl instance from the model URL.
+
+    @param modelURL URL to the .mlmodelc directory for whisper_encoder_impl.
+    @param configuration The model configuration object
+    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
+*/
+- (nullable instancetype)initWithContentsOfURL:(NSURL *)modelURL configuration:(MLModelConfiguration *)configuration error:(NSError * _Nullable __autoreleasing * _Nullable)error {
+    MLModel *model = [MLModel modelWithContentsOfURL:modelURL configuration:configuration error:error];
+    if (model == nil) { return nil; }
+    return [self initWithMLModel:model];
+}
+
+
+/**
+    Construct whisper_encoder_impl instance asynchronously with configuration.
+    Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
+
+    @param configuration The model configuration
+    @param handler When the model load completes successfully or unsuccessfully, the completion handler is invoked with a valid whisper_encoder_impl instance or NSError object.
+*/
++ (void)loadWithConfiguration:(MLModelConfiguration *)configuration completionHandler:(void (^)(whisper_encoder_impl * _Nullable model, NSError * _Nullable error))handler {
+    [self loadContentsOfURL:(NSURL * _Nonnull)[self URLOfModelInThisBundle]
+              configuration:configuration
+          completionHandler:handler];
+}
+
+
+/**
+    Construct whisper_encoder_impl instance asynchronously with URL of .mlmodelc directory and optional configuration.
+
+    Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
+
+    @param modelURL The model URL.
+    @param configuration The model configuration
+    @param handler When the model load completes successfully or unsuccessfully, the completion handler is invoked with a valid whisper_encoder_impl instance or NSError object.
+*/
++ (void)loadContentsOfURL:(NSURL *)modelURL configuration:(MLModelConfiguration *)configuration completionHandler:(void (^)(whisper_encoder_impl * _Nullable model, NSError * _Nullable error))handler {
+    [MLModel loadContentsOfURL:modelURL
+                 configuration:configuration
+             completionHandler:^(MLModel *model, NSError *error) {
+        if (model != nil) {
+            whisper_encoder_impl *typedModel = [[whisper_encoder_impl alloc] initWithMLModel:model];
+            handler(typedModel, nil);
+        } else {
+            handler(nil, error);
+        }
+    }];
+}
+
+- (nullable whisper_encoder_implOutput *)predictionFromFeatures:(whisper_encoder_implInput *)input error:(NSError * _Nullable __autoreleasing * _Nullable)error {
+    return [self predictionFromFeatures:input options:[[MLPredictionOptions alloc] init] error:error];
+}
+
+- (nullable whisper_encoder_implOutput *)predictionFromFeatures:(whisper_encoder_implInput *)input options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error {
+    id<MLFeatureProvider> outFeatures = [self.model predictionFromFeatures:input options:options error:error];
+    if (!outFeatures) { return nil; }
+    return [[whisper_encoder_implOutput alloc] initWithOutput:(MLMultiArray *)[outFeatures featureValueForName:@"output"].multiArrayValue];
+}
+
+- (nullable whisper_encoder_implOutput *)predictionFromLogmel_data:(MLMultiArray *)logmel_data error:(NSError * _Nullable __autoreleasing * _Nullable)error {
+    whisper_encoder_implInput *input_ = [[whisper_encoder_implInput alloc] initWithLogmel_data:logmel_data];
+    return [self predictionFromFeatures:input_ error:error];
+}
+
+- (nullable NSArray<whisper_encoder_implOutput *> *)predictionsFromInputs:(NSArray<whisper_encoder_implInput*> *)inputArray options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error {
+    id<MLBatchProvider> inBatch = [[MLArrayBatchProvider alloc] initWithFeatureProviderArray:inputArray];
+    id<MLBatchProvider> outBatch = [self.model predictionsFromBatch:inBatch options:options error:error];
+    if (!outBatch) { return nil; }
+    NSMutableArray<whisper_encoder_implOutput*> *results = [NSMutableArray arrayWithCapacity:(NSUInteger)outBatch.count];
+    for (NSInteger i = 0; i < outBatch.count; i++) {
+        id<MLFeatureProvider> resultProvider = [outBatch featuresAtIndex:i];
+        whisper_encoder_implOutput * result = [[whisper_encoder_implOutput alloc] initWithOutput:(MLMultiArray *)[resultProvider featureValueForName:@"output"].multiArrayValue];
+        [results addObject:result];
+    }
+    return results;
+}
+
+@end

coreml/whisper-encoder.h

@@ -0,0 +1,22 @@
+// Wrapper of the Core ML Whisper Encoder model
+//
+// Code is derived from the work of Github user @wangchou
+// ref: https://github.com/wangchou/callCoreMLFromCpp
+
+#if __cplusplus
+extern "C" {
+#endif
+
+struct whisper_coreml_context;
+
+struct whisper_coreml_context * whisper_coreml_init(const char * path_model);
+void whisper_coreml_free(struct whisper_coreml_context * ctx);
+
+void whisper_coreml_encode(
+        const whisper_coreml_context * ctx,
+                               float * mel,
+                               float * out);
+
+#if __cplusplus
+}
+#endif

coreml/whisper-encoder.mm

@@ -0,0 +1,63 @@
+#if !__has_feature(objc_arc)
+#error This file must be compiled with automatic reference counting enabled (-fobjc-arc)
+#endif
+
+#import "whisper-encoder.h"
+#import "whisper-encoder-impl.h"
+
+#import <CoreML/CoreML.h>
+
+#include <stdlib.h>
+
+#if __cplusplus
+extern "C" {
+#endif
+
+struct whisper_coreml_context {
+    const void * data;
+};
+
+struct whisper_coreml_context * whisper_coreml_init(const char * path_model) {
+    NSString * path_model_str = [[NSString alloc] initWithUTF8String:path_model];
+
+    NSURL * url_model = [NSURL fileURLWithPath: path_model_str];
+
+    const void * data = CFBridgingRetain([[whisper_encoder_impl alloc] initWithContentsOfURL:url_model error:nil]);
+
+    if (data == NULL) {
+        return NULL;
+    }
+
+    whisper_coreml_context * ctx = new whisper_coreml_context;
+
+    ctx->data = data;
+
+    return ctx;
+}
+
+void whisper_coreml_free(struct whisper_coreml_context * ctx) {
+    CFRelease(ctx->data);
+    delete ctx;
+}
+
+void whisper_coreml_encode(
+        const whisper_coreml_context * ctx,
+                               float * mel,
+                               float * out) {
+    MLMultiArray * inMultiArray = [
+        [MLMultiArray alloc] initWithDataPointer: mel
+                                           shape: @[@1, @80, @3000]
+                                        dataType: MLMultiArrayDataTypeFloat32
+                                         strides: @[@(240000), @(3000), @1]
+                                     deallocator: nil
+                                           error: nil
+    ];
+
+    whisper_encoder_implOutput * outCoreML = [(__bridge id) ctx->data predictionFromLogmel_data:inMultiArray error:nil];
+
+    memcpy(out, outCoreML.output.dataPointer, outCoreML.output.count * sizeof(float));
+}
+
+#if __cplusplus
+}
+#endif

examples/CMakeLists.txt

@@ -4,7 +4,7 @@ find_package(Threads REQUIRED)
 
 # third-party
 
-if (WHISPER_SUPPORT_SDL2)
+if (WHISPER_SDL2)
     # SDL2
     find_package(SDL2 REQUIRED)
 
@@ -21,13 +21,17 @@ set(TARGET common)
 add_library(${TARGET} STATIC
     common.h
     common.cpp
+    common-ggml.h
+    common-ggml.cpp
     )
 
 include(DefaultTargetOptions)
 
+target_link_libraries(${TARGET} PRIVATE whisper)
+
 set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
 
-if (WHISPER_SUPPORT_SDL2)
+if (WHISPER_SDL2)
     # common-sdl
 
     set(TARGET common-sdl)
@@ -62,5 +66,7 @@ else()
     add_subdirectory(stream)
     add_subdirectory(command)
     add_subdirectory(bench)
+    add_subdirectory(quantize)
     add_subdirectory(talk)
+    add_subdirectory(talk-llama)
 endif()

examples/addon.node/__test__/whisper.spec.js

@@ -1,15 +1,23 @@
-const path = require('path');
-const { whisper } = require(path.join(__dirname, '../../../build/Release/whisper-addon'));
+const path = require("path");
+const { whisper } = require(path.join(
+  __dirname,
+  "../../../build/Release/whisper-addon"
+));
+const { promisify } = require("util");
+
+const whisperAsync = promisify(whisper);
 
 const whisperParamsMock = {
-    language: 'en',
-    model: path.join(__dirname, '../../../models/ggml-base.en.bin'),
-    fname_inp: path.join(__dirname, '../../../samples/jfk.wav'),
+  language: "en",
+  model: path.join(__dirname, "../../../models/ggml-base.en.bin"),
+  fname_inp: path.join(__dirname, "../../../samples/jfk.wav"),
 };
 
 describe("Run whisper.node", () => {
+    test("it should receive a non-empty value", async () => {
+        let result = await whisperAsync(whisperParamsMock);
 
-    test("it should receive a non-empty value", () => {
-        expect(whisper(whisperParamsMock).length).toBeGreaterThan(0);
-    });
+        expect(result.length).toBeGreaterThan(0);
+    }, 10000);
 });
+

examples/addon.node/addon.cpp

@@ -160,22 +160,6 @@ int run(whisper_params &params, std::vector<std::vector<std::string>> &result) {
         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];
         const auto fname_out = f < (int)params.fname_out.size() && !params.fname_out[f].empty() ? params.fname_out[f] : params.fname_inp[f];
@@ -243,8 +227,7 @@ int run(whisper_params &params, std::vector<std::vector<std::string>> &result) {
             wparams.greedy.best_of        = params.best_of;
             wparams.beam_search.beam_size = params.beam_size;
 
-            wparams.prompt_tokens     = prompt_tokens.empty() ? nullptr : prompt_tokens.data();
-            wparams.prompt_n_tokens   = prompt_tokens.empty() ? 0       : prompt_tokens.size();
+            wparams.initial_prompt   = params.prompt.c_str();
 
             whisper_print_user_data user_data = { &params, &pcmf32s };
 

examples/bench.wasm/CMakeLists.txt

@@ -31,9 +31,9 @@ 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 PTHREAD_POOL_SIZE_STRICT=0 \
+    -s INITIAL_MEMORY=2000MB \
+    -s TOTAL_MEMORY=2000MB \
     -s FORCE_FILESYSTEM=1 \
     -s EXPORTED_RUNTIME_METHODS=\"['print', 'printErr', 'ccall', 'cwrap']\" \
     ${EXTRA_FLAGS} \

examples/bench.wasm/index-tmpl.html

@@ -35,6 +35,15 @@
 
             <br><br>
 
+            <b>More examples:</b>
+                <a href="https://whisper.ggerganov.com/">main</a> |
+                <a href="https://whisper.ggerganov.com/bench">bench</a> |
+                <a href="https://whisper.ggerganov.com/stream">stream</a> |
+                <a href="https://whisper.ggerganov.com/command">command</a> |
+                <a href="https://whisper.ggerganov.com/talk">talk</a> |
+
+            <br><br>
+
             <hr>
 
             Select the model you would like to use and click the "Bench" button.<br>
@@ -44,11 +53,18 @@
 
             <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>
-
+                <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>
+                <button id="fetch-whisper-small-en" onclick="loadWhisper('small.en')">small.en (466 MB)</button>
                 <input type="file" id="whisper-file" name="file" onchange="loadFile(event, 'whisper.bin')" />
+                <br><br>
+                Quantized models:<br><br>
+                <button id="fetch-whisper-tiny-en-q5_1"   onclick="loadWhisper('tiny-en-q5_1')">tiny.en (Q5_1, 31 MB)</button>
+                <button id="fetch-whisper-base-en-q5_1"   onclick="loadWhisper('base-en-q5_1')">base.en (Q5_1, 57 MB)</button>
+                <button id="fetch-whisper-small-en-q5_1"  onclick="loadWhisper('small-en-q5_1')">small.en (Q5_1, 182 MB)</button>
+                <button id="fetch-whisper-medium-en-q5_0" onclick="loadWhisper('medium-en-q5_0')">medium.en (Q5_0, 515 MB)</button>
+                <button id="fetch-whisper-large-q5_0"     onclick="loadWhisper('large-q5_0')">large (Q5_0, 1030 MB)</button>
+                <span id="fetch-whisper-progress"></span>
             </div>
 
             <br>
@@ -160,6 +176,14 @@
 
                 document.getElementById('fetch-whisper-tiny-en').style.display = 'none';
                 document.getElementById('fetch-whisper-base-en').style.display = 'none';
+                document.getElementById('fetch-whisper-small-en').style.display = 'none';
+
+                document.getElementById('fetch-whisper-tiny-en-q5_1'  ).style.display = 'none';
+                document.getElementById('fetch-whisper-base-en-q5_1'  ).style.display = 'none';
+                document.getElementById('fetch-whisper-small-en-q5_1' ).style.display = 'none';
+                document.getElementById('fetch-whisper-medium-en-q5_0').style.display = 'none';
+                document.getElementById('fetch-whisper-large-q5_0'    ).style.display = 'none';
+
                 document.getElementById('whisper-file'         ).style.display = 'none';
                 document.getElementById('model-whisper-status' ).innerHTML = 'loaded model: ' + file.name;
             }
@@ -168,19 +192,42 @@
                 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',
+                    'small.en': 'https://whisper.ggerganov.com/ggml-model-whisper-small.en.bin',
+
+                    'tiny-en-q5_1':  'https://whisper.ggerganov.com/ggml-model-whisper-tiny.en-q5_1.bin',
+                    'base-en-q5_1':  'https://whisper.ggerganov.com/ggml-model-whisper-base.en-q5_1.bin',
+                    'small-en-q5_1': 'https://whisper.ggerganov.com/ggml-model-whisper-small.en-q5_1.bin',
+                    'medium-en-q5_0':'https://whisper.ggerganov.com/ggml-model-whisper-medium.en-q5_0.bin',
+                    'large-q5_0':    'https://whisper.ggerganov.com/ggml-model-whisper-large-q5_0.bin',
                 };
 
                 let sizes = {
                     'tiny.en': 75,
                     'base.en': 142,
+                    'small.en': 466,
+
+                    'tiny-en-q5_1':   31,
+                    'base-en-q5_1':   57,
+                    'small-en-q5_1':  182,
+                    'medium-en-q5_0': 515,
+                    'large-q5_0':     1030,
                 };
 
                 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('fetch-whisper-tiny-en').style.display  = 'none';
+                document.getElementById('fetch-whisper-base-en').style.display  = 'none';
+                document.getElementById('fetch-whisper-small-en').style.display = 'none';
+
+                document.getElementById('fetch-whisper-tiny-en-q5_1'  ).style.display = 'none';
+                document.getElementById('fetch-whisper-base-en-q5_1'  ).style.display = 'none';
+                document.getElementById('fetch-whisper-small-en-q5_1' ).style.display = 'none';
+                document.getElementById('fetch-whisper-medium-en-q5_0').style.display = 'none';
+                document.getElementById('fetch-whisper-large-q5_0'    ).style.display = 'none';
+
+                document.getElementById('whisper-file'        ).style.display = 'none';
                 document.getElementById('model-whisper-status').innerHTML = 'loading "' + model + '" ... ';
 
                 cbProgress = function(p) {
@@ -190,9 +237,18 @@
 
                 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 = '';
+                    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('fetch-whisper-small-en'); if (el) el.style.display = 'inline-block';
+
+                    el = document.getElementById('fetch-whisper-tiny-en-q5_1'  ); if (el) el.style.display = 'inline-block';
+                    el = document.getElementById('fetch-whisper-base-en-q5_1'  ); if (el) el.style.display = 'inline-block';
+                    el = document.getElementById('fetch-whisper-small-en-q5_1' ); if (el) el.style.display = 'inline-block';
+                    el = document.getElementById('fetch-whisper-medium-en-q5_0'); if (el) el.style.display = 'inline-block';
+                    el = document.getElementById('fetch-whisper-large-q5_0'    ); if (el) el.style.display = 'inline-block';
+
+                    el = document.getElementById('whisper-file'        ); 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);

examples/command.wasm/emscripten.cpp

@@ -28,31 +28,6 @@ std::string g_transcribed   = "";
 
 std::vector<float> g_pcmf32;
 
-// 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;

examples/command.wasm/index-tmpl.html

@@ -35,6 +35,15 @@
 
             <br><br>
 
+            <b>More examples:</b>
+                <a href="https://whisper.ggerganov.com/">main</a> |
+                <a href="https://whisper.ggerganov.com/bench">bench</a> |
+                <a href="https://whisper.ggerganov.com/stream">stream</a> |
+                <a href="https://whisper.ggerganov.com/command">command</a> |
+                <a href="https://whisper.ggerganov.com/talk">talk</a> |
+
+            <br><br>
+
             <hr>
 
             Select the model you would like to use, click the "Start" button and follow the instructions.
@@ -45,6 +54,10 @@
                 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>
+                <br><br>
+                Quantized models:<br><br>
+                <button id="fetch-whisper-tiny-en-q5_1"   onclick="loadWhisper('tiny-en-q5_1')">tiny.en (Q5_1, 31 MB)</button>
+                <button id="fetch-whisper-base-en-q5_1"   onclick="loadWhisper('base-en-q5_1')">base.en (Q5_1, 57 MB)</button>
                 <span id="fetch-whisper-progress"></span>
 
                 <!--
@@ -162,11 +175,17 @@
                 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',
+
+                    'tiny-en-q5_1':  'https://whisper.ggerganov.com/ggml-model-whisper-tiny.en-q5_1.bin',
+                    'base-en-q5_1':  'https://whisper.ggerganov.com/ggml-model-whisper-base.en-q5_1.bin',
                 };
 
                 let sizes = {
                     'tiny.en': 75,
                     'base.en': 142,
+
+                    'tiny-en-q5_1':   31,
+                    'base-en-q5_1':   57,
                 };
 
                 let url     = urls[model];
@@ -177,6 +196,10 @@
 
                 document.getElementById('fetch-whisper-tiny-en').style.display = 'none';
                 document.getElementById('fetch-whisper-base-en').style.display = 'none';
+
+                document.getElementById('fetch-whisper-tiny-en-q5_1').style.display = 'none';
+                document.getElementById('fetch-whisper-base-en-q5_1').style.display = 'none';
+
                 document.getElementById('model-whisper-status').innerHTML = 'loading "' + model + '" ... ';
 
                 cbProgress = function(p) {
@@ -188,6 +211,10 @@
                     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('fetch-whisper-tiny-en-q5_1'); if (el) el.style.display = 'inline-block';
+                    el = document.getElementById('fetch-whisper-base-en-q5_1'); if (el) el.style.display = 'inline-block';
+
                     el = document.getElementById('model-whisper-status');  if (el) el.innerHTML = '';
                 };
 

examples/command/CMakeLists.txt

@@ -1,4 +1,4 @@
-if (WHISPER_SUPPORT_SDL2)
+if (WHISPER_SDL2)
     # command
     set(TARGET command)
     add_executable(${TARGET} command.cpp)

examples/command/command.cpp

@@ -163,31 +163,6 @@ std::string transcribe(whisper_context * ctx, const whisper_params & params, con
     return result;
 }
 
-// compute similarity between two strings using Levenshtein distance
-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()));
-}
-
 std::vector<std::string> read_allowed_commands(const std::string & fname) {
     std::vector<std::string> allowed_commands;
 

examples/common-ggml.cpp

@@ -0,0 +1,246 @@
+#include "common-ggml.h"
+
+#include <regex>
+#include <map>
+
+static const std::map<std::string, enum ggml_ftype> GGML_FTYPE_MAP = {
+    {"q4_0", GGML_FTYPE_MOSTLY_Q4_0},
+    {"q4_1", GGML_FTYPE_MOSTLY_Q4_1},
+    {"q5_0", GGML_FTYPE_MOSTLY_Q5_0},
+    {"q5_1", GGML_FTYPE_MOSTLY_Q5_1},
+    {"q8_0", GGML_FTYPE_MOSTLY_Q8_0},
+};
+
+void ggml_print_ftypes(FILE * fp) {
+    for (auto it = GGML_FTYPE_MAP.begin(); it != GGML_FTYPE_MAP.end(); it++) {
+        fprintf(fp, "  type = \"%s\" or %d\n", it->first.c_str(), it->second);
+    }
+}
+
+enum ggml_ftype ggml_parse_ftype(const char * str) {
+    enum ggml_ftype ftype;
+    if (str[0] == 'q') {
+        const auto it = GGML_FTYPE_MAP.find(str);
+        if (it == GGML_FTYPE_MAP.end()) {
+            fprintf(stderr, "%s: unknown ftype '%s'\n", __func__, str);
+            return GGML_FTYPE_UNKNOWN;
+        }
+        ftype = it->second;
+    } else {
+        ftype = (enum ggml_ftype) atoi(str);
+    }
+
+    return ftype;
+}
+
+bool ggml_common_quantize_0(
+        std::ifstream & finp,
+        std::ofstream & fout,
+        const ggml_ftype ftype,
+        const std::vector<std::string> & to_quant,
+        const std::vector<std::string> & to_skip) {
+
+    ggml_type qtype = GGML_TYPE_F32;
+
+    switch (ftype) {
+        case GGML_FTYPE_MOSTLY_Q4_0: qtype = GGML_TYPE_Q4_0; break;
+        case GGML_FTYPE_MOSTLY_Q4_1: qtype = GGML_TYPE_Q4_1; break;
+        case GGML_FTYPE_MOSTLY_Q5_0: qtype = GGML_TYPE_Q5_0; break;
+        case GGML_FTYPE_MOSTLY_Q5_1: qtype = GGML_TYPE_Q5_1; break;
+        case GGML_FTYPE_MOSTLY_Q8_0: qtype = GGML_TYPE_Q8_0; break;
+        case GGML_FTYPE_UNKNOWN:
+        case GGML_FTYPE_ALL_F32:
+        case GGML_FTYPE_MOSTLY_F16:
+        case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16:
+        case GGML_FTYPE_MOSTLY_Q2_K:
+        case GGML_FTYPE_MOSTLY_Q3_K:
+        case GGML_FTYPE_MOSTLY_Q4_K:
+        case GGML_FTYPE_MOSTLY_Q5_K:
+        case GGML_FTYPE_MOSTLY_Q6_K:
+                {
+                    fprintf(stderr, "%s: invalid model type %d\n", __func__, ftype);
+                    return false;
+                }
+    };
+
+    if (!ggml_is_quantized(qtype)) {
+        fprintf(stderr, "%s: invalid quantization type %d (%s)\n", __func__, qtype, ggml_type_name(qtype));
+        return false;
+    }
+
+    size_t total_size_org = 0;
+    size_t total_size_new = 0;
+
+    std::vector<float> work;
+
+    std::vector<uint8_t>     data_u8;
+    std::vector<ggml_fp16_t> data_f16;
+    std::vector<float>       data_f32;
+
+    std::vector<int64_t> hist_all(1 << 4, 0);
+
+    while (true) {
+        int32_t n_dims;
+        int32_t length;
+        int32_t ttype;
+
+        finp.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
+        finp.read(reinterpret_cast<char *>(&length), sizeof(length));
+        finp.read(reinterpret_cast<char *>(&ttype),  sizeof(ttype));
+
+        if (finp.eof()) {
+            break;
+        }
+
+        int32_t nelements = 1;
+        int32_t ne[4] = { 1, 1, 1, 1 };
+        for (int i = 0; i < n_dims; ++i) {
+            finp.read (reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
+            nelements *= ne[i];
+        }
+
+        std::string name(length, 0);
+        finp.read (&name[0], length);
+
+        printf("%64s - [%5d, %5d, %5d], type = %6s ", name.data(), ne[0], ne[1], ne[2], ggml_type_name((ggml_type) ttype));
+
+        bool quantize = false;
+
+        // check if we should quantize this tensor
+        for (const auto & s : to_quant) {
+            if (std::regex_match(name, std::regex(s))) {
+                quantize = true;
+                break;
+            }
+        }
+
+        // check if we should skip this tensor
+        for (const auto & s : to_skip) {
+            if (std::regex_match(name, std::regex(s))) {
+                quantize = false;
+                break;
+            }
+        }
+
+        // quantize only 2D tensors
+        quantize &= (n_dims == 2);
+
+        if (quantize) {
+            if (ttype != GGML_TYPE_F32 && ttype != GGML_TYPE_F16) {
+                fprintf(stderr, "%s: unsupported ttype %d (%s) for integer quantization\n", __func__, ttype, ggml_type_name((ggml_type) ttype));
+                return false;
+            }
+
+            if (ttype == GGML_TYPE_F16) {
+                data_f16.resize(nelements);
+                finp.read(reinterpret_cast<char *>(data_f16.data()), nelements * sizeof(ggml_fp16_t));
+                data_f32.resize(nelements);
+                for (int i = 0; i < nelements; ++i) {
+                    data_f32[i] = ggml_fp16_to_fp32(data_f16[i]);
+                }
+            } else {
+                data_f32.resize(nelements);
+                finp.read(reinterpret_cast<char *>(data_f32.data()), nelements * sizeof(float));
+            }
+
+            ttype = qtype;
+        } else {
+            const int bpe = (ttype == 0) ? sizeof(float) : sizeof(uint16_t);
+
+            data_u8.resize(nelements*bpe);
+            finp.read(reinterpret_cast<char *>(data_u8.data()), nelements * bpe);
+        }
+
+        fout.write(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
+        fout.write(reinterpret_cast<char *>(&length), sizeof(length));
+        fout.write(reinterpret_cast<char *>(&ttype),  sizeof(ttype));
+        for (int i = 0; i < n_dims; ++i) {
+            fout.write(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
+        }
+        fout.write(&name[0], length);
+
+        if (quantize) {
+            work.resize(nelements); // for quantization
+
+            size_t cur_size = 0;
+            std::vector<int64_t> hist_cur(1 << 4, 0);
+
+            switch ((ggml_type) ttype) {
+                case GGML_TYPE_Q4_0:
+                    {
+                        cur_size = ggml_quantize_q4_0(data_f32.data(), work.data(), nelements, ne[0], hist_cur.data());
+                    } break;
+                case GGML_TYPE_Q4_1:
+                    {
+                        cur_size = ggml_quantize_q4_1(data_f32.data(), work.data(), nelements, ne[0], hist_cur.data());
+                    } break;
+                case GGML_TYPE_Q5_0:
+                    {
+                        cur_size = ggml_quantize_q5_0(data_f32.data(), work.data(), nelements, ne[0], hist_cur.data());
+                    } break;
+                case GGML_TYPE_Q5_1:
+                    {
+                        cur_size = ggml_quantize_q5_1(data_f32.data(), work.data(), nelements, ne[0], hist_cur.data());
+                    } break;
+                case GGML_TYPE_Q8_0:
+                    {
+                        cur_size = ggml_quantize_q8_0(data_f32.data(), work.data(), nelements, ne[0], hist_cur.data());
+                    } break;
+                case GGML_TYPE_F32:
+                case GGML_TYPE_F16:
+                case GGML_TYPE_I8:
+                case GGML_TYPE_I16:
+                case GGML_TYPE_I32:
+                case GGML_TYPE_Q8_1:
+                case GGML_TYPE_Q2_K:
+                case GGML_TYPE_Q3_K:
+                case GGML_TYPE_Q4_K:
+                case GGML_TYPE_Q5_K:
+                case GGML_TYPE_Q6_K:
+                case GGML_TYPE_Q8_K:
+                case GGML_TYPE_COUNT:
+                    {
+                        fprintf(stderr, "%s: unsupported quantization type %d (%s)\n", __func__, ttype, ggml_type_name((ggml_type) ttype));
+                        return false;
+                    }
+            }
+
+            fout.write(reinterpret_cast<char *>(work.data()), cur_size);
+            total_size_new += cur_size;
+
+            printf("size = %8.2f MB -> %8.2f MB | hist: ", nelements * sizeof(float)/1024.0/1024.0, cur_size/1024.0/1024.0);
+            for (int i = 0; i < (int) hist_cur.size(); ++i) {
+                hist_all[i] += hist_cur[i];
+            }
+
+            for (int i = 0; i < (int) hist_cur.size(); ++i) {
+                printf("%5.3f ", hist_cur[i] / (float)nelements);
+            }
+            printf("\n");
+        } else {
+            printf("size = %8.3f MB\n", data_u8.size()/1024.0/1024.0);
+            fout.write(reinterpret_cast<char *>(data_u8.data()), data_u8.size());
+            total_size_new += data_u8.size();
+        }
+
+        total_size_org += nelements * sizeof(float);
+    }
+
+    printf("%s: model size  = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0);
+    printf("%s: quant size  = %8.2f MB | ftype = %d (%s)\n", __func__, total_size_new/1024.0/1024.0, ftype, ggml_type_name(qtype));
+
+    {
+        int64_t sum_all = 0;
+        for (int i = 0; i < (int) hist_all.size(); ++i) {
+            sum_all += hist_all[i];
+        }
+
+        printf("%s: hist: ", __func__);
+        for (int i = 0; i < (int) hist_all.size(); ++i) {
+            printf("%5.3f ", hist_all[i] / (float)sum_all);
+        }
+        printf("\n");
+    }
+
+    return true;
+}

examples/common-ggml.h

@@ -0,0 +1,18 @@
+#pragma once
+
+#include "ggml.h"
+
+#include <fstream>
+#include <vector>
+#include <string>
+
+enum ggml_ftype ggml_parse_ftype(const char * str);
+
+void ggml_print_ftypes(FILE * fp = stderr);
+
+bool ggml_common_quantize_0(
+        std::ifstream & finp,
+        std::ofstream & fout,
+        const ggml_ftype ftype,
+        const std::vector<std::string> & to_quant,
+        const std::vector<std::string> & to_skip);

examples/common.cpp

@@ -6,12 +6,126 @@
 #include "dr_wav.h"
 
 #include <cmath>
+#include <cstring>
+#include <fstream>
 #include <regex>
+#include <locale>
+#include <codecvt>
+#include <sstream>
 
 #ifndef M_PI
 #define M_PI 3.14159265358979323846
 #endif
 
+#if defined(_MSC_VER)
+#pragma warning(disable: 4244 4267) // possible loss of data
+#endif
+
+bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
+    for (int i = 1; i < argc; i++) {
+        std::string arg = argv[i];
+
+        if (arg == "-s" || arg == "--seed") {
+            params.seed = std::stoi(argv[++i]);
+        } else if (arg == "-t" || arg == "--threads") {
+            params.n_threads = std::stoi(argv[++i]);
+        } else if (arg == "-p" || arg == "--prompt") {
+            params.prompt = argv[++i];
+        } else if (arg == "-n" || arg == "--n_predict") {
+            params.n_predict = std::stoi(argv[++i]);
+        } else if (arg == "--top_k") {
+            params.top_k = std::max(1, std::stoi(argv[++i]));
+        } else if (arg == "--top_p") {
+            params.top_p = std::stof(argv[++i]);
+        } else if (arg == "--temp") {
+            params.temp = std::stof(argv[++i]);
+        } else if (arg == "--repeat-last-n") {
+            params.repeat_last_n = std::stof(argv[++i]);
+        } else if (arg == "--repeat-penalty") {
+            params.repeat_penalty = std::stof(argv[++i]);
+        } else if (arg == "-b" || arg == "--batch_size") {
+            params.n_batch = std::stoi(argv[++i]);
+        } else if (arg == "-m" || arg == "--model") {
+            params.model = argv[++i];
+        } else if (arg == "-i" || arg == "--interactive") {
+            params.interactive = true;
+        } else if (arg == "-ip" || arg == "--interactive-port") {
+            params.interactive = true;
+            params.interactive_port = std::stoi(argv[++i]);
+        } else if (arg == "-h" || arg == "--help") {
+            gpt_print_usage(argc, argv, params);
+            exit(0);
+        } else if (arg == "-f" || arg == "--file") {
+            if (++i > argc) {
+                fprintf(stderr, "Invalid file param");
+                break;
+            }
+            std::ifstream file(argv[i]);
+            if (!file) {
+                fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
+                break;
+            }
+            std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
+            if (params.prompt.back() == '\n') {
+                params.prompt.pop_back();
+            }
+        } else if (arg == "-tt" || arg == "--token_test") {
+            params.token_test = argv[++i];
+        }
+        else {
+            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
+            gpt_print_usage(argc, argv, params);
+            exit(0);
+        }
+    }
+
+    return true;
+}
+
+void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
+    fprintf(stderr, "usage: %s [options]\n", argv[0]);
+    fprintf(stderr, "\n");
+    fprintf(stderr, "options:\n");
+    fprintf(stderr, "  -h, --help            show this help message and exit\n");
+    fprintf(stderr, "  -s SEED, --seed SEED  RNG seed (default: -1)\n");
+    fprintf(stderr, "  -t N, --threads N     number of threads to use during computation (default: %d)\n", params.n_threads);
+    fprintf(stderr, "  -p PROMPT, --prompt PROMPT\n");
+    fprintf(stderr, "                        prompt to start generation with (default: random)\n");
+    fprintf(stderr, "  -f FNAME, --file FNAME\n");
+    fprintf(stderr, "                        load prompt from a file\n");
+    fprintf(stderr, "  -tt TOKEN_TEST, --token_test TOKEN_TEST\n");
+    fprintf(stderr, "                        test tokenization\n");
+    fprintf(stderr, "  -n N, --n_predict N   number of tokens to predict (default: %d)\n", params.n_predict);
+    fprintf(stderr, "  --top_k N             top-k sampling (default: %d)\n", params.top_k);
+    fprintf(stderr, "  --top_p N             top-p sampling (default: %.1f)\n", params.top_p);
+    fprintf(stderr, "  --temp N              temperature (default: %.1f)\n", params.temp);
+    fprintf(stderr, "  --repeat-last-n N     last n tokens to consider for penalize (default: %d, 0 = disabled)\n", params.repeat_last_n);
+    fprintf(stderr, "  --repeat-penalty N    penalize repeat sequence of tokens (default: %.2f, 1.0 = disabled)\n", (double)params.repeat_penalty);
+    fprintf(stderr, "  -b N, --batch_size N  batch size for prompt processing (default: %d)\n", params.n_batch);
+    fprintf(stderr, "  -m FNAME, --model FNAME\n");
+    fprintf(stderr, "                        model path (default: %s)\n", params.model.c_str());
+    fprintf(stderr, "\n");
+}
+
+std::string gpt_random_prompt(std::mt19937 & rng) {
+    const int r = rng() % 10;
+    switch (r) {
+        case 0: return "So";
+        case 1: return "Once upon a time";
+        case 2: return "When";
+        case 3: return "The";
+        case 4: return "After";
+        case 5: return "If";
+        case 6: return "import";
+        case 7: return "He";
+        case 8: return "She";
+        case 9: return "They";
+        default: return "To";
+    }
+
+    return "The";
+}
+
 std::string trim(const std::string & s) {
     std::regex e("^\\s+|\\s+$");
     return std::regex_replace(s, e, "");
@@ -27,6 +141,463 @@ std::string replace(const std::string & s, const std::string & from, const std::
     return result;
 }
 
+void gpt_vocab::add_special_token(const std::string & token) {
+    special_tokens.push_back(token);
+}
+
+std::map<std::string, int32_t> json_parse(const std::string & fname) {
+    std::map<std::string, int32_t> result;
+
+    // read file into string
+    std::string json;
+    {
+        std::ifstream ifs(fname);
+        if (!ifs) {
+            fprintf(stderr, "Failed to open %s\n", fname.c_str());
+            exit(1);
+        }
+
+        json = std::string((std::istreambuf_iterator<char>(ifs)),
+                (std::istreambuf_iterator<char>()));
+    }
+
+    if (json[0] != '{') {
+        return result;
+    }
+
+    // parse json
+    {
+        bool has_key  = false;
+        bool in_token = false;
+
+        std::string str_key = "";
+        std::string str_val = "";
+
+        int n = json.size();
+        for (int i = 1; i < n; ++i) {
+            if (!in_token) {
+                if (json[i] == ' ') continue;
+                if (json[i] == '"') {
+                    in_token = true;
+                    continue;
+                }
+            } else {
+                if (json[i] == '\\' && i+1 < n) {
+                    if (has_key == false) {
+                        str_key += json[i];
+                    } else {
+                        str_val += json[i];
+                    }
+                    ++i;
+                } else if (json[i] == '"') {
+                    if (has_key == false) {
+                        has_key = true;
+                        ++i;
+                        while (json[i] == ' ') ++i;
+                        ++i; // :
+                        while (json[i] == ' ') ++i;
+                        if (json[i] != '\"') {
+                            while (json[i] != ',' && json[i] != '}') {
+                                str_val += json[i++];
+                            }
+                            has_key = false;
+                        } else {
+                            in_token = true;
+                            continue;
+                        }
+                    } else {
+                        has_key = false;
+                    }
+
+                    str_key = ::replace(str_key, "\\u0120", " " ); // \u0120 -> space
+                    str_key = ::replace(str_key, "\\u010a", "\n"); // \u010a -> new line
+                    str_key = ::replace(str_key, "\\\"",    "\""); // \\\"   -> "
+
+                    try {
+                        result[str_key] = std::stoi(str_val);
+                    } catch (...) {
+                        //fprintf(stderr, "%s: ignoring key '%s' with value '%s'\n", fname.c_str(), str_key.c_str(), str_val.c_str());
+
+                    }
+                    str_key = "";
+                    str_val = "";
+                    in_token = false;
+                    continue;
+                }
+                if (has_key == false) {
+                    str_key += json[i];
+                } else {
+                    str_val += json[i];
+                }
+            }
+        }
+    }
+
+    return result;
+}
+
+std::string convert_to_utf8(const std::wstring & input) {
+    std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
+    return converter.to_bytes(input);
+}
+
+
+std::wstring convert_to_wstring(const std::string & input) {
+    std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
+    return converter.from_bytes(input);
+}
+
+void gpt_split_words(std::string str, std::vector<std::string>& words) {
+    const std::string pattern = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
+    const std::regex re(pattern);
+    std::smatch m;
+
+    while (std::regex_search(str, m, re)) {
+        for (auto x : m) {
+            words.push_back(x);
+        }
+        str = m.suffix();
+    }
+}
+
+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;
+
+        // Generate the subpattern from the special_tokens vector if it's not empty
+        if (!vocab.special_tokens.empty()) {
+            const std::regex escape(R"([\[\\\^\$\.\|\?\*\+\(\)\{\}])");
+            std::string special_tokens_subpattern;
+            for (const auto & token : vocab.special_tokens) {
+                if (!special_tokens_subpattern.empty()) {
+                    special_tokens_subpattern += "|";
+                }
+                special_tokens_subpattern += std::regex_replace(token, escape, R"(\$&)");
+            }
+
+            std::regex re(special_tokens_subpattern);
+            std::smatch m;
+            // Split the text by special tokens.
+            while (std::regex_search(str, m, re)) {
+                // Split the substrings in-between special tokens into words.
+                gpt_split_words(m.prefix(), words);
+                // Add matched special tokens as words.
+                for (auto x : m) {
+                    words.push_back(x);
+                }
+                str = m.suffix();
+            }
+            // Remaining text without special tokens will be handled below.
+        }
+
+        gpt_split_words(str, words);
+    }
+
+    // find the longest token that forms each word in words:
+    std::vector<gpt_vocab::id> tokens;
+    for (const auto & word : words) {
+        for (int i = 0; i < (int) word.size(); ){
+            for (int j = word.size() - 1; j >= i; j--){
+                auto cand = word.substr(i, j-i+1);
+                auto it = vocab.token_to_id.find(cand);
+                if (it != vocab.token_to_id.end()){ // word.substr(i, j-i+1) in vocab
+                    tokens.push_back(it->second);
+                    i = j + 1;
+                    break;
+                }
+                else if (j == i){ // word.substr(i, 1) has no matching
+                    fprintf(stderr, "%s: unknown token '%s'\n", __func__, word.substr(i, 1).data());
+                    i++;
+                }
+            }
+        }
+    }
+
+    return tokens;
+}
+
+std::vector<gpt_vocab::id> parse_tokens_from_string(const std::string& input, char delimiter) {
+    std::vector<gpt_vocab::id> output;
+    std::stringstream ss(input);
+    std::string token;
+
+    while (std::getline(ss, token, delimiter)) {
+        output.push_back(std::stoi(token));
+    }
+
+    return output;
+}
+
+std::map<std::string, std::vector<gpt_vocab::id>> extract_tests_from_file(const std::string & fpath_test){
+    if (fpath_test.empty()){
+        fprintf(stderr, "%s : No test file found.\n", __func__);
+        return std::map<std::string, std::vector<gpt_vocab::id>>();
+    }
+
+    std::map<std::string, std::vector<gpt_vocab::id>> tests;
+
+    auto fin = std::ifstream(fpath_test, std::ios_base::in);
+    const char * delimeter = " => ";
+    const char del_tok = ',';
+    std::string line;
+    while (std::getline(fin, line)) {
+        size_t delimiterPos = line.find(delimeter);
+        if (delimiterPos != std::string::npos) {
+            std::string text = line.substr(0, delimiterPos);
+            std::string s_tokens = line.substr(delimiterPos + std::strlen(delimeter));
+            tests[text] = parse_tokens_from_string(s_tokens, del_tok);
+        }
+    }
+    return tests;
+}
+
+void test_gpt_tokenizer(gpt_vocab & vocab, const std::string & fpath_test){
+    std::map<std::string, std::vector<gpt_vocab::id>> tests = extract_tests_from_file(fpath_test);
+
+    size_t n_fails = 0;
+
+    for (const auto & test : tests) {
+        std::vector<gpt_vocab::id> tokens = gpt_tokenize(vocab, test.first);
+
+        if (tokens != test.second){
+            n_fails++;
+
+            // print out failure cases
+            fprintf(stderr, "%s : failed test: '%s'\n", __func__, test.first.c_str());
+            fprintf(stderr, "%s : tokens in hf:   ", __func__);
+            for (const auto & t : test.second) {
+                fprintf(stderr, "%s(%d), ", vocab.id_to_token[t].c_str(), t);
+            }
+            fprintf(stderr, "\n");
+            fprintf(stderr, "%s : tokens in ggml: ", __func__);
+            for (const auto & t : tokens) {
+                fprintf(stderr, "%s(%d), ", vocab.id_to_token[t].c_str(), t);
+            }
+            fprintf(stderr, "\n");
+        }
+    }
+
+    fprintf(stderr, "%s : %zu tests failed out of %zu tests.\n", __func__, n_fails, tests.size());
+}
+
+bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab) {
+    printf("%s: loading vocab from '%s'\n", __func__, fname.c_str());
+
+    vocab.token_to_id = ::json_parse(fname);
+
+    for (const auto & kv : vocab.token_to_id) {
+        vocab.id_to_token[kv.second] = kv.first;
+    }
+
+    printf("%s: vocab size = %d\n", __func__, (int) vocab.token_to_id.size());
+
+    // print the vocabulary
+    //for (auto kv : vocab.token_to_id) {
+    //    printf("'%s' -> %d\n", kv.first.data(), kv.second);
+    //}
+
+    return true;
+}
+
+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);
+
+    {
+        const double scale = 1.0/temp;
+        for (int i = 0; i < n_logits; ++i) {
+            logits_id.push_back(std::make_pair(logits[i]*scale, 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);
+
+    double maxl = -INFINITY;
+    for (const auto & kv : logits_id) {
+        maxl = std::max(maxl, kv.first);
+    }
+
+    // compute probs for the top K tokens
+    std::vector<double> probs;
+    probs.reserve(logits_id.size());
+
+    double sum = 0.0;
+    for (const auto & kv : logits_id) {
+        double p = exp(kv.first - maxl);
+        probs.push_back(p);
+        sum += p;
+    }
+
+    // normalize the probs
+    for (auto & p : probs) {
+        p /= sum;
+    }
+
+    if (top_p < 1.0f) {
+        double cumsum = 0.0f;
+        for (int i = 0; i < top_k; i++) {
+            cumsum += probs[i];
+            if (cumsum >= top_p) {
+                top_k = i + 1;
+                probs.resize(top_k);
+                logits_id.resize(top_k);
+                break;
+            }
+        }
+
+        cumsum = 1.0/cumsum;
+        for (int i = 0; i < (int) probs.size(); i++) {
+            probs[i] *= cumsum;
+        }
+    }
+
+    //printf("\n");
+    //for (int i = 0; i < (int) probs.size(); i++) {
+    //    printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
+    //}
+    //exit(0);
+
+    std::discrete_distribution<> dist(probs.begin(), probs.end());
+    int idx = dist(rng);
+
+    return logits_id[idx].second;
+}
+
+gpt_vocab::id gpt_sample_top_k_top_p_repeat(
+        const gpt_vocab & vocab,
+        const float * logits,
+        const int32_t * last_n_tokens_data,
+        size_t last_n_tokens_data_size,
+        int    top_k,
+        double top_p,
+        double temp,
+        int repeat_last_n,
+        float repeat_penalty,
+        std::mt19937 & rng) {
+
+    int n_logits = vocab.id_to_token.size();
+
+    const auto * plogits = logits;
+
+    const auto last_n_tokens = std::vector<int32_t>(last_n_tokens_data, last_n_tokens_data + last_n_tokens_data_size);
+
+    if (temp <= 0) {
+        // select the token with the highest logit directly
+        float max_logit = plogits[0];
+        gpt_vocab::id max_id = 0;
+
+        for (int i = 1; i < n_logits; ++i) {
+            if (plogits[i] > max_logit) {
+                max_logit = plogits[i];
+                max_id = i;
+            }
+        }
+        return max_id;
+    }
+
+
+    std::vector<std::pair<double, gpt_vocab::id>> logits_id;
+    logits_id.reserve(n_logits);
+
+    {
+        const float scale = 1.0f/temp;
+        for (int i = 0; i < n_logits; ++i) {
+            // repetition penalty from ctrl paper (https://arxiv.org/abs/1909.05858)
+            // credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main
+            if (repeat_last_n > 0 && std::find(last_n_tokens.end()-repeat_last_n, last_n_tokens.end(), i) != last_n_tokens.end()) {
+                // if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
+                if (plogits[i] < 0.0f) {
+                    logits_id.push_back(std::make_pair(plogits[i]*scale*repeat_penalty, i));
+                } else {
+                    logits_id.push_back(std::make_pair(plogits[i]*scale/repeat_penalty, i));
+                }
+            } else {
+                logits_id.push_back(std::make_pair(plogits[i]*scale, 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);
+
+    double maxl = -INFINITY;
+    for (const auto & kv : logits_id) {
+        maxl = std::max(maxl, kv.first);
+    }
+
+    // compute probs for the top K tokens
+    std::vector<double> probs;
+    probs.reserve(logits_id.size());
+
+    double sum = 0.0;
+    for (const auto & kv : logits_id) {
+        double p = exp(kv.first - maxl);
+        probs.push_back(p);
+        sum += p;
+    }
+
+    // normalize the probs
+    for (auto & p : probs) {
+        p /= sum;
+    }
+
+    if (top_p < 1.0f) {
+        double cumsum = 0.0f;
+        for (int i = 0; i < top_k; i++) {
+            cumsum += probs[i];
+            if (cumsum >= top_p) {
+                top_k = i + 1;
+                probs.resize(top_k);
+                logits_id.resize(top_k);
+                break;
+            }
+        }
+
+        cumsum = 1.0/cumsum;
+        for (int i = 0; i < (int) probs.size(); i++) {
+            probs[i] *= cumsum;
+        }
+    }
+
+//    printf("\n");
+//    for (int i = 0; i < (int) probs.size(); i++) {
+//    for (int i = 0; i < 10; i++) {
+//        printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
+//    }
+
+    std::discrete_distribution<> dist(probs.begin(), probs.end());
+    int idx = dist(rng);
+
+    return logits_id[idx].second;
+
+}
+
 bool read_wav(const std::string & fname, std::vector<float>& pcmf32, std::vector<std::vector<float>>& pcmf32s, bool stereo) {
     drwav wav;
     std::vector<uint8_t> wav_data; // used for pipe input from stdin
@@ -160,3 +731,27 @@ bool vad_simple(std::vector<float> & pcmf32, int sample_rate, int last_ms, float
 
     return true;
 }
+
+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] + (i > 0 && 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()));
+}

examples/common.h

@@ -1,10 +1,49 @@
+// Various helper functions and utilities
+
 #pragma once
 
-// needs to match WHISPER_SAMPLE_RATE
+#include <string>
+#include <map>
+#include <vector>
+#include <random>
+#include <thread>
+
 #define COMMON_SAMPLE_RATE 16000
 
-#include <vector>
-#include <string>
+//
+// CLI argument parsing
+//
+
+struct gpt_params {
+    int32_t seed      = -1;  // RNG seed
+    int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
+    int32_t n_predict = 200; // new tokens to predict
+    int32_t n_batch   = 8;   // batch size for prompt processing
+
+    // sampling parameters
+    int32_t top_k          = 40;
+    float   top_p          = 0.9f;
+    float   temp           = 0.9f;
+    int32_t repeat_last_n  = 64;
+    float   repeat_penalty = 1.00f;
+
+    std::string model      = "models/gpt-2-117M/ggml-model.bin"; // model path
+    std::string prompt     = "";
+    std::string token_test = "";
+
+    bool    interactive      = false;
+    int32_t interactive_port = -1;
+};
+
+bool gpt_params_parse(int argc, char ** argv, gpt_params & params);
+
+void gpt_print_usage(int argc, char ** argv, const gpt_params & params);
+
+std::string gpt_random_prompt(std::mt19937 & rng);
+
+//
+// Vocab utils
+//
 
 std::string trim(const std::string & s);
 
@@ -13,6 +52,82 @@ std::string replace(
         const std::string & from,
         const std::string & to);
 
+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;
+    std::vector<std::string> special_tokens;
+
+    void add_special_token(const std::string & token);
+};
+
+// poor-man's JSON parsing
+std::map<std::string, int32_t> json_parse(const std::string & fname);
+
+std::string convert_to_utf8(const std::wstring & input);
+
+std::wstring convert_to_wstring(const std::string & input);
+
+void gpt_split_words(std::string str, std::vector<std::string>& words);
+
+// split text into tokens
+//
+// ref: https://github.com/openai/gpt-2/blob/a74da5d99abaaba920de8131d64da2862a8f213b/src/encoder.py#L53
+//
+// Regex (Python):
+// r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
+//
+// Regex (C++):
+// R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)"
+//
+std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text);
+
+// test outputs of gpt_tokenize
+//
+//   - compare with tokens generated by the huggingface tokenizer
+//   - test cases are chosen based on the model's main language (under 'prompt' directory)
+//   - if all sentences are tokenized identically, print 'All tests passed.'
+//   - otherwise, print sentence, huggingface tokens, ggml tokens
+//
+void test_gpt_tokenizer(gpt_vocab & vocab, const std::string & fpath_test);
+
+// load the tokens from encoder.json
+bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab);
+
+// sample next token given probabilities for each embedding
+//
+//   - consider only the top K tokens
+//   - from them, consider only the top tokens with cumulative probability > P
+//
+// TODO: not sure if this implementation is correct
+// TODO: temperature is not implemented
+//
+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);
+
+gpt_vocab::id gpt_sample_top_k_top_p_repeat(
+        const gpt_vocab & vocab,
+        const float * logits,
+        const int32_t * last_n_tokens_data,
+        size_t last_n_tokens_data_size,
+        int    top_k,
+        double top_p,
+        double temp,
+        int repeat_last_n,
+        float repeat_penalty,
+        std::mt19937 & rng);
+
+//
+// Audio utils
+//
+
 // Read WAV audio file and store the PCM data into pcmf32
 // The sample rate of the audio must be equal to COMMON_SAMPLE_RATE
 // If stereo flag is set and the audio has 2 channels, the pcmf32s will contain 2 channel PCM
@@ -38,3 +153,5 @@ bool vad_simple(
         float freq_thold,
         bool  verbose);
 
+// compute similarity between two strings using Levenshtein distance
+float similarity(const std::string & s0, const std::string & s1);

examples/helpers.js

@@ -145,7 +145,15 @@ function loadRemote(url, dst, size_mb, cbProgress, cbReady, cbCancel, cbPrint) {
                             var db = event.target.result;
                             var tx = db.transaction(['models'], 'readwrite');
                             var os = tx.objectStore('models');
-                            var rq = os.put(data, url);
+
+                            var rq = null;
+                            try {
+                                var rq = os.put(data, url);
+                            } catch (e) {
+                                cbPrint('loadRemote: failed to store "' + url + '" in the IndexedDB: \n' + e);
+                                cbCancel();
+                                return;
+                            }
 
                             rq.onsuccess = function (event) {
                                 cbPrint('loadRemote: "' + url + '" stored in the IndexedDB');
@@ -180,7 +188,6 @@ function loadRemote(url, dst, size_mb, cbProgress, cbReady, cbCancel, cbPrint) {
 
     rq.onabort = function (event) {
         cbPrint('loadRemote: failed to open IndexedDB: abort');
-
+        cbCancel();
     };
 }
-

examples/main/main.cpp

@@ -8,6 +8,11 @@
 #include <string>
 #include <thread>
 #include <vector>
+#include <cstring>
+
+#if defined(_MSC_VER)
+#pragma warning(disable: 4244 4267) // possible loss of data
+#endif
 
 // Terminal color map. 10 colors grouped in ranges [0.0, 0.1, ..., 0.9]
 // Lowest is red, middle is yellow, highest is green.
@@ -56,33 +61,41 @@ struct whisper_params {
     int32_t duration_ms  =  0;
     int32_t max_context  = -1;
     int32_t max_len      =  0;
-    int32_t best_of      =  5;
+    int32_t best_of      =  2;
     int32_t beam_size    = -1;
 
     float word_thold    =  0.01f;
     float entropy_thold =  2.40f;
     float logprob_thold = -1.00f;
 
-    bool speed_up       = false;
-    bool translate      = false;
-    bool diarize        = false;
-    bool split_on_word  = false;
-    bool no_fallback    = false;
-    bool output_txt     = false;
-    bool output_vtt     = false;
-    bool output_srt     = false;
-    bool output_wts     = false;
-    bool output_csv     = false;
-    bool output_jsn     = false;
-    bool print_special  = false;
-    bool print_colors   = false;
-    bool print_progress = false;
-    bool no_timestamps  = false;
+    bool speed_up        = false;
+    bool translate       = false;
+    bool detect_language = false;
+    bool diarize         = false;
+    bool tinydiarize     = false;
+    bool split_on_word   = false;
+    bool no_fallback     = false;
+    bool output_txt      = false;
+    bool output_vtt      = false;
+    bool output_srt      = false;
+    bool output_wts      = false;
+    bool output_csv      = false;
+    bool output_jsn      = false;
+    bool output_lrc      = false;
+    bool print_special   = false;
+    bool print_colors    = false;
+    bool print_progress  = false;
+    bool no_timestamps   = false;
 
-    std::string language = "en";
+    std::string language  = "en";
     std::string prompt;
     std::string font_path = "/System/Library/Fonts/Supplemental/Courier New Bold.ttf";
-    std::string model    = "models/ggml-base.en.bin";
+    std::string model     = "models/ggml-base.en.bin";
+
+    // [TDRZ] speaker turn string
+    std::string tdrz_speaker_turn = " [SPEAKER_TURN]"; // TODO: set from command line
+
+    std::string openvino_encode_device = "CPU";
 
     std::vector<std::string> fname_inp = {};
     std::vector<std::string> fname_out = {};
@@ -108,39 +121,43 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
             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 == "-sow"  || arg == "--split-on-word")  { params.split_on_word  = true; }
-        else if (arg == "-nf"   || arg == "--no-fallback")    { params.no_fallback    = 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 == "-fp"   || arg == "--font-path")      { params.font_path      = argv[++i]; }
-        else if (arg == "-ocsv" || arg == "--output-csv")     { params.output_csv     = true; }
-        else if (arg == "-oj"   || arg == "--output-json")    { params.output_jsn     = true; }
-        else if (arg == "-of"   || arg == "--output-file")    { params.fname_out.emplace_back(argv[++i]); }
-        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 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 == "-tdrz" || arg == "--tinydiarize")     { params.tinydiarize     = true; }
+        else if (arg == "-sow"  || arg == "--split-on-word")   { params.split_on_word   = true; }
+        else if (arg == "-nf"   || arg == "--no-fallback")     { params.no_fallback     = 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 == "-olrc" || arg == "--output-lrc")      { params.output_lrc      = true; }
+        else if (arg == "-fp"   || arg == "--font-path")       { params.font_path       = argv[++i]; }
+        else if (arg == "-ocsv" || arg == "--output-csv")      { params.output_csv      = true; }
+        else if (arg == "-oj"   || arg == "--output-json")     { params.output_jsn      = true; }
+        else if (arg == "-of"   || arg == "--output-file")     { params.fname_out.emplace_back(argv[++i]); }
+        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 == "-dl"   || arg == "--detect-language") { params.detect_language = true; }
+        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 if (arg == "-oved" || arg == "--ov-e-device")     { params.openvino_encode_device = argv[++i]; }
         else {
             fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
             whisper_print_usage(argc, argv, params);
@@ -173,10 +190,12 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
     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, "  -tdrz,     --tinydiarize       [%-7s] enable tinydiarize (requires a tdrz model)\n",     params.tinydiarize ? "true" : "false");
     fprintf(stderr, "  -nf,       --no-fallback       [%-7s] do not use temperature fallback while decoding\n", params.no_fallback ? "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, "  -olrc,     --output-lrc        [%-7s] output result in a lrc file\n",                    params.output_lrc ? "true" : "false");
     fprintf(stderr, "  -owts,     --output-words      [%-7s] output script for generating karaoke video\n",     params.output_wts ? "true" : "false");
     fprintf(stderr, "  -fp,       --font-path         [%-7s] path to a monospace font for karaoke video\n",     params.font_path.c_str());
     fprintf(stderr, "  -ocsv,     --output-csv        [%-7s] output result in a CSV file\n",                    params.output_csv ? "true" : "false");
@@ -185,11 +204,13 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
     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, "  -nt,       --no-timestamps     [%-7s] do not print timestamps\n",                        params.no_timestamps ? "true" : "false");
     fprintf(stderr, "  -l LANG,   --language LANG     [%-7s] spoken language ('auto' for auto-detect)\n",       params.language.c_str());
+    fprintf(stderr, "  -dl,       --detect-language   [%-7s] exit after automatically detecting language\n",    params.detect_language ? "true" : "false");
     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, "  -oved D,   --ov-e-device DNAME [%-7s] the OpenVINO device used for encode inference\n",  params.openvino_encode_device.c_str());
     fprintf(stderr, "\n");
 }
 
@@ -199,6 +220,39 @@ struct whisper_print_user_data {
     const std::vector<std::vector<float>> * pcmf32s;
 };
 
+std::string estimate_diarization_speaker(std::vector<std::vector<float>> pcmf32s, int64_t t0, int64_t t1, bool id_only = false) {
+    std::string speaker = "";
+    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 = "0";
+    } else if (energy1 > 1.1*energy0) {
+        speaker = "1";
+    } else {
+        speaker = "?";
+    }
+
+    //printf("is0 = %lld, is1 = %lld, energy0 = %f, energy1 = %f, speaker = %s\n", is0, is1, energy0, energy1, speaker.c_str());
+
+    if (!id_only) {
+        speaker.insert(0, "(speaker ");
+        speaker.append(")");
+    }
+
+    return speaker;
+}
+
 void whisper_print_segment_callback(struct whisper_context * ctx, struct whisper_state * /*state*/, 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;
@@ -207,8 +261,8 @@ void whisper_print_segment_callback(struct whisper_context * ctx, struct whisper
 
     std::string speaker = "";
 
-    int64_t t0;
-    int64_t t1;
+    int64_t t0 = 0;
+    int64_t t1 = 0;
 
     // print the last n_new segments
     const int s0 = n_segments - n_new;
@@ -228,28 +282,7 @@ void whisper_print_segment_callback(struct whisper_context * ctx, struct whisper
         }
 
         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());
+            speaker = estimate_diarization_speaker(pcmf32s, t0, t1);
         }
 
         if (params.print_colors) {
@@ -274,6 +307,12 @@ void whisper_print_segment_callback(struct whisper_context * ctx, struct whisper
             printf("%s%s", speaker.c_str(), text);
         }
 
+        if (params.tinydiarize) {
+            if (whisper_full_get_segment_speaker_turn_next(ctx, i)) {
+                printf("%s", params.tdrz_speaker_turn.c_str());
+            }
+        }
+
         // with timestamps or speakers: each segment on new line
         if (!params.no_timestamps || params.diarize) {
             printf("\n");
@@ -283,7 +322,7 @@ void whisper_print_segment_callback(struct whisper_context * ctx, struct whisper
     }
 }
 
-bool output_txt(struct whisper_context * ctx, const char * fname) {
+bool output_txt(struct whisper_context * ctx, const char * fname, const whisper_params & params, std::vector<std::vector<float>> pcmf32s) {
     std::ofstream fout(fname);
     if (!fout.is_open()) {
         fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname);
@@ -295,13 +334,22 @@ bool output_txt(struct whisper_context * ctx, const char * 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";
+        std::string speaker = "";
+
+        if (params.diarize && pcmf32s.size() == 2)
+        {
+            const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
+            const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
+            speaker = estimate_diarization_speaker(pcmf32s, t0, t1);
+        }
+
+        fout << speaker << text << "\n";
     }
 
     return true;
 }
 
-bool output_vtt(struct whisper_context * ctx, const char * fname) {
+bool output_vtt(struct whisper_context * ctx, const char * fname, const whisper_params & params, std::vector<std::vector<float>> pcmf32s) {
     std::ofstream fout(fname);
     if (!fout.is_open()) {
         fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname);
@@ -317,15 +365,23 @@ bool output_vtt(struct whisper_context * ctx, const char * fname) {
         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);
+        std::string speaker = "";
+
+        if (params.diarize && pcmf32s.size() == 2)
+        {
+            speaker = estimate_diarization_speaker(pcmf32s, t0, t1, true);
+            speaker.insert(0, "<v Speaker");
+            speaker.append(">");
+        }
 
         fout << to_timestamp(t0) << " --> " << to_timestamp(t1) << "\n";
-        fout << text << "\n\n";
+        fout << speaker << text << "\n\n";
     }
 
     return true;
 }
 
-bool output_srt(struct whisper_context * ctx, const char * fname, const whisper_params & params) {
+bool output_srt(struct whisper_context * ctx, const char * fname, const whisper_params & params, std::vector<std::vector<float>> pcmf32s) {
     std::ofstream fout(fname);
     if (!fout.is_open()) {
         fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname);
@@ -339,16 +395,53 @@ bool output_srt(struct whisper_context * ctx, const char * fname, const whisper_
         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);
+        std::string speaker = "";
+
+        if (params.diarize && pcmf32s.size() == 2)
+        {
+            speaker = estimate_diarization_speaker(pcmf32s, t0, t1);
+        }
 
         fout << i + 1 + params.offset_n << "\n";
         fout << to_timestamp(t0, true) << " --> " << to_timestamp(t1, true) << "\n";
-        fout << text << "\n\n";
+        fout << speaker << text << "\n\n";
     }
 
     return true;
 }
 
-bool output_csv(struct whisper_context * ctx, const char * fname) {
+char *escape_double_quotes_and_backslashes(const char *str) {
+    if (str == NULL) {
+        return NULL;
+    }
+
+    size_t escaped_length = strlen(str) + 1;
+
+    for (size_t i = 0; str[i] != '\0'; i++) {
+        if (str[i] == '"' || str[i] == '\\') {
+            escaped_length++;
+        }
+    }
+
+    char *escaped = (char *)calloc(escaped_length, 1); // pre-zeroed
+    if (escaped == NULL) {
+        return NULL;
+    }
+
+    size_t pos = 0;
+    for (size_t i = 0; str[i] != '\0'; i++) {
+        if (str[i] == '"' || str[i] == '\\') {
+            escaped[pos++] = '\\';
+        }
+        escaped[pos++] = str[i];
+    }
+
+    // no need to set zero due to calloc() being used prior
+
+    return escaped;
+}
+
+bool output_csv(struct whisper_context * ctx, const char * fname, const whisper_params & params, std::vector<std::vector<float>> pcmf32s) {
     std::ofstream fout(fname);
     if (!fout.is_open()) {
         fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname);
@@ -358,20 +451,32 @@ bool output_csv(struct whisper_context * ctx, const char * fname) {
     fprintf(stderr, "%s: saving output to '%s'\n", __func__, fname);
 
     const int n_segments = whisper_full_n_segments(ctx);
-    fout << "start,end,text\n";
+    fout << "start,end,";
+    if (params.diarize && pcmf32s.size() == 2)
+    {
+        fout << "speaker,";
+    }
+    fout << "text\n";
+
     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);
+        char * text_escaped = escape_double_quotes_and_backslashes(text);
 
         //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";
+        fout << 10 * t0 << "," << 10 * t1 << ",";
+        if (params.diarize && pcmf32s.size() == 2)
+        {
+            fout << estimate_diarization_speaker(pcmf32s, t0, t1, true) << ",";
+        }
+        fout << "\"" << text_escaped << "\"\n";
     }
 
     return true;
 }
 
-bool output_json(struct whisper_context * ctx, const char * fname, const whisper_params & params) {
+bool output_json(struct whisper_context * ctx, const char * fname, const whisper_params & params, std::vector<std::vector<float>> pcmf32s) {
     std::ofstream fout(fname);
     int indent = 0;
 
@@ -385,13 +490,13 @@ bool output_json(struct whisper_context * ctx, const char * fname, const whisper
         indent++;
     };
 
-    auto end_arr = [&](bool end = false) {
+    auto end_arr = [&](bool end) {
         indent--;
         doindent();
         fout << (end ? "]\n" : "},\n");
     };
 
-    auto start_obj = [&](const char *name = nullptr) {
+    auto start_obj = [&](const char *name) {
         doindent();
         if (name) {
             fout << "\"" << name << "\": {\n";
@@ -401,7 +506,7 @@ bool output_json(struct whisper_context * ctx, const char * fname, const whisper
         indent++;
     };
 
-    auto end_obj = [&](bool end = false) {
+    auto end_obj = [&](bool end) {
         indent--;
         doindent();
         fout << (end ? "}\n" : "},\n");
@@ -412,22 +517,24 @@ bool output_json(struct whisper_context * ctx, const char * fname, const whisper
         fout << "\"" << name << "\": ";
     };
 
-    auto value_s = [&](const char *name, const char *val, bool end = false) {
+    auto value_s = [&](const char *name, const char *val, bool end) {
         start_value(name);
-        fout << "\"" << val << (end ? "\"\n" : "\",\n");
+        char * val_escaped = escape_double_quotes_and_backslashes(val);
+        fout << "\"" << val_escaped << (end ? "\"\n" : "\",\n");
+        free(val_escaped);
     };
 
-    auto end_value = [&](bool end = false) {
+    auto end_value = [&](bool end) {
         fout << (end ? "\n" : ",\n");
     };
 
-    auto value_i = [&](const char *name, const int64_t val, bool end = false) {
+    auto value_i = [&](const char *name, const int64_t val, bool end) {
         start_value(name);
         fout << val;
         end_value(end);
     };
 
-    auto value_b = [&](const char *name, const bool val, bool end = false) {
+    auto value_b = [&](const char *name, const bool val, bool end) {
         start_value(name);
         fout << (val ? "true" : "false");
         end_value(end);
@@ -439,53 +546,62 @@ bool output_json(struct whisper_context * ctx, const char * fname, const whisper
     }
 
     fprintf(stderr, "%s: saving output to '%s'\n", __func__, fname);
-    start_obj();
-        value_s("systeminfo", whisper_print_system_info());
+    start_obj(nullptr);
+        value_s("systeminfo", whisper_print_system_info(), false);
         start_obj("model");
-            value_s("type", whisper_model_type_readable(ctx));
-            value_b("multilingual", whisper_is_multilingual(ctx));
-            value_i("vocab", whisper_model_n_vocab(ctx));
+            value_s("type", whisper_model_type_readable(ctx), false);
+            value_b("multilingual", whisper_is_multilingual(ctx), false);
+            value_i("vocab", whisper_model_n_vocab(ctx), false);
             start_obj("audio");
-                value_i("ctx", whisper_model_n_audio_ctx(ctx));
-                value_i("state", whisper_model_n_audio_state(ctx));
-                value_i("head", whisper_model_n_audio_head(ctx));
+                value_i("ctx", whisper_model_n_audio_ctx(ctx), false);
+                value_i("state", whisper_model_n_audio_state(ctx), false);
+                value_i("head", whisper_model_n_audio_head(ctx), false);
                 value_i("layer", whisper_model_n_audio_layer(ctx), true);
-            end_obj();
+            end_obj(false);
             start_obj("text");
-                value_i("ctx", whisper_model_n_text_ctx(ctx));
-                value_i("state", whisper_model_n_text_state(ctx));
-                value_i("head", whisper_model_n_text_head(ctx));
-                value_i("leyer", whisper_model_n_text_layer(ctx), true);
-            end_obj();
-            value_i("mels", whisper_model_n_mels(ctx));
-            value_i("f16", whisper_model_f16(ctx), true);
-        end_obj();
+                value_i("ctx", whisper_model_n_text_ctx(ctx), false);
+                value_i("state", whisper_model_n_text_state(ctx), false);
+                value_i("head", whisper_model_n_text_head(ctx), false);
+                value_i("layer", whisper_model_n_text_layer(ctx), true);
+            end_obj(false);
+            value_i("mels", whisper_model_n_mels(ctx), false);
+            value_i("ftype", whisper_model_ftype(ctx), true);
+        end_obj(false);
         start_obj("params");
-            value_s("model", params.model.c_str());
-            value_s("language", params.language.c_str());
+            value_s("model", params.model.c_str(), false);
+            value_s("language", params.language.c_str(), false);
             value_b("translate", params.translate, true);
-        end_obj();
+        end_obj(false);
         start_obj("result");
             value_s("language", whisper_lang_str(whisper_full_lang_id(ctx)), true);
-        end_obj();
+        end_obj(false);
         start_arr("transcription");
 
             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);
 
-                start_obj();
-                    start_obj("timestanps");
-                        value_s("from", to_timestamp(t0, true).c_str());
+                start_obj(nullptr);
+                    start_obj("timestamps");
+                        value_s("from", to_timestamp(t0, true).c_str(), false);
                         value_s("to", to_timestamp(t1, true).c_str(), true);
-                    end_obj();
+                    end_obj(false);
                     start_obj("offsets");
-                        value_i("from", t0 * 10);
+                        value_i("from", t0 * 10, false);
                         value_i("to", t1 * 10, true);
-                    end_obj();
-                    value_s("text", text, true);
+                    end_obj(false);
+                    value_s("text", text, !params.diarize && !params.tinydiarize);
+
+                    if (params.diarize && pcmf32s.size() == 2) {
+                        value_s("speaker", estimate_diarization_speaker(pcmf32s, t0, t1, true).c_str(), true);
+                    }
+
+                    if (params.tinydiarize) {
+                        value_b("speaker_turn_next", whisper_full_get_segment_speaker_turn_next(ctx, i), true);
+                    }
                 end_obj(i == (n_segments - 1));
             }
 
@@ -497,7 +613,7 @@ bool output_json(struct whisper_context * ctx, const char * fname, const whisper
 // 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) {
+bool output_wts(struct whisper_context * ctx, const char * fname, const char * fname_inp, const whisper_params & params, float t_sec, std::vector<std::vector<float>> pcmf32s) {
     std::ofstream fout(fname);
 
     fprintf(stderr, "%s: saving output to '%s'\n", __func__, fname);
@@ -534,6 +650,11 @@ bool output_wts(struct whisper_context * ctx, const char * fname, const char * f
         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;
+        std::string speaker = "";
+
+        if (params.diarize && pcmf32s.size() == 2) {
+            speaker = estimate_diarization_speaker(pcmf32s, t0, t1);
+        }
 
         for (int j = 0; j < n; ++j) {
             const auto & token = tokens[j];
@@ -542,13 +663,19 @@ bool output_wts(struct whisper_context * ctx, const char * fname, const char * f
                 continue;
             }
 
-            std::string txt_bg;
-            std::string txt_fg; // highlight token
-            std::string txt_ul; // underline
+            std::string txt_bg = "";
+            std::string txt_fg = ""; // highlight token
+            std::string txt_ul = ""; // underline
 
-            txt_bg = "> ";
-            txt_fg = "> ";
-            txt_ul = "\\ \\ ";
+            if (params.diarize && pcmf32s.size() == 2) {
+                txt_bg = speaker;
+                txt_fg = speaker;
+                txt_ul = "\\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ ";
+            }
+
+            txt_bg.append("> ");
+            txt_fg.append("> ");
+            txt_ul.append("\\ \\ ");
 
             {
                 for (int k = 0; k < n; ++k) {
@@ -611,10 +738,51 @@ bool output_wts(struct whisper_context * ctx, const char * fname, const char * f
     return true;
 }
 
+bool output_lrc(struct whisper_context * ctx, const char * fname, const whisper_params & params, std::vector<std::vector<float>> pcmf32s) {
+    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 << "[by:whisper.cpp]\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 t = whisper_full_get_segment_t0(ctx, i);
+
+        int64_t msec = t * 10;
+        int64_t min = msec / (1000 * 60);
+        msec = msec - min * (1000 * 60);
+        int64_t sec = msec / 1000;
+        msec = msec - sec * 1000;
+
+        char buf[16];
+        snprintf(buf, sizeof(buf), "%02d:%02d.%02d", (int) min, (int) sec, (int) ( msec / 10));
+        std::string timestamp_lrc = std::string(buf);
+        std::string speaker = "";
+
+        if (params.diarize && pcmf32s.size() == 2)
+        {
+            const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
+            const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
+            speaker = estimate_diarization_speaker(pcmf32s, t0, t1);
+        }
+
+        fout <<  '[' << timestamp_lrc << ']' << speaker << text << "\n";
+    }
+
+    return true;
+}
+
 int main(int argc, char ** argv) {
     whisper_params params;
 
     if (whisper_params_parse(argc, argv, params) == false) {
+        whisper_print_usage(argc, argv, params);
         return 1;
     }
 
@@ -630,6 +798,12 @@ int main(int argc, char ** argv) {
         exit(0);
     }
 
+    if (params.diarize && params.tinydiarize) {
+        fprintf(stderr, "error: cannot use both --diarize and --tinydiarize\n");
+        whisper_print_usage(argc, argv, params);
+        exit(0);
+    }
+
     // whisper init
 
     struct whisper_context * ctx = whisper_init_from_file(params.model.c_str());
@@ -639,21 +813,8 @@ int main(int argc, char ** argv) {
         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");
-    }
+    // initialize openvino encoder. this has no effect on whisper.cpp builds that don't have OpenVINO configured
+    whisper_ctx_init_openvino_encoder(ctx, nullptr, params.openvino_encode_device.c_str(), nullptr);
 
     for (int f = 0; f < (int) params.fname_inp.size(); ++f) {
         const auto fname_inp = params.fname_inp[f];
@@ -684,11 +845,15 @@ int main(int argc, char ** argv) {
                     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",
+            if (params.detect_language) {
+                params.language = "auto";
+            }
+            fprintf(stderr, "%s: processing '%s' (%d samples, %.1f sec), %d threads, %d processors, lang = %s, task = %s, %stimestamps = %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.tinydiarize ? "tdrz = 1, " : "",
                     params.no_timestamps ? 0 : 1);
 
             fprintf(stderr, "\n");
@@ -706,6 +871,7 @@ int main(int argc, char ** argv) {
             wparams.print_special    = params.print_special;
             wparams.translate        = params.translate;
             wparams.language         = params.language.c_str();
+            wparams.detect_language  = params.detect_language;
             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;
@@ -718,8 +884,9 @@ int main(int argc, char ** argv) {
 
             wparams.speed_up         = params.speed_up;
 
-            wparams.prompt_tokens     = prompt_tokens.empty() ? nullptr : prompt_tokens.data();
-            wparams.prompt_n_tokens   = prompt_tokens.empty() ? 0       : prompt_tokens.size();
+            wparams.tdrz_enable      = params.tinydiarize; // [TDRZ]
+
+            wparams.initial_prompt   = params.prompt.c_str();
 
             wparams.greedy.best_of        = params.best_of;
             wparams.beam_search.beam_size = params.beam_size;
@@ -762,37 +929,43 @@ int main(int argc, char ** argv) {
             // output to text file
             if (params.output_txt) {
                 const auto fname_txt = fname_out + ".txt";
-                output_txt(ctx, fname_txt.c_str());
+                output_txt(ctx, fname_txt.c_str(), params, pcmf32s);
             }
 
             // output to VTT file
             if (params.output_vtt) {
                 const auto fname_vtt = fname_out + ".vtt";
-                output_vtt(ctx, fname_vtt.c_str());
+                output_vtt(ctx, fname_vtt.c_str(), params, pcmf32s);
             }
 
             // output to SRT file
             if (params.output_srt) {
                 const auto fname_srt = fname_out + ".srt";
-                output_srt(ctx, fname_srt.c_str(), params);
+                output_srt(ctx, fname_srt.c_str(), params, pcmf32s);
             }
 
             // output to WTS file
             if (params.output_wts) {
                 const auto fname_wts = fname_out + ".wts";
-                output_wts(ctx, fname_wts.c_str(), fname_inp.c_str(), params, float(pcmf32.size() + 1000)/WHISPER_SAMPLE_RATE);
+                output_wts(ctx, fname_wts.c_str(), fname_inp.c_str(), params, float(pcmf32.size() + 1000)/WHISPER_SAMPLE_RATE, pcmf32s);
             }
 
             // output to CSV file
             if (params.output_csv) {
                 const auto fname_csv = fname_out + ".csv";
-                output_csv(ctx, fname_csv.c_str());
+                output_csv(ctx, fname_csv.c_str(), params, pcmf32s);
             }
 
             // output to JSON file
             if (params.output_jsn) {
                 const auto fname_jsn = fname_out + ".json";
-                output_json(ctx, fname_jsn.c_str(), params);
+                output_json(ctx, fname_jsn.c_str(), params, pcmf32s);
+            }
+
+            // output to LRC file
+            if (params.output_lrc) {
+                const auto fname_lrc = fname_out + ".lrc";
+                output_lrc(ctx, fname_lrc.c_str(), params, pcmf32s);
             }
         }
     }

examples/quantize/CMakeLists.txt

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

examples/quantize/README.md

@@ -0,0 +1,3 @@
+# quantize
+
+Tool for integer quantization of Whisper `ggml` model files

examples/quantize/quantize.cpp

@@ -0,0 +1,223 @@
+#include "ggml.h"
+
+#include "common.h"
+#include "common-ggml.h"
+
+#include <cassert>
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <fstream>
+#include <map>
+#include <string>
+#include <vector>
+#include <regex>
+
+// default hparams (Whisper tiny)
+struct whisper_hparams {
+    int32_t n_vocab       = 51864;
+    int32_t n_audio_ctx   = 1500;
+    int32_t n_audio_state = 384;
+    int32_t n_audio_head  = 6;
+    int32_t n_audio_layer = 4;
+    int32_t n_text_ctx    = 448;
+    int32_t n_text_state  = 384;
+    int32_t n_text_head   = 6;
+    int32_t n_text_layer  = 4;
+    int32_t n_mels        = 80;
+    int32_t ftype         = 1;
+};
+
+struct whisper_filters {
+    int32_t n_mel;
+    int32_t n_fft;
+
+    std::vector<float> data;
+};
+
+// quantize a model
+bool whisper_model_quantize(const std::string & fname_inp, const std::string & fname_out, ggml_ftype ftype) {
+    gpt_vocab vocab;
+
+    printf("%s: loading model from '%s'\n", __func__, fname_inp.c_str());
+
+    auto finp = std::ifstream(fname_inp, std::ios::binary);
+    if (!finp) {
+        fprintf(stderr, "%s: failed to open '%s' for reading\n", __func__, fname_inp.c_str());
+        return false;
+    }
+
+    auto fout = std::ofstream(fname_out, std::ios::binary);
+    if (!fout) {
+        fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname_out.c_str());
+        return false;
+    }
+
+    // verify magic
+    {
+        uint32_t magic;
+        finp.read((char *) &magic, sizeof(magic));
+        if (magic != GGML_FILE_MAGIC) {
+            fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname_inp.c_str());
+            return false;
+        }
+
+        fout.write((char *) &magic, sizeof(magic));
+    }
+
+    whisper_hparams hparams;
+
+    // load hparams
+    {
+        finp.read((char *) &hparams.n_vocab,       sizeof(hparams.n_vocab));
+        finp.read((char *) &hparams.n_audio_ctx,   sizeof(hparams.n_audio_ctx));
+        finp.read((char *) &hparams.n_audio_state, sizeof(hparams.n_audio_state));
+        finp.read((char *) &hparams.n_audio_head,  sizeof(hparams.n_audio_head));
+        finp.read((char *) &hparams.n_audio_layer, sizeof(hparams.n_audio_layer));
+        finp.read((char *) &hparams.n_text_ctx,    sizeof(hparams.n_text_ctx));
+        finp.read((char *) &hparams.n_text_state,  sizeof(hparams.n_text_state));
+        finp.read((char *) &hparams.n_text_head,   sizeof(hparams.n_text_head));
+        finp.read((char *) &hparams.n_text_layer,  sizeof(hparams.n_text_layer));
+        finp.read((char *) &hparams.n_mels,        sizeof(hparams.n_mels));
+        finp.read((char *) &hparams.ftype,         sizeof(hparams.ftype));
+
+        const int32_t qntvr_src =    hparams.ftype / GGML_QNT_VERSION_FACTOR;
+        const int32_t ftype_dst = GGML_QNT_VERSION * GGML_QNT_VERSION_FACTOR + ftype;
+
+        fprintf(stderr, "%s: n_vocab       = %d\n", __func__, hparams.n_vocab);
+        fprintf(stderr, "%s: n_audio_ctx   = %d\n", __func__, hparams.n_audio_ctx);
+        fprintf(stderr, "%s: n_audio_state = %d\n", __func__, hparams.n_audio_state);
+        fprintf(stderr, "%s: n_audio_head  = %d\n", __func__, hparams.n_audio_head);
+        fprintf(stderr, "%s: n_audio_layer = %d\n", __func__, hparams.n_audio_layer);
+        fprintf(stderr, "%s: n_text_ctx    = %d\n", __func__, hparams.n_text_ctx);
+        fprintf(stderr, "%s: n_text_state  = %d\n", __func__, hparams.n_text_state);
+        fprintf(stderr, "%s: n_text_head   = %d\n", __func__, hparams.n_text_head);
+        fprintf(stderr, "%s: n_text_layer  = %d\n", __func__, hparams.n_text_layer);
+        fprintf(stderr, "%s: n_mels        = %d\n", __func__, hparams.n_mels);
+        fprintf(stderr, "%s: ftype (src)   = %d\n", __func__, hparams.ftype);
+        fprintf(stderr, "%s: qntvr (src)   = %d\n", __func__, qntvr_src);
+        fprintf(stderr, "%s: ftype (dst)   = %d\n", __func__, ftype_dst);
+        fprintf(stderr, "%s: qntvr (dst)   = %d\n", __func__, GGML_QNT_VERSION);
+
+        fout.write((const char *) &hparams.n_vocab,       sizeof(hparams.n_vocab));
+        fout.write((const char *) &hparams.n_audio_ctx,   sizeof(hparams.n_audio_ctx));
+        fout.write((const char *) &hparams.n_audio_state, sizeof(hparams.n_audio_state));
+        fout.write((const char *) &hparams.n_audio_head,  sizeof(hparams.n_audio_head));
+        fout.write((const char *) &hparams.n_audio_layer, sizeof(hparams.n_audio_layer));
+        fout.write((const char *) &hparams.n_text_ctx,    sizeof(hparams.n_text_ctx));
+        fout.write((const char *) &hparams.n_text_state,  sizeof(hparams.n_text_state));
+        fout.write((const char *) &hparams.n_text_head,   sizeof(hparams.n_text_head));
+        fout.write((const char *) &hparams.n_text_layer,  sizeof(hparams.n_text_layer));
+        fout.write((const char *) &hparams.n_mels,        sizeof(hparams.n_mels));
+        fout.write((const char *) &ftype_dst,             sizeof(hparams.ftype));
+    }
+
+    // load mel filters
+    {
+        whisper_filters filters;
+
+        finp.read ((char *) &filters.n_mel, sizeof(filters.n_mel));
+        fout.write((char *) &filters.n_mel, sizeof(filters.n_mel));
+        finp.read ((char *) &filters.n_fft, sizeof(filters.n_fft));
+        fout.write((char *) &filters.n_fft, sizeof(filters.n_fft));
+
+        filters.data.resize(filters.n_mel * filters.n_fft);
+        finp.read ((char *) filters.data.data(), filters.data.size() * sizeof(float));
+        fout.write((char *) filters.data.data(), filters.data.size() * sizeof(float));
+    }
+
+    // load vocab
+    {
+        int32_t n_vocab = 0;
+        finp.read ((char *) &n_vocab, sizeof(n_vocab));
+        fout.write((char *) &n_vocab, sizeof(n_vocab));
+
+        //if (n_vocab != hparams.n_vocab) {
+        //    fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
+        //            __func__, fname_inp.c_str(), n_vocab, hparams.n_vocab);
+        //    return false;
+        //}
+
+        char word[128];
+
+        for (int i = 0; i < n_vocab; i++) {
+            uint32_t len;
+            finp.read ((char *) &len, sizeof(len));
+            fout.write((char *) &len, sizeof(len));
+
+            word[len] = '\0';
+
+            finp.read ((char *) word, len);
+            fout.write((char *) word, len);
+
+            vocab.token_to_id[word] = i;
+            vocab.id_to_token[i] = word;
+        }
+    }
+
+    // regexes of tensor names to not be quantized
+    const std::vector<std::string> to_skip = {
+        //"encoder.*",
+        "encoder.conv1.bias",
+        "encoder.conv2.bias",
+        "encoder.positional_embedding",
+        "decoder.positional_embedding",
+    };
+
+    if (!ggml_common_quantize_0(finp, fout, ftype, { ".*" }, to_skip)) {
+        fprintf(stderr, "%s: failed to quantize model '%s'\n", __func__, fname_inp.c_str());
+        return false;
+    }
+
+    finp.close();
+    fout.close();
+
+    return true;
+}
+
+int main(int argc, char ** argv) {
+    if (argc != 4) {
+        fprintf(stderr, "usage: %s model-f32.bin model-quant.bin type\n", argv[0]);
+        ggml_print_ftypes(stderr);
+        return 1;
+    }
+
+    // needed to initialize f16 tables
+    {
+        struct ggml_init_params params = { 0, NULL, false };
+        struct ggml_context * ctx = ggml_init(params);
+        ggml_free(ctx);
+    }
+
+    const std::string fname_inp = argv[1];
+    const std::string fname_out = argv[2];
+
+    const ggml_ftype ftype = ggml_parse_ftype(argv[3]);
+
+    const int64_t t_main_start_us = ggml_time_us();
+
+    int64_t t_quantize_us = 0;
+
+    // load the model
+    {
+        const int64_t t_start_us = ggml_time_us();
+
+        if (!whisper_model_quantize(fname_inp, fname_out, ggml_ftype(ftype))) {
+            fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str());
+            return 1;
+        }
+
+        t_quantize_us = ggml_time_us() - t_start_us;
+    }
+
+    // report timing
+    {
+        const int64_t t_main_end_us = ggml_time_us();
+
+        printf("\n");
+        printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us/1000.0f);
+        printf("%s:    total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
+    }
+
+    return 0;
+}

examples/stream.wasm/index-tmpl.html

@@ -35,6 +35,15 @@
 
             <br><br>
 
+            <b>More examples:</b>
+                <a href="https://whisper.ggerganov.com/">main</a> |
+                <a href="https://whisper.ggerganov.com/bench">bench</a> |
+                <a href="https://whisper.ggerganov.com/stream">stream</a> |
+                <a href="https://whisper.ggerganov.com/command">command</a> |
+                <a href="https://whisper.ggerganov.com/talk">talk</a> |
+
+            <br><br>
+
             <hr>
 
             Select the model you would like to use, click the "Start" button and start speaking
@@ -45,6 +54,10 @@
                 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>
+                <br><br>
+                Quantized models:<br><br>
+                <button id="fetch-whisper-tiny-en-q5_1"   onclick="loadWhisper('tiny-en-q5_1')">tiny.en (Q5_1, 31 MB)</button>
+                <button id="fetch-whisper-base-en-q5_1"   onclick="loadWhisper('base-en-q5_1')">base.en (Q5_1, 57 MB)</button>
                 <span id="fetch-whisper-progress"></span>
 
                 <!--
@@ -162,11 +175,17 @@
                 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',
+
+                    'tiny-en-q5_1':  'https://whisper.ggerganov.com/ggml-model-whisper-tiny.en-q5_1.bin',
+                    'base-en-q5_1':  'https://whisper.ggerganov.com/ggml-model-whisper-base.en-q5_1.bin',
                 };
 
                 let sizes = {
                     'tiny.en': 75,
                     'base.en': 142,
+
+                    'tiny-en-q5_1':   31,
+                    'base-en-q5_1':   57,
                 };
 
                 let url     = urls[model];
@@ -177,6 +196,10 @@
 
                 document.getElementById('fetch-whisper-tiny-en').style.display = 'none';
                 document.getElementById('fetch-whisper-base-en').style.display = 'none';
+
+                document.getElementById('fetch-whisper-tiny-en-q5_1').style.display = 'none';
+                document.getElementById('fetch-whisper-base-en-q5_1').style.display = 'none';
+
                 document.getElementById('model-whisper-status').innerHTML = 'loading "' + model + '" ... ';
 
                 cbProgress = function(p) {
@@ -188,6 +211,10 @@
                     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('fetch-whisper-tiny-en-q5_1'); if (el) el.style.display = 'inline-block';
+                    el = document.getElementById('fetch-whisper-base-en-q5_1'); if (el) el.style.display = 'inline-block';
+
                     el = document.getElementById('model-whisper-status');  if (el) el.innerHTML = '';
                 };
 

examples/stream/CMakeLists.txt

@@ -1,4 +1,4 @@
-if (WHISPER_SUPPORT_SDL2)
+if (WHISPER_SDL2)
     # stream
     set(TARGET stream)
     add_executable(${TARGET} stream.cpp)

examples/stream/stream.cpp

@@ -43,6 +43,7 @@ struct whisper_params {
 
     bool speed_up      = false;
     bool translate     = false;
+    bool no_fallback   = false;
     bool print_special = false;
     bool no_context    = true;
     bool no_timestamps = false;
@@ -73,6 +74,7 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
         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 == "-nf"  || arg == "--no-fallback")   { params.no_fallback   = 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]; }
@@ -94,22 +96,23 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
     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, "  -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, "  -nf,      --no-fallback   [%-7s] do not use temperature fallback while decoding\n", params.no_fallback ? "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");
 }
 
@@ -148,7 +151,7 @@ int main(int argc, char ** argv) {
 
     // whisper init
 
-    if (whisper_lang_id(params.language.c_str()) == -1) {
+    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);
@@ -297,7 +300,8 @@ int main(int argc, char ** argv) {
             wparams.speed_up         = params.speed_up;
 
             // disable temperature fallback
-            wparams.temperature_inc  = -1.0f;
+            //wparams.temperature_inc  = -1.0f;
+            wparams.temperature_inc  = params.no_fallback ? 0.0f : wparams.temperature_inc;
 
             wparams.prompt_tokens    = params.no_context ? nullptr : prompt_tokens.data();
             wparams.prompt_n_tokens  = params.no_context ? 0       : prompt_tokens.size();
@@ -379,6 +383,7 @@ int main(int argc, char ** argv) {
                     }
                 }
             }
+            fflush(stdout);
         }
     }
 

examples/talk-llama/.gitignore

@@ -0,0 +1 @@
+audio.mp3

examples/talk-llama/CMakeLists.txt

@@ -0,0 +1,16 @@
+if (WHISPER_SDL2)
+    # talk-llama
+    set(TARGET talk-llama)
+    #add_executable(${TARGET} talk-llama.cpp llama.cpp)
+    #target_include_directories(${TARGET} PRIVATE ${SDL2_INCLUDE_DIRS})
+    #target_link_libraries(${TARGET} PRIVATE common common-sdl 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-llama.cpp llama.cpp ../common.cpp ../common-sdl.cpp ../../ggml.c ../../whisper.cpp)
+
+    target_include_directories(${TARGET} PRIVATE ${SDL2_INCLUDE_DIRS} ../../)
+    target_link_libraries(${TARGET} PRIVATE ${SDL2_LIBRARIES} ${CMAKE_THREAD_LIBS_INIT})
+
+    include(DefaultTargetOptions)
+endif ()

examples/talk-llama/README.md

@@ -0,0 +1,50 @@
+# talk-llama
+
+Talk with an LLaMA AI in your terminal
+
+[Demo Talk](https://user-images.githubusercontent.com/1991296/228024237-848f998c-c334-46a6-bef8-3271590da83b.mp4)
+
+## Building
+
+The `talk-llama` 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-llama" executable
+make talk-llama
+
+# Run it
+./talk-llama -mw ./models/ggml-small.en.bin -ml ../llama.cpp/models/13B/ggml-model-q4_0.bin -p "Georgi" -t 8
+```
+
+- The `-mw` argument specifies the Whisper model that you would like to use. Recommended `base` or `small` for real-time experience
+- The `-ml` argument specifies the LLaMA model that you would like to use. Read the instructions in https://github.com/ggerganov/llama.cpp for information about how to obtain a `ggml` compatible LLaMA model
+
+## Session
+
+The `talk-llama` tool supports session management to enable more coherent and continuous conversations. By maintaining context from previous interactions, it can better understand and respond to user requests in a more natural way.
+
+To enable session support, use the `--session FILE` command line option when running the program. The `talk-llama` model state will be saved to the specified file after each interaction. If the file does not exist, it will be created. If the file exists, the model state will be loaded from it, allowing you to resume a previous session.
+
+This feature is especially helpful for maintaining context in long conversations or when interacting with the AI assistant across multiple sessions. It ensures that the assistant remembers the previous interactions and can provide more relevant and contextual responses.
+
+Example usage:
+
+```bash
+./talk-llama --session ./my-session-file -mw ./models/ggml-small.en.bin -ml ../llama.cpp/models/13B/ggml-model-q4_0.bin -p "Georgi" -t 8
+```
+
+## 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](speak) script to your needs.
+By default, it is configured to use MacOS's `say` or Windows SpeechSynthesizer, but you can use whatever you wish.
+
+## Discussion
+
+If you have any feedback, please let "us" know in the following discussion: https://github.com/ggerganov/whisper.cpp/discussions/672?converting=1

examples/talk-llama/eleven-labs.py

@@ -0,0 +1,20 @@
+import sys
+import importlib.util
+
+if importlib.util.find_spec("elevenlabs") is None:
+    print("elevenlabs library is not installed, you can install it to your enviroment using 'pip install elevenlabs'")
+    sys.exit()
+
+from elevenlabs import generate, play, save
+
+# Get a Voice object, by name or UUID
+voice = "Arnold" #Possible Voices: Adam Antoni Arnold Bella Domi Elli Josh
+
+# Generate the TTS
+audio = generate(
+  text=str(sys.argv[2:]),
+  voice=voice
+)
+
+# Save the TTS to a file
+save(audio, "audio.mp3") 

examples/talk-llama/llama-util.h

@@ -0,0 +1,474 @@
+// Internal header to be included only by llama.cpp.
+// Contains wrappers around OS interfaces.
+
+#ifndef LLAMA_UTIL_H
+#define LLAMA_UTIL_H
+
+#include <cstdio>
+#include <cstdint>
+#include <cerrno>
+#include <cstring>
+#include <cstdarg>
+#include <cstdlib>
+#include <climits>
+
+#include <string>
+#include <vector>
+#include <stdexcept>
+
+#ifdef __has_include
+    #if __has_include(<unistd.h>)
+        #include <unistd.h>
+        #if defined(_POSIX_MAPPED_FILES)
+            #include <sys/mman.h>
+        #endif
+        #if defined(_POSIX_MEMLOCK_RANGE)
+            #include <sys/resource.h>
+        #endif
+    #endif
+#endif
+
+#if defined(_WIN32)
+    #define WIN32_LEAN_AND_MEAN
+    #ifndef NOMINMAX
+        #define NOMINMAX
+    #endif
+    #include <windows.h>
+    #include <io.h>
+    #include <stdio.h> // for _fseeki64
+#endif
+
+#define LLAMA_ASSERT(x) \
+    do { \
+        if (!(x)) { \
+            fprintf(stderr, "LLAMA_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
+            abort(); \
+        } \
+    } while (0)
+
+#ifdef __GNUC__
+#ifdef __MINGW32__
+__attribute__((format(gnu_printf, 1, 2)))
+#else
+__attribute__((format(printf, 1, 2)))
+#endif
+#endif
+static std::string format(const char * fmt, ...) {
+    va_list ap, ap2;
+    va_start(ap, fmt);
+    va_copy(ap2, ap);
+    int size = vsnprintf(NULL, 0, fmt, ap);
+    LLAMA_ASSERT(size >= 0 && size < INT_MAX);
+    std::vector<char> buf(size + 1);
+    int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
+    LLAMA_ASSERT(size2 == size);
+    va_end(ap2);
+    va_end(ap);
+    return std::string(buf.data(), size);
+}
+
+struct llama_file {
+    // use FILE * so we don't have to re-open the file to mmap
+    FILE * fp;
+    size_t size;
+
+    llama_file(const char * fname, const char * mode) {
+        fp = std::fopen(fname, mode);
+        if (fp == NULL) {
+            throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
+        }
+        seek(0, SEEK_END);
+        size = tell();
+        seek(0, SEEK_SET);
+    }
+
+    size_t tell() const {
+#ifdef _WIN32
+        __int64 ret = _ftelli64(fp);
+#else
+        long ret = std::ftell(fp);
+#endif
+        LLAMA_ASSERT(ret != -1); // this really shouldn't fail
+        return (size_t) ret;
+    }
+
+    void seek(size_t offset, int whence) {
+#ifdef _WIN32
+        int ret = _fseeki64(fp, (__int64) offset, whence);
+#else
+        int ret = std::fseek(fp, (long) offset, whence);
+#endif
+        LLAMA_ASSERT(ret == 0); // same
+    }
+
+    void read_raw(void * ptr, size_t len) const {
+        if (len == 0) {
+            return;
+        }
+        errno = 0;
+        std::size_t ret = std::fread(ptr, len, 1, fp);
+        if (ferror(fp)) {
+            throw std::runtime_error(format("read error: %s", strerror(errno)));
+        }
+        if (ret != 1) {
+            throw std::runtime_error(std::string("unexpectedly reached end of file"));
+        }
+    }
+
+    std::uint32_t read_u32() {
+        std::uint32_t ret;
+        read_raw(&ret, sizeof(ret));
+        return ret;
+    }
+
+    std::string read_string(std::uint32_t len) {
+        std::vector<char> chars(len);
+        read_raw(chars.data(), len);
+        return std::string(chars.data(), len);
+    }
+
+    void write_raw(const void * ptr, size_t len) const {
+        if (len == 0) {
+            return;
+        }
+        errno = 0;
+        size_t ret = std::fwrite(ptr, len, 1, fp);
+        if (ret != 1) {
+            throw std::runtime_error(format("write error: %s", strerror(errno)));
+        }
+    }
+
+    void write_u32(std::uint32_t val) {
+        write_raw(&val, sizeof(val));
+    }
+
+    ~llama_file() {
+        if (fp) {
+            std::fclose(fp);
+        }
+    }
+};
+
+#if defined(_WIN32)
+static std::string llama_format_win_err(DWORD err) {
+    LPSTR buf;
+    size_t size = FormatMessageA(FORMAT_MESSAGE_ALLOCATE_BUFFER | FORMAT_MESSAGE_FROM_SYSTEM | FORMAT_MESSAGE_IGNORE_INSERTS,
+                                 NULL, err, MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT), (LPSTR)&buf, 0, NULL);
+    if (!size) {
+        return "FormatMessageA failed";
+    }
+    std::string ret(buf, size);
+    LocalFree(buf);
+    return ret;
+}
+#endif
+
+struct llama_mmap {
+    void * addr;
+    size_t size;
+
+    llama_mmap(const llama_mmap &) = delete;
+
+#ifdef _POSIX_MAPPED_FILES
+    static constexpr bool SUPPORTED = true;
+
+    llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1 /* -1 = max value */) {
+        size = file->size;
+        int fd = fileno(file->fp);
+        int flags = MAP_SHARED;
+#ifdef __linux__
+        flags |= MAP_POPULATE;
+#endif
+        addr = mmap(NULL, file->size, PROT_READ, flags, fd, 0);
+        if (addr == MAP_FAILED) {
+            throw std::runtime_error(format("mmap failed: %s", strerror(errno)));
+        }
+
+        if (prefetch > 0) {
+            // Advise the kernel to preload the mapped memory
+            if (posix_madvise(addr, std::min(file->size, prefetch), POSIX_MADV_WILLNEED)) {
+                fprintf(stderr, "warning: posix_madvise(.., POSIX_MADV_WILLNEED) failed: %s\n",
+                        strerror(errno));
+            }
+        }
+    }
+
+    ~llama_mmap() {
+        munmap(addr, size);
+    }
+#elif defined(_WIN32)
+    static constexpr bool SUPPORTED = true;
+
+    llama_mmap(struct llama_file * file, bool prefetch = true) {
+        size = file->size;
+
+        HANDLE hFile = (HANDLE) _get_osfhandle(_fileno(file->fp));
+
+        HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);
+        DWORD error = GetLastError();
+
+        if (hMapping == NULL) {
+            throw std::runtime_error(format("CreateFileMappingA failed: %s", llama_format_win_err(error).c_str()));
+        }
+
+        addr = MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0);
+        error = GetLastError();
+        CloseHandle(hMapping);
+
+        if (addr == NULL) {
+            throw std::runtime_error(format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str()));
+        }
+
+        #if _WIN32_WINNT >= _WIN32_WINNT_WIN8
+        if (prefetch) {
+            // Advise the kernel to preload the mapped memory
+            WIN32_MEMORY_RANGE_ENTRY range;
+            range.VirtualAddress = addr;
+            range.NumberOfBytes = (SIZE_T)size;
+            if (!PrefetchVirtualMemory(GetCurrentProcess(), 1, &range, 0)) {
+                fprintf(stderr, "warning: PrefetchVirtualMemory failed: %s\n",
+                        llama_format_win_err(GetLastError()).c_str());
+            }
+        }
+        #else
+        #pragma message("warning: You are building for pre-Windows 8; prefetch not supported")
+        #endif // _WIN32_WINNT >= _WIN32_WINNT_WIN8
+    }
+
+    ~llama_mmap() {
+        if (!UnmapViewOfFile(addr)) {
+            fprintf(stderr, "warning: UnmapViewOfFile failed: %s\n",
+                    llama_format_win_err(GetLastError()).c_str());
+        }
+    }
+#else
+    static constexpr bool SUPPORTED = false;
+
+    llama_mmap(struct llama_file *, bool prefetch = true) {
+        (void)prefetch;
+        throw std::runtime_error(std::string("mmap not supported"));
+    }
+#endif
+};
+
+// Represents some region of memory being locked using mlock or VirtualLock;
+// will automatically unlock on destruction.
+struct llama_mlock {
+    void * addr = NULL;
+    size_t size = 0;
+    bool failed_already = false;
+
+    llama_mlock() {}
+    llama_mlock(const llama_mlock &) = delete;
+
+    ~llama_mlock() {
+        if (size) {
+            raw_unlock(addr, size);
+        }
+    }
+
+    void init(void * ptr) {
+        LLAMA_ASSERT(addr == NULL && size == 0);
+        addr = ptr;
+    }
+
+    void grow_to(size_t target_size) {
+        LLAMA_ASSERT(addr);
+        if (failed_already) {
+            return;
+        }
+        size_t granularity = lock_granularity();
+        target_size = (target_size + granularity - 1) & ~(granularity - 1);
+        if (target_size > size) {
+            if (raw_lock((uint8_t *) addr + size, target_size - size)) {
+                size = target_size;
+            } else {
+                failed_already = true;
+            }
+        }
+    }
+
+#ifdef _POSIX_MEMLOCK_RANGE
+    static constexpr bool SUPPORTED = true;
+
+    size_t lock_granularity() {
+        return (size_t) sysconf(_SC_PAGESIZE);
+    }
+
+    #ifdef __APPLE__
+        #define MLOCK_SUGGESTION \
+            "Try increasing the sysctl values 'vm.user_wire_limit' and 'vm.global_user_wire_limit' and/or " \
+            "decreasing 'vm.global_no_user_wire_amount'.  Also try increasing RLIMIT_MLOCK (ulimit -l).\n"
+    #else
+        #define MLOCK_SUGGESTION \
+            "Try increasing RLIMIT_MLOCK ('ulimit -l' as root).\n"
+    #endif
+
+    bool raw_lock(const void * addr, size_t size) {
+        if (!mlock(addr, size)) {
+            return true;
+        } else {
+            char* errmsg = std::strerror(errno);
+            bool suggest = (errno == ENOMEM);
+
+            // Check if the resource limit is fine after all
+            struct rlimit lock_limit;
+            if (suggest && getrlimit(RLIMIT_MEMLOCK, &lock_limit))
+                suggest = false;
+            if (suggest && (lock_limit.rlim_max > lock_limit.rlim_cur + size))
+                suggest = false;
+
+            fprintf(stderr, "warning: failed to mlock %zu-byte buffer (after previously locking %zu bytes): %s\n%s",
+                    size, this->size, errmsg, suggest ? MLOCK_SUGGESTION : "");
+            return false;
+        }
+    }
+
+    #undef MLOCK_SUGGESTION
+
+    void raw_unlock(void * addr, size_t size) {
+        if (munlock(addr, size)) {
+            fprintf(stderr, "warning: failed to munlock buffer: %s\n", std::strerror(errno));
+        }
+    }
+#elif defined(_WIN32)
+    static constexpr bool SUPPORTED = true;
+
+    size_t lock_granularity() {
+        SYSTEM_INFO si;
+        GetSystemInfo(&si);
+        return (size_t) si.dwPageSize;
+    }
+
+    bool raw_lock(void * ptr, size_t len) {
+        for (int tries = 1; ; tries++) {
+            if (VirtualLock(ptr, len)) {
+                return true;
+            }
+            if (tries == 2) {
+                fprintf(stderr, "warning: failed to VirtualLock %zu-byte buffer (after previously locking %zu bytes): %s\n",
+                    len, size, llama_format_win_err(GetLastError()).c_str());
+                return false;
+            }
+
+            // It failed but this was only the first try; increase the working
+            // set size and try again.
+            SIZE_T min_ws_size, max_ws_size;
+            if (!GetProcessWorkingSetSize(GetCurrentProcess(), &min_ws_size, &max_ws_size)) {
+                fprintf(stderr, "warning: GetProcessWorkingSetSize failed: %s\n",
+                        llama_format_win_err(GetLastError()).c_str());
+                return false;
+            }
+            // Per MSDN: "The maximum number of pages that a process can lock
+            // is equal to the number of pages in its minimum working set minus
+            // a small overhead."
+            // Hopefully a megabyte is enough overhead:
+            size_t increment = len + 1048576;
+            // The minimum must be <= the maximum, so we need to increase both:
+            min_ws_size += increment;
+            max_ws_size += increment;
+            if (!SetProcessWorkingSetSize(GetCurrentProcess(), min_ws_size, max_ws_size)) {
+                fprintf(stderr, "warning: SetProcessWorkingSetSize failed: %s\n",
+                        llama_format_win_err(GetLastError()).c_str());
+                return false;
+            }
+        }
+    }
+
+    void raw_unlock(void * ptr, size_t len) {
+        if (!VirtualUnlock(ptr, len)) {
+            fprintf(stderr, "warning: failed to VirtualUnlock buffer: %s\n",
+                    llama_format_win_err(GetLastError()).c_str());
+        }
+    }
+#else
+    static constexpr bool SUPPORTED = false;
+
+    size_t lock_granularity() {
+        return (size_t) 65536;
+    }
+
+    bool raw_lock(const void * addr, size_t len) {
+        fprintf(stderr, "warning: mlock not supported on this system\n");
+        return false;
+    }
+
+    void raw_unlock(const void * addr, size_t len) {}
+#endif
+};
+
+// Replacement for std::vector<uint8_t> that doesn't require zero-initialization.
+struct llama_buffer {
+    uint8_t * addr = NULL;
+    size_t size = 0;
+
+    llama_buffer() = default;
+
+    void resize(size_t len) {
+        delete[] addr;
+        addr = new uint8_t[len];
+        size = len;
+    }
+
+    ~llama_buffer() {
+        delete[] addr;
+    }
+
+    // disable copy and move
+    llama_buffer(const llama_buffer&) = delete;
+    llama_buffer(llama_buffer&&) = delete;
+    llama_buffer& operator=(const llama_buffer&) = delete;
+    llama_buffer& operator=(llama_buffer&&) = delete;
+};
+
+#ifdef GGML_USE_CUBLAS
+#include "ggml-cuda.h"
+struct llama_ctx_buffer {
+    uint8_t * addr = NULL;
+    bool is_cuda;
+    size_t size = 0;
+
+    llama_ctx_buffer() = default;
+
+    void resize(size_t size) {
+        free();
+
+        addr = (uint8_t *) ggml_cuda_host_malloc(size);
+        if (addr) {
+            is_cuda = true;
+        }
+        else {
+            // fall back to pageable memory
+            addr = new uint8_t[size];
+            is_cuda = false;
+        }
+        this->size = size;
+    }
+
+    void free() {
+        if (addr) {
+            if (is_cuda) {
+                ggml_cuda_host_free(addr);
+            }
+            else {
+                delete[] addr;
+            }
+        }
+        addr = NULL;
+    }
+
+    ~llama_ctx_buffer() {
+        free();
+    }
+
+    // disable copy and move
+    llama_ctx_buffer(const llama_ctx_buffer&) = delete;
+    llama_ctx_buffer(llama_ctx_buffer&&) = delete;
+    llama_ctx_buffer& operator=(const llama_ctx_buffer&) = delete;
+    llama_ctx_buffer& operator=(llama_ctx_buffer&&) = delete;
+};
+#else
+typedef llama_buffer llama_ctx_buffer;
+#endif
+
+#endif

examples/talk-llama/llama.h

@@ -0,0 +1,273 @@
+#ifndef LLAMA_H
+#define LLAMA_H
+
+#include <stddef.h>
+#include <stdint.h>
+#include <stdbool.h>
+
+#ifdef LLAMA_SHARED
+#    if defined(_WIN32) && !defined(__MINGW32__)
+#        ifdef LLAMA_BUILD
+#            define LLAMA_API __declspec(dllexport)
+#        else
+#            define LLAMA_API __declspec(dllimport)
+#        endif
+#    else
+#        define LLAMA_API __attribute__ ((visibility ("default")))
+#    endif
+#else
+#    define LLAMA_API
+#endif
+
+#define LLAMA_FILE_MAGIC_GGJT        0x67676a74u // 'ggjt'
+#define LLAMA_FILE_MAGIC_GGLA        0x67676c61u // 'ggla'
+#define LLAMA_FILE_MAGIC_GGMF        0x67676d66u // 'ggmf'
+#define LLAMA_FILE_MAGIC_GGML        0x67676d6cu // 'ggml'
+#define LLAMA_FILE_MAGIC_GGSN        0x6767736eu // 'ggsn'
+
+#define LLAMA_FILE_VERSION           3
+#define LLAMA_FILE_MAGIC             LLAMA_FILE_MAGIC_GGJT
+#define LLAMA_FILE_MAGIC_UNVERSIONED LLAMA_FILE_MAGIC_GGML
+#define LLAMA_SESSION_MAGIC          LLAMA_FILE_MAGIC_GGSN
+#define LLAMA_SESSION_VERSION        1
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+    //
+    // C interface
+    //
+    // TODO: show sample usage
+    //
+
+    struct llama_context;
+
+    typedef int llama_token;
+
+    typedef struct llama_token_data {
+        llama_token id; // token id
+        float logit;    // log-odds of the token
+        float p;        // probability of the token
+    } llama_token_data;
+
+    typedef struct llama_token_data_array {
+        llama_token_data * data;
+        size_t size;
+        bool sorted;
+    } llama_token_data_array;
+
+    typedef void (*llama_progress_callback)(float progress, void *ctx);
+
+    struct llama_context_params {
+        int n_ctx;        // text context
+        int n_gpu_layers; // number of layers to store in VRAM
+        int seed;         // RNG seed, -1 for random
+
+        bool f16_kv;     // use fp16 for KV cache
+        bool logits_all; // the llama_eval() call computes all logits, not just the last one
+        bool vocab_only; // only load the vocabulary, no weights
+        bool use_mmap;   // use mmap if possible
+        bool use_mlock;  // force system to keep model in RAM
+        bool embedding;  // embedding mode only
+
+        // called with a progress value between 0 and 1, pass NULL to disable
+        llama_progress_callback progress_callback;
+        // context pointer passed to the progress callback
+        void * progress_callback_user_data;
+    };
+
+    // model file types
+    enum llama_ftype {
+        LLAMA_FTYPE_ALL_F32              = 0,
+        LLAMA_FTYPE_MOSTLY_F16           = 1, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q4_0          = 2, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q4_1          = 3, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
+        // LLAMA_FTYPE_MOSTLY_Q4_2       = 5, // support has been removed
+        // LLAMA_FTYPE_MOSTLY_Q4_3       = 6, // support has been removed
+        LLAMA_FTYPE_MOSTLY_Q8_0          = 7, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q5_0          = 8, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q5_1          = 9, // except 1d tensors
+    };
+
+    LLAMA_API struct llama_context_params llama_context_default_params();
+
+    LLAMA_API bool llama_mmap_supported();
+    LLAMA_API bool llama_mlock_supported();
+
+    // TODO: not great API - very likely to change
+    // Initialize the llama + ggml backend
+    // Call once at the start of the program
+    LLAMA_API void llama_init_backend();
+
+    LLAMA_API int64_t llama_time_us();
+
+    // Various functions for loading a ggml llama model.
+    // Allocate (almost) all memory needed for the model.
+    // Return NULL on failure
+    LLAMA_API struct llama_context * llama_init_from_file(
+                             const char * path_model,
+            struct llama_context_params   params);
+
+    // Frees all allocated memory
+    LLAMA_API void llama_free(struct llama_context * ctx);
+
+    // TODO: not great API - very likely to change
+    // Returns 0 on success
+    // nthread - how many threads to use. If <=0, will use std::thread::hardware_concurrency(), else the number given
+    LLAMA_API int llama_model_quantize(
+            const char * fname_inp,
+            const char * fname_out,
+      enum llama_ftype   ftype,
+            int          nthread);
+
+    // Apply a LoRA adapter to a loaded model
+    // path_base_model is the path to a higher quality model to use as a base for
+    // the layers modified by the adapter. Can be NULL to use the current loaded model.
+    // The model needs to be reloaded before applying a new adapter, otherwise the adapter
+    // will be applied on top of the previous one
+    // Returns 0 on success
+    LLAMA_API int llama_apply_lora_from_file(
+            struct llama_context * ctx,
+                      const char * path_lora,
+                      const char * path_base_model,
+                             int   n_threads);
+
+    // Returns the number of tokens in the KV cache
+    LLAMA_API int llama_get_kv_cache_token_count(const struct llama_context * ctx);
+
+    // Sets the current rng seed.
+    LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, int seed);
+
+    // Returns the maximum size in bytes of the state (rng, logits, embedding
+    // and kv_cache) - will often be smaller after compacting tokens
+    LLAMA_API size_t llama_get_state_size(const struct llama_context * ctx);
+
+    // Copies the state to the specified destination address.
+    // Destination needs to have allocated enough memory.
+    // Returns the number of bytes copied
+    LLAMA_API size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst);
+
+    // Set the state reading from the specified address
+    // Returns the number of bytes read
+    LLAMA_API size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src);
+
+    // Save/load session file
+    LLAMA_API bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out);
+    LLAMA_API bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count);
+
+    // Run the llama inference to obtain the logits and probabilities for the next token.
+    // tokens + n_tokens is the provided batch of new tokens to process
+    // n_past is the number of tokens to use from previous eval calls
+    // Returns 0 on success
+    LLAMA_API int llama_eval(
+            struct llama_context * ctx,
+               const llama_token * tokens,
+                             int   n_tokens,
+                             int   n_past,
+                             int   n_threads);
+
+    // 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, no more than n_max_tokens
+    // Returns a negative number on failure - the number of tokens that would have been returned
+    // TODO: not sure if correct
+    LLAMA_API int llama_tokenize(
+            struct llama_context * ctx,
+                      const char * text,
+                     llama_token * tokens,
+                             int   n_max_tokens,
+                            bool   add_bos);
+
+    LLAMA_API int llama_n_vocab(const struct llama_context * ctx);
+    LLAMA_API int llama_n_ctx  (const struct llama_context * ctx);
+    LLAMA_API int llama_n_embd (const struct llama_context * ctx);
+
+    // Token logits obtained from the last call to llama_eval()
+    // The logits for the last token are stored in the last row
+    // Can be mutated in order to change the probabilities of the next token
+    // Rows: n_tokens
+    // Cols: n_vocab
+    LLAMA_API float * llama_get_logits(struct llama_context * ctx);
+
+    // Get the embeddings for the input
+    // shape: [n_embd] (1-dimensional)
+    LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
+
+    // Token Id -> String. Uses the vocabulary in the provided context
+    LLAMA_API const char * llama_token_to_str(const struct llama_context * ctx, llama_token token);
+
+    // Special tokens
+    LLAMA_API llama_token llama_token_bos();
+    LLAMA_API llama_token llama_token_eos();
+    LLAMA_API llama_token llama_token_nl();
+
+    // Sampling functions
+
+    /// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
+    LLAMA_API void llama_sample_repetition_penalty(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float penalty);
+
+    /// @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
+    LLAMA_API void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float alpha_frequency, float alpha_presence);
+
+    /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
+    LLAMA_API void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates);
+
+    /// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
+    LLAMA_API void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int k, size_t min_keep);
+
+    /// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
+    LLAMA_API void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep);
+
+    /// @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
+    LLAMA_API void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep);
+
+    /// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
+    LLAMA_API void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep);
+    LLAMA_API void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates, float temp);
+
+    /// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
+    /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
+    /// @param tau  The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
+    /// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
+    /// @param m The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm.
+    /// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
+    LLAMA_API llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int m, float * mu);
+
+    /// @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
+    /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
+    /// @param tau  The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
+    /// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
+    /// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
+    LLAMA_API llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu);
+
+    /// @details Selects the token with the highest probability.
+    LLAMA_API llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates);
+
+    /// @details Randomly selects a token from the candidates based on their probabilities.
+    LLAMA_API llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates);
+
+    // Performance information
+    LLAMA_API void llama_print_timings(struct llama_context * ctx);
+    LLAMA_API void llama_reset_timings(struct llama_context * ctx);
+
+    // Print system information
+    LLAMA_API const char * llama_print_system_info(void);
+
+#ifdef __cplusplus
+}
+#endif
+
+// Internal API to be implemented by llama.cpp and used by tests/benchmarks only
+#ifdef LLAMA_API_INTERNAL
+
+#include <vector>
+#include <string>
+struct ggml_tensor;
+
+std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx);
+
+#endif
+
+#endif // LLAMA_H

examples/talk-llama/prompts/talk-alpaca.txt

@@ -0,0 +1,23 @@
+Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+
+Write a text transcript of a never ending dialog, where {0} interacts with an AI assistant named {1}.
+{1} is helpful, kind, honest, friendly, good at writing and never fails to answer {0}’s requests immediately and with details and precision.
+There are no annotations like (30 seconds passed...) or (to himself), just what {0} and {1} say aloud to each other.
+The transcript only includes text, it does not include markup like HTML and Markdown.
+{1} responds with short and concise answers.
+
+### Response:
+
+{0}{4} Hello, {1}!
+{1}{4} Hello {0}! How may I help you today?
+{0}{4} What time is it?
+{1}{4} It is {2} o'clock.
+{0}{4} What year is it?
+{1}{4} We are in {3}.
+{0}{4} What is a cat?
+{1}{4} A cat is a domestic species of small carnivorous mammal. It is the only domesticated species in the family Felidae.
+{0}{4} Name a color.
+{1}{4} Blue
+{0}{4}

examples/talk-llama/speak

@@ -0,0 +1,24 @@
+#!/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 225 -p 50 -a 200 -g 5 -k 5 "$2"
+
+# for Mac
+say "$2"
+
+# Eleven Labs
+# To use it, install the elevenlabs module from pip (pip install elevenlabs)
+# It's possible to use the API for free with limited number of characters. To increase this limit register to https://beta.elevenlabs.io to get an api key and paste it after 'ELEVEN_API_KEY='
+#Keep the line commented to use the free version whitout api key
+#
+#export ELEVEN_API_KEY=your_api_key
+#wd=$(dirname $0)
+#script=$wd/eleven-labs.py
+#python3 $script $1 "$2" >/dev/null 2>&1
+#ffplay -autoexit -nodisp -loglevel quiet -hide_banner -i ./audio.mp3 >/dev/null 2>&1

examples/talk-llama/speak.bat

@@ -0,0 +1 @@
+@powershell -ExecutionPolicy Bypass -F examples\talk\speak.ps1 %1 %2

examples/talk-llama/speak.ps1

@@ -0,0 +1,12 @@
+# Set-ExecutionPolicy -ExecutionPolicy Bypass -Scope CurrentUser
+param(
+  # voice options are David or Zira
+  [Parameter(Mandatory=$true)][string]$voice,
+  [Parameter(Mandatory=$true)][string]$text
+)
+
+Add-Type -AssemblyName System.Speech;
+$speak = New-Object System.Speech.Synthesis.SpeechSynthesizer;
+$speak.SelectVoice("Microsoft $voice Desktop");
+$speak.Rate="0";
+$speak.Speak($text);

examples/talk-llama/talk-llama.cpp

@@ -0,0 +1,670 @@
+// Talk with AI
+//
+
+#include "common.h"
+#include "common-sdl.h"
+#include "whisper.h"
+#include "llama.h"
+
+#include <cassert>
+#include <cstdio>
+#include <fstream>
+#include <regex>
+#include <string>
+#include <thread>
+#include <vector>
+#include <regex>
+
+std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::string & text, bool add_bos) {
+    // initialize to prompt numer of chars, since n_tokens <= n_prompt_chars
+    std::vector<llama_token> res(text.size() + (int)add_bos);
+    int n = llama_tokenize(ctx, text.c_str(), res.data(), res.size(), add_bos);
+    assert(n >= 0);
+    res.resize(n);
+
+    return res;
+}
+
+// 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;
+    bool verbose_prompt = false;
+
+    std::string person      = "Georgi";
+    std::string language    = "en";
+    std::string model_wsp   = "models/ggml-base.en.bin";
+    std::string model_llama = "models/ggml-llama-7B.bin";
+    std::string speak       = "./examples/talk-llama/speak";
+    std::string prompt      = "";
+    std::string fname_out;
+    std::string path_session = "";       // path to file for saving/loading model eval state
+};
+
+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 == "--verbose-prompt")                 { params.verbose_prompt = true; }
+        else if (arg == "-p"   || arg == "--person")        { params.person        = argv[++i]; }
+        else if (arg == "--session")                        { params.path_session  = 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 == "-ml"  || arg == "--model-llama")   { params.model_llama   = argv[++i]; }
+        else if (arg == "-s"   || arg == "--speak")         { params.speak         = argv[++i]; }
+        else if (arg == "--prompt-file")                    {
+            std::ifstream file(argv[++i]);
+            std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
+            if (params.prompt.back() == '\n') {
+                params.prompt.pop_back();
+            }
+        }
+        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, "  -ml FILE, --model-llama   [%-7s] llama model file\n",                            params.model_llama.c_str());
+    fprintf(stderr, "  -s FILE,  --speak TEXT    [%-7s] command for TTS\n",                             params.speak.c_str());
+    fprintf(stderr, "  --prompt-file FNAME       [%-7s] file with custom prompt to start dialog\n",     "");
+    fprintf(stderr, "  --session FNAME       file to cache model state in (may be large!) (default: none)\n");
+    fprintf(stderr, "  --verbose-prompt          [%-7s] print prompt at start\n",                       params.verbose_prompt ? "true" : "false");
+    fprintf(stderr, "  -f FNAME, --file FNAME    [%-7s] text output file name\n",                       params.fname_out.c_str());
+    fprintf(stderr, "\n");
+}
+
+std::string transcribe(
+        whisper_context * ctx,
+        const whisper_params & params,
+        const std::vector<float> & pcmf32,
+        const std::string prompt_text,
+        float & prob,
+        int64_t & t_ms) {
+    const auto t_start = std::chrono::high_resolution_clock::now();
+
+    prob = 0.0f;
+    t_ms = 0;
+
+    std::vector<whisper_token> prompt_tokens;
+
+    whisper_full_params wparams = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
+
+    prompt_tokens.resize(1024);
+    prompt_tokens.resize(whisper_tokenize(ctx, prompt_text.c_str(), prompt_tokens.data(), prompt_tokens.size()));
+
+    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.prompt_tokens    = prompt_tokens.empty() ? nullptr : prompt_tokens.data();
+    wparams.prompt_n_tokens  = prompt_tokens.empty() ? 0       : prompt_tokens.size();
+
+    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_whisper = R"(A conversation with a person called {1}.)";
+
+const std::string k_prompt_llama = R"(Text transcript of a never ending dialog, where {0} interacts with an AI assistant named {1}.
+{1} is helpful, kind, honest, friendly, good at writing and never fails to answer {0}’s requests immediately and with details and precision.
+There are no annotations like (30 seconds passed...) or (to himself), just what {0} and {1} say aloud to each other.
+The transcript only includes text, it does not include markup like HTML and Markdown.
+{1} responds with short and concise answers.
+
+{0}{4} Hello, {1}!
+{1}{4} Hello {0}! How may I help you today?
+{0}{4} What time is it?
+{1}{4} It is {2} o'clock.
+{0}{4} What year is it?
+{1}{4} We are in {3}.
+{0}{4} What is a cat?
+{1}{4} A cat is a domestic species of small carnivorous mammal. It is the only domesticated species in the family Felidae.
+{0}{4} Name a color.
+{1}{4} Blue
+{0}{4})";
+
+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());
+
+    // llama init
+
+    llama_init_backend();
+
+    auto lparams = llama_context_default_params();
+
+    // tune these to your liking
+    lparams.n_ctx      = 2048;
+    lparams.seed       = 1;
+    lparams.f16_kv     = true;
+
+    struct llama_context * ctx_llama = llama_init_from_file(params.model_llama.c_str(), lparams);
+
+    // 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;
+
+    const std::string chat_symb = ":";
+    const std::string bot_name  = "LLaMA";
+
+    std::vector<float> pcmf32_cur;
+    std::vector<float> pcmf32_prompt;
+
+    const std::string prompt_whisper = ::replace(k_prompt_whisper, "{1}", bot_name);
+
+    // construct the initial prompt for LLaMA inference
+    std::string prompt_llama = params.prompt.empty() ? k_prompt_llama : params.prompt;
+
+    // need to have leading ' '
+    prompt_llama.insert(0, 1, ' ');
+
+    prompt_llama = ::replace(prompt_llama, "{0}", params.person);
+    prompt_llama = ::replace(prompt_llama, "{1}", bot_name);
+
+    {
+        // get time string
+        std::string time_str;
+        {
+            time_t t = time(0);
+            struct tm * now = localtime(&t);
+            char buf[128];
+            strftime(buf, sizeof(buf), "%H:%M", now);
+            time_str = buf;
+        }
+        prompt_llama = ::replace(prompt_llama, "{2}", time_str);
+    }
+
+    {
+        // get year string
+        std::string year_str;
+        {
+            time_t t = time(0);
+            struct tm * now = localtime(&t);
+            char buf[128];
+            strftime(buf, sizeof(buf), "%Y", now);
+            year_str = buf;
+        }
+        prompt_llama = ::replace(prompt_llama, "{3}", year_str);
+    }
+
+    prompt_llama = ::replace(prompt_llama, "{4}", chat_symb);
+
+    // init session
+    std::string path_session = params.path_session;
+    std::vector<llama_token> session_tokens;
+    auto embd_inp = ::llama_tokenize(ctx_llama, prompt_llama, true);
+
+    if (!path_session.empty()) {
+        fprintf(stderr, "%s: attempting to load saved session from %s\n", __func__, path_session.c_str());
+
+        // fopen to check for existing session
+        FILE * fp = std::fopen(path_session.c_str(), "rb");
+        if (fp != NULL) {
+            std::fclose(fp);
+
+            session_tokens.resize(lparams.n_ctx);
+            size_t n_token_count_out = 0;
+            if (!llama_load_session_file(ctx_llama, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) {
+                fprintf(stderr, "%s: error: failed to load session file '%s'\n", __func__, path_session.c_str());
+                return 1;
+            }
+            session_tokens.resize(n_token_count_out);
+            for (size_t i = 0; i < session_tokens.size(); i++) {
+                embd_inp[i] = session_tokens[i];
+            }
+
+            fprintf(stderr, "%s: loaded a session with prompt size of %d tokens\n", __func__, (int) session_tokens.size());
+        } else {
+            fprintf(stderr, "%s: session file does not exist, will create\n", __func__);
+        }
+    }
+
+    // evaluate the initial prompt
+
+    printf("\n");
+    printf("%s : initializing - please wait ...\n", __func__);
+
+    if (llama_eval(ctx_llama, embd_inp.data(), embd_inp.size(), 0, params.n_threads)) {
+        fprintf(stderr, "%s : failed to eval\n", __func__);
+        return 1;
+    }
+
+    if (params.verbose_prompt) {
+        fprintf(stdout, "\n");
+        fprintf(stdout, "%s", prompt_llama.c_str());
+        fflush(stdout);
+    }
+
+     // debug message about similarity of saved session, if applicable
+    size_t n_matching_session_tokens = 0;
+    if (session_tokens.size()) {
+        for (llama_token id : session_tokens) {
+            if (n_matching_session_tokens >= embd_inp.size() || id != embd_inp[n_matching_session_tokens]) {
+                break;
+            }
+            n_matching_session_tokens++;
+        }
+        if (n_matching_session_tokens >= embd_inp.size()) {
+            fprintf(stderr, "%s: session file has exact match for prompt!\n", __func__);
+        } else if (n_matching_session_tokens < (embd_inp.size() / 2)) {
+            fprintf(stderr, "%s: warning: session file has low similarity to prompt (%zu / %zu tokens); will mostly be reevaluated\n",
+                __func__, n_matching_session_tokens, embd_inp.size());
+        } else {
+            fprintf(stderr, "%s: session file matches %zu / %zu tokens of prompt\n",
+                __func__, n_matching_session_tokens, embd_inp.size());
+        }
+    }
+
+    // HACK - because session saving incurs a non-negligible delay, for now skip re-saving session
+    // if we loaded a session with at least 75% similarity. It's currently just used to speed up the
+    // initial prompt so it doesn't need to be an exact match.
+    bool need_to_save_session = !path_session.empty() && n_matching_session_tokens < (embd_inp.size() * 3 / 4);
+
+    printf("%s : done! start speaking in the microphone\n", __func__);
+    printf("\n");
+    printf("%s%s", params.person.c_str(), chat_symb.c_str());
+    fflush(stdout);
+
+    // clear audio buffer
+    audio.clear();
+
+    // text inference variables
+    const int voice_id = 2;
+    const int n_keep   = embd_inp.size();
+    const int n_ctx    = llama_n_ctx(ctx_llama);
+
+    int n_past = n_keep;
+    int n_prev = 64; // TODO arg
+    int n_session_consumed = !path_session.empty() && session_tokens.size() > 0 ? session_tokens.size() : 0;
+
+    std::vector<llama_token> embd;
+
+    // reverse prompts for detecting when it's time to stop speaking
+    std::vector<std::string> antiprompts = {
+        params.person + chat_symb,
+    };
+
+    // main loop
+    while (is_running) {
+        // handle Ctrl + C
+        is_running = sdl_poll_events();
+
+        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, prompt_whisper, 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<llama_token> tokens = llama_tokenize(ctx_llama, text_heard.c_str(), false);
+
+                if (text_heard.empty() || tokens.empty() || force_speak) {
+                    //fprintf(stdout, "%s: Heard nothing, skipping ...\n", __func__);
+                    audio.clear();
+
+                    continue;
+                }
+
+                force_speak = false;
+
+                text_heard.insert(0, 1, ' ');
+                text_heard += "\n" + bot_name + chat_symb;
+                fprintf(stdout, "%s%s%s", "\033[1m", text_heard.c_str(), "\033[0m");
+                fflush(stdout);
+
+                embd = ::llama_tokenize(ctx_llama, text_heard, false);
+
+                // Append the new input tokens to the session_tokens vector
+                if (!path_session.empty()) {
+                    session_tokens.insert(session_tokens.end(), tokens.begin(), tokens.end());
+                }
+
+                // text inference
+                bool done = false;
+                std::string text_to_speak;
+                while (true) {
+                    // predict
+                    if (embd.size() > 0) {
+                        if (n_past + (int) embd.size() > n_ctx) {
+                            n_past = n_keep;
+
+                            // insert n_left/2 tokens at the start of embd from last_n_tokens
+                            embd.insert(embd.begin(), embd_inp.begin() + embd_inp.size() - n_prev, embd_inp.end());
+                            // stop saving session if we run out of context
+                            path_session = "";
+                            //printf("\n---\n");
+                            //printf("resetting: '");
+                            //for (int i = 0; i < (int) embd.size(); i++) {
+                            //    printf("%s", llama_token_to_str(ctx_llama, embd[i]));
+                            //}
+                            //printf("'\n");
+                            //printf("\n---\n");
+                        }
+
+                        // try to reuse a matching prefix from the loaded session instead of re-eval (via n_past)
+                        // REVIEW
+                        if (n_session_consumed < (int) session_tokens.size()) {
+                            size_t i = 0;
+                            for ( ; i < embd.size(); i++) {
+                                if (embd[i] != session_tokens[n_session_consumed]) {
+                                    session_tokens.resize(n_session_consumed);
+                                    break;
+                                }
+
+                                n_past++;
+                                n_session_consumed++;
+
+                                if (n_session_consumed >= (int) session_tokens.size()) {
+                                    i++;
+                                    break;
+                                }
+                            }
+                            if (i > 0) {
+                                embd.erase(embd.begin(), embd.begin() + i);
+                            }
+                        }
+
+                        if (embd.size() > 0 && !path_session.empty()) {
+                            session_tokens.insert(session_tokens.end(), embd.begin(), embd.end());
+                            n_session_consumed = session_tokens.size();
+                        }
+
+                        if (llama_eval(ctx_llama, embd.data(), embd.size(), n_past, params.n_threads)) {
+                            fprintf(stderr, "%s : failed to eval\n", __func__);
+                            return 1;
+                        }
+                    }
+
+
+                    embd_inp.insert(embd_inp.end(), embd.begin(), embd.end());
+                    n_past += embd.size();
+
+                    embd.clear();
+
+                    if (done) break;
+
+                    {
+                        // out of user input, sample next token
+                        const float top_k          = 5;
+                        const float top_p          = 0.80f;
+                        const float temp           = 0.30f;
+                        const float repeat_penalty = 1.1764f;
+
+                        const int repeat_last_n    = 256;
+
+                        if (!path_session.empty() && need_to_save_session) {
+                            need_to_save_session = false;
+                            llama_save_session_file(ctx_llama, path_session.c_str(), session_tokens.data(), session_tokens.size());
+                        }
+
+                        llama_token id = 0;
+
+                        {
+                            auto logits = llama_get_logits(ctx_llama);
+                            auto n_vocab = llama_n_vocab(ctx_llama);
+
+                            logits[llama_token_eos()] = 0;
+
+                            std::vector<llama_token_data> candidates;
+                            candidates.reserve(n_vocab);
+                            for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
+                                candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
+                            }
+
+                            llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
+
+                            // apply repeat penalty
+                            const float nl_logit = logits[llama_token_nl()];
+
+                            llama_sample_repetition_penalty(ctx_llama, &candidates_p,
+                                    embd_inp.data() + std::max(0, n_past - repeat_last_n),
+                                    repeat_last_n, repeat_penalty);
+
+                            logits[llama_token_nl()] = nl_logit;
+
+                            if (temp <= 0) {
+                                // Greedy sampling
+                                id = llama_sample_token_greedy(ctx_llama, &candidates_p);
+                            } else {
+                                // Temperature sampling
+                                llama_sample_top_k(ctx_llama, &candidates_p, top_k, 1);
+                                llama_sample_top_p(ctx_llama, &candidates_p, top_p, 1);
+                                llama_sample_temperature(ctx_llama, &candidates_p, temp);
+                                id = llama_sample_token(ctx_llama, &candidates_p);
+                            }
+                        }
+
+                        if (id != llama_token_eos()) {
+                            // add it to the context
+                            embd.push_back(id);
+
+                            text_to_speak += llama_token_to_str(ctx_llama, id);
+
+                            printf("%s", llama_token_to_str(ctx_llama, id));
+                        }
+                    }
+
+                    {
+                        std::string last_output;
+                        for (int i = embd_inp.size() - 16; i < (int) embd_inp.size(); i++) {
+                            last_output += llama_token_to_str(ctx_llama, embd_inp[i]);
+                        }
+                        last_output += llama_token_to_str(ctx_llama, embd[0]);
+
+                        for (std::string & antiprompt : antiprompts) {
+                            if (last_output.find(antiprompt.c_str(), last_output.length() - antiprompt.length(), antiprompt.length()) != std::string::npos) {
+                                done = true;
+                                text_to_speak = ::replace(text_to_speak, antiprompt, "");
+                                fflush(stdout);
+                                need_to_save_session = true;
+                                break;
+                            }
+                        }
+                    }
+
+                    is_running = sdl_poll_events();
+
+                    if (!is_running) {
+                        break;
+                    }
+                }
+
+                text_to_speak = ::replace(text_to_speak, "\"", "");
+                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);
+
+    llama_print_timings(ctx_llama);
+    llama_free(ctx_llama);
+
+    return 0;
+}

examples/talk.wasm/CMakeLists.txt

@@ -13,6 +13,7 @@ include(DefaultTargetOptions)
 
 target_link_libraries(${TARGET} PRIVATE
     whisper
+    common
     )
 
 unset(EXTRA_FLAGS)

examples/talk.wasm/gpt-2.cpp

@@ -1,4 +1,6 @@
 #include "ggml.h"
+#include "common-ggml.h"
+
 #include "gpt-2.h"
 
 #include <cmath>
@@ -14,150 +16,6 @@
 
 /////////////////////// 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;
@@ -165,7 +23,7 @@ struct gpt2_hparams {
     int32_t n_embd  = 768;
     int32_t n_head  = 12;
     int32_t n_layer = 12;
-    int32_t f16     = 1;
+    int32_t ftype   = 1;
 };
 
 struct gpt2_layer {
@@ -187,7 +45,7 @@ struct gpt2_layer {
     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_w;
     struct ggml_tensor * c_mlp_proj_b;
 };
 
@@ -198,8 +56,9 @@ struct gpt2_model {
     struct ggml_tensor * ln_f_g;
     struct ggml_tensor * ln_f_b;
 
-    struct ggml_tensor * wte; // position embedding
-    struct ggml_tensor * wpe; //    token embedding
+    struct ggml_tensor * wte;     // position embedding
+    struct ggml_tensor * wpe;     //    token embedding
+    struct ggml_tensor * lm_head; // language model head
 
     std::vector<gpt2_layer> layers;
 
@@ -241,14 +100,14 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
         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));
+        fin.read((char *) &hparams.ftype,   sizeof(hparams.ftype));
 
         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);
+        printf("%s: ftype   = %d\n", __func__, hparams.ftype);
     }
 
     // load vocab
@@ -275,9 +134,14 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
         }
     }
 
-    // for the big tensors, we have the option to store the data in 16-bit floats
+    // for the big tensors, we have the option to store the data in 16-bit floats or quantized
     // 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;
+    ggml_type wtype = ggml_ftype_to_ggml_type((ggml_ftype) (model.hparams.ftype));
+    if (wtype == GGML_TYPE_COUNT) {
+        fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n",
+                __func__, fname.c_str(), model.hparams.ftype);
+        return false;
+    }
 
     auto & ctx = model.ctx;
 
@@ -291,32 +155,33 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
         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_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_g
+        ctx_size += n_embd*ggml_type_sizef(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_vocab*n_embd*ggml_type_sizef(wtype);         // wte
+        ctx_size +=   n_ctx*n_embd*ggml_type_sizef(GGML_TYPE_F32); // wpe
+        ctx_size += n_vocab*n_embd*ggml_type_sizef(wtype);         // lm_head
 
-        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_sizef(GGML_TYPE_F32)); // ln_1_g
+        ctx_size += n_layer*(n_embd*ggml_type_sizef(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*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_2_g
+        ctx_size += n_layer*(n_embd*ggml_type_sizef(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*(3*n_embd*n_embd*ggml_type_sizef(wtype));         // c_attn_attn_w
+        ctx_size += n_layer*(       3*n_embd*ggml_type_sizef(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*(n_embd*n_embd*ggml_type_sizef(wtype));           // c_attn_proj_w
+        ctx_size += n_layer*(       n_embd*ggml_type_sizef(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_sizef(wtype));         // c_mlp_fc_w
+        ctx_size += n_layer*(       4*n_embd*ggml_type_sizef(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_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype));         // c_mlp_proj_w
+        ctx_size += n_layer*(         n_embd*ggml_type_sizef(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 += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_k
+        ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_v
 
         ctx_size += (6 + 12*n_layer)*256; // object overhead
 
@@ -325,9 +190,11 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
 
     // create the ggml context
     {
-        struct ggml_init_params params;
-        params.mem_size   = ctx_size;
-        params.mem_buffer = NULL;
+        struct ggml_init_params params = {
+            .mem_size   = ctx_size,
+            .mem_buffer = NULL,
+            .no_alloc   = false,
+        };
 
         model.ctx = ggml_init(params);
         if (!model.ctx) {
@@ -350,36 +217,38 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
         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);
+        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);
+        model.lm_head = ggml_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);
 
         // 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;
+        model.tensors["model/wte"]     = model.wte;
+        model.tensors["model/wpe"]     = model.wpe;
+        model.tensors["model/lm_head"] = model.lm_head;
 
         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_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.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_attn_w = ggml_new_tensor_2d(ctx, wtype,           n_embd, 3*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_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_fc_w    = ggml_new_tensor_2d(ctx, wtype,           n_embd, 4*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);
+            layer.c_mlp_proj_w  = 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;
@@ -397,7 +266,7 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
             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/w"]  = layer.c_mlp_proj_w;
             model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/b"]  = layer.c_mlp_proj_b;
         }
     }
@@ -425,14 +294,16 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
     {
         size_t total_size = 0;
 
+        bool has_lm_head = false;
+
         while (true) {
             int32_t n_dims;
             int32_t length;
-            int32_t ftype;
+            int32_t ttype;
 
             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));
+            fin.read(reinterpret_cast<char *>(&ttype),  sizeof(ttype));
 
             if (fin.eof()) {
                 break;
@@ -461,13 +332,18 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
 
             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]);
+                        __func__, name.data(), (int) tensor->ne[0], (int) tensor->ne[1], ne[0], ne[1]);
                 return false;
             }
 
-            const size_t bpe = (ftype == 0) ? sizeof(float) : sizeof(ggml_fp16_t);
+            // for debugging
+            if (0) {
+                printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ggml_type_name(ggml_type(ttype)), ggml_nbytes(tensor)/1024.0/1024.0, ggml_nbytes(tensor));
+            }
 
-            if (nelements*bpe != ggml_nbytes(tensor)) {
+            const size_t bpe = ggml_type_size(ggml_type(ttype));
+
+            if ((nelements*bpe)/ggml_blck_size(tensor->type) != 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;
@@ -475,7 +351,15 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
 
             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);
+            // GPT-2 models share the WTE tensor as the LM head
+            if (name == "model/wte" && has_lm_head == false) {
+                memcpy(model.lm_head->data, tensor->data, ggml_nbytes(tensor));
+            }
+
+            if (name == "model/lm_head") {
+                has_lm_head = true;
+            }
+
             total_size += ggml_nbytes(tensor);
         }
 
@@ -493,7 +377,7 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
 //   - 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
+//   - embd_w:    the predicted logits for the next token
 //
 bool gpt2_eval(
         const gpt2_model & model,
@@ -512,12 +396,12 @@ bool gpt2_eval(
     const int n_head  = hparams.n_head;
     const int n_vocab = hparams.n_vocab;
 
-    static size_t buf_size = 640u*1024*1024;
+    static size_t buf_size = 512u*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);
+        //printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
 
         // reallocate
         buf_size = buf_size_new;
@@ -528,13 +412,14 @@ bool gpt2_eval(
         }
     }
 
-    struct ggml_init_params params;
-    params.mem_size   = buf_size;
-    params.mem_buffer = buf;
+    struct ggml_init_params params = {
+        /*.mem_size   =*/ buf_size,
+        /*.mem_buffer =*/ buf,
+        /*.no_alloc   =*/ false,
+    };
 
     struct ggml_context * ctx0 = ggml_init(params);
-
-    struct ggml_cgraph gf = { };
+    struct ggml_cgraph gf = {};
     gf.n_threads = n_threads;
 
     struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
@@ -578,7 +463,7 @@ bool gpt2_eval(
         // [2304, N]
         {
             cur = ggml_mul_mat(ctx0,
-                    ggml_transpose(ctx0, model.layers[il].c_attn_attn_w),
+                    model.layers[il].c_attn_attn_w,
                     cur);
 
             cur = ggml_add(ctx0,
@@ -654,11 +539,13 @@ bool gpt2_eval(
             // 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);
+                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, model.memory_v->type, n_past + N, n_embd/n_head, n_head));
 
             // KQV = transpose(V) * KQ_soft_max
             // [64, N, 12]
@@ -685,7 +572,7 @@ bool gpt2_eval(
         // [768, N]
         {
             cur = ggml_mul_mat(ctx0,
-                    ggml_transpose(ctx0, model.layers[il].c_attn_proj_w),
+                    model.layers[il].c_attn_proj_w,
                     cur);
 
             cur = ggml_add(ctx0,
@@ -722,7 +609,7 @@ bool gpt2_eval(
             // cur = fc_w*cur + fc_b
             // [3072, N]
             cur = ggml_mul_mat(ctx0,
-                    ggml_transpose(ctx0, model.layers[il].c_mlp_fc_w),
+                    model.layers[il].c_mlp_fc_w,
                     cur);
 
             cur = ggml_add(ctx0,
@@ -742,7 +629,7 @@ bool gpt2_eval(
             // cur = proj_w*cur + proj_b
             // [768, N]
             cur = ggml_mul_mat(ctx0,
-                    model.layers[il].c_mlp_proj_w_trans,
+                    model.layers[il].c_mlp_proj_w,
                     cur);
 
             cur = ggml_add(ctx0,
@@ -769,12 +656,12 @@ bool gpt2_eval(
     }
 
     // inpL = WTE * inpL
-    // [ 768, 50257] - model.wte
+    // [ 768, 50257] - model.lm_head
     // [ 768, N]     - inpL
-    inpL = ggml_mul_mat(ctx0, model.wte, inpL);
+    inpL = ggml_mul_mat(ctx0, model.lm_head, inpL);
 
     // logits -> probs
-    inpL = ggml_soft_max(ctx0, inpL);
+    //inpL = ggml_soft_max(ctx0, inpL);
 
     // run the computation
     ggml_build_forward_expand(&gf, inpL);
@@ -788,7 +675,7 @@ bool gpt2_eval(
     //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
+    // return result just for 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);
 
@@ -825,7 +712,7 @@ Me too.
     int32_t n_threads = std::min(N_THREAD, (int) std::thread::hardware_concurrency());
 
     // sampling parameters
-    int32_t top_k = 40;
+    int32_t top_k = 5;
     float   top_p = 0.9f;
     float   temp  = 1.0f;
 };
@@ -833,14 +720,15 @@ Me too.
 struct gpt2_context * gpt2_init(const char * path_model) {
     gpt2_context * ctx = new gpt2_context;
 
-    ctx->rng = std::mt19937(time(NULL));
+    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__, "gpt-2.bin");
+            fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, path_model);
+            delete ctx;
             return nullptr;
         }
 
@@ -884,9 +772,9 @@ std::string gpt2_gen_text(gpt2_context * ctx, const char * text, int max_tokens)
 
     std::string result;
 
-    for (int i = embd.size(); i < embd_inp.size() + n_predict; i++) {
+    for (int i = embd.size(); i < (int) embd_inp.size() + n_predict; i++) {
         // predict
-        if (embd.size() > 0) {
+        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 "";
@@ -913,10 +801,7 @@ std::string gpt2_gen_text(gpt2_context * ctx, const char * text, int max_tokens)
         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()] == "?") {
+        if (embd.back() == 50256) {
             break;
         }
     }

examples/talk.wasm/gpt-2.h

@@ -2,18 +2,12 @@
 
 // TODO: Change to C-style API and move to ./examples for easy reuse.
 
+#include "common.h"
+
 #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);

examples/talk.wasm/index-tmpl.html

@@ -44,6 +44,15 @@
 
             <br><br>
 
+            <b>More examples:</b>
+                <a href="https://whisper.ggerganov.com/">main</a> |
+                <a href="https://whisper.ggerganov.com/bench">bench</a> |
+                <a href="https://whisper.ggerganov.com/stream">stream</a> |
+                <a href="https://whisper.ggerganov.com/command">command</a> |
+                <a href="https://whisper.ggerganov.com/talk">talk</a> |
+
+            <br><br>
+
             <hr>
 
             Select the models you would like to use and click the "Start" button to begin the conversation
@@ -54,6 +63,10 @@
                 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>
+                <br><br>
+                Quantized models:<br><br>
+                <button id="fetch-whisper-tiny-en-q5_1"   onclick="loadWhisper('tiny-en-q5_1')">tiny.en (Q5_1, 31 MB)</button>
+                <button id="fetch-whisper-base-en-q5_1"   onclick="loadWhisper('base-en-q5_1')">base.en (Q5_1, 57 MB)</button>
                 <span id="fetch-whisper-progress"></span>
 
                 <!--
@@ -266,11 +279,17 @@
                 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',
+
+                    'tiny-en-q5_1':  'https://whisper.ggerganov.com/ggml-model-whisper-tiny.en-q5_1.bin',
+                    'base-en-q5_1':  'https://whisper.ggerganov.com/ggml-model-whisper-base.en-q5_1.bin',
                 };
 
                 let sizes = {
                     'tiny.en': 75,
                     'base.en': 142,
+
+                    'tiny-en-q5_1':   31,
+                    'base-en-q5_1':   57,
                 };
 
                 let url     = urls[model];
@@ -281,6 +300,10 @@
 
                 document.getElementById('fetch-whisper-tiny-en').style.display = 'none';
                 document.getElementById('fetch-whisper-base-en').style.display = 'none';
+
+                document.getElementById('fetch-whisper-tiny-en-q5_1').style.display = 'none';
+                document.getElementById('fetch-whisper-base-en-q5_1').style.display = 'none';
+
                 document.getElementById('model-whisper-status').innerHTML = 'loading "' + model + '" ... ';
 
                 cbProgress = function(p) {
@@ -292,6 +315,10 @@
                     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('fetch-whisper-tiny-en-q5_1'); if (el) el.style.display = 'inline-block';
+                    el = document.getElementById('fetch-whisper-base-en-q5_1'); if (el) el.style.display = 'inline-block';
+
                     el = document.getElementById('model-whisper-status');  if (el) el.innerHTML = '';
                 };
 

examples/talk/.gitignore

@@ -1 +1 @@
-eleven-labs.py
+audio.mp3

examples/talk/CMakeLists.txt

@@ -1,16 +1,8 @@
-if (WHISPER_SUPPORT_SDL2)
+if (WHISPER_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 ../common.cpp ../common-sdl.cpp ../../ggml.c ../../whisper.cpp)
+    add_executable(${TARGET} talk.cpp gpt-2.cpp)
+    target_link_libraries(${TARGET} PRIVATE common common-sdl whisper ${CMAKE_THREAD_LIBS_INIT})
 
     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

@@ -37,5 +37,5 @@ wget --quiet --show-progress -O models/ggml-gpt-2-117M.bin https://huggingface.c
 ## 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.
+You can use any TTS engine that you would like - simply edit the [speak](speak) script to your needs.
+By default, it is configured to use MacOS's `say` or `espeak` or Windows SpeechSynthesizer, but you can use whatever you wish.

examples/talk/eleven-labs.py

@@ -0,0 +1,20 @@
+import sys
+import importlib.util
+
+if importlib.util.find_spec("elevenlabs") is None:
+    print("elevenlabs library is not installed, you can install it to your enviroment using 'pip install elevenlabs'")
+    sys.exit()
+
+from elevenlabs import generate, play, save
+
+# Get a Voice object, by name or UUID
+voice = "Arnold" #Possible Voices: Adam Antoni Arnold Bella Domi Elli Josh
+
+# Generate the TTS
+audio = generate(
+  text=str(sys.argv[2:]),
+  voice=voice
+)
+
+# Save the TTS to a file
+save(audio, "audio.mp3") 

examples/talk/gpt-2.cpp

@@ -1,4 +1,6 @@
 #include "ggml.h"
+#include "common-ggml.h"
+
 #include "gpt-2.h"
 
 #include <cmath>
@@ -14,150 +16,6 @@
 
 /////////////////////// 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;
@@ -165,7 +23,7 @@ struct gpt2_hparams {
     int32_t n_embd  = 768;
     int32_t n_head  = 12;
     int32_t n_layer = 12;
-    int32_t f16     = 1;
+    int32_t ftype   = 1;
 };
 
 struct gpt2_layer {
@@ -187,7 +45,7 @@ struct gpt2_layer {
     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_w;
     struct ggml_tensor * c_mlp_proj_b;
 };
 
@@ -198,8 +56,9 @@ struct gpt2_model {
     struct ggml_tensor * ln_f_g;
     struct ggml_tensor * ln_f_b;
 
-    struct ggml_tensor * wte; // position embedding
-    struct ggml_tensor * wpe; //    token embedding
+    struct ggml_tensor * wte;     // position embedding
+    struct ggml_tensor * wpe;     //    token embedding
+    struct ggml_tensor * lm_head; // language model head
 
     std::vector<gpt2_layer> layers;
 
@@ -241,14 +100,14 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
         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));
+        fin.read((char *) &hparams.ftype,   sizeof(hparams.ftype));
 
         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);
+        printf("%s: ftype   = %d\n", __func__, hparams.ftype);
     }
 
     // load vocab
@@ -268,16 +127,21 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
             fin.read((char *) &len, sizeof(len));
 
             word.resize(len);
-            fin.read((char *) &word[0], 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
+    // for the big tensors, we have the option to store the data in 16-bit floats or quantized
     // 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;
+    ggml_type wtype = ggml_ftype_to_ggml_type((ggml_ftype) (model.hparams.ftype));
+    if (wtype == GGML_TYPE_COUNT) {
+        fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n",
+                __func__, fname.c_str(), model.hparams.ftype);
+        return false;
+    }
 
     auto & ctx = model.ctx;
 
@@ -291,32 +155,33 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
         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_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_g
+        ctx_size += n_embd*ggml_type_sizef(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_vocab*n_embd*ggml_type_sizef(wtype);         // wte
+        ctx_size +=   n_ctx*n_embd*ggml_type_sizef(GGML_TYPE_F32); // wpe
+        ctx_size += n_vocab*n_embd*ggml_type_sizef(wtype);         // lm_head
 
-        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_sizef(GGML_TYPE_F32)); // ln_1_g
+        ctx_size += n_layer*(n_embd*ggml_type_sizef(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*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_2_g
+        ctx_size += n_layer*(n_embd*ggml_type_sizef(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*(3*n_embd*n_embd*ggml_type_sizef(wtype));         // c_attn_attn_w
+        ctx_size += n_layer*(       3*n_embd*ggml_type_sizef(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*(n_embd*n_embd*ggml_type_sizef(wtype));           // c_attn_proj_w
+        ctx_size += n_layer*(       n_embd*ggml_type_sizef(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_sizef(wtype));         // c_mlp_fc_w
+        ctx_size += n_layer*(       4*n_embd*ggml_type_sizef(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_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype));         // c_mlp_proj_w
+        ctx_size += n_layer*(         n_embd*ggml_type_sizef(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 += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_k
+        ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_v
 
         ctx_size += (6 + 12*n_layer)*256; // object overhead
 
@@ -325,9 +190,11 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
 
     // create the ggml context
     {
-        struct ggml_init_params params;
-        params.mem_size   = ctx_size;
-        params.mem_buffer = nullptr;
+        struct ggml_init_params params = {
+            .mem_size   = ctx_size,
+            .mem_buffer = NULL,
+            .no_alloc   = false,
+        };
 
         model.ctx = ggml_init(params);
         if (!model.ctx) {
@@ -350,36 +217,38 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
         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);
+        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);
+        model.lm_head = ggml_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);
 
         // 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;
+        model.tensors["model/wte"]     = model.wte;
+        model.tensors["model/wpe"]     = model.wpe;
+        model.tensors["model/lm_head"] = model.lm_head;
 
         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_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.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_attn_w = ggml_new_tensor_2d(ctx, wtype,           n_embd, 3*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_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_fc_w    = ggml_new_tensor_2d(ctx, wtype,           n_embd, 4*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);
+            layer.c_mlp_proj_w  = 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;
@@ -397,7 +266,7 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
             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/w"]  = layer.c_mlp_proj_w;
             model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/b"]  = layer.c_mlp_proj_b;
         }
     }
@@ -425,14 +294,16 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
     {
         size_t total_size = 0;
 
+        bool has_lm_head = false;
+
         while (true) {
             int32_t n_dims;
             int32_t length;
-            int32_t ftype;
+            int32_t ttype;
 
             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));
+            fin.read(reinterpret_cast<char *>(&ttype),  sizeof(ttype));
 
             if (fin.eof()) {
                 break;
@@ -448,7 +319,7 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
             std::string name(length, 0);
             fin.read(&name[0], length);
 
-            if (model.tensors.find(name) == model.tensors.end()) {
+            if (model.tensors.find(name.data()) == model.tensors.end()) {
                 fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
                 return false;
             }
@@ -461,13 +332,18 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
 
             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]);
+                        __func__, name.data(), (int) tensor->ne[0], (int) tensor->ne[1], ne[0], ne[1]);
                 return false;
             }
 
-            const size_t bpe = (ftype == 0) ? sizeof(float) : sizeof(ggml_fp16_t);
+            // for debugging
+            if (0) {
+                printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ggml_type_name(ggml_type(ttype)), ggml_nbytes(tensor)/1024.0/1024.0, ggml_nbytes(tensor));
+            }
 
-            if (nelements*bpe != ggml_nbytes(tensor)) {
+            const size_t bpe = ggml_type_size(ggml_type(ttype));
+
+            if ((nelements*bpe)/ggml_blck_size(tensor->type) != 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;
@@ -475,7 +351,15 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
 
             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);
+            // GPT-2 models share the WTE tensor as the LM head
+            if (name == "model/wte" && has_lm_head == false) {
+                memcpy(model.lm_head->data, tensor->data, ggml_nbytes(tensor));
+            }
+
+            if (name == "model/lm_head") {
+                has_lm_head = true;
+            }
+
             total_size += ggml_nbytes(tensor);
         }
 
@@ -493,7 +377,7 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
 //   - 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
+//   - embd_w:    the predicted logits for the next token
 //
 bool gpt2_eval(
         const gpt2_model & model,
@@ -512,12 +396,12 @@ bool gpt2_eval(
     const int n_head  = hparams.n_head;
     const int n_vocab = hparams.n_vocab;
 
-    static size_t buf_size = 5640ull*1024*1024;
+    static size_t buf_size = 512u*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);
+        //printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
 
         // reallocate
         buf_size = buf_size_new;
@@ -528,13 +412,14 @@ bool gpt2_eval(
         }
     }
 
-    struct ggml_init_params params;
-    params.mem_size   = buf_size;
-    params.mem_buffer = buf;
+    struct ggml_init_params params = {
+        /*.mem_size   =*/ buf_size,
+        /*.mem_buffer =*/ buf,
+        /*.no_alloc   =*/ false,
+    };
 
     struct ggml_context * ctx0 = ggml_init(params);
-
-    struct ggml_cgraph gf = { };
+    struct ggml_cgraph gf = {};
     gf.n_threads = n_threads;
 
     struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
@@ -578,7 +463,7 @@ bool gpt2_eval(
         // [2304, N]
         {
             cur = ggml_mul_mat(ctx0,
-                    ggml_transpose(ctx0, model.layers[il].c_attn_attn_w),
+                    model.layers[il].c_attn_attn_w,
                     cur);
 
             cur = ggml_add(ctx0,
@@ -654,11 +539,13 @@ bool gpt2_eval(
             // 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);
+                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, model.memory_v->type, n_past + N, n_embd/n_head, n_head));
 
             // KQV = transpose(V) * KQ_soft_max
             // [64, N, 12]
@@ -685,7 +572,7 @@ bool gpt2_eval(
         // [768, N]
         {
             cur = ggml_mul_mat(ctx0,
-                    ggml_transpose(ctx0, model.layers[il].c_attn_proj_w),
+                    model.layers[il].c_attn_proj_w,
                     cur);
 
             cur = ggml_add(ctx0,
@@ -722,7 +609,7 @@ bool gpt2_eval(
             // cur = fc_w*cur + fc_b
             // [3072, N]
             cur = ggml_mul_mat(ctx0,
-                    ggml_transpose(ctx0, model.layers[il].c_mlp_fc_w),
+                    model.layers[il].c_mlp_fc_w,
                     cur);
 
             cur = ggml_add(ctx0,
@@ -742,7 +629,7 @@ bool gpt2_eval(
             // cur = proj_w*cur + proj_b
             // [768, N]
             cur = ggml_mul_mat(ctx0,
-                    model.layers[il].c_mlp_proj_w_trans,
+                    model.layers[il].c_mlp_proj_w,
                     cur);
 
             cur = ggml_add(ctx0,
@@ -769,12 +656,12 @@ bool gpt2_eval(
     }
 
     // inpL = WTE * inpL
-    // [ 768, 50257] - model.wte
+    // [ 768, 50257] - model.lm_head
     // [ 768, N]     - inpL
-    inpL = ggml_mul_mat(ctx0, model.wte, inpL);
+    inpL = ggml_mul_mat(ctx0, model.lm_head, inpL);
 
     // logits -> probs
-    inpL = ggml_soft_max(ctx0, inpL);
+    //inpL = ggml_soft_max(ctx0, inpL);
 
     // run the computation
     ggml_build_forward_expand(&gf, inpL);
@@ -788,7 +675,7 @@ bool gpt2_eval(
     //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
+    // return result just for 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);
 

examples/talk/gpt-2.h

@@ -2,18 +2,12 @@
 
 // TODO: Change to C-style API and move to ./examples for easy reuse.
 
+#include "common.h"
+
 #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);

examples/talk/speak

@@ -0,0 +1,24 @@
+#!/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"
+
+# Mac OS "say" command
+say "$2"
+
+# Eleven Labs
+# To use it, install the elevenlabs module from pip (pip install elevenlabs)
+# It's possible to use the API for free with limited number of characters. To increase this limit register to https://beta.elevenlabs.io to get an api key and paste it after 'ELEVEN_API_KEY='
+#Keep the line commented to use the free version without api key
+#
+#export ELEVEN_API_KEY=your_api_key
+#wd=$(dirname $0)
+#script=$wd/eleven-labs.py
+#python3 $script $1 "$2"
+#ffplay -autoexit -nodisp -loglevel quiet -hide_banner -i ./audio.mp3

examples/talk/speak.bat

@@ -0,0 +1 @@
+@powershell -ExecutionPolicy Bypass -F examples\talk\speak.ps1 %1 %2