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Author SHA1 Message Date
59c997ca2d wip ignore 2023-02-15 19:11:12 +02:00
71 changed files with 2994 additions and 8159 deletions

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@ -1,22 +0,0 @@
name: Bindings Tests (Ruby)
on:
push:
paths:
- bindings/ruby/**
- whisper.h
pull_request:
paths:
- bindings/ruby/**
- whisper.h
jobs:
ubuntu-latest:
runs-on: ubuntu-latest
steps:
- uses: ruby/setup-ruby@v1
with:
ruby-version: '3.0'
- uses: actions/checkout@v1
- run: |
cd bindings/ruby/ext
ruby extconf.rb && make

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@ -1,4 +1,4 @@
name: Bindings Tests (Go)
name: Bindings Tests
on:
push:
paths:

5
.gitignore vendored
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@ -1,7 +1,5 @@
*.o
*.a
*.mlmodel
*.mlmodelc
.cache/
.vs/
.vscode/
@ -12,7 +10,6 @@ build-em/
build-debug/
build-release/
build-static/
build-no-accel/
build-sanitize-addr/
build-sanitize-thread/
@ -34,5 +31,3 @@ examples/whisper.objc/whisper.objc.xcodeproj/xcuserdata/
examples/whisper.objc/whisper.objc.xcodeproj/project.xcworkspace/xcuserdata
extra/bench-gg.txt
*.mlmodel*

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@ -1,6 +1,6 @@
cmake_minimum_required (VERSION 3.0)
project(whisper.cpp VERSION 1.2.1)
project(whisper.cpp VERSION 1.2.0)
# Add path to modules
list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/")
@ -54,8 +54,6 @@ if (APPLE)
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_COREML "whisper: enable Core ML framework" OFF)
else()
option(WHISPER_SUPPORT_OPENBLAS "whisper: support for OpenBLAS" OFF)
endif()
@ -88,33 +86,16 @@ endif()
find_package(Threads REQUIRED)
# on APPLE
if (APPLE)
# include Accelerate framework
if (NOT WHISPER_NO_ACCELERATE)
find_library(ACCELERATE_FRAMEWORK Accelerate)
# 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")
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()
set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} ${ACCELERATE_FRAMEWORK})
set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DGGML_USE_ACCELERATE)
else()
message(WARNING "Accelerate framework not found")
endif()
endif()
@ -191,9 +172,7 @@ else()
if(NOT WHISPER_NO_FMA)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mfma")
endif()
if(NOT WHISPER_NO_F16C)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mf16c")
endif()
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mf16c")
endif()
endif()
endif()
@ -202,33 +181,6 @@ 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()
#
# whisper - this is the main library of the project
#
@ -248,10 +200,6 @@ target_include_directories(${TARGET} PUBLIC
.
)
if (WHISPER_COREML)
target_link_libraries(${TARGET} PRIVATE whisper.coreml)
endif()
if (MSVC)
target_link_libraries(${TARGET} PRIVATE ${WHISPER_EXTRA_LIBS} ${CMAKE_THREAD_LIBS_INIT})

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@ -30,16 +30,10 @@ endif
# Compile flags
#
CFLAGS = -I. -O3 -DNDEBUG -std=c11 -fPIC
CXXFLAGS = -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC
CFLAGS = -I. -O3 -std=c11 -fPIC
CXXFLAGS = -I. -I./examples -O3 -std=c++11 -fPIC
LDFLAGS =
# ref: https://github.com/ggerganov/whisper.cpp/issues/37
ifneq ($(wildcard /usr/include/musl/*),)
CFLAGS += -D_POSIX_SOURCE -D_GNU_SOURCE
CXXFLAGS += -D_POSIX_SOURCE -D_GNU_SOURCE
endif
# OS specific
# TODO: support Windows
ifeq ($(UNAME_S),Linux)
@ -138,10 +132,6 @@ ifndef WHISPER_NO_ACCELERATE
LDFLAGS += -framework Accelerate
endif
endif
ifdef WHISPER_COREML
CXXFLAGS += -DWHISPER_USE_COREML
LDFLAGS += -framework Foundation -framework CoreML
endif
ifdef WHISPER_OPENBLAS
CFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/openblas
LDFLAGS += -lopenblas
@ -151,8 +141,6 @@ ifdef WHISPER_GPROF
CXXFLAGS += -pg
endif
ifneq ($(filter aarch64%,$(UNAME_M)),)
CFLAGS += -mcpu=native
CXXFLAGS += -mcpu=native
endif
ifneq ($(filter armv6%,$(UNAME_M)),)
# Raspberry Pi 1, 2, 3
@ -194,23 +182,11 @@ ggml.o: ggml.c ggml.h
whisper.o: whisper.cpp whisper.h
$(CXX) $(CXXFLAGS) -c whisper.cpp -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 . -c coreml/whisper-encoder.mm -o whisper-encoder.o
libwhisper.a: ggml.o whisper.o
$(AR) rcs libwhisper.a ggml.o whisper.o
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)
libwhisper.so: ggml.o whisper.o
$(CXX) $(CXXFLAGS) -shared -o libwhisper.so ggml.o whisper.o $(LDFLAGS)
clean:
rm -f *.o main stream command talk bench libwhisper.a libwhisper.so
@ -221,24 +197,21 @@ clean:
CC_SDL=`sdl2-config --cflags --libs`
SRC_COMMON = examples/common.cpp
SRC_COMMON_SDL = examples/common-sdl.cpp
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: examples/main/main.cpp ggml.o whisper.o
$(CXX) $(CXXFLAGS) examples/main/main.cpp ggml.o whisper.o -o main $(LDFLAGS)
./main -h
stream: examples/stream/stream.cpp $(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)
stream: examples/stream/stream.cpp ggml.o whisper.o
$(CXX) $(CXXFLAGS) examples/stream/stream.cpp ggml.o whisper.o -o stream $(CC_SDL) $(LDFLAGS)
command: examples/command/command.cpp $(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)
command: examples/command/command.cpp ggml.o whisper.o
$(CXX) $(CXXFLAGS) examples/command/command.cpp ggml.o whisper.o -o command $(CC_SDL) $(LDFLAGS)
talk: examples/talk/talk.cpp examples/talk/gpt-2.cpp $(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: examples/talk/talk.cpp examples/talk/gpt-2.cpp ggml.o whisper.o
$(CXX) $(CXXFLAGS) examples/talk/talk.cpp examples/talk/gpt-2.cpp ggml.o whisper.o -o talk $(CC_SDL) $(LDFLAGS)
bench: examples/bench/bench.cpp ggml.o $(WHISPER_OBJ)
$(CXX) $(CXXFLAGS) examples/bench/bench.cpp ggml.o $(WHISPER_OBJ) -o bench $(LDFLAGS)
bench: examples/bench/bench.cpp ggml.o whisper.o
$(CXX) $(CXXFLAGS) examples/bench/bench.cpp ggml.o whisper.o -o bench $(LDFLAGS)
#
# Audio samples

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@ -4,7 +4,7 @@
[![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)
Stable: [v1.2.0](https://github.com/ggerganov/whisper.cpp/releases/tag/v1.2.0) / [Roadmap | F.A.Q.](https://github.com/ggerganov/whisper.cpp/discussions/126)
High-performance inference of [OpenAI's Whisper](https://github.com/openai/whisper) automatic speech recognition (ASR) model:
@ -433,19 +433,6 @@ https://user-images.githubusercontent.com/1991296/199337538-b7b0c7a3-2753-4a88-a
---
## Video comparison of different models
Use the [extra/bench-wts.sh](https://github.com/ggerganov/whisper.cpp/blob/master/extra/bench-wts.sh) script to generate a video in the following format:
```java
./extra/bench-wts.sh samples/jfk.wav
ffplay ./samples/jfk.wav.all.mp4
```
https://user-images.githubusercontent.com/1991296/223206245-2d36d903-cf8e-4f09-8c3b-eb9f9c39d6fc.mp4
---
## Benchmarks
In order to have an objective comparison of the performance of the inference across different system configurations,
@ -466,7 +453,7 @@ The original models are converted to a custom binary format. This allows to pack
You can download the converted models using the [models/download-ggml-model.sh](models/download-ggml-model.sh) script
or manually from here:
- https://huggingface.co/ggerganov/whisper.cpp
- https://huggingface.co/datasets/ggerganov/whisper.cpp
- https://ggml.ggerganov.com
For more details, see the conversion script [models/convert-pt-to-ggml.py](models/convert-pt-to-ggml.py) or the README
@ -476,17 +463,12 @@ in [models](models).
- [X] Rust: [tazz4843/whisper-rs](https://github.com/tazz4843/whisper-rs) | [#310](https://github.com/ggerganov/whisper.cpp/discussions/310)
- [X] Javascript: [bindings/javascript](bindings/javascript) | [#309](https://github.com/ggerganov/whisper.cpp/discussions/309)
- 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] 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)
- [X] .NET: | [#422](https://github.com/ggerganov/whisper.cpp/discussions/422)
- [X] .NET:
- [sandrohanea/whisper.net](https://github.com/sandrohanea/whisper.net)
- [NickDarvey/whisper](https://github.com/NickDarvey/whisper)
- [X] Python: | [#9](https://github.com/ggerganov/whisper.cpp/issues/9)
- [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)
- [ ] Python: soon | [WIP](https://github.com/ggerganov/whisper.cpp/issues/9)
## Examples

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@ -17,9 +17,9 @@ import (
// CONSTANTS
const (
srcUrl = "https://huggingface.co/ggerganov/whisper.cpp/resolve/main" // The location of the models
srcExt = ".bin" // Filename extension
bufSize = 1024 * 64 // Size of the buffer used for downloading the model
srcUrl = "https://huggingface.co/datasets/ggerganov/whisper.cpp/resolve/main" // The location of the models
srcExt = ".bin" // Filename extension
bufSize = 1024 * 64 // Size of the buffer used for downloading the model
)
var (

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@ -94,7 +94,6 @@ func (model *model) NewContext() (Context, error) {
params.SetPrintRealtime(false)
params.SetPrintTimestamps(false)
params.SetThreads(runtime.NumCPU())
params.SetNoContext(true)
// Return new context
return newContext(model, params)

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@ -20,7 +20,7 @@ extern bool callEncoderBegin(void* user_data);
// Text segment callback
// Called on every newly generated text segment
// Use the whisper_full_...() functions to obtain the text segments
static void whisper_new_segment_cb(struct whisper_context* ctx, struct whisper_state* state, int n_new, void* user_data) {
static void whisper_new_segment_cb(struct whisper_context* ctx, int n_new, void* user_data) {
if(user_data != NULL && ctx != NULL) {
callNewSegment(user_data, n_new);
}
@ -29,7 +29,7 @@ static void whisper_new_segment_cb(struct whisper_context* ctx, struct whisper_s
// Encoder begin callback
// If not NULL, called before the encoder starts
// If it returns false, the computation is aborted
static bool whisper_encoder_begin_cb(struct whisper_context* ctx, struct whisper_state* state, void* user_data) {
static bool whisper_encoder_begin_cb(struct whisper_context* ctx, void* user_data) {
if(user_data != NULL && ctx != NULL) {
return callEncoderBegin(user_data);
}

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@ -1,6 +1,6 @@
{
"name": "whisper.cpp",
"version": "1.2.1",
"version": "1.2.0",
"description": "Whisper speech recognition",
"main": "whisper.js",
"scripts": {

File diff suppressed because one or more lines are too long

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@ -1,7 +0,0 @@
Makefile
ggml.c
ggml.h
whisper.bundle
whisper.cpp
whisper.h
dr_wav.h

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@ -1,21 +0,0 @@
require 'mkmf'
system("cp #{File.join(File.dirname(__FILE__),'..','..','..','whisper.cpp')} .")
system("cp #{File.join(File.dirname(__FILE__),'..','..','..','whisper.h')} .")
system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml.h')} .")
system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml.c')} .")
system("cp #{File.join(File.dirname(__FILE__),'..','..','..','examples','dr_wav.h')} .")
# need to use c++ compiler flags
$CXXFLAGS << ' -std=c++11'
# Set to true when building binary gems
if enable_config('static-stdlib', false)
$LDFLAGS << ' -static-libgcc -static-libstdc++'
end
if enable_config('march-tune-native', false)
$CFLAGS << ' -march=native -mtune=native'
$CXXFLAGS << ' -march=native -mtune=native'
end
create_makefile('whisper')

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@ -1,426 +0,0 @@
#include <ruby.h>
#include "ruby_whisper.h"
#define DR_WAV_IMPLEMENTATION
#include "dr_wav.h"
#include <cmath>
#include <fstream>
#include <cstdio>
#include <string>
#include <thread>
#include <vector>
#ifdef __cplusplus
extern "C" {
#endif
#define BOOL_PARAMS_SETTER(self, prop, value) \
ruby_whisper_params *rwp; \
Data_Get_Struct(self, ruby_whisper_params, rwp); \
if (value == Qfalse || value == Qnil) { \
rwp->params.prop = false; \
} else { \
rwp->params.prop = true; \
} \
return value; \
#define BOOL_PARAMS_GETTER(self, prop) \
ruby_whisper_params *rwp; \
Data_Get_Struct(self, ruby_whisper_params, rwp); \
if (rwp->params.prop) { \
return Qtrue; \
} else { \
return Qfalse; \
}
VALUE mWhisper;
VALUE cContext;
VALUE cParams;
static void ruby_whisper_free(ruby_whisper *rw) {
if (rw->context) {
whisper_free(rw->context);
rw->context = NULL;
}
}
static void ruby_whisper_params_free(ruby_whisper_params *rwp) {
}
void rb_whisper_mark(ruby_whisper *rw) {
// call rb_gc_mark on any ruby references in rw
}
void rb_whisper_free(ruby_whisper *rw) {
ruby_whisper_free(rw);
free(rw);
}
void rb_whisper_params_mark(ruby_whisper_params *rwp) {
}
void rb_whisper_params_free(ruby_whisper_params *rwp) {
ruby_whisper_params_free(rwp);
free(rwp);
}
static VALUE ruby_whisper_allocate(VALUE klass) {
ruby_whisper *rw;
rw = ALLOC(ruby_whisper);
rw->context = NULL;
return Data_Wrap_Struct(klass, rb_whisper_mark, rb_whisper_free, rw);
}
static VALUE ruby_whisper_params_allocate(VALUE klass) {
ruby_whisper_params *rwp;
rwp = ALLOC(ruby_whisper_params);
rwp->params = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
return Data_Wrap_Struct(klass, rb_whisper_params_mark, rb_whisper_params_free, rwp);
}
static VALUE ruby_whisper_initialize(int argc, VALUE *argv, VALUE self) {
ruby_whisper *rw;
VALUE whisper_model_file_path;
// TODO: we can support init from buffer here too maybe another ruby object to expose
rb_scan_args(argc, argv, "01", &whisper_model_file_path);
Data_Get_Struct(self, ruby_whisper, rw);
if (!rb_respond_to(whisper_model_file_path, rb_intern("to_s"))) {
rb_raise(rb_eRuntimeError, "Expected file path to model to initialize Whisper::Context");
}
rw->context = whisper_init_from_file(StringValueCStr(whisper_model_file_path));
if (rw->context == nullptr) {
rb_raise(rb_eRuntimeError, "error: failed to initialize whisper context");
}
return self;
}
/*
* transcribe a single file
* can emit to a block results
*
**/
static VALUE ruby_whisper_transcribe(int argc, VALUE *argv, VALUE self) {
ruby_whisper *rw;
ruby_whisper_params *rwp;
VALUE wave_file_path, blk, params;
rb_scan_args(argc, argv, "02&", &wave_file_path, &params, &blk);
Data_Get_Struct(self, ruby_whisper, rw);
Data_Get_Struct(params, ruby_whisper_params, rwp);
if (!rb_respond_to(wave_file_path, rb_intern("to_s"))) {
rb_raise(rb_eRuntimeError, "Expected file path to wave file");
}
std::string fname_inp = StringValueCStr(wave_file_path);
std::vector<float> pcmf32; // mono-channel F32 PCM
std::vector<std::vector<float>> pcmf32s; // stereo-channel F32 PCM
// WAV input - this is directly from main.cpp example
{
drwav wav;
std::vector<uint8_t> wav_data; // used for pipe input from stdin
if (fname_inp == "-") {
{
uint8_t buf[1024];
while (true) {
const size_t n = fread(buf, 1, sizeof(buf), stdin);
if (n == 0) {
break;
}
wav_data.insert(wav_data.end(), buf, buf + n);
}
}
if (drwav_init_memory(&wav, wav_data.data(), wav_data.size(), nullptr) == false) {
fprintf(stderr, "error: failed to open WAV file from stdin\n");
return self;
}
fprintf(stderr, "%s: read %zu bytes from stdin\n", __func__, wav_data.size());
} else if (drwav_init_file(&wav, fname_inp.c_str(), nullptr) == false) {
fprintf(stderr, "error: failed to open '%s' as WAV file\n", fname_inp.c_str());
return self;
}
if (wav.channels != 1 && wav.channels != 2) {
fprintf(stderr, "WAV file '%s' must be mono or stereo\n", fname_inp.c_str());
return self;
}
if (rwp->diarize && wav.channels != 2 && rwp->params.print_timestamps == false) {
fprintf(stderr, "WAV file '%s' must be stereo for diarization and timestamps have to be enabled\n", fname_inp.c_str());
return self;
}
if (wav.sampleRate != WHISPER_SAMPLE_RATE) {
fprintf(stderr, "WAV file '%s' must be %i kHz\n", fname_inp.c_str(), WHISPER_SAMPLE_RATE/1000);
return self;
}
if (wav.bitsPerSample != 16) {
fprintf(stderr, "WAV file '%s' must be 16-bit\n", fname_inp.c_str());
return self;
}
const uint64_t n = wav_data.empty() ? wav.totalPCMFrameCount : wav_data.size()/(wav.channels*wav.bitsPerSample/8);
std::vector<int16_t> pcm16;
pcm16.resize(n*wav.channels);
drwav_read_pcm_frames_s16(&wav, n, pcm16.data());
drwav_uninit(&wav);
// convert to mono, float
pcmf32.resize(n);
if (wav.channels == 1) {
for (uint64_t i = 0; i < n; i++) {
pcmf32[i] = float(pcm16[i])/32768.0f;
}
} else {
for (uint64_t i = 0; i < n; i++) {
pcmf32[i] = float(pcm16[2*i] + pcm16[2*i + 1])/65536.0f;
}
}
if (rwp->diarize) {
// convert to stereo, float
pcmf32s.resize(2);
pcmf32s[0].resize(n);
pcmf32s[1].resize(n);
for (uint64_t i = 0; i < n; i++) {
pcmf32s[0][i] = float(pcm16[2*i])/32768.0f;
pcmf32s[1][i] = float(pcm16[2*i + 1])/32768.0f;
}
}
}
{
static bool is_aborted = false; // NOTE: this should be atomic to avoid data race
rwp->params.encoder_begin_callback = [](struct whisper_context * /*ctx*/, struct whisper_state * /*state*/, void * user_data) {
bool is_aborted = *(bool*)user_data;
return !is_aborted;
};
rwp->params.encoder_begin_callback_user_data = &is_aborted;
}
if (whisper_full_parallel(rw->context, rwp->params, pcmf32.data(), pcmf32.size(), 1) != 0) {
fprintf(stderr, "failed to process audio\n");
return self;
}
const int n_segments = whisper_full_n_segments(rw->context);
VALUE output = rb_str_new2("");
for (int i = 0; i < n_segments; ++i) {
const char * text = whisper_full_get_segment_text(rw->context, i);
output = rb_str_concat(output, rb_str_new2(text));
}
VALUE idCall = rb_intern("call");
if (blk != Qnil) {
rb_funcall(blk, idCall, 1, output);
}
return self;
}
/*
* params.language = "auto" | "en", etc...
*/
static VALUE ruby_whisper_params_set_language(VALUE self, VALUE value) {
ruby_whisper_params *rwp;
Data_Get_Struct(self, ruby_whisper_params, rwp);
if (value == Qfalse || value == Qnil) {
rwp->params.language = "auto";
} else {
rwp->params.language = StringValueCStr(value);
}
return value;
}
static VALUE ruby_whisper_params_get_language(VALUE self) {
ruby_whisper_params *rwp;
Data_Get_Struct(self, ruby_whisper_params, rwp);
if (rwp->params.language) {
return rb_str_new2(rwp->params.language);
} else {
return rb_str_new2("auto");
}
}
static VALUE ruby_whisper_params_set_translate(VALUE self, VALUE value) {
BOOL_PARAMS_SETTER(self, translate, value)
}
static VALUE ruby_whisper_params_get_translate(VALUE self) {
BOOL_PARAMS_GETTER(self, translate)
}
static VALUE ruby_whisper_params_set_no_context(VALUE self, VALUE value) {
BOOL_PARAMS_SETTER(self, no_context, value)
}
static VALUE ruby_whisper_params_get_no_context(VALUE self) {
BOOL_PARAMS_GETTER(self, no_context)
}
static VALUE ruby_whisper_params_set_single_segment(VALUE self, VALUE value) {
BOOL_PARAMS_SETTER(self, single_segment, value)
}
static VALUE ruby_whisper_params_get_single_segment(VALUE self) {
BOOL_PARAMS_GETTER(self, single_segment)
}
static VALUE ruby_whisper_params_set_print_special(VALUE self, VALUE value) {
BOOL_PARAMS_SETTER(self, print_special, value)
}
static VALUE ruby_whisper_params_get_print_special(VALUE self) {
BOOL_PARAMS_GETTER(self, print_special)
}
static VALUE ruby_whisper_params_set_print_progress(VALUE self, VALUE value) {
BOOL_PARAMS_SETTER(self, print_progress, value)
}
static VALUE ruby_whisper_params_get_print_progress(VALUE self) {
BOOL_PARAMS_GETTER(self, print_progress)
}
static VALUE ruby_whisper_params_set_print_realtime(VALUE self, VALUE value) {
BOOL_PARAMS_SETTER(self, print_realtime, value)
}
static VALUE ruby_whisper_params_get_print_realtime(VALUE self) {
BOOL_PARAMS_GETTER(self, print_realtime)
}
static VALUE ruby_whisper_params_set_print_timestamps(VALUE self, VALUE value) {
BOOL_PARAMS_SETTER(self, print_timestamps, value)
}
static VALUE ruby_whisper_params_get_print_timestamps(VALUE self) {
BOOL_PARAMS_GETTER(self, print_timestamps)
}
static VALUE ruby_whisper_params_set_suppress_blank(VALUE self, VALUE value) {
BOOL_PARAMS_SETTER(self, suppress_blank, value)
}
static VALUE ruby_whisper_params_get_suppress_blank(VALUE self) {
BOOL_PARAMS_GETTER(self, suppress_blank)
}
static VALUE ruby_whisper_params_set_suppress_non_speech_tokens(VALUE self, VALUE value) {
BOOL_PARAMS_SETTER(self, suppress_non_speech_tokens, value)
}
static VALUE ruby_whisper_params_get_suppress_non_speech_tokens(VALUE self) {
BOOL_PARAMS_GETTER(self, suppress_non_speech_tokens)
}
static VALUE ruby_whisper_params_get_token_timestamps(VALUE self) {
BOOL_PARAMS_GETTER(self, token_timestamps)
}
static VALUE ruby_whisper_params_set_token_timestamps(VALUE self, VALUE value) {
BOOL_PARAMS_SETTER(self, token_timestamps, value)
}
static VALUE ruby_whisper_params_get_split_on_word(VALUE self) {
BOOL_PARAMS_GETTER(self, split_on_word)
}
static VALUE ruby_whisper_params_set_split_on_word(VALUE self, VALUE value) {
BOOL_PARAMS_SETTER(self, split_on_word, value)
}
static VALUE ruby_whisper_params_get_speed_up(VALUE self) {
BOOL_PARAMS_GETTER(self, speed_up)
}
static VALUE ruby_whisper_params_set_speed_up(VALUE self, VALUE value) {
BOOL_PARAMS_SETTER(self, speed_up, value)
}
static VALUE ruby_whisper_params_get_diarize(VALUE self) {
ruby_whisper_params *rwp;
Data_Get_Struct(self, ruby_whisper_params, rwp);
if (rwp->diarize) {
return Qtrue;
} else {
return Qfalse;
}
}
static VALUE ruby_whisper_params_set_diarize(VALUE self, VALUE value) {
ruby_whisper_params *rwp;
Data_Get_Struct(self, ruby_whisper_params, rwp);
if (value == Qfalse || value == Qnil) {
rwp->diarize = false;
} else {
rwp->diarize = true;
} \
return value;
}
static VALUE ruby_whisper_params_get_offset(VALUE self) {
ruby_whisper_params *rwp;
Data_Get_Struct(self, ruby_whisper_params, rwp);
return INT2NUM(rwp->params.offset_ms);
}
static VALUE ruby_whisper_params_set_offset(VALUE self, VALUE value) {
ruby_whisper_params *rwp;
Data_Get_Struct(self, ruby_whisper_params, rwp);
rwp->params.offset_ms = NUM2INT(value);
return value;
}
static VALUE ruby_whisper_params_get_duration(VALUE self) {
ruby_whisper_params *rwp;
Data_Get_Struct(self, ruby_whisper_params, rwp);
return INT2NUM(rwp->params.duration_ms);
}
static VALUE ruby_whisper_params_set_duration(VALUE self, VALUE value) {
ruby_whisper_params *rwp;
Data_Get_Struct(self, ruby_whisper_params, rwp);
rwp->params.duration_ms = NUM2INT(value);
return value;
}
static VALUE ruby_whisper_params_get_max_text_tokens(VALUE self) {
ruby_whisper_params *rwp;
Data_Get_Struct(self, ruby_whisper_params, rwp);
return INT2NUM(rwp->params.n_max_text_ctx);
}
static VALUE ruby_whisper_params_set_max_text_tokens(VALUE self, VALUE value) {
ruby_whisper_params *rwp;
Data_Get_Struct(self, ruby_whisper_params, rwp);
rwp->params.n_max_text_ctx = NUM2INT(value);
return value;
}
void Init_whisper() {
mWhisper = rb_define_module("Whisper");
cContext = rb_define_class_under(mWhisper, "Context", rb_cObject);
cParams = rb_define_class_under(mWhisper, "Params", rb_cObject);
rb_define_alloc_func(cContext, ruby_whisper_allocate);
rb_define_method(cContext, "initialize", ruby_whisper_initialize, -1);
rb_define_method(cContext, "transcribe", ruby_whisper_transcribe, -1);
rb_define_alloc_func(cParams, ruby_whisper_params_allocate);
rb_define_method(cParams, "language=", ruby_whisper_params_set_language, 1);
rb_define_method(cParams, "language", ruby_whisper_params_get_language, 0);
rb_define_method(cParams, "translate=", ruby_whisper_params_set_translate, 1);
rb_define_method(cParams, "translate", ruby_whisper_params_get_translate, 0);
rb_define_method(cParams, "no_context=", ruby_whisper_params_set_no_context, 1);
rb_define_method(cParams, "no_context", ruby_whisper_params_get_no_context, 0);
rb_define_method(cParams, "single_segment=", ruby_whisper_params_set_single_segment, 1);
rb_define_method(cParams, "single_segment", ruby_whisper_params_get_single_segment, 0);
rb_define_method(cParams, "print_special", ruby_whisper_params_get_print_special, 0);
rb_define_method(cParams, "print_special=", ruby_whisper_params_set_print_special, 1);
rb_define_method(cParams, "print_progress", ruby_whisper_params_get_print_progress, 0);
rb_define_method(cParams, "print_progress=", ruby_whisper_params_set_print_progress, 1);
rb_define_method(cParams, "print_realtime", ruby_whisper_params_get_print_realtime, 0);
rb_define_method(cParams, "print_realtime=", ruby_whisper_params_set_print_realtime, 1);
rb_define_method(cParams, "print_timestamps", ruby_whisper_params_get_print_timestamps, 0);
rb_define_method(cParams, "print_timestamps=", ruby_whisper_params_set_print_timestamps, 1);
rb_define_method(cParams, "suppress_blank", ruby_whisper_params_get_suppress_blank, 0);
rb_define_method(cParams, "suppress_blank=", ruby_whisper_params_set_suppress_blank, 1);
rb_define_method(cParams, "suppress_non_speech_tokens", ruby_whisper_params_get_suppress_non_speech_tokens, 0);
rb_define_method(cParams, "suppress_non_speech_tokens=", ruby_whisper_params_set_suppress_non_speech_tokens, 1);
rb_define_method(cParams, "token_timestamps", ruby_whisper_params_get_token_timestamps, 0);
rb_define_method(cParams, "token_timestamps=", ruby_whisper_params_set_token_timestamps, 1);
rb_define_method(cParams, "split_on_word", ruby_whisper_params_get_split_on_word, 0);
rb_define_method(cParams, "split_on_word=", ruby_whisper_params_set_split_on_word, 1);
rb_define_method(cParams, "speed_up", ruby_whisper_params_get_speed_up, 0);
rb_define_method(cParams, "speed_up=", ruby_whisper_params_set_speed_up, 1);
rb_define_method(cParams, "diarize", ruby_whisper_params_get_diarize, 0);
rb_define_method(cParams, "diarize=", ruby_whisper_params_set_diarize, 1);
rb_define_method(cParams, "offset", ruby_whisper_params_get_offset, 0);
rb_define_method(cParams, "offset=", ruby_whisper_params_set_offset, 1);
rb_define_method(cParams, "duration", ruby_whisper_params_get_duration, 0);
rb_define_method(cParams, "duration=", ruby_whisper_params_set_duration, 1);
rb_define_method(cParams, "max_text_tokens", ruby_whisper_params_get_max_text_tokens, 0);
rb_define_method(cParams, "max_text_tokens=", ruby_whisper_params_set_max_text_tokens, 1);
}
#ifdef __cplusplus
}
#endif

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@ -1,15 +0,0 @@
#ifndef __RUBY_WHISPER_H
#define __RUBY_WHISPER_H
#include "whisper.h"
typedef struct {
struct whisper_context *context;
} ruby_whisper;
typedef struct {
struct whisper_full_params params;
bool diarize;
} ruby_whisper_params;
#endif

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@ -1,138 +0,0 @@
TOPDIR = File.expand_path(File.join(File.dirname(__FILE__), '..'))
EXTDIR = File.join(TOPDIR, 'ext')
#$LIBDIR = File.join(TOPDIR, 'lib')
#$:.unshift(LIBDIR)
$:.unshift(EXTDIR)
require 'whisper'
require 'test/unit'
class TestWhisper < Test::Unit::TestCase
def setup
@params = Whisper::Params.new
end
def test_language
@params.language = "en"
assert_equal @params.language, "en"
@params.language = "auto"
assert_equal @params.language, "auto"
end
def test_offset
@params.offset = 10_000
assert_equal @params.offset, 10_000
@params.offset = 0
assert_equal @params.offset, 0
end
def test_duration
@params.duration = 60_000
assert_equal @params.duration, 60_000
@params.duration = 0
assert_equal @params.duration, 0
end
def test_max_text_tokens
@params.max_text_tokens = 300
assert_equal @params.max_text_tokens, 300
@params.max_text_tokens = 0
assert_equal @params.max_text_tokens, 0
end
def test_translate
@params.translate = true
assert @params.translate
@params.translate = false
assert !@params.translate
end
def test_no_context
@params.no_context = true
assert @params.no_context
@params.no_context = false
assert !@params.no_context
end
def test_single_segment
@params.single_segment = true
assert @params.single_segment
@params.single_segment = false
assert !@params.single_segment
end
def test_print_special
@params.print_special = true
assert @params.print_special
@params.print_special = false
assert !@params.print_special
end
def test_print_progress
@params.print_progress = true
assert @params.print_progress
@params.print_progress = false
assert !@params.print_progress
end
def test_print_realtime
@params.print_realtime = true
assert @params.print_realtime
@params.print_realtime = false
assert !@params.print_realtime
end
def test_print_timestamps
@params.print_timestamps = true
assert @params.print_timestamps
@params.print_timestamps = false
assert !@params.print_timestamps
end
def test_suppress_blank
@params.suppress_blank = true
assert @params.suppress_blank
@params.suppress_blank = false
assert !@params.suppress_blank
end
def test_suppress_non_speech_tokens
@params.suppress_non_speech_tokens = true
assert @params.suppress_non_speech_tokens
@params.suppress_non_speech_tokens = false
assert !@params.suppress_non_speech_tokens
end
def test_token_timestamps
@params.token_timestamps = true
assert @params.token_timestamps
@params.token_timestamps = false
assert !@params.token_timestamps
end
def test_split_on_word
@params.split_on_word = true
assert @params.split_on_word
@params.split_on_word = false
assert !@params.split_on_word
end
def test_speed_up
@params.speed_up = true
assert @params.speed_up
@params.speed_up = false
assert !@params.speed_up
end
def test_whisper
@whisper = Whisper::Context.new(File.join(TOPDIR, '..', '..', 'models', 'ggml-base.en.bin'))
params = Whisper::Params.new
params.print_timestamps = false
jfk = File.join(TOPDIR, '..', '..', 'samples', 'jfk.wav')
@whisper.transcribe(jfk, params) {|text|
assert_match /ask not what your country can do for you, ask what you can do for your country/, text
}
end
end

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@ -1,142 +0,0 @@
//
// CoremlEncoder.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(10.15), ios(13.0), watchos(6.0), tvos(13.0)) __attribute__((visibility("hidden")))
@interface CoremlEncoderInput : NSObject<MLFeatureProvider>
/// melSegment as 1 × 80 × 3000 3-dimensional array of floats
@property (readwrite, nonatomic, strong) MLMultiArray * melSegment;
- (instancetype)init NS_UNAVAILABLE;
- (instancetype)initWithMelSegment:(MLMultiArray *)melSegment NS_DESIGNATED_INITIALIZER;
@end
/// Model Prediction Output Type
API_AVAILABLE(macos(10.15), ios(13.0), watchos(6.0), tvos(13.0)) __attribute__((visibility("hidden")))
@interface CoremlEncoderOutput : 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(10.15), ios(13.0), watchos(6.0), tvos(13.0)) __attribute__((visibility("hidden")))
@interface CoremlEncoder : NSObject
@property (readonly, nonatomic, nullable) MLModel * model;
/**
URL of the underlying .mlmodelc directory.
*/
+ (nullable NSURL *)URLOfModelInThisBundle;
/**
Initialize CoremlEncoder instance from an existing MLModel object.
Usually the application does not use this initializer unless it makes a subclass of CoremlEncoder.
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 CoremlEncoder instance with the model in this bundle.
*/
- (nullable instancetype)init;
/**
Initialize CoremlEncoder 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 CoremlEncoder instance from the model URL.
@param modelURL URL to the .mlmodelc directory for CoremlEncoder.
@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 CoremlEncoder instance from the model URL.
@param modelURL URL to the .mlmodelc directory for CoremlEncoder.
@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 CoremlEncoder 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 CoremlEncoder instance or NSError object.
*/
+ (void)loadWithConfiguration:(MLModelConfiguration *)configuration completionHandler:(void (^)(CoremlEncoder * _Nullable model, NSError * _Nullable error))handler API_AVAILABLE(macos(11.0), ios(14.0), watchos(7.0), tvos(14.0)) __attribute__((visibility("hidden")));
/**
Construct CoremlEncoder 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 CoremlEncoder instance or NSError object.
*/
+ (void)loadContentsOfURL:(NSURL *)modelURL configuration:(MLModelConfiguration *)configuration completionHandler:(void (^)(CoremlEncoder * _Nullable model, NSError * _Nullable error))handler API_AVAILABLE(macos(11.0), ios(14.0), watchos(7.0), tvos(14.0)) __attribute__((visibility("hidden")));
/**
Make a prediction using the standard interface
@param input an instance of CoremlEncoderInput 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 CoremlEncoderOutput
*/
- (nullable CoremlEncoderOutput *)predictionFromFeatures:(CoremlEncoderInput *)input error:(NSError * _Nullable __autoreleasing * _Nullable)error;
/**
Make a prediction using the standard interface
@param input an instance of CoremlEncoderInput 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 CoremlEncoderOutput
*/
- (nullable CoremlEncoderOutput *)predictionFromFeatures:(CoremlEncoderInput *)input options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error;
/**
Make a prediction using the convenience interface
@param melSegment 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 CoremlEncoderOutput
*/
- (nullable CoremlEncoderOutput *)predictionFromMelSegment:(MLMultiArray *)melSegment error:(NSError * _Nullable __autoreleasing * _Nullable)error;
/**
Batch prediction
@param inputArray array of CoremlEncoderInput 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<CoremlEncoderOutput *>
*/
- (nullable NSArray<CoremlEncoderOutput *> *)predictionsFromInputs:(NSArray<CoremlEncoderInput*> *)inputArray options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error;
@end
NS_ASSUME_NONNULL_END

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@ -1,197 +0,0 @@
//
// CoremlEncoder.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 CoremlEncoderInput
- (instancetype)initWithMelSegment:(MLMultiArray *)melSegment {
self = [super init];
if (self) {
_melSegment = melSegment;
}
return self;
}
- (NSSet<NSString *> *)featureNames {
return [NSSet setWithArray:@[@"melSegment"]];
}
- (nullable MLFeatureValue *)featureValueForName:(NSString *)featureName {
if ([featureName isEqualToString:@"melSegment"]) {
return [MLFeatureValue featureValueWithMultiArray:self.melSegment];
}
return nil;
}
@end
@implementation CoremlEncoderOutput
- (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 CoremlEncoder
/**
URL of the underlying .mlmodelc directory.
*/
+ (nullable NSURL *)URLOfModelInThisBundle {
NSString *assetPath = [[NSBundle bundleForClass:[self class]] pathForResource:@"CoremlEncoder" ofType:@"mlmodelc"];
if (nil == assetPath) { os_log_error(OS_LOG_DEFAULT, "Could not load CoremlEncoder.mlmodelc in the bundle resource"); return nil; }
return [NSURL fileURLWithPath:assetPath];
}
/**
Initialize CoremlEncoder instance from an existing MLModel object.
Usually the application does not use this initializer unless it makes a subclass of CoremlEncoder.
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 CoremlEncoder instance with the model in this bundle.
*/
- (nullable instancetype)init {
return [self initWithContentsOfURL:(NSURL * _Nonnull)self.class.URLOfModelInThisBundle error:nil];
}
/**
Initialize CoremlEncoder 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 CoremlEncoder instance from the model URL.
@param modelURL URL to the .mlmodelc directory for CoremlEncoder.
@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 CoremlEncoder instance from the model URL.
@param modelURL URL to the .mlmodelc directory for CoremlEncoder.
@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 CoremlEncoder 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 CoremlEncoder instance or NSError object.
*/
+ (void)loadWithConfiguration:(MLModelConfiguration *)configuration completionHandler:(void (^)(CoremlEncoder * _Nullable model, NSError * _Nullable error))handler {
[self loadContentsOfURL:(NSURL * _Nonnull)[self URLOfModelInThisBundle]
configuration:configuration
completionHandler:handler];
}
/**
Construct CoremlEncoder 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 CoremlEncoder instance or NSError object.
*/
+ (void)loadContentsOfURL:(NSURL *)modelURL configuration:(MLModelConfiguration *)configuration completionHandler:(void (^)(CoremlEncoder * _Nullable model, NSError * _Nullable error))handler {
[MLModel loadContentsOfURL:modelURL
configuration:configuration
completionHandler:^(MLModel *model, NSError *error) {
if (model != nil) {
CoremlEncoder *typedModel = [[CoremlEncoder alloc] initWithMLModel:model];
handler(typedModel, nil);
} else {
handler(nil, error);
}
}];
}
- (nullable CoremlEncoderOutput *)predictionFromFeatures:(CoremlEncoderInput *)input error:(NSError * _Nullable __autoreleasing * _Nullable)error {
return [self predictionFromFeatures:input options:[[MLPredictionOptions alloc] init] error:error];
}
- (nullable CoremlEncoderOutput *)predictionFromFeatures:(CoremlEncoderInput *)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 [[CoremlEncoderOutput alloc] initWithOutput:(MLMultiArray *)[outFeatures featureValueForName:@"output"].multiArrayValue];
}
- (nullable CoremlEncoderOutput *)predictionFromMelSegment:(MLMultiArray *)melSegment error:(NSError * _Nullable __autoreleasing * _Nullable)error {
CoremlEncoderInput *input_ = [[CoremlEncoderInput alloc] initWithMelSegment:melSegment];
return [self predictionFromFeatures:input_ error:error];
}
- (nullable NSArray<CoremlEncoderOutput *> *)predictionsFromInputs:(NSArray<CoremlEncoderInput*> *)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<CoremlEncoderOutput*> *results = [NSMutableArray arrayWithCapacity:(NSUInteger)outBatch.count];
for (NSInteger i = 0; i < outBatch.count; i++) {
id<MLFeatureProvider> resultProvider = [outBatch featuresAtIndex:i];
CoremlEncoderOutput * result = [[CoremlEncoderOutput alloc] initWithOutput:(MLMultiArray *)[resultProvider featureValueForName:@"output"].multiArrayValue];
[results addObject:result];
}
return results;
}
@end

View File

@ -1,22 +0,0 @@
// 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

View File

@ -1,61 +0,0 @@
#import "coreml/whisper-encoder.h"
#import "coreml/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([[CoremlEncoder 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
];
CoremlEncoderOutput * outCoreML = [(__bridge id) ctx->data predictionFromMelSegment:inMultiArray error:nil];
MLMultiArray * outMA = outCoreML.output;
memcpy(out, outMA.dataPointer, outMA.count * sizeof(float));
}
#if __cplusplus
}
#endif

View File

@ -14,37 +14,6 @@ if (WHISPER_SUPPORT_SDL2)
message(STATUS "SDL2_LIBRARIES = ${SDL2_LIBRARIES}")
endif()
# common
set(TARGET common)
add_library(${TARGET} STATIC
common.h
common.cpp
)
include(DefaultTargetOptions)
set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
if (WHISPER_SUPPORT_SDL2)
# common-sdl
set(TARGET common-sdl)
add_library(${TARGET} STATIC
common-sdl.h
common-sdl.cpp
)
include(DefaultTargetOptions)
target_include_directories(${TARGET} PUBLIC ${SDL2_INCLUDE_DIRS})
target_link_libraries(${TARGET} PRIVATE ${SDL2_LIBRARIES})
set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
endif()
# examples
include_directories(${CMAKE_CURRENT_SOURCE_DIR})
@ -63,5 +32,4 @@ else()
add_subdirectory(command)
add_subdirectory(bench)
add_subdirectory(talk)
add_subdirectory(talk.llama)
endif()

View File

@ -23,7 +23,7 @@ string(REPLACE "\"" "" NODE_ADDON_API_DIR ${NODE_ADDON_API_DIR})
target_include_directories(${TARGET} PRIVATE ${NODE_ADDON_API_DIR})
#==================================================================
target_link_libraries(${TARGET} ${CMAKE_JS_LIB} common whisper ${CMAKE_THREAD_LIBS_INIT})
target_link_libraries(${TARGET} ${CMAKE_JS_LIB} whisper ${CMAKE_THREAD_LIBS_INIT})
if(MSVC AND CMAKE_JS_NODELIB_DEF AND CMAKE_JS_NODELIB_TARGET)
# Generate node.lib

View File

@ -1,13 +1,15 @@
#include "napi.h"
#include "common.h"
#include "whisper.h"
#include <cstdint>
#include <string>
#include <thread>
#include <vector>
#include <cmath>
#include <cstdint>
#include "napi.h"
#define DR_WAV_IMPLEMENTATION
#include "dr_wav.h"
#include "whisper.h"
struct whisper_params {
int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
@ -42,7 +44,7 @@ struct whisper_params {
std::string model = "../../ggml-large.bin";
std::vector<std::string> fname_inp = {};
std::vector<std::string> fname_out = {};
std::vector<std::string> fname_outp = {};
};
struct whisper_print_user_data {
@ -72,7 +74,7 @@ int timestamp_to_sample(int64_t t, int n_samples) {
return std::max(0, std::min((int) n_samples - 1, (int) ((t*WHISPER_SAMPLE_RATE)/100)));
}
void whisper_print_segment_callback(struct whisper_context * ctx, struct whisper_state * state, int n_new, void * user_data) {
void whisper_print_segment_callback(struct whisper_context * ctx, int n_new, void * user_data) {
const auto & params = *((whisper_print_user_data *) user_data)->params;
const auto & pcmf32s = *((whisper_print_user_data *) user_data)->pcmf32s;
@ -141,6 +143,7 @@ void whisper_print_segment_callback(struct whisper_context * ctx, struct whisper
}
int run(whisper_params &params, std::vector<std::vector<std::string>> &result) {
if (params.fname_inp.empty()) {
fprintf(stderr, "error: no input files specified\n");
return 2;
@ -178,14 +181,91 @@ int run(whisper_params &params, std::vector<std::vector<std::string>> &result) {
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];
const auto fname_outp = f < (int)params.fname_outp.size() && !params.fname_outp[f].empty() ? params.fname_outp[f] : params.fname_inp[f];
std::vector<float> pcmf32; // mono-channel F32 PCM
std::vector<std::vector<float>> pcmf32s; // stereo-channel F32 PCM
if (!::read_wav(fname_inp, pcmf32, pcmf32s, params.diarize)) {
fprintf(stderr, "error: failed to read WAV file '%s'\n", fname_inp.c_str());
continue;
// WAV input
{
drwav wav;
std::vector<uint8_t> wav_data; // used for pipe input from stdin
if (fname_inp == "-") {
{
uint8_t buf[1024];
while (true)
{
const size_t n = fread(buf, 1, sizeof(buf), stdin);
if (n == 0) {
break;
}
wav_data.insert(wav_data.end(), buf, buf + n);
}
}
if (drwav_init_memory(&wav, wav_data.data(), wav_data.size(), nullptr) == false) {
fprintf(stderr, "error: failed to open WAV file from stdin\n");
return 4;
}
fprintf(stderr, "%s: read %zu bytes from stdin\n", __func__, wav_data.size());
}
else if (drwav_init_file(&wav, fname_inp.c_str(), nullptr) == false) {
fprintf(stderr, "error: failed to open '%s' as WAV file\n", fname_inp.c_str());
return 5;
}
if (wav.channels != 1 && wav.channels != 2) {
fprintf(stderr, "error: WAV file '%s' must be mono or stereo\n", fname_inp.c_str());
return 6;
}
if (params.diarize && wav.channels != 2 && params.no_timestamps == false) {
fprintf(stderr, "error: WAV file '%s' must be stereo for diarization and timestamps have to be enabled\n", fname_inp.c_str());
return 6;
}
if (wav.sampleRate != WHISPER_SAMPLE_RATE) {
fprintf(stderr, "error: WAV file '%s' must be %i kHz\n", fname_inp.c_str(), WHISPER_SAMPLE_RATE/1000);
return 8;
}
if (wav.bitsPerSample != 16) {
fprintf(stderr, "error: WAV file '%s' must be 16-bit\n", fname_inp.c_str());
return 9;
}
const uint64_t n = wav_data.empty() ? wav.totalPCMFrameCount : wav_data.size()/(wav.channels*wav.bitsPerSample/8);
std::vector<int16_t> pcm16;
pcm16.resize(n*wav.channels);
drwav_read_pcm_frames_s16(&wav, n, pcm16.data());
drwav_uninit(&wav);
// convert to mono, float
pcmf32.resize(n);
if (wav.channels == 1) {
for (uint64_t i = 0; i < n; i++) {
pcmf32[i] = float(pcm16[i])/32768.0f;
}
} else {
for (uint64_t i = 0; i < n; i++) {
pcmf32[i] = float(pcm16[2*i] + pcm16[2*i + 1])/65536.0f;
}
}
if (params.diarize) {
// convert to stereo, float
pcmf32s.resize(2);
pcmf32s[0].resize(n);
pcmf32s[1].resize(n);
for (uint64_t i = 0; i < n; i++) {
pcmf32s[0][i] = float(pcm16[2*i])/32768.0f;
pcmf32s[1][i] = float(pcm16[2*i + 1])/32768.0f;
}
}
}
// print system information
@ -260,7 +340,7 @@ int run(whisper_params &params, std::vector<std::vector<std::string>> &result) {
{
static bool is_aborted = false; // NOTE: this should be atomic to avoid data race
wparams.encoder_begin_callback = [](struct whisper_context * /*ctx*/, struct whisper_state * /*state*/, void * user_data) {
wparams.encoder_begin_callback = [](struct whisper_context * /*ctx*/, void * user_data) {
bool is_aborted = *(bool*)user_data;
return !is_aborted;
};
@ -292,64 +372,51 @@ int run(whisper_params &params, std::vector<std::vector<std::string>> &result) {
return 0;
}
class Worker : public Napi::AsyncWorker {
public:
Worker(Napi::Function& callback, whisper_params params)
: Napi::AsyncWorker(callback), params(params) {}
void Execute() override {
run(params, result);
}
void OnOK() override {
Napi::HandleScope scope(Env());
Napi::Object res = Napi::Array::New(Env(), result.size());
for (uint64_t i = 0; i < result.size(); ++i) {
Napi::Object tmp = Napi::Array::New(Env(), 3);
for (uint64_t j = 0; j < 3; ++j) {
tmp[j] = Napi::String::New(Env(), result[i][j]);
}
res[i] = tmp;
Napi::Object whisper(const Napi::CallbackInfo& info) {
Napi::Env env = info.Env();
if (info.Length() <= 0 || !info[0].IsObject()) {
Napi::TypeError::New(env, "object expected").ThrowAsJavaScriptException();
}
Callback().Call({Env().Null(), res});
}
whisper_params params;
std::vector<std::vector<std::string>> result;
private:
whisper_params params;
std::vector<std::vector<std::string>> result;
};
Napi::Object whisper_params = info[0].As<Napi::Object>();
std::string language = whisper_params.Get("language").As<Napi::String>();
std::string model = whisper_params.Get("model").As<Napi::String>();
std::string input = whisper_params.Get("fname_inp").As<Napi::String>();
params.language = language;
params.model = model;
params.fname_inp.emplace_back(input);
// run model
run(params, result);
Napi::Value whisper(const Napi::CallbackInfo& info) {
Napi::Env env = info.Env();
if (info.Length() <= 0 || !info[0].IsObject()) {
Napi::TypeError::New(env, "object expected").ThrowAsJavaScriptException();
}
whisper_params params;
fprintf(stderr, "RESULT:\n");
for (auto sentence:result) {
fprintf(stderr, "t0: %s, t1: %s, content: %s \n",
sentence[0].c_str(), sentence[1].c_str(), sentence[2].c_str());
}
Napi::Object whisper_params = info[0].As<Napi::Object>();
std::string language = whisper_params.Get("language").As<Napi::String>();
std::string model = whisper_params.Get("model").As<Napi::String>();
std::string input = whisper_params.Get("fname_inp").As<Napi::String>();
Napi::Object res = Napi::Array::New(env, result.size());
for (uint64_t i = 0; i < result.size(); ++i) {
Napi::Object tmp = Napi::Array::New(env, 3);
for (uint64_t j = 0; j < 3; ++j) {
tmp[j] = Napi::String::New(env, result[i][j]);
}
res[i] = tmp;
}
params.language = language;
params.model = model;
params.fname_inp.emplace_back(input);
Napi::Function callback = info[1].As<Napi::Function>();
Worker* worker = new Worker(callback, params);
worker->Queue();
return env.Undefined();
return res;
}
Napi::Object Init(Napi::Env env, Napi::Object exports) {
exports.Set(
Napi::String::New(env, "whisper"),
Napi::Function::New(env, whisper)
);
return exports;
exports.Set(
Napi::String::New(env, "whisper"),
Napi::Function::New(env, whisper)
);
return exports;
}
NODE_API_MODULE(whisper, Init);

View File

@ -1,36 +1,27 @@
const path = require("path");
const { whisper } = require(path.join(
__dirname,
"../../build/Release/whisper-addon"
));
const { promisify } = require("util");
const whisperAsync = promisify(whisper);
const path = require('path');
const { whisper } = require(path.join(__dirname, '../../build/Release/whisper-addon'));
const whisperParams = {
language: "en",
model: path.join(__dirname, "../../models/ggml-base.en.bin"),
fname_inp: "../../samples/jfk.wav",
language: 'en',
model: path.join(__dirname, '../../models/ggml-base.en.bin'),
fname_inp: '',
};
const arguments = process.argv.slice(2);
const params = Object.fromEntries(
arguments.reduce((pre, item) => {
if (item.startsWith("--")) {
return [...pre, item.slice(2).split("=")];
}
return pre;
}, [])
arguments.reduce((pre, item) => {
if (item.startsWith("--")) {
return [...pre, item.slice(2).split("=")];
}
return pre;
}, []),
);
for (const key in params) {
if (whisperParams.hasOwnProperty(key)) {
whisperParams[key] = params[key];
}
if (whisperParams.hasOwnProperty(key)) {
whisperParams[key] = params[key];
}
}
console.log("whisperParams =", whisperParams);
whisperAsync(whisperParams).then((result) => {
console.log(`Result from whisper: ${result}`);
});
console.log('whisperParams =', whisperParams);
console.log(whisper(whisperParams));

View File

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

634
examples/chess/chess.cpp Normal file
View File

@ -0,0 +1,634 @@
// Input chess moves via voice
//
#include "common.h"
#include "whisper.h"
#include <SDL.h>
#include <SDL_audio.h>
#include <atomic>
#include <cassert>
#include <cstdio>
#include <string>
#include <thread>
#include <vector>
#include <fstream>
#include <mutex>
// 500 -> 00:05.000
// 6000 -> 01:00.000
std::string to_timestamp(int64_t t) {
int64_t sec = t/100;
int64_t msec = t - sec*100;
int64_t min = sec/60;
sec = sec - min*60;
char buf[32];
snprintf(buf, sizeof(buf), "%02d:%02d.%03d", (int) min, (int) sec, (int) msec);
return std::string(buf);
}
// command-line parameters
struct whisper_params {
int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
int32_t step_ms = 3000;
int32_t length_ms = 10000;
int32_t keep_ms = 200;
int32_t capture_id = -1;
int32_t max_tokens = 32;
int32_t audio_ctx = 0;
float vad_thold = 0.6f;
float freq_thold = 100.0f;
bool translate = false;
bool print_special = false;
bool no_context = true;
bool no_timestamps = false;
std::string language = "en";
std::string model = "models/ggml-base.en.bin";
std::string fname_inp;
};
void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
for (int i = 1; i < argc; i++) {
std::string arg = argv[i];
if (arg == "-h" || arg == "--help") {
whisper_print_usage(argc, argv, params);
exit(0);
}
else if (arg == "-t" || arg == "--threads") { params.n_threads = std::stoi(argv[++i]); }
else if ( arg == "--step") { params.step_ms = std::stoi(argv[++i]); }
else if ( arg == "--length") { params.length_ms = std::stoi(argv[++i]); }
else if ( arg == "--keep") { params.keep_ms = std::stoi(argv[++i]); }
else if (arg == "-c" || arg == "--capture") { params.capture_id = std::stoi(argv[++i]); }
else if (arg == "-mt" || arg == "--max-tokens") { params.max_tokens = std::stoi(argv[++i]); }
else if (arg == "-ac" || arg == "--audio-ctx") { params.audio_ctx = std::stoi(argv[++i]); }
else if (arg == "-vth" || arg == "--vad-thold") { params.vad_thold = std::stof(argv[++i]); }
else if (arg == "-fth" || arg == "--freq-thold") { params.freq_thold = std::stof(argv[++i]); }
else if (arg == "-tr" || arg == "--translate") { params.translate = true; }
else if (arg == "-ps" || arg == "--print-special") { params.print_special = true; }
else if (arg == "-kc" || arg == "--keep-context") { params.no_context = false; }
else if (arg == "-l" || arg == "--language") { params.language = argv[++i]; }
else if (arg == "-m" || arg == "--model") { params.model = argv[++i]; }
else if (arg == "-f" || arg == "--file") { params.fname_inp = argv[++i]; }
else {
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
whisper_print_usage(argc, argv, params);
exit(0);
}
}
return true;
}
void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & params) {
fprintf(stderr, "\n");
fprintf(stderr, "usage: %s [options]\n", argv[0]);
fprintf(stderr, "\n");
fprintf(stderr, "options:\n");
fprintf(stderr, " -h, --help [default] show this help message and exit\n");
fprintf(stderr, " -t N, --threads N [%-7d] number of threads to use during computation\n", params.n_threads);
fprintf(stderr, " --step N [%-7d] audio step size in milliseconds\n", params.step_ms);
fprintf(stderr, " --length N [%-7d] audio length in milliseconds\n", params.length_ms);
fprintf(stderr, " --keep N [%-7d] audio to keep from previous step in ms\n", params.keep_ms);
fprintf(stderr, " -c ID, --capture ID [%-7d] capture device ID\n", params.capture_id);
fprintf(stderr, " -mt N, --max-tokens N [%-7d] maximum number of tokens per audio chunk\n", params.max_tokens);
fprintf(stderr, " -ac N, --audio-ctx N [%-7d] audio context size (0 - all)\n", params.audio_ctx);
fprintf(stderr, " -vth N, --vad-thold N [%-7.2f] voice activity detection threshold\n", params.vad_thold);
fprintf(stderr, " -fth N, --freq-thold N [%-7.2f] high-pass frequency cutoff\n", params.freq_thold);
fprintf(stderr, " -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] input WAV file path\n", params.fname_inp.c_str());
fprintf(stderr, "\n");
}
//
// SDL Audio capture
//
class audio_async {
public:
audio_async(int len_ms);
~audio_async();
bool init(int capture_id, int sample_rate);
// start capturing audio via the provided SDL callback
// keep last len_ms seconds of audio in a circular buffer
bool resume();
bool pause();
bool clear();
// callback to be called by SDL
void callback(uint8_t * stream, int len);
// get audio data from the circular buffer
void get(int ms, std::vector<float> & audio);
private:
SDL_AudioDeviceID m_dev_id_in = 0;
int m_len_ms = 0;
int m_sample_rate = 0;
std::atomic_bool m_running;
std::mutex m_mutex;
std::vector<float> m_audio;
std::vector<float> m_audio_new;
size_t m_audio_pos = 0;
size_t m_audio_len = 0;
};
audio_async::audio_async(int len_ms) {
m_len_ms = len_ms;
m_running = false;
}
audio_async::~audio_async() {
if (m_dev_id_in) {
SDL_CloseAudioDevice(m_dev_id_in);
}
}
bool audio_async::init(int capture_id, int sample_rate) {
SDL_LogSetPriority(SDL_LOG_CATEGORY_APPLICATION, SDL_LOG_PRIORITY_INFO);
if (SDL_Init(SDL_INIT_AUDIO) < 0) {
SDL_LogError(SDL_LOG_CATEGORY_APPLICATION, "Couldn't initialize SDL: %s\n", SDL_GetError());
return false;
}
SDL_SetHintWithPriority(SDL_HINT_AUDIO_RESAMPLING_MODE, "medium", SDL_HINT_OVERRIDE);
{
int nDevices = SDL_GetNumAudioDevices(SDL_TRUE);
fprintf(stderr, "%s: found %d capture devices:\n", __func__, nDevices);
for (int i = 0; i < nDevices; i++) {
fprintf(stderr, "%s: - Capture device #%d: '%s'\n", __func__, i, SDL_GetAudioDeviceName(i, SDL_TRUE));
}
}
SDL_AudioSpec capture_spec_requested;
SDL_AudioSpec capture_spec_obtained;
SDL_zero(capture_spec_requested);
SDL_zero(capture_spec_obtained);
capture_spec_requested.freq = sample_rate;
capture_spec_requested.format = AUDIO_F32;
capture_spec_requested.channels = 1;
capture_spec_requested.samples = 1024;
capture_spec_requested.callback = [](void * userdata, uint8_t * stream, int len) {
audio_async * audio = (audio_async *) userdata;
audio->callback(stream, len);
};
capture_spec_requested.userdata = this;
if (capture_id >= 0) {
fprintf(stderr, "%s: attempt to open capture device %d : '%s' ...\n", __func__, capture_id, SDL_GetAudioDeviceName(capture_id, SDL_TRUE));
m_dev_id_in = SDL_OpenAudioDevice(SDL_GetAudioDeviceName(capture_id, SDL_TRUE), SDL_TRUE, &capture_spec_requested, &capture_spec_obtained, 0);
} else {
fprintf(stderr, "%s: attempt to open default capture device ...\n", __func__);
m_dev_id_in = SDL_OpenAudioDevice(nullptr, SDL_TRUE, &capture_spec_requested, &capture_spec_obtained, 0);
}
if (!m_dev_id_in) {
fprintf(stderr, "%s: couldn't open an audio device for capture: %s!\n", __func__, SDL_GetError());
m_dev_id_in = 0;
return false;
} else {
fprintf(stderr, "%s: obtained spec for input device (SDL Id = %d):\n", __func__, m_dev_id_in);
fprintf(stderr, "%s: - sample rate: %d\n", __func__, capture_spec_obtained.freq);
fprintf(stderr, "%s: - format: %d (required: %d)\n", __func__, capture_spec_obtained.format,
capture_spec_requested.format);
fprintf(stderr, "%s: - channels: %d (required: %d)\n", __func__, capture_spec_obtained.channels,
capture_spec_requested.channels);
fprintf(stderr, "%s: - samples per frame: %d\n", __func__, capture_spec_obtained.samples);
}
m_sample_rate = capture_spec_obtained.freq;
m_audio.resize((m_sample_rate*m_len_ms)/1000);
return true;
}
bool audio_async::resume() {
if (!m_dev_id_in) {
fprintf(stderr, "%s: no audio device to resume!\n", __func__);
return false;
}
if (m_running) {
fprintf(stderr, "%s: already running!\n", __func__);
return false;
}
SDL_PauseAudioDevice(m_dev_id_in, 0);
m_running = true;
return true;
}
bool audio_async::pause() {
if (!m_dev_id_in) {
fprintf(stderr, "%s: no audio device to pause!\n", __func__);
return false;
}
if (!m_running) {
fprintf(stderr, "%s: already paused!\n", __func__);
return false;
}
SDL_PauseAudioDevice(m_dev_id_in, 1);
m_running = false;
return true;
}
bool audio_async::clear() {
if (!m_dev_id_in) {
fprintf(stderr, "%s: no audio device to clear!\n", __func__);
return false;
}
if (!m_running) {
fprintf(stderr, "%s: not running!\n", __func__);
return false;
}
{
std::lock_guard<std::mutex> lock(m_mutex);
m_audio_pos = 0;
m_audio_len = 0;
}
return true;
}
// callback to be called by SDL
void audio_async::callback(uint8_t * stream, int len) {
if (!m_running) {
return;
}
const size_t n_samples = len / sizeof(float);
m_audio_new.resize(n_samples);
memcpy(m_audio_new.data(), stream, n_samples * sizeof(float));
//fprintf(stderr, "%s: %zu samples, pos %zu, len %zu\n", __func__, n_samples, m_audio_pos, m_audio_len);
{
std::lock_guard<std::mutex> lock(m_mutex);
if (m_audio_pos + n_samples > m_audio.size()) {
const size_t n0 = m_audio.size() - m_audio_pos;
memcpy(&m_audio[m_audio_pos], stream, n0 * sizeof(float));
memcpy(&m_audio[0], &stream[n0], (n_samples - n0) * sizeof(float));
m_audio_pos = (m_audio_pos + n_samples) % m_audio.size();
m_audio_len = m_audio.size();
} else {
memcpy(&m_audio[m_audio_pos], stream, n_samples * sizeof(float));
m_audio_pos = (m_audio_pos + n_samples) % m_audio.size();
m_audio_len = std::min(m_audio_len + n_samples, m_audio.size());
}
}
}
void audio_async::get(int ms, std::vector<float> & result) {
if (!m_dev_id_in) {
fprintf(stderr, "%s: no audio device to get audio from!\n", __func__);
return;
}
if (!m_running) {
fprintf(stderr, "%s: not running!\n", __func__);
return;
}
result.clear();
{
std::lock_guard<std::mutex> lock(m_mutex);
if (ms <= 0) {
ms = m_len_ms;
}
size_t n_samples = (m_sample_rate * ms) / 1000;
if (n_samples > m_audio_len) {
n_samples = m_audio_len;
}
result.resize(n_samples);
int s0 = m_audio_pos - n_samples;
if (s0 < 0) {
s0 += m_audio.size();
}
if (s0 + n_samples > m_audio.size()) {
const size_t n0 = m_audio.size() - s0;
memcpy(result.data(), &m_audio[s0], n0 * sizeof(float));
memcpy(&result[n0], &m_audio[0], (n_samples - n0) * sizeof(float));
} else {
memcpy(result.data(), &m_audio[s0], n_samples * sizeof(float));
}
}
}
///////////////////////////
int main(int argc, char ** argv) {
whisper_params params;
if (whisper_params_parse(argc, argv, params) == false) {
return 1;
}
params.keep_ms = std::min(params.keep_ms, params.step_ms);
params.length_ms = std::max(params.length_ms, params.step_ms);
const int n_samples_step = (1e-3*params.step_ms )*WHISPER_SAMPLE_RATE;
const int n_samples_len = (1e-3*params.length_ms)*WHISPER_SAMPLE_RATE;
const int n_samples_keep = (1e-3*params.keep_ms )*WHISPER_SAMPLE_RATE;
const int n_samples_30s = (1e-3*30000.0 )*WHISPER_SAMPLE_RATE;
const bool use_vad = n_samples_step <= 0; // sliding window mode uses VAD
const int n_new_line = !use_vad ? std::max(1, params.length_ms / params.step_ms - 1) : 1; // number of steps to print new line
params.no_timestamps = !use_vad;
params.no_context |= use_vad;
params.max_tokens = 0;
// init audio
audio_async audio(params.length_ms);
if (!audio.init(params.capture_id, WHISPER_SAMPLE_RATE)) {
fprintf(stderr, "%s: audio.init() failed!\n", __func__);
return 1;
}
audio.resume();
// whisper init
if (whisper_lang_id(params.language.c_str()) == -1) {
fprintf(stderr, "error: unknown language '%s'\n", params.language.c_str());
whisper_print_usage(argc, argv, params);
exit(0);
}
struct whisper_context * ctx = whisper_init_from_file(params.model.c_str());
std::vector<float> pcmf32 (n_samples_30s, 0.0f);
std::vector<float> pcmf32_old;
std::vector<float> pcmf32_new(n_samples_30s, 0.0f);
std::vector<whisper_token> prompt_tokens;
// print some info about the processing
{
fprintf(stderr, "\n");
if (!whisper_is_multilingual(ctx)) {
if (params.language != "en" || params.translate) {
params.language = "en";
params.translate = false;
fprintf(stderr, "%s: WARNING: model is not multilingual, ignoring language and translation options\n", __func__);
}
}
fprintf(stderr, "%s: processing %d samples (step = %.1f sec / len = %.1f sec / keep = %.1f sec), %d threads, lang = %s, task = %s, timestamps = %d ...\n",
__func__,
n_samples_step,
float(n_samples_step)/WHISPER_SAMPLE_RATE,
float(n_samples_len )/WHISPER_SAMPLE_RATE,
float(n_samples_keep)/WHISPER_SAMPLE_RATE,
params.n_threads,
params.language.c_str(),
params.translate ? "translate" : "transcribe",
params.no_timestamps ? 0 : 1);
if (!use_vad) {
fprintf(stderr, "%s: n_new_line = %d, no_context = %d\n", __func__, n_new_line, params.no_context);
} else {
fprintf(stderr, "%s: using VAD, will transcribe on speech activity\n", __func__);
}
fprintf(stderr, "\n");
}
int n_iter = 0;
bool is_running = true;
printf("[Start speaking]");
fflush(stdout);
auto t_last = std::chrono::high_resolution_clock::now();
const auto t_start = t_last;
// main audio loop
while (is_running) {
// handle Ctrl + C
{
SDL_Event event;
while (SDL_PollEvent(&event)) {
switch (event.type) {
case SDL_QUIT:
{
is_running = false;
} break;
default:
break;
}
}
if (!is_running) {
break;
}
}
if (!is_running) {
break;
}
// process new audio
if (!use_vad) {
while (true) {
audio.get(params.step_ms, pcmf32_new);
if ((int) pcmf32_new.size() > 2*n_samples_step) {
fprintf(stderr, "\n\n%s: WARNING: cannot process audio fast enough, dropping audio ...\n\n", __func__);
audio.clear();
continue;
}
if ((int) pcmf32_new.size() >= n_samples_step) {
audio.clear();
break;
}
SDL_Delay(1);
}
const int n_samples_new = pcmf32_new.size();
// take up to params.length_ms audio from previous iteration
const int n_samples_take = std::min((int) pcmf32_old.size(), std::max(0, n_samples_keep + n_samples_len - n_samples_new));
//printf("processing: take = %d, new = %d, old = %d\n", n_samples_take, n_samples_new, (int) pcmf32_old.size());
pcmf32.resize(n_samples_new + n_samples_take);
for (int i = 0; i < n_samples_take; i++) {
pcmf32[i] = pcmf32_old[pcmf32_old.size() - n_samples_take + i];
}
memcpy(pcmf32.data() + n_samples_take, pcmf32_new.data(), n_samples_new*sizeof(float));
pcmf32_old = pcmf32;
} else {
const auto t_now = std::chrono::high_resolution_clock::now();
const auto t_diff = std::chrono::duration_cast<std::chrono::milliseconds>(t_now - t_last).count();
if (t_diff < 2000) {
std::this_thread::sleep_for(std::chrono::milliseconds(100));
continue;
}
audio.get(2000, pcmf32_new);
if (vad_simple(pcmf32_new, WHISPER_SAMPLE_RATE, 1000, params.vad_thold, params.freq_thold, false)) {
audio.get(params.length_ms, pcmf32);
} else {
std::this_thread::sleep_for(std::chrono::milliseconds(100));
continue;
}
t_last = t_now;
}
// run the inference
{
whisper_full_params wparams = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
wparams.print_progress = false;
wparams.print_special = params.print_special;
wparams.print_realtime = false;
wparams.print_timestamps = !params.no_timestamps;
wparams.translate = params.translate;
wparams.no_context = true;
wparams.single_segment = !use_vad;
wparams.max_tokens = params.max_tokens;
wparams.language = params.language.c_str();
wparams.n_threads = params.n_threads;
wparams.audio_ctx = params.audio_ctx;
// disable temperature fallback
wparams.temperature_inc = -1.0f;
wparams.prompt_tokens = params.no_context ? nullptr : prompt_tokens.data();
wparams.prompt_n_tokens = params.no_context ? 0 : prompt_tokens.size();
if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
fprintf(stderr, "%s: failed to process audio\n", argv[0]);
return 6;
}
// print result;
{
if (!use_vad) {
printf("\33[2K\r");
// print long empty line to clear the previous line
printf("%s", std::string(100, ' ').c_str());
printf("\33[2K\r");
} else {
const int64_t t1 = (t_last - t_start).count()/1000000;
const int64_t t0 = std::max(0.0, t1 - pcmf32.size()*1000.0/WHISPER_SAMPLE_RATE);
printf("\n");
printf("### Transcription %d START | t0 = %d ms | t1 = %d ms\n", n_iter, (int) t0, (int) t1);
printf("\n");
}
const int n_segments = whisper_full_n_segments(ctx);
for (int i = 0; i < n_segments; ++i) {
const char * text = whisper_full_get_segment_text(ctx, i);
if (params.no_timestamps) {
printf("%s", text);
fflush(stdout);
} else {
const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
printf ("[%s --> %s] %s\n", to_timestamp(t0).c_str(), to_timestamp(t1).c_str(), text);
}
}
if (use_vad){
printf("\n");
printf("### Transcription %d END\n", n_iter);
}
}
++n_iter;
if (!use_vad && (n_iter % n_new_line) == 0) {
printf("\n");
// keep part of the audio for next iteration to try to mitigate word boundary issues
pcmf32_old = std::vector<float>(pcmf32.end() - n_samples_keep, pcmf32.end());
// Add tokens of the last full length segment as the prompt
if (!params.no_context) {
prompt_tokens.clear();
const int n_segments = whisper_full_n_segments(ctx);
for (int i = 0; i < n_segments; ++i) {
const int token_count = whisper_full_n_tokens(ctx, i);
for (int j = 0; j < token_count; ++j) {
prompt_tokens.push_back(whisper_full_get_token_id(ctx, i, j));
}
}
}
}
}
}
audio.pause();
whisper_print_timings(ctx);
whisper_free(ctx);
return 0;
}

View File

@ -11,7 +11,6 @@ add_executable(${TARGET}
include(DefaultTargetOptions)
target_link_libraries(${TARGET} PRIVATE
common
whisper
)

View File

@ -1,5 +1,4 @@
#include "ggml.h"
#include "common.h"
#include "whisper.h"
#include <emscripten.h>
@ -28,6 +27,24 @@ std::string g_transcribed = "";
std::vector<float> g_pcmf32;
static std::string trim(const std::string & s) {
std::regex e("^\\s+|\\s+$");
return std::regex_replace(s, e, "");
}
static void high_pass_filter(std::vector<float> & data, float cutoff, float sample_rate) {
const float rc = 1.0f / (2.0f * M_PI * cutoff);
const float dt = 1.0f / sample_rate;
const float alpha = dt / (rc + dt);
float y = data[0];
for (size_t i = 1; i < data.size(); i++) {
y = alpha * (y + data[i] - data[i - 1]);
data[i] = y;
}
}
// compute similarity between two strings using Levenshtein distance
static float similarity(const std::string & s0, const std::string & s1) {
const size_t len0 = s0.size() + 1;
@ -58,6 +75,44 @@ void command_set_status(const std::string & status) {
g_status = status;
}
bool command_vad_simple(std::vector<float> & pcmf32, int sample_rate, int last_ms, float vad_thold, float freq_thold, bool verbose) {
const int n_samples = pcmf32.size();
const int n_samples_last = (sample_rate * last_ms) / 1000;
if (n_samples_last >= n_samples) {
// not enough samples - assume no speech
return false;
}
if (freq_thold > 0.0f) {
high_pass_filter(pcmf32, freq_thold, sample_rate);
}
float energy_all = 0.0f;
float energy_last = 0.0f;
for (size_t i = 0; i < n_samples; i++) {
energy_all += fabsf(pcmf32[i]);
if (i >= n_samples - n_samples_last) {
energy_last += fabsf(pcmf32[i]);
}
}
energy_all /= n_samples;
energy_last /= n_samples_last;
if (verbose) {
fprintf(stderr, "%s: energy_all: %f, energy_last: %f, vad_thold: %f, freq_thold: %f\n", __func__, energy_all, energy_last, vad_thold, freq_thold);
}
if (energy_last > vad_thold*energy_all) {
return false;
}
return true;
}
std::string command_transcribe(whisper_context * ctx, const whisper_full_params & wparams, const std::vector<float> & pcmf32, float & prob, int64_t & t_ms) {
const auto t_start = std::chrono::high_resolution_clock::now();
@ -100,7 +155,7 @@ void command_get_audio(int ms, int sample_rate, std::vector<float> & audio) {
const int64_t n_samples = (ms * sample_rate) / 1000;
int64_t n_take = 0;
if (n_samples > (int) g_pcmf32.size()) {
if (g_pcmf32.size() < n_samples) {
n_take = g_pcmf32.size();
} else {
n_take = n_samples;
@ -132,6 +187,7 @@ void command_main(size_t index) {
printf("command: using %d threads\n", wparams.n_threads);
bool is_running = true;
bool have_prompt = false;
bool ask_prompt = true;
bool print_energy = false;
@ -177,7 +233,7 @@ void command_main(size_t index) {
{
command_get_audio(vad_ms, WHISPER_SAMPLE_RATE, pcmf32_cur);
if (::vad_simple(pcmf32_cur, WHISPER_SAMPLE_RATE, 1000, vad_thold, freq_thold, print_energy)) {
if (command_vad_simple(pcmf32_cur, WHISPER_SAMPLE_RATE, 1000, vad_thold, freq_thold, print_energy)) {
fprintf(stdout, "%s: Speech detected! Processing ...\n", __func__);
command_set_status("Speech detected! Processing ...");

View File

@ -5,5 +5,6 @@ if (WHISPER_SUPPORT_SDL2)
include(DefaultTargetOptions)
target_link_libraries(${TARGET} PRIVATE common common-sdl whisper ${CMAKE_THREAD_LIBS_INIT})
target_include_directories(${TARGET} PRIVATE ${SDL2_INCLUDE_DIRS})
target_link_libraries(${TARGET} PRIVATE whisper ${SDL2_LIBRARIES} ${CMAKE_THREAD_LIBS_INIT})
endif ()

View File

@ -6,10 +6,11 @@
// ref: https://github.com/ggerganov/whisper.cpp/issues/171
//
#include "common.h"
#include "common-sdl.h"
#include "whisper.h"
#include <SDL.h>
#include <SDL_audio.h>
#include <sstream>
#include <cassert>
#include <cstdio>
@ -109,6 +110,309 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
fprintf(stderr, "\n");
}
//
// SDL Audio capture
//
class audio_async {
public:
audio_async(int len_ms);
~audio_async();
bool init(int capture_id, int sample_rate);
// start capturing audio via the provided SDL callback
// keep last len_ms seconds of audio in a circular buffer
bool resume();
bool pause();
bool clear();
// callback to be called by SDL
void callback(uint8_t * stream, int len);
// get audio data from the circular buffer
void get(int ms, std::vector<float> & audio);
private:
SDL_AudioDeviceID m_dev_id_in = 0;
int m_len_ms = 0;
int m_sample_rate = 0;
bool m_running = false;
std::mutex m_mutex;
std::vector<float> m_audio;
std::vector<float> m_audio_new;
size_t m_audio_pos = 0;
size_t m_audio_len = 0;
};
audio_async::audio_async(int len_ms) {
m_len_ms = len_ms;
}
audio_async::~audio_async() {
if (m_dev_id_in) {
SDL_CloseAudioDevice(m_dev_id_in);
}
}
bool audio_async::init(int capture_id, int sample_rate) {
SDL_LogSetPriority(SDL_LOG_CATEGORY_APPLICATION, SDL_LOG_PRIORITY_INFO);
if (SDL_Init(SDL_INIT_AUDIO) < 0) {
SDL_LogError(SDL_LOG_CATEGORY_APPLICATION, "Couldn't initialize SDL: %s\n", SDL_GetError());
return false;
}
SDL_SetHintWithPriority(SDL_HINT_AUDIO_RESAMPLING_MODE, "medium", SDL_HINT_OVERRIDE);
{
int nDevices = SDL_GetNumAudioDevices(SDL_TRUE);
fprintf(stderr, "%s: found %d capture devices:\n", __func__, nDevices);
for (int i = 0; i < nDevices; i++) {
fprintf(stderr, "%s: - Capture device #%d: '%s'\n", __func__, i, SDL_GetAudioDeviceName(i, SDL_TRUE));
}
}
SDL_AudioSpec capture_spec_requested;
SDL_AudioSpec capture_spec_obtained;
SDL_zero(capture_spec_requested);
SDL_zero(capture_spec_obtained);
capture_spec_requested.freq = sample_rate;
capture_spec_requested.format = AUDIO_F32;
capture_spec_requested.channels = 1;
capture_spec_requested.samples = 1024;
capture_spec_requested.callback = [](void * userdata, uint8_t * stream, int len) {
audio_async * audio = (audio_async *) userdata;
audio->callback(stream, len);
};
capture_spec_requested.userdata = this;
if (capture_id >= 0) {
fprintf(stderr, "%s: attempt to open capture device %d : '%s' ...\n", __func__, capture_id, SDL_GetAudioDeviceName(capture_id, SDL_TRUE));
m_dev_id_in = SDL_OpenAudioDevice(SDL_GetAudioDeviceName(capture_id, SDL_TRUE), SDL_TRUE, &capture_spec_requested, &capture_spec_obtained, 0);
} else {
fprintf(stderr, "%s: attempt to open default capture device ...\n", __func__);
m_dev_id_in = SDL_OpenAudioDevice(nullptr, SDL_TRUE, &capture_spec_requested, &capture_spec_obtained, 0);
}
if (!m_dev_id_in) {
fprintf(stderr, "%s: couldn't open an audio device for capture: %s!\n", __func__, SDL_GetError());
m_dev_id_in = 0;
return false;
} else {
fprintf(stderr, "%s: obtained spec for input device (SDL Id = %d):\n", __func__, m_dev_id_in);
fprintf(stderr, "%s: - sample rate: %d\n", __func__, capture_spec_obtained.freq);
fprintf(stderr, "%s: - format: %d (required: %d)\n", __func__, capture_spec_obtained.format,
capture_spec_requested.format);
fprintf(stderr, "%s: - channels: %d (required: %d)\n", __func__, capture_spec_obtained.channels,
capture_spec_requested.channels);
fprintf(stderr, "%s: - samples per frame: %d\n", __func__, capture_spec_obtained.samples);
}
m_sample_rate = capture_spec_obtained.freq;
m_audio.resize((m_sample_rate*m_len_ms)/1000);
return true;
}
bool audio_async::resume() {
if (!m_dev_id_in) {
fprintf(stderr, "%s: no audio device to resume!\n", __func__);
return false;
}
if (m_running) {
fprintf(stderr, "%s: already running!\n", __func__);
return false;
}
SDL_PauseAudioDevice(m_dev_id_in, 0);
m_running = true;
return true;
}
bool audio_async::pause() {
if (!m_dev_id_in) {
fprintf(stderr, "%s: no audio device to pause!\n", __func__);
return false;
}
if (!m_running) {
fprintf(stderr, "%s: already paused!\n", __func__);
return false;
}
SDL_PauseAudioDevice(m_dev_id_in, 1);
m_running = false;
return true;
}
bool audio_async::clear() {
if (!m_dev_id_in) {
fprintf(stderr, "%s: no audio device to clear!\n", __func__);
return false;
}
if (!m_running) {
fprintf(stderr, "%s: not running!\n", __func__);
return false;
}
{
std::lock_guard<std::mutex> lock(m_mutex);
m_audio_pos = 0;
m_audio_len = 0;
}
return true;
}
// callback to be called by SDL
void audio_async::callback(uint8_t * stream, int len) {
if (!m_running) {
return;
}
const size_t n_samples = len / sizeof(float);
m_audio_new.resize(n_samples);
memcpy(m_audio_new.data(), stream, n_samples * sizeof(float));
//fprintf(stderr, "%s: %zu samples, pos %zu, len %zu\n", __func__, n_samples, m_audio_pos, m_audio_len);
{
std::lock_guard<std::mutex> lock(m_mutex);
if (m_audio_pos + n_samples > m_audio.size()) {
const size_t n0 = m_audio.size() - m_audio_pos;
memcpy(&m_audio[m_audio_pos], stream, n0 * sizeof(float));
memcpy(&m_audio[0], &stream[n0], (n_samples - n0) * sizeof(float));
m_audio_pos = (m_audio_pos + n_samples) % m_audio.size();
m_audio_len = m_audio.size();
} else {
memcpy(&m_audio[m_audio_pos], stream, n_samples * sizeof(float));
m_audio_pos = (m_audio_pos + n_samples) % m_audio.size();
m_audio_len = std::min(m_audio_len + n_samples, m_audio.size());
}
}
}
void audio_async::get(int ms, std::vector<float> & result) {
if (!m_dev_id_in) {
fprintf(stderr, "%s: no audio device to get audio from!\n", __func__);
return;
}
if (!m_running) {
fprintf(stderr, "%s: not running!\n", __func__);
return;
}
result.clear();
{
std::lock_guard<std::mutex> lock(m_mutex);
if (ms <= 0) {
ms = m_len_ms;
}
size_t n_samples = (m_sample_rate * ms) / 1000;
if (n_samples > m_audio_len) {
n_samples = m_audio_len;
}
result.resize(n_samples);
int s0 = m_audio_pos - n_samples;
if (s0 < 0) {
s0 += m_audio.size();
}
if (s0 + n_samples > m_audio.size()) {
const size_t n0 = m_audio.size() - s0;
memcpy(result.data(), &m_audio[s0], n0 * sizeof(float));
memcpy(&result[n0], &m_audio[0], (n_samples - n0) * sizeof(float));
} else {
memcpy(result.data(), &m_audio[s0], n_samples * sizeof(float));
}
}
}
///////////////////////////
std::string trim(const std::string & s) {
std::regex e("^\\s+|\\s+$");
return std::regex_replace(s, e, "");
}
void high_pass_filter(std::vector<float> & data, float cutoff, float sample_rate) {
const float rc = 1.0f / (2.0f * M_PI * cutoff);
const float dt = 1.0f / sample_rate;
const float alpha = dt / (rc + dt);
float y = data[0];
for (size_t i = 1; i < data.size(); i++) {
y = alpha * (y + data[i] - data[i - 1]);
data[i] = y;
}
}
bool vad_simple(std::vector<float> & pcmf32, int sample_rate, int last_ms, float vad_thold, float freq_thold, bool verbose) {
const int n_samples = pcmf32.size();
const int n_samples_last = (sample_rate * last_ms) / 1000;
if (n_samples_last >= n_samples) {
// not enough samples - assume no speech
return false;
}
if (freq_thold > 0.0f) {
high_pass_filter(pcmf32, freq_thold, sample_rate);
}
float energy_all = 0.0f;
float energy_last = 0.0f;
for (int i = 0; i < n_samples; i++) {
energy_all += fabsf(pcmf32[i]);
if (i >= n_samples - n_samples_last) {
energy_last += fabsf(pcmf32[i]);
}
}
energy_all /= n_samples;
energy_last /= n_samples_last;
if (verbose) {
fprintf(stderr, "%s: energy_all: %f, energy_last: %f, vad_thold: %f, freq_thold: %f\n", __func__, energy_all, energy_last, vad_thold, freq_thold);
}
if (energy_last > vad_thold*energy_all) {
return false;
}
return true;
}
std::string transcribe(whisper_context * ctx, const whisper_params & params, const std::vector<float> & pcmf32, float & prob, int64_t & t_ms) {
const auto t_start = std::chrono::high_resolution_clock::now();
@ -198,7 +502,7 @@ std::vector<std::string> read_allowed_commands(const std::string & fname) {
std::string line;
while (std::getline(ifs, line)) {
line = ::trim(line);
line = trim(line);
if (line.empty()) {
continue;
}
@ -222,6 +526,23 @@ std::vector<std::string> get_words(const std::string &txt) {
return words;
}
// returns true if no exit event was received
bool process_sdl_events() {
SDL_Event event;
while (SDL_PollEvent(&event)) {
switch (event.type) {
case SDL_QUIT:
{
return false;
} break;
default:
break;
}
}
return true;
}
// command-list mode
// guide the transcription to match the most likely command from a provided list
int process_command_list(struct whisper_context * ctx, audio_async &audio, const whisper_params &params) {
@ -313,14 +634,14 @@ int process_command_list(struct whisper_context * ctx, audio_async &audio, const
// main loop
while (is_running) {
// handle Ctrl + C
is_running = sdl_poll_events();
is_running = process_sdl_events();
// delay
std::this_thread::sleep_for(std::chrono::milliseconds(100));
audio.get(2000, pcmf32_cur);
if (::vad_simple(pcmf32_cur, WHISPER_SAMPLE_RATE, 1000, params.vad_thold, params.freq_thold, params.print_energy)) {
if (vad_simple(pcmf32_cur, WHISPER_SAMPLE_RATE, 1000, params.vad_thold, params.freq_thold, params.print_energy)) {
fprintf(stdout, "%s: Speech detected! Processing ...\n", __func__);
const auto t_start = std::chrono::high_resolution_clock::now();
@ -454,7 +775,7 @@ int always_prompt_transcription(struct whisper_context * ctx, audio_async & audi
// main loop
while (is_running) {
// handle Ctrl + C
is_running = sdl_poll_events();
is_running = process_sdl_events();
// delay
std::this_thread::sleep_for(std::chrono::milliseconds(100));
@ -470,7 +791,7 @@ int always_prompt_transcription(struct whisper_context * ctx, audio_async & audi
{
audio.get(2000, pcmf32_cur);
if (::vad_simple(pcmf32_cur, WHISPER_SAMPLE_RATE, 1000, params.vad_thold, params.freq_thold, params.print_energy)) {
if (vad_simple(pcmf32_cur, WHISPER_SAMPLE_RATE, 1000, params.vad_thold, params.freq_thold, params.print_energy)) {
fprintf(stdout, "%s: Speech detected! Processing ...\n", __func__);
int64_t t_ms = 0;
@ -533,7 +854,7 @@ int process_general_transcription(struct whisper_context * ctx, audio_async &aud
// main loop
while (is_running) {
// handle Ctrl + C
is_running = sdl_poll_events();
is_running = process_sdl_events();
// delay
std::this_thread::sleep_for(std::chrono::milliseconds(100));
@ -549,7 +870,7 @@ int process_general_transcription(struct whisper_context * ctx, audio_async &aud
{
audio.get(2000, pcmf32_cur);
if (::vad_simple(pcmf32_cur, WHISPER_SAMPLE_RATE, 1000, params.vad_thold, params.freq_thold, params.print_energy)) {
if (vad_simple(pcmf32_cur, WHISPER_SAMPLE_RATE, 1000, params.vad_thold, params.freq_thold, params.print_energy)) {
fprintf(stdout, "%s: Speech detected! Processing ...\n", __func__);
int64_t t_ms = 0;

View File

@ -1,226 +0,0 @@
#include "common-sdl.h"
audio_async::audio_async(int len_ms) {
m_len_ms = len_ms;
m_running = false;
}
audio_async::~audio_async() {
if (m_dev_id_in) {
SDL_CloseAudioDevice(m_dev_id_in);
}
}
bool audio_async::init(int capture_id, int sample_rate) {
SDL_LogSetPriority(SDL_LOG_CATEGORY_APPLICATION, SDL_LOG_PRIORITY_INFO);
if (SDL_Init(SDL_INIT_AUDIO) < 0) {
SDL_LogError(SDL_LOG_CATEGORY_APPLICATION, "Couldn't initialize SDL: %s\n", SDL_GetError());
return false;
}
SDL_SetHintWithPriority(SDL_HINT_AUDIO_RESAMPLING_MODE, "medium", SDL_HINT_OVERRIDE);
{
int nDevices = SDL_GetNumAudioDevices(SDL_TRUE);
fprintf(stderr, "%s: found %d capture devices:\n", __func__, nDevices);
for (int i = 0; i < nDevices; i++) {
fprintf(stderr, "%s: - Capture device #%d: '%s'\n", __func__, i, SDL_GetAudioDeviceName(i, SDL_TRUE));
}
}
SDL_AudioSpec capture_spec_requested;
SDL_AudioSpec capture_spec_obtained;
SDL_zero(capture_spec_requested);
SDL_zero(capture_spec_obtained);
capture_spec_requested.freq = sample_rate;
capture_spec_requested.format = AUDIO_F32;
capture_spec_requested.channels = 1;
capture_spec_requested.samples = 1024;
capture_spec_requested.callback = [](void * userdata, uint8_t * stream, int len) {
audio_async * audio = (audio_async *) userdata;
audio->callback(stream, len);
};
capture_spec_requested.userdata = this;
if (capture_id >= 0) {
fprintf(stderr, "%s: attempt to open capture device %d : '%s' ...\n", __func__, capture_id, SDL_GetAudioDeviceName(capture_id, SDL_TRUE));
m_dev_id_in = SDL_OpenAudioDevice(SDL_GetAudioDeviceName(capture_id, SDL_TRUE), SDL_TRUE, &capture_spec_requested, &capture_spec_obtained, 0);
} else {
fprintf(stderr, "%s: attempt to open default capture device ...\n", __func__);
m_dev_id_in = SDL_OpenAudioDevice(nullptr, SDL_TRUE, &capture_spec_requested, &capture_spec_obtained, 0);
}
if (!m_dev_id_in) {
fprintf(stderr, "%s: couldn't open an audio device for capture: %s!\n", __func__, SDL_GetError());
m_dev_id_in = 0;
return false;
} else {
fprintf(stderr, "%s: obtained spec for input device (SDL Id = %d):\n", __func__, m_dev_id_in);
fprintf(stderr, "%s: - sample rate: %d\n", __func__, capture_spec_obtained.freq);
fprintf(stderr, "%s: - format: %d (required: %d)\n", __func__, capture_spec_obtained.format,
capture_spec_requested.format);
fprintf(stderr, "%s: - channels: %d (required: %d)\n", __func__, capture_spec_obtained.channels,
capture_spec_requested.channels);
fprintf(stderr, "%s: - samples per frame: %d\n", __func__, capture_spec_obtained.samples);
}
m_sample_rate = capture_spec_obtained.freq;
m_audio.resize((m_sample_rate*m_len_ms)/1000);
return true;
}
bool audio_async::resume() {
if (!m_dev_id_in) {
fprintf(stderr, "%s: no audio device to resume!\n", __func__);
return false;
}
if (m_running) {
fprintf(stderr, "%s: already running!\n", __func__);
return false;
}
SDL_PauseAudioDevice(m_dev_id_in, 0);
m_running = true;
return true;
}
bool audio_async::pause() {
if (!m_dev_id_in) {
fprintf(stderr, "%s: no audio device to pause!\n", __func__);
return false;
}
if (!m_running) {
fprintf(stderr, "%s: already paused!\n", __func__);
return false;
}
SDL_PauseAudioDevice(m_dev_id_in, 1);
m_running = false;
return true;
}
bool audio_async::clear() {
if (!m_dev_id_in) {
fprintf(stderr, "%s: no audio device to clear!\n", __func__);
return false;
}
if (!m_running) {
fprintf(stderr, "%s: not running!\n", __func__);
return false;
}
{
std::lock_guard<std::mutex> lock(m_mutex);
m_audio_pos = 0;
m_audio_len = 0;
}
return true;
}
// callback to be called by SDL
void audio_async::callback(uint8_t * stream, int len) {
if (!m_running) {
return;
}
const size_t n_samples = len / sizeof(float);
m_audio_new.resize(n_samples);
memcpy(m_audio_new.data(), stream, n_samples * sizeof(float));
//fprintf(stderr, "%s: %zu samples, pos %zu, len %zu\n", __func__, n_samples, m_audio_pos, m_audio_len);
{
std::lock_guard<std::mutex> lock(m_mutex);
if (m_audio_pos + n_samples > m_audio.size()) {
const size_t n0 = m_audio.size() - m_audio_pos;
memcpy(&m_audio[m_audio_pos], stream, n0 * sizeof(float));
memcpy(&m_audio[0], &stream[n0], (n_samples - n0) * sizeof(float));
m_audio_pos = (m_audio_pos + n_samples) % m_audio.size();
m_audio_len = m_audio.size();
} else {
memcpy(&m_audio[m_audio_pos], stream, n_samples * sizeof(float));
m_audio_pos = (m_audio_pos + n_samples) % m_audio.size();
m_audio_len = std::min(m_audio_len + n_samples, m_audio.size());
}
}
}
void audio_async::get(int ms, std::vector<float> & result) {
if (!m_dev_id_in) {
fprintf(stderr, "%s: no audio device to get audio from!\n", __func__);
return;
}
if (!m_running) {
fprintf(stderr, "%s: not running!\n", __func__);
return;
}
result.clear();
{
std::lock_guard<std::mutex> lock(m_mutex);
if (ms <= 0) {
ms = m_len_ms;
}
size_t n_samples = (m_sample_rate * ms) / 1000;
if (n_samples > m_audio_len) {
n_samples = m_audio_len;
}
result.resize(n_samples);
int s0 = m_audio_pos - n_samples;
if (s0 < 0) {
s0 += m_audio.size();
}
if (s0 + n_samples > m_audio.size()) {
const size_t n0 = m_audio.size() - s0;
memcpy(result.data(), &m_audio[s0], n0 * sizeof(float));
memcpy(&result[n0], &m_audio[0], (n_samples - n0) * sizeof(float));
} else {
memcpy(result.data(), &m_audio[s0], n_samples * sizeof(float));
}
}
}
bool sdl_poll_events() {
SDL_Event event;
while (SDL_PollEvent(&event)) {
switch (event.type) {
case SDL_QUIT:
{
return false;
} break;
default:
break;
}
}
return true;
}

View File

@ -1,50 +0,0 @@
#pragma once
#include <SDL.h>
#include <SDL_audio.h>
#include <atomic>
#include <cstdint>
#include <vector>
#include <mutex>
//
// SDL Audio capture
//
class audio_async {
public:
audio_async(int len_ms);
~audio_async();
bool init(int capture_id, int sample_rate);
// start capturing audio via the provided SDL callback
// keep last len_ms seconds of audio in a circular buffer
bool resume();
bool pause();
bool clear();
// callback to be called by SDL
void callback(uint8_t * stream, int len);
// get audio data from the circular buffer
void get(int ms, std::vector<float> & audio);
private:
SDL_AudioDeviceID m_dev_id_in = 0;
int m_len_ms = 0;
int m_sample_rate = 0;
std::atomic_bool m_running;
std::mutex m_mutex;
std::vector<float> m_audio;
std::vector<float> m_audio_new;
size_t m_audio_pos = 0;
size_t m_audio_len = 0;
};
// Return false if need to quit
bool sdl_poll_events();

View File

@ -1,162 +0,0 @@
#include "common.h"
// third-party utilities
// use your favorite implementations
#define DR_WAV_IMPLEMENTATION
#include "dr_wav.h"
#include <cmath>
#include <regex>
#ifndef M_PI
#define M_PI 3.14159265358979323846
#endif
std::string trim(const std::string & s) {
std::regex e("^\\s+|\\s+$");
return std::regex_replace(s, e, "");
}
std::string replace(const std::string & s, const std::string & from, const std::string & to) {
std::string result = s;
size_t pos = 0;
while ((pos = result.find(from, pos)) != std::string::npos) {
result.replace(pos, from.length(), to);
pos += to.length();
}
return result;
}
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
if (fname == "-") {
{
uint8_t buf[1024];
while (true)
{
const size_t n = fread(buf, 1, sizeof(buf), stdin);
if (n == 0) {
break;
}
wav_data.insert(wav_data.end(), buf, buf + n);
}
}
if (drwav_init_memory(&wav, wav_data.data(), wav_data.size(), nullptr) == false) {
fprintf(stderr, "error: failed to open WAV file from stdin\n");
return false;
}
fprintf(stderr, "%s: read %zu bytes from stdin\n", __func__, wav_data.size());
}
else if (drwav_init_file(&wav, fname.c_str(), nullptr) == false) {
fprintf(stderr, "error: failed to open '%s' as WAV file\n", fname.c_str());
return false;
}
if (wav.channels != 1 && wav.channels != 2) {
fprintf(stderr, "%s: WAV file '%s' must be mono or stereo\n", __func__, fname.c_str());
return false;
}
if (stereo && wav.channels != 2) {
fprintf(stderr, "%s: WAV file '%s' must be stereo for diarization\n", __func__, fname.c_str());
return false;
}
if (wav.sampleRate != COMMON_SAMPLE_RATE) {
fprintf(stderr, "%s: WAV file '%s' must be %i kHz\n", __func__, fname.c_str(), COMMON_SAMPLE_RATE/1000);
return false;
}
if (wav.bitsPerSample != 16) {
fprintf(stderr, "%s: WAV file '%s' must be 16-bit\n", __func__, fname.c_str());
return false;
}
const uint64_t n = wav_data.empty() ? wav.totalPCMFrameCount : wav_data.size()/(wav.channels*wav.bitsPerSample/8);
std::vector<int16_t> pcm16;
pcm16.resize(n*wav.channels);
drwav_read_pcm_frames_s16(&wav, n, pcm16.data());
drwav_uninit(&wav);
// convert to mono, float
pcmf32.resize(n);
if (wav.channels == 1) {
for (uint64_t i = 0; i < n; i++) {
pcmf32[i] = float(pcm16[i])/32768.0f;
}
} else {
for (uint64_t i = 0; i < n; i++) {
pcmf32[i] = float(pcm16[2*i] + pcm16[2*i + 1])/65536.0f;
}
}
if (stereo) {
// convert to stereo, float
pcmf32s.resize(2);
pcmf32s[0].resize(n);
pcmf32s[1].resize(n);
for (uint64_t i = 0; i < n; i++) {
pcmf32s[0][i] = float(pcm16[2*i])/32768.0f;
pcmf32s[1][i] = float(pcm16[2*i + 1])/32768.0f;
}
}
return true;
}
void high_pass_filter(std::vector<float> & data, float cutoff, float sample_rate) {
const float rc = 1.0f / (2.0f * M_PI * cutoff);
const float dt = 1.0f / sample_rate;
const float alpha = dt / (rc + dt);
float y = data[0];
for (size_t i = 1; i < data.size(); i++) {
y = alpha * (y + data[i] - data[i - 1]);
data[i] = y;
}
}
bool vad_simple(std::vector<float> & pcmf32, int sample_rate, int last_ms, float vad_thold, float freq_thold, bool verbose) {
const int n_samples = pcmf32.size();
const int n_samples_last = (sample_rate * last_ms) / 1000;
if (n_samples_last >= n_samples) {
// not enough samples - assume no speech
return false;
}
if (freq_thold > 0.0f) {
high_pass_filter(pcmf32, freq_thold, sample_rate);
}
float energy_all = 0.0f;
float energy_last = 0.0f;
for (int i = 0; i < n_samples; i++) {
energy_all += fabsf(pcmf32[i]);
if (i >= n_samples - n_samples_last) {
energy_last += fabsf(pcmf32[i]);
}
}
energy_all /= n_samples;
energy_last /= n_samples_last;
if (verbose) {
fprintf(stderr, "%s: energy_all: %f, energy_last: %f, vad_thold: %f, freq_thold: %f\n", __func__, energy_all, energy_last, vad_thold, freq_thold);
}
if (energy_last > vad_thold*energy_all) {
return false;
}
return true;
}

View File

@ -1,40 +0,0 @@
#pragma once
// needs to match WHISPER_SAMPLE_RATE
#define COMMON_SAMPLE_RATE 16000
#include <vector>
#include <string>
std::string trim(const std::string & s);
std::string replace(
const std::string & s,
const std::string & from,
const std::string & to);
// 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
bool read_wav(
const std::string & fname,
std::vector<float> & pcmf32,
std::vector<std::vector<float>> & pcmf32s,
bool stereo);
// Apply a high-pass frequency filter to PCM audio
// Suppresses frequencies below cutoff Hz
void high_pass_filter(
std::vector<float> & data,
float cutoff,
float sample_rate);
// Basic voice activity detection (VAD) using audio energy adaptive threshold
bool vad_simple(
std::vector<float> & pcmf32,
int sample_rate,
int last_ms,
float vad_thold,
float freq_thold,
bool verbose);

View File

@ -3,4 +3,4 @@ add_executable(${TARGET} main.cpp)
include(DefaultTargetOptions)
target_link_libraries(${TARGET} PRIVATE common whisper ${CMAKE_THREAD_LIBS_INIT})
target_link_libraries(${TARGET} PRIVATE whisper ${CMAKE_THREAD_LIBS_INIT})

View File

@ -31,7 +31,6 @@ options:
-osrt, --output-srt [false ] output result in a srt file
-owts, --output-words [false ] output script for generating karaoke video
-ocsv, --output-csv [false ] output result in a CSV file
-oj, --output-json [false ] output result in a JSON file
-of FNAME, --output-file FNAME [ ] output file path (without file extension)
-ps, --print-special [false ] print special tokens
-pc, --print-colors [false ] print colors

View File

@ -1,7 +1,10 @@
#include "common.h"
#include "whisper.h"
// third-party utilities
// use your favorite implementations
#define DR_WAV_IMPLEMENTATION
#include "dr_wav.h"
#include <cmath>
#include <fstream>
#include <cstdio>
@ -73,7 +76,6 @@ struct whisper_params {
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;
@ -81,11 +83,10 @@ struct whisper_params {
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::vector<std::string> fname_inp = {};
std::vector<std::string> fname_out = {};
std::vector<std::string> fname_outp = {};
};
void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
@ -94,11 +95,6 @@ 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 == "-"){
params.fname_inp.push_back(arg);
continue;
}
if (arg[0] != '-') {
params.fname_inp.push_back(arg);
continue;
@ -129,10 +125,8 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
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 == "-of" || arg == "--output-file") { params.fname_outp.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; }
@ -178,9 +172,7 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
fprintf(stderr, " -ovtt, --output-vtt [%-7s] output result in a vtt file\n", params.output_vtt ? "true" : "false");
fprintf(stderr, " -osrt, --output-srt [%-7s] output result in a srt file\n", params.output_srt ? "true" : "false");
fprintf(stderr, " -owts, --output-words [%-7s] output script for generating karaoke video\n", params.output_wts ? "true" : "false");
fprintf(stderr, " -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");
fprintf(stderr, " -oj, --output-json [%-7s] output result in a JSON file\n", params.output_jsn ? "true" : "false");
fprintf(stderr, " -of FNAME, --output-file FNAME [%-7s] output file path (without file extension)\n", "");
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");
@ -199,7 +191,7 @@ struct whisper_print_user_data {
const std::vector<std::vector<float>> * pcmf32s;
};
void whisper_print_segment_callback(struct whisper_context * ctx, struct whisper_state * /*state*/, int n_new, void * user_data) {
void whisper_print_segment_callback(struct whisper_context * ctx, int n_new, void * user_data) {
const auto & params = *((whisper_print_user_data *) user_data)->params;
const auto & pcmf32s = *((whisper_print_user_data *) user_data)->pcmf32s;
@ -358,157 +350,28 @@ 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";
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);
//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 << ", \"" << text << "\"\n";
}
return true;
}
bool output_json(struct whisper_context * ctx, const char * fname, const whisper_params & params) {
std::ofstream fout(fname);
int indent = 0;
auto doindent = [&]() {
for (int i = 0; i < indent; i++) fout << "\t";
};
auto start_arr = [&](const char *name) {
doindent();
fout << "\"" << name << "\": [\n";
indent++;
};
auto end_arr = [&](bool end = false) {
indent--;
doindent();
fout << (end ? "]\n" : "},\n");
};
auto start_obj = [&](const char *name = nullptr) {
doindent();
if (name) {
fout << "\"" << name << "\": {\n";
} else {
fout << "{\n";
}
indent++;
};
auto end_obj = [&](bool end = false) {
indent--;
doindent();
fout << (end ? "}\n" : "},\n");
};
auto start_value = [&](const char *name) {
doindent();
fout << "\"" << name << "\": ";
};
auto value_s = [&](const char *name, const char *val, bool end = false) {
start_value(name);
fout << "\"" << val << (end ? "\"\n" : "\",\n");
};
auto end_value = [&](bool end = false) {
fout << (end ? "\n" : ",\n");
};
auto value_i = [&](const char *name, const int64_t val, bool end = false) {
start_value(name);
fout << val;
end_value(end);
};
auto value_b = [&](const char *name, const bool val, bool end = false) {
start_value(name);
fout << (val ? "true" : "false");
end_value(end);
};
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);
start_obj();
value_s("systeminfo", whisper_print_system_info());
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));
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("layer", whisper_model_n_audio_layer(ctx), true);
end_obj();
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();
start_obj("params");
value_s("model", params.model.c_str());
value_s("language", params.language.c_str());
value_b("translate", params.translate, true);
end_obj();
start_obj("result");
value_s("language", whisper_lang_str(whisper_full_lang_id(ctx)), true);
end_obj();
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());
value_s("to", to_timestamp(t1, true).c_str(), true);
end_obj();
start_obj("offsets");
value_i("from", t0 * 10);
value_i("to", t1 * 10, true);
end_obj();
value_s("text", text, true);
end_obj(i == (n_segments - 1));
}
end_arr(true);
end_obj(true);
return true;
}
// karaoke video generation
// outputs a bash script that uses ffmpeg to generate a video with the subtitles
// TODO: font parameter adjustments
bool output_wts(struct whisper_context * ctx, const char * fname, const char * fname_inp, const whisper_params & params, float t_sec) {
bool output_wts(struct whisper_context * ctx, const char * fname, const char * fname_inp, const whisper_params & /*params*/, float t_sec) {
std::ofstream fout(fname);
fprintf(stderr, "%s: saving output to '%s'\n", __func__, fname);
static const char * font = params.font_path.c_str();
std::ifstream fin(font);
if (!fin.is_open()) {
fprintf(stderr, "%s: font not found at '%s', please specify a monospace font with -fp\n", __func__, font);
return false;
}
// TODO: become parameter
static const char * font = "/System/Library/Fonts/Supplemental/Courier New Bold.ttf";
fout << "#!/bin/bash" << "\n";
fout << "\n";
@ -657,14 +520,91 @@ int main(int argc, char ** argv) {
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];
const auto fname_outp = f < (int) params.fname_outp.size() && !params.fname_outp[f].empty() ? params.fname_outp[f] : params.fname_inp[f];
std::vector<float> pcmf32; // mono-channel F32 PCM
std::vector<float> pcmf32; // mono-channel F32 PCM
std::vector<std::vector<float>> pcmf32s; // stereo-channel F32 PCM
if (!::read_wav(fname_inp, pcmf32, pcmf32s, params.diarize)) {
fprintf(stderr, "error: failed to read WAV file '%s'\n", fname_inp.c_str());
continue;
// WAV input
{
drwav wav;
std::vector<uint8_t> wav_data; // used for pipe input from stdin
if (fname_inp == "-") {
{
uint8_t buf[1024];
while (true)
{
const size_t n = fread(buf, 1, sizeof(buf), stdin);
if (n == 0) {
break;
}
wav_data.insert(wav_data.end(), buf, buf + n);
}
}
if (drwav_init_memory(&wav, wav_data.data(), wav_data.size(), nullptr) == false) {
fprintf(stderr, "error: failed to open WAV file from stdin\n");
return 4;
}
fprintf(stderr, "%s: read %zu bytes from stdin\n", __func__, wav_data.size());
}
else if (drwav_init_file(&wav, fname_inp.c_str(), nullptr) == false) {
fprintf(stderr, "error: failed to open '%s' as WAV file\n", fname_inp.c_str());
return 5;
}
if (wav.channels != 1 && wav.channels != 2) {
fprintf(stderr, "%s: WAV file '%s' must be mono or stereo\n", argv[0], fname_inp.c_str());
return 6;
}
if (params.diarize && wav.channels != 2 && params.no_timestamps == false) {
fprintf(stderr, "%s: WAV file '%s' must be stereo for diarization and timestamps have to be enabled\n", argv[0], fname_inp.c_str());
return 6;
}
if (wav.sampleRate != WHISPER_SAMPLE_RATE) {
fprintf(stderr, "%s: WAV file '%s' must be %i kHz\n", argv[0], fname_inp.c_str(), WHISPER_SAMPLE_RATE/1000);
return 8;
}
if (wav.bitsPerSample != 16) {
fprintf(stderr, "%s: WAV file '%s' must be 16-bit\n", argv[0], fname_inp.c_str());
return 9;
}
const uint64_t n = wav_data.empty() ? wav.totalPCMFrameCount : wav_data.size()/(wav.channels*wav.bitsPerSample/8);
std::vector<int16_t> pcm16;
pcm16.resize(n*wav.channels);
drwav_read_pcm_frames_s16(&wav, n, pcm16.data());
drwav_uninit(&wav);
// convert to mono, float
pcmf32.resize(n);
if (wav.channels == 1) {
for (uint64_t i = 0; i < n; i++) {
pcmf32[i] = float(pcm16[i])/32768.0f;
}
} else {
for (uint64_t i = 0; i < n; i++) {
pcmf32[i] = float(pcm16[2*i] + pcm16[2*i + 1])/65536.0f;
}
}
if (params.diarize) {
// convert to stereo, float
pcmf32s.resize(2);
pcmf32s[0].resize(n);
pcmf32s[1].resize(n);
for (uint64_t i = 0; i < n; i++) {
pcmf32s[0][i] = float(pcm16[2*i])/32768.0f;
pcmf32s[1][i] = float(pcm16[2*i + 1])/32768.0f;
}
}
}
// print system information
@ -742,7 +682,7 @@ int main(int argc, char ** argv) {
{
static bool is_aborted = false; // NOTE: this should be atomic to avoid data race
wparams.encoder_begin_callback = [](struct whisper_context * /*ctx*/, struct whisper_state * /*state*/, void * user_data) {
wparams.encoder_begin_callback = [](struct whisper_context * /*ctx*/, void * user_data) {
bool is_aborted = *(bool*)user_data;
return !is_aborted;
};
@ -761,39 +701,34 @@ int main(int argc, char ** argv) {
// output to text file
if (params.output_txt) {
const auto fname_txt = fname_out + ".txt";
const auto fname_txt = fname_outp + ".txt";
output_txt(ctx, fname_txt.c_str());
}
// output to VTT file
if (params.output_vtt) {
const auto fname_vtt = fname_out + ".vtt";
const auto fname_vtt = fname_outp + ".vtt";
output_vtt(ctx, fname_vtt.c_str());
}
// output to SRT file
if (params.output_srt) {
const auto fname_srt = fname_out + ".srt";
const auto fname_srt = fname_outp + ".srt";
output_srt(ctx, fname_srt.c_str(), params);
}
// output to WTS file
if (params.output_wts) {
const auto fname_wts = fname_out + ".wts";
const auto fname_wts = fname_outp + ".wts";
output_wts(ctx, fname_wts.c_str(), fname_inp.c_str(), params, float(pcmf32.size() + 1000)/WHISPER_SAMPLE_RATE);
}
// output to CSV file
// output to CSV file
if (params.output_csv) {
const auto fname_csv = fname_out + ".csv";
const auto fname_csv = fname_outp + ".csv";
output_csv(ctx, fname_csv.c_str());
}
// output to JSON file
if (params.output_jsn) {
const auto fname_jsn = fname_out + ".json";
output_json(ctx, fname_jsn.c_str(), params);
}
}
}

View File

@ -5,5 +5,6 @@ if (WHISPER_SUPPORT_SDL2)
include(DefaultTargetOptions)
target_link_libraries(${TARGET} PRIVATE common common-sdl whisper ${CMAKE_THREAD_LIBS_INIT})
target_include_directories(${TARGET} PRIVATE ${SDL2_INCLUDE_DIRS})
target_link_libraries(${TARGET} PRIVATE whisper ${SDL2_LIBRARIES} ${CMAKE_THREAD_LIBS_INIT})
endif ()

View File

@ -3,16 +3,19 @@
// A very quick-n-dirty implementation serving mainly as a proof of concept.
//
#include "common.h"
#include "common-sdl.h"
#include "whisper.h"
#include <SDL.h>
#include <SDL_audio.h>
#include <atomic>
#include <cassert>
#include <cstdio>
#include <string>
#include <thread>
#include <vector>
#include <fstream>
#include <mutex>
// 500 -> 00:05.000
// 6000 -> 01:00.000
@ -113,6 +116,306 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
fprintf(stderr, "\n");
}
//
// SDL Audio capture
//
class audio_async {
public:
audio_async(int len_ms);
~audio_async();
bool init(int capture_id, int sample_rate);
// start capturing audio via the provided SDL callback
// keep last len_ms seconds of audio in a circular buffer
bool resume();
bool pause();
bool clear();
// callback to be called by SDL
void callback(uint8_t * stream, int len);
// get audio data from the circular buffer
void get(int ms, std::vector<float> & audio);
private:
SDL_AudioDeviceID m_dev_id_in = 0;
int m_len_ms = 0;
int m_sample_rate = 0;
std::atomic_bool m_running;
std::mutex m_mutex;
std::vector<float> m_audio;
std::vector<float> m_audio_new;
size_t m_audio_pos = 0;
size_t m_audio_len = 0;
};
audio_async::audio_async(int len_ms) {
m_len_ms = len_ms;
m_running = false;
}
audio_async::~audio_async() {
if (m_dev_id_in) {
SDL_CloseAudioDevice(m_dev_id_in);
}
}
bool audio_async::init(int capture_id, int sample_rate) {
SDL_LogSetPriority(SDL_LOG_CATEGORY_APPLICATION, SDL_LOG_PRIORITY_INFO);
if (SDL_Init(SDL_INIT_AUDIO) < 0) {
SDL_LogError(SDL_LOG_CATEGORY_APPLICATION, "Couldn't initialize SDL: %s\n", SDL_GetError());
return false;
}
SDL_SetHintWithPriority(SDL_HINT_AUDIO_RESAMPLING_MODE, "medium", SDL_HINT_OVERRIDE);
{
int nDevices = SDL_GetNumAudioDevices(SDL_TRUE);
fprintf(stderr, "%s: found %d capture devices:\n", __func__, nDevices);
for (int i = 0; i < nDevices; i++) {
fprintf(stderr, "%s: - Capture device #%d: '%s'\n", __func__, i, SDL_GetAudioDeviceName(i, SDL_TRUE));
}
}
SDL_AudioSpec capture_spec_requested;
SDL_AudioSpec capture_spec_obtained;
SDL_zero(capture_spec_requested);
SDL_zero(capture_spec_obtained);
capture_spec_requested.freq = sample_rate;
capture_spec_requested.format = AUDIO_F32;
capture_spec_requested.channels = 1;
capture_spec_requested.samples = 1024;
capture_spec_requested.callback = [](void * userdata, uint8_t * stream, int len) {
audio_async * audio = (audio_async *) userdata;
audio->callback(stream, len);
};
capture_spec_requested.userdata = this;
if (capture_id >= 0) {
fprintf(stderr, "%s: attempt to open capture device %d : '%s' ...\n", __func__, capture_id, SDL_GetAudioDeviceName(capture_id, SDL_TRUE));
m_dev_id_in = SDL_OpenAudioDevice(SDL_GetAudioDeviceName(capture_id, SDL_TRUE), SDL_TRUE, &capture_spec_requested, &capture_spec_obtained, 0);
} else {
fprintf(stderr, "%s: attempt to open default capture device ...\n", __func__);
m_dev_id_in = SDL_OpenAudioDevice(nullptr, SDL_TRUE, &capture_spec_requested, &capture_spec_obtained, 0);
}
if (!m_dev_id_in) {
fprintf(stderr, "%s: couldn't open an audio device for capture: %s!\n", __func__, SDL_GetError());
m_dev_id_in = 0;
return false;
} else {
fprintf(stderr, "%s: obtained spec for input device (SDL Id = %d):\n", __func__, m_dev_id_in);
fprintf(stderr, "%s: - sample rate: %d\n", __func__, capture_spec_obtained.freq);
fprintf(stderr, "%s: - format: %d (required: %d)\n", __func__, capture_spec_obtained.format,
capture_spec_requested.format);
fprintf(stderr, "%s: - channels: %d (required: %d)\n", __func__, capture_spec_obtained.channels,
capture_spec_requested.channels);
fprintf(stderr, "%s: - samples per frame: %d\n", __func__, capture_spec_obtained.samples);
}
m_sample_rate = capture_spec_obtained.freq;
m_audio.resize((m_sample_rate*m_len_ms)/1000);
return true;
}
bool audio_async::resume() {
if (!m_dev_id_in) {
fprintf(stderr, "%s: no audio device to resume!\n", __func__);
return false;
}
if (m_running) {
fprintf(stderr, "%s: already running!\n", __func__);
return false;
}
SDL_PauseAudioDevice(m_dev_id_in, 0);
m_running = true;
return true;
}
bool audio_async::pause() {
if (!m_dev_id_in) {
fprintf(stderr, "%s: no audio device to pause!\n", __func__);
return false;
}
if (!m_running) {
fprintf(stderr, "%s: already paused!\n", __func__);
return false;
}
SDL_PauseAudioDevice(m_dev_id_in, 1);
m_running = false;
return true;
}
bool audio_async::clear() {
if (!m_dev_id_in) {
fprintf(stderr, "%s: no audio device to clear!\n", __func__);
return false;
}
if (!m_running) {
fprintf(stderr, "%s: not running!\n", __func__);
return false;
}
{
std::lock_guard<std::mutex> lock(m_mutex);
m_audio_pos = 0;
m_audio_len = 0;
}
return true;
}
// callback to be called by SDL
void audio_async::callback(uint8_t * stream, int len) {
if (!m_running) {
return;
}
const size_t n_samples = len / sizeof(float);
m_audio_new.resize(n_samples);
memcpy(m_audio_new.data(), stream, n_samples * sizeof(float));
//fprintf(stderr, "%s: %zu samples, pos %zu, len %zu\n", __func__, n_samples, m_audio_pos, m_audio_len);
{
std::lock_guard<std::mutex> lock(m_mutex);
if (m_audio_pos + n_samples > m_audio.size()) {
const size_t n0 = m_audio.size() - m_audio_pos;
memcpy(&m_audio[m_audio_pos], stream, n0 * sizeof(float));
memcpy(&m_audio[0], &stream[n0], (n_samples - n0) * sizeof(float));
m_audio_pos = (m_audio_pos + n_samples) % m_audio.size();
m_audio_len = m_audio.size();
} else {
memcpy(&m_audio[m_audio_pos], stream, n_samples * sizeof(float));
m_audio_pos = (m_audio_pos + n_samples) % m_audio.size();
m_audio_len = std::min(m_audio_len + n_samples, m_audio.size());
}
}
}
void audio_async::get(int ms, std::vector<float> & result) {
if (!m_dev_id_in) {
fprintf(stderr, "%s: no audio device to get audio from!\n", __func__);
return;
}
if (!m_running) {
fprintf(stderr, "%s: not running!\n", __func__);
return;
}
result.clear();
{
std::lock_guard<std::mutex> lock(m_mutex);
if (ms <= 0) {
ms = m_len_ms;
}
size_t n_samples = (m_sample_rate * ms) / 1000;
if (n_samples > m_audio_len) {
n_samples = m_audio_len;
}
result.resize(n_samples);
int s0 = m_audio_pos - n_samples;
if (s0 < 0) {
s0 += m_audio.size();
}
if (s0 + n_samples > m_audio.size()) {
const size_t n0 = m_audio.size() - s0;
memcpy(result.data(), &m_audio[s0], n0 * sizeof(float));
memcpy(&result[n0], &m_audio[0], (n_samples - n0) * sizeof(float));
} else {
memcpy(result.data(), &m_audio[s0], n_samples * sizeof(float));
}
}
}
///////////////////////////
void high_pass_filter(std::vector<float> & data, float cutoff, float sample_rate) {
const float rc = 1.0f / (2.0f * M_PI * cutoff);
const float dt = 1.0f / sample_rate;
const float alpha = dt / (rc + dt);
float y = data[0];
for (size_t i = 1; i < data.size(); i++) {
y = alpha * (y + data[i] - data[i - 1]);
data[i] = y;
}
}
bool vad_simple(std::vector<float> & pcmf32, int sample_rate, int last_ms, float vad_thold, float freq_thold, bool verbose) {
const int n_samples = pcmf32.size();
const int n_samples_last = (sample_rate * last_ms) / 1000;
if (n_samples_last >= n_samples) {
// not enough samples - assume no speech
return false;
}
if (freq_thold > 0.0f) {
high_pass_filter(pcmf32, freq_thold, sample_rate);
}
float energy_all = 0.0f;
float energy_last = 0.0f;
for (int i = 0; i < n_samples; i++) {
energy_all += fabsf(pcmf32[i]);
if (i >= n_samples - n_samples_last) {
energy_last += fabsf(pcmf32[i]);
}
}
energy_all /= n_samples;
energy_last /= n_samples_last;
if (verbose) {
fprintf(stderr, "%s: energy_all: %f, energy_last: %f, vad_thold: %f, freq_thold: %f\n", __func__, energy_all, energy_last, vad_thold, freq_thold);
}
if (energy_last > vad_thold*energy_all) {
return false;
}
return true;
}
int main(int argc, char ** argv) {
whisper_params params;
@ -123,10 +426,10 @@ int main(int argc, char ** argv) {
params.keep_ms = std::min(params.keep_ms, params.step_ms);
params.length_ms = std::max(params.length_ms, params.step_ms);
const int n_samples_step = (1e-3*params.step_ms )*WHISPER_SAMPLE_RATE;
const int n_samples_len = (1e-3*params.length_ms)*WHISPER_SAMPLE_RATE;
const int n_samples_keep = (1e-3*params.keep_ms )*WHISPER_SAMPLE_RATE;
const int n_samples_30s = (1e-3*30000.0 )*WHISPER_SAMPLE_RATE;
const int n_samples_step = (params.step_ms *1e-3)*WHISPER_SAMPLE_RATE;
const int n_samples_len = (params.length_ms*1e-3)*WHISPER_SAMPLE_RATE;
const int n_samples_keep = (params.keep_ms *1e-3)*WHISPER_SAMPLE_RATE;
const int n_samples_30s = (30000 *1e-3)*WHISPER_SAMPLE_RATE;
const bool use_vad = n_samples_step <= 0; // sliding window mode uses VAD
@ -214,7 +517,23 @@ int main(int argc, char ** argv) {
// main audio loop
while (is_running) {
// handle Ctrl + C
is_running = sdl_poll_events();
{
SDL_Event event;
while (SDL_PollEvent(&event)) {
switch (event.type) {
case SDL_QUIT:
{
is_running = false;
} break;
default:
break;
}
}
if (!is_running) {
break;
}
}
if (!is_running) {
break;
@ -237,7 +556,7 @@ int main(int argc, char ** argv) {
break;
}
std::this_thread::sleep_for(std::chrono::milliseconds(1));
SDL_Delay(1);
}
const int n_samples_new = pcmf32_new.size();
@ -268,7 +587,7 @@ int main(int argc, char ** argv) {
audio.get(2000, pcmf32_new);
if (::vad_simple(pcmf32_new, WHISPER_SAMPLE_RATE, 1000, params.vad_thold, params.freq_thold, false)) {
if (vad_simple(pcmf32_new, WHISPER_SAMPLE_RATE, 1000, params.vad_thold, params.freq_thold, false)) {
audio.get(params.length_ms, pcmf32);
} else {
std::this_thread::sleep_for(std::chrono::milliseconds(100));
@ -288,6 +607,7 @@ int main(int argc, char ** argv) {
wparams.print_realtime = false;
wparams.print_timestamps = !params.no_timestamps;
wparams.translate = params.translate;
wparams.no_context = true;
wparams.single_segment = !use_vad;
wparams.max_tokens = params.max_tokens;
wparams.language = params.language.c_str();

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@ -1,2 +0,0 @@
eleven-labs.py
audio.mp3

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@ -1,12 +0,0 @@
if (WHISPER_SUPPORT_SDL2)
# talk.llama
set(TARGET talk-llama)
# TODO: this is temporary
# need to export ggml symbols for MSVC, but too lazy ..
add_executable(${TARGET} talk-llama.cpp llama.cpp)
include(DefaultTargetOptions)
target_link_libraries(${TARGET} PRIVATE common common-sdl whisper ${SDL2_LIBRARIES} ${CMAKE_THREAD_LIBS_INIT})
endif ()

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@ -1,2 +0,0 @@
# talk.llama

File diff suppressed because it is too large Load Diff

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@ -1,153 +0,0 @@
#ifndef LLAMA_H
#define LLAMA_H
#include <stddef.h>
#include <stdint.h>
#include <stdbool.h>
#ifdef LLAMA_SHARED
# ifdef _WIN32
# 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_VERSION 1
#define LLAMA_FILE_MAGIC 0x67676d66 // 'ggmf' in hex
#define LLAMA_FILE_MAGIC_UNVERSIONED 0x67676d6c // pre-versioned files
#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 p; // probability of the token
float plog; // log probability of the token
} llama_token_data;
typedef void (*llama_progress_callback)(double progress, void *ctx);
struct llama_context_params {
int n_ctx; // text context
int n_parts; // -1 for default
int seed; // RNG seed, 0 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_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;
};
LLAMA_API struct llama_context_params llama_context_default_params();
// 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
LLAMA_API int llama_model_quantize(
const char * fname_inp,
const char * fname_out,
int itype,
int qk);
// 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(struct llama_context * ctx);
LLAMA_API int llama_n_ctx (struct llama_context * ctx);
LLAMA_API int llama_n_embd (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(struct llama_context * ctx, llama_token token);
// Special tokens
LLAMA_API llama_token llama_token_bos();
LLAMA_API llama_token llama_token_eos();
// TODO: improve the last_n_tokens interface ?
LLAMA_API llama_token llama_sample_top_p_top_k(
struct llama_context * ctx,
const llama_token * last_n_tokens_data,
int last_n_tokens_size,
int top_k,
double top_p,
double temp,
double repeat_penalty);
// 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
#endif

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@ -1,20 +0,0 @@
#!/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 "$2"
# Eleven Labs
#
#wd=$(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

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@ -1,511 +0,0 @@
// 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;
std::string person = "Santa";
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/speak.sh";
std::string fname_out;
};
void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
for (int i = 1; i < argc; i++) {
std::string arg = argv[i];
if (arg == "-h" || arg == "--help") {
whisper_print_usage(argc, argv, params);
exit(0);
}
else if (arg == "-t" || arg == "--threads") { params.n_threads = std::stoi(argv[++i]); }
else if (arg == "-vms" || arg == "--voice-ms") { params.voice_ms = std::stoi(argv[++i]); }
else if (arg == "-c" || arg == "--capture") { params.capture_id = std::stoi(argv[++i]); }
else if (arg == "-mt" || arg == "--max-tokens") { params.max_tokens = std::stoi(argv[++i]); }
else if (arg == "-ac" || arg == "--audio-ctx") { params.audio_ctx = std::stoi(argv[++i]); }
else if (arg == "-vth" || arg == "--vad-thold") { params.vad_thold = std::stof(argv[++i]); }
else if (arg == "-fth" || arg == "--freq-thold") { params.freq_thold = std::stof(argv[++i]); }
else if (arg == "-su" || arg == "--speed-up") { params.speed_up = true; }
else if (arg == "-tr" || arg == "--translate") { params.translate = true; }
else if (arg == "-ps" || arg == "--print-special") { params.print_special = true; }
else if (arg == "-pe" || arg == "--print-energy") { params.print_energy = true; }
else if (arg == "-p" || arg == "--person") { params.person = argv[++i]; }
else if (arg == "-l" || arg == "--language") { params.language = argv[++i]; }
else if (arg == "-mw" || arg == "--model-whisper") { params.model_wsp = argv[++i]; }
else if (arg == "-ml" || arg == "--model-llama") { params.model_llama = argv[++i]; }
else if (arg == "-s" || arg == "--speak") { params.speak = argv[++i]; }
else if (arg == "-f" || arg == "--file") { params.fname_out = argv[++i]; }
else {
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
whisper_print_usage(argc, argv, params);
exit(0);
}
}
return true;
}
void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & params) {
fprintf(stderr, "\n");
fprintf(stderr, "usage: %s [options]\n", argv[0]);
fprintf(stderr, "\n");
fprintf(stderr, "options:\n");
fprintf(stderr, " -h, --help [default] show this help message and exit\n");
fprintf(stderr, " -t N, --threads N [%-7d] number of threads to use during computation\n", params.n_threads);
fprintf(stderr, " -vms N, --voice-ms N [%-7d] voice duration in milliseconds\n", params.voice_ms);
fprintf(stderr, " -c ID, --capture ID [%-7d] capture device ID\n", params.capture_id);
fprintf(stderr, " -mt N, --max-tokens N [%-7d] maximum number of tokens per audio chunk\n", params.max_tokens);
fprintf(stderr, " -ac N, --audio-ctx N [%-7d] audio context size (0 - all)\n", params.audio_ctx);
fprintf(stderr, " -vth N, --vad-thold N [%-7.2f] voice activity detection threshold\n", params.vad_thold);
fprintf(stderr, " -fth N, --freq-thold N [%-7.2f] high-pass frequency cutoff\n", params.freq_thold);
fprintf(stderr, " -su, --speed-up [%-7s] speed up audio by x2 (reduced accuracy)\n", params.speed_up ? "true" : "false");
fprintf(stderr, " -tr, --translate [%-7s] translate from source language to english\n", params.translate ? "true" : "false");
fprintf(stderr, " -ps, --print-special [%-7s] print special tokens\n", params.print_special ? "true" : "false");
fprintf(stderr, " -pe, --print-energy [%-7s] print sound energy (for debugging)\n", params.print_energy ? "true" : "false");
fprintf(stderr, " -p NAME, --person NAME [%-7s] person name (for prompt selection)\n", params.person.c_str());
fprintf(stderr, " -l LANG, --language LANG [%-7s] spoken language\n", params.language.c_str());
fprintf(stderr, " -mw FILE, --model-whisper [%-7s] whisper model file\n", params.model_wsp.c_str());
fprintf(stderr, " -mg FILE, --model-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, " -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, float & prob, int64_t & t_ms) {
const auto t_start = std::chrono::high_resolution_clock::now();
prob = 0.0f;
t_ms = 0;
whisper_full_params wparams = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
wparams.print_progress = false;
wparams.print_special = params.print_special;
wparams.print_realtime = false;
wparams.print_timestamps = !params.no_timestamps;
wparams.translate = params.translate;
wparams.no_context = true;
wparams.single_segment = true;
wparams.max_tokens = params.max_tokens;
wparams.language = params.language.c_str();
wparams.n_threads = params.n_threads;
wparams.audio_ctx = params.audio_ctx;
wparams.speed_up = params.speed_up;
if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
return "";
}
int prob_n = 0;
std::string result;
const int n_segments = whisper_full_n_segments(ctx);
for (int i = 0; i < n_segments; ++i) {
const char * text = whisper_full_get_segment_text(ctx, i);
result += text;
const int n_tokens = whisper_full_n_tokens(ctx, i);
for (int j = 0; j < n_tokens; ++j) {
const auto token = whisper_full_get_token_data(ctx, i, j);
prob += token.p;
++prob_n;
}
}
if (prob_n > 0) {
prob /= prob_n;
}
const auto t_end = std::chrono::high_resolution_clock::now();
t_ms = std::chrono::duration_cast<std::chrono::milliseconds>(t_end - t_start).count();
return result;
}
// need to have leading ' '
//const std::string k_prompt = R"( Transcript of a dialog, where {1} interacts with an Assistant named Bob. Bob is helpful, kind, honest, good at writing, and never fails to answer {1}'s requests immediately and with precision.
//
//{0}: Hello, Bob.
//{1}: Hello {0}. How may I help you today?
//{0}:)";
const std::string k_prompt = 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} answers 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
auto lparams = llama_context_default_params();
lparams.n_ctx = 512;
lparams.n_parts = 2; // TODO fix
lparams.seed = 1; // TODO fix
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;
std::string prompt_org = k_prompt;
prompt_org = ::replace(prompt_org, "{0}", params.person);
prompt_org = ::replace(prompt_org, "{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_org = ::replace(prompt_org, "{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_org = ::replace(prompt_org, "{3}", year_str);
}
prompt_org = ::replace(prompt_org, "{4}", chat_symb);
auto embd_inp = ::llama_tokenize(ctx_llama, prompt_org, true);
const int n_ctx = llama_n_ctx(ctx_llama);
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;
}
//fprintf(stdout, "\n");
//fprintf(stdout, "%s", prompt_org.c_str());
//fflush(stdout);
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);
audio.clear();
const int n_keep = embd_inp.size();
const int voice_id = 2;
int n_past = n_keep;
int n_prev = 64; // TODO arg
std::vector<llama_token> embd;
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, 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);
// 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());
//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");
}
if (llama_eval(ctx_llama, embd.data(), embd.size(), n_past, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return 1;
}
}
//printf("n_iter = %d, n_past = %d, n_ctx = %d, n_keep = %d, n_prev = %d, embd.size() = %d\n", n_iter, n_past, n_ctx, n_keep, n_prev, (int) embd.size());
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;
llama_token id = 0;
{
//auto logits = llama_get_logits(ctx_llama);
//logits[llama_token_eos()] = 0;
id = llama_sample_top_p_top_k(ctx_llama,
embd_inp.data() + std::max(0, n_past - repeat_last_n),
repeat_last_n, top_k, top_p, temp, repeat_penalty);
}
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));
} else {
// TODO
printf("EOS TOKEN - SHOULD NOT HAPPEN\n");
exit(0);
}
}
{
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);
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);
return 0;
}

View File

@ -7,7 +7,7 @@ if (WHISPER_SUPPORT_SDL2)
# 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 ../../ggml.c ../../whisper.cpp)
include(DefaultTargetOptions)

View File

@ -31,7 +31,7 @@ To run this, you will need a ggml GPT-2 model: [instructions](https://github.com
Alternatively, you can simply download the smallest ggml GPT-2 117M model (240 MB) like this:
```
wget --quiet --show-progress -O models/ggml-gpt-2-117M.bin https://huggingface.co/ggerganov/ggml/raw/main/ggml-model-gpt-2-117M.bin
wget --quiet --show-progress -O models/ggml-gpt-2-117M.bin https://huggingface.co/datasets/ggerganov/ggml/raw/main/ggml-model-gpt-2-117M.bin
```
## TTS

View File

@ -1,14 +1,16 @@
// Talk with AI
//
#include "common.h"
#include "common-sdl.h"
#include "whisper.h"
#include "gpt-2.h"
#include <SDL.h>
#include <SDL_audio.h>
#include <cassert>
#include <cstdio>
#include <fstream>
#include <mutex>
#include <regex>
#include <string>
#include <thread>
@ -103,6 +105,320 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
fprintf(stderr, "\n");
}
//
// SDL Audio capture
//
class audio_async {
public:
audio_async(int len_ms);
~audio_async();
bool init(int capture_id, int sample_rate);
// start capturing audio via the provided SDL callback
// keep last len_ms seconds of audio in a circular buffer
bool resume();
bool pause();
bool clear();
// callback to be called by SDL
void callback(uint8_t * stream, int len);
// get audio data from the circular buffer
void get(int ms, std::vector<float> & audio);
private:
SDL_AudioDeviceID m_dev_id_in = 0;
int m_len_ms = 0;
int m_sample_rate = 0;
bool m_running = false;
std::mutex m_mutex;
std::vector<float> m_audio;
std::vector<float> m_audio_new;
size_t m_audio_pos = 0;
size_t m_audio_len = 0;
};
audio_async::audio_async(int len_ms) {
m_len_ms = len_ms;
}
audio_async::~audio_async() {
if (m_dev_id_in) {
SDL_CloseAudioDevice(m_dev_id_in);
}
}
bool audio_async::init(int capture_id, int sample_rate) {
SDL_LogSetPriority(SDL_LOG_CATEGORY_APPLICATION, SDL_LOG_PRIORITY_INFO);
if (SDL_Init(SDL_INIT_AUDIO) < 0) {
SDL_LogError(SDL_LOG_CATEGORY_APPLICATION, "Couldn't initialize SDL: %s\n", SDL_GetError());
return false;
}
SDL_SetHintWithPriority(SDL_HINT_AUDIO_RESAMPLING_MODE, "medium", SDL_HINT_OVERRIDE);
{
int nDevices = SDL_GetNumAudioDevices(SDL_TRUE);
fprintf(stderr, "%s: found %d capture devices:\n", __func__, nDevices);
for (int i = 0; i < nDevices; i++) {
fprintf(stderr, "%s: - Capture device #%d: '%s'\n", __func__, i, SDL_GetAudioDeviceName(i, SDL_TRUE));
}
}
SDL_AudioSpec capture_spec_requested;
SDL_AudioSpec capture_spec_obtained;
SDL_zero(capture_spec_requested);
SDL_zero(capture_spec_obtained);
capture_spec_requested.freq = sample_rate;
capture_spec_requested.format = AUDIO_F32;
capture_spec_requested.channels = 1;
capture_spec_requested.samples = 1024;
capture_spec_requested.callback = [](void * userdata, uint8_t * stream, int len) {
audio_async * audio = (audio_async *) userdata;
audio->callback(stream, len);
};
capture_spec_requested.userdata = this;
if (capture_id >= 0) {
fprintf(stderr, "%s: attempt to open capture device %d : '%s' ...\n", __func__, capture_id, SDL_GetAudioDeviceName(capture_id, SDL_TRUE));
m_dev_id_in = SDL_OpenAudioDevice(SDL_GetAudioDeviceName(capture_id, SDL_TRUE), SDL_TRUE, &capture_spec_requested, &capture_spec_obtained, 0);
} else {
fprintf(stderr, "%s: attempt to open default capture device ...\n", __func__);
m_dev_id_in = SDL_OpenAudioDevice(nullptr, SDL_TRUE, &capture_spec_requested, &capture_spec_obtained, 0);
}
if (!m_dev_id_in) {
fprintf(stderr, "%s: couldn't open an audio device for capture: %s!\n", __func__, SDL_GetError());
m_dev_id_in = 0;
return false;
} else {
fprintf(stderr, "%s: obtained spec for input device (SDL Id = %d):\n", __func__, m_dev_id_in);
fprintf(stderr, "%s: - sample rate: %d\n", __func__, capture_spec_obtained.freq);
fprintf(stderr, "%s: - format: %d (required: %d)\n", __func__, capture_spec_obtained.format,
capture_spec_requested.format);
fprintf(stderr, "%s: - channels: %d (required: %d)\n", __func__, capture_spec_obtained.channels,
capture_spec_requested.channels);
fprintf(stderr, "%s: - samples per frame: %d\n", __func__, capture_spec_obtained.samples);
fprintf(stderr, "\n");
}
m_sample_rate = capture_spec_obtained.freq;
m_audio.resize((m_sample_rate*m_len_ms)/1000);
return true;
}
bool audio_async::resume() {
if (!m_dev_id_in) {
fprintf(stderr, "%s: no audio device to resume!\n", __func__);
return false;
}
if (m_running) {
fprintf(stderr, "%s: already running!\n", __func__);
return false;
}
SDL_PauseAudioDevice(m_dev_id_in, 0);
m_running = true;
return true;
}
bool audio_async::pause() {
if (!m_dev_id_in) {
fprintf(stderr, "%s: no audio device to pause!\n", __func__);
return false;
}
if (!m_running) {
fprintf(stderr, "%s: already paused!\n", __func__);
return false;
}
SDL_PauseAudioDevice(m_dev_id_in, 1);
m_running = false;
return true;
}
bool audio_async::clear() {
if (!m_dev_id_in) {
fprintf(stderr, "%s: no audio device to clear!\n", __func__);
return false;
}
if (!m_running) {
fprintf(stderr, "%s: not running!\n", __func__);
return false;
}
{
std::lock_guard<std::mutex> lock(m_mutex);
m_audio_pos = 0;
m_audio_len = 0;
}
return true;
}
// callback to be called by SDL
void audio_async::callback(uint8_t * stream, int len) {
if (!m_running) {
return;
}
const size_t n_samples = len / sizeof(float);
m_audio_new.resize(n_samples);
memcpy(m_audio_new.data(), stream, n_samples * sizeof(float));
//fprintf(stderr, "%s: %zu samples, pos %zu, len %zu\n", __func__, n_samples, m_audio_pos, m_audio_len);
{
std::lock_guard<std::mutex> lock(m_mutex);
if (m_audio_pos + n_samples > m_audio.size()) {
const size_t n0 = m_audio.size() - m_audio_pos;
memcpy(&m_audio[m_audio_pos], stream, n0 * sizeof(float));
memcpy(&m_audio[0], &stream[n0], (n_samples - n0) * sizeof(float));
m_audio_pos = (m_audio_pos + n_samples) % m_audio.size();
m_audio_len = m_audio.size();
} else {
memcpy(&m_audio[m_audio_pos], stream, n_samples * sizeof(float));
m_audio_pos = (m_audio_pos + n_samples) % m_audio.size();
m_audio_len = std::min(m_audio_len + n_samples, m_audio.size());
}
}
}
void audio_async::get(int ms, std::vector<float> & result) {
if (!m_dev_id_in) {
fprintf(stderr, "%s: no audio device to get audio from!\n", __func__);
return;
}
if (!m_running) {
fprintf(stderr, "%s: not running!\n", __func__);
return;
}
result.clear();
{
std::lock_guard<std::mutex> lock(m_mutex);
if (ms <= 0) {
ms = m_len_ms;
}
size_t n_samples = (m_sample_rate * ms) / 1000;
if (n_samples > m_audio_len) {
n_samples = m_audio_len;
}
result.resize(n_samples);
int s0 = m_audio_pos - n_samples;
if (s0 < 0) {
s0 += m_audio.size();
}
if (s0 + n_samples > m_audio.size()) {
const size_t n0 = m_audio.size() - s0;
memcpy(result.data(), &m_audio[s0], n0 * sizeof(float));
memcpy(&result[n0], &m_audio[0], (n_samples - n0) * sizeof(float));
} else {
memcpy(result.data(), &m_audio[s0], n_samples * sizeof(float));
}
}
}
///////////////////////////
std::string trim(const std::string & s) {
std::regex e("^\\s+|\\s+$");
return std::regex_replace(s, e, "");
}
std::string replace(const std::string & s, const std::string & from, const std::string & to) {
std::string result = s;
size_t pos = 0;
while ((pos = result.find(from, pos)) != std::string::npos) {
result.replace(pos, from.length(), to);
pos += to.length();
}
return result;
}
void high_pass_filter(std::vector<float> & data, float cutoff, float sample_rate) {
const float rc = 1.0f / (2.0f * M_PI * cutoff);
const float dt = 1.0f / sample_rate;
const float alpha = dt / (rc + dt);
float y = data[0];
for (size_t i = 1; i < data.size(); i++) {
y = alpha * (y + data[i] - data[i - 1]);
data[i] = y;
}
}
bool vad_simple(std::vector<float> & pcmf32, int sample_rate, int last_ms, float vad_thold, float freq_thold, bool verbose) {
const int n_samples = pcmf32.size();
const int n_samples_last = (sample_rate * last_ms) / 1000;
if (n_samples_last >= n_samples) {
// not enough samples - assume no speech
return false;
}
if (freq_thold > 0.0f) {
high_pass_filter(pcmf32, freq_thold, sample_rate);
}
float energy_all = 0.0f;
float energy_last = 0.0f;
for (int i = 0; i < n_samples; i++) {
energy_all += fabsf(pcmf32[i]);
if (i >= n_samples - n_samples_last) {
energy_last += fabsf(pcmf32[i]);
}
}
energy_all /= n_samples;
energy_last /= n_samples_last;
if (verbose) {
fprintf(stderr, "%s: energy_all: %f, energy_last: %f, vad_thold: %f, freq_thold: %f\n", __func__, energy_all, energy_last, vad_thold, freq_thold);
}
if (energy_last > vad_thold*energy_all) {
return false;
}
return true;
}
std::string transcribe(whisper_context * ctx, const whisper_params & params, const std::vector<float> & pcmf32, float & prob, int64_t & t_ms) {
const auto t_start = std::chrono::high_resolution_clock::now();
@ -241,10 +557,22 @@ int main(int argc, char ** argv) {
// main loop
while (is_running) {
// handle Ctrl + C
is_running = sdl_poll_events();
{
SDL_Event event;
while (SDL_PollEvent(&event)) {
switch (event.type) {
case SDL_QUIT:
{
is_running = false;
} break;
default:
break;
}
}
if (!is_running) {
break;
if (!is_running) {
break;
}
}
// delay
@ -255,7 +583,7 @@ int main(int argc, char ** argv) {
{
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) {
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);

View File

@ -9,4 +9,4 @@ To use:
5. Select the "release" active build variant, and use Android Studio to run and deploy to your device.
[^1]: I recommend the tiny or base models for running on an Android device.
<img width="300" alt="image" src="https://user-images.githubusercontent.com/1670775/221613663-a17bf770-27ef-45ab-9a46-a5f99ba65d2a.jpg">
<img width="300" alt="image" src="https://user-images.githubusercontent.com/1991296/208154256-82d972dc-221b-48c4-bfcb-36ce68602f93.png">

View File

@ -2,7 +2,6 @@ package com.whispercppdemo.ui.main
import androidx.compose.foundation.layout.*
import androidx.compose.foundation.rememberScrollState
import androidx.compose.foundation.text.selection.SelectionContainer
import androidx.compose.foundation.verticalScroll
import androidx.compose.material3.*
import androidx.compose.runtime.Composable
@ -20,7 +19,6 @@ fun MainScreen(viewModel: MainScreenViewModel) {
canTranscribe = viewModel.canTranscribe,
isRecording = viewModel.isRecording,
messageLog = viewModel.dataLog,
onBenchmarkTapped = viewModel::benchmark,
onTranscribeSampleTapped = viewModel::transcribeSample,
onRecordTapped = viewModel::toggleRecord
)
@ -32,7 +30,6 @@ private fun MainScreen(
canTranscribe: Boolean,
isRecording: Boolean,
messageLog: String,
onBenchmarkTapped: () -> Unit,
onTranscribeSampleTapped: () -> Unit,
onRecordTapped: () -> Unit
) {
@ -48,11 +45,8 @@ private fun MainScreen(
.padding(innerPadding)
.padding(16.dp)
) {
Column(verticalArrangement = Arrangement.SpaceBetween) {
Row(horizontalArrangement = Arrangement.SpaceBetween, modifier = Modifier.fillMaxWidth()) {
BenchmarkButton(enabled = canTranscribe, onClick = onBenchmarkTapped)
TranscribeSampleButton(enabled = canTranscribe, onClick = onTranscribeSampleTapped)
}
Row(horizontalArrangement = Arrangement.SpaceBetween) {
TranscribeSampleButton(enabled = canTranscribe, onClick = onTranscribeSampleTapped)
RecordButton(
enabled = canTranscribe,
isRecording = isRecording,
@ -66,16 +60,7 @@ private fun MainScreen(
@Composable
private fun MessageLog(log: String) {
SelectionContainer() {
Text(modifier = Modifier.verticalScroll(rememberScrollState()), text = log)
}
}
@Composable
private fun BenchmarkButton(enabled: Boolean, onClick: () -> Unit) {
Button(onClick = onClick, enabled = enabled) {
Text("Benchmark")
}
Text(modifier = Modifier.verticalScroll(rememberScrollState()), text = log)
}
@Composable

View File

@ -41,15 +41,10 @@ class MainScreenViewModel(private val application: Application) : ViewModel() {
init {
viewModelScope.launch {
printSystemInfo()
loadData()
}
}
private suspend fun printSystemInfo() {
printMessage(String.format("System Info: %s\n", WhisperContext.getSystemInfo()));
}
private suspend fun loadData() {
printMessage("Loading data...\n")
try {
@ -86,29 +81,10 @@ class MainScreenViewModel(private val application: Application) : ViewModel() {
//whisperContext = WhisperContext.createContextFromFile(firstModel.absolutePath)
}
fun benchmark() = viewModelScope.launch {
runBenchmark(6)
}
fun transcribeSample() = viewModelScope.launch {
transcribeAudio(getFirstSample())
}
private suspend fun runBenchmark(nthreads: Int) {
if (!canTranscribe) {
return
}
canTranscribe = false
printMessage("Running benchmark. This will take minutes...\n")
whisperContext?.benchMemory(nthreads)?.let{ printMessage(it) }
printMessage("\n")
whisperContext?.benchGgmlMulMat(nthreads)?.let{ printMessage(it) }
canTranscribe = true
}
private suspend fun getFirstSample(): File = withContext(Dispatchers.IO) {
samplesPath.listFiles()!!.first()
}
@ -138,14 +114,11 @@ class MainScreenViewModel(private val application: Application) : ViewModel() {
canTranscribe = false
try {
printMessage("Reading wave samples... ")
printMessage("Reading wave samples...\n")
val data = readAudioSamples(file)
printMessage("${data.size / (16000 / 1000)} ms\n")
printMessage("Transcribing data...\n")
val start = System.currentTimeMillis()
val text = whisperContext?.transcribeData(data)
val elapsed = System.currentTimeMillis() - start
printMessage("Done ($elapsed ms): $text\n")
printMessage("Done: $text\n")
} catch (e: Exception) {
Log.w(LOG_TAG, e)
printMessage("${e.localizedMessage}\n")

View File

@ -27,14 +27,6 @@ class WhisperContext private constructor(private var ptr: Long) {
}
}
suspend fun benchMemory(nthreads: Int): String = withContext(scope.coroutineContext) {
return@withContext WhisperLib.benchMemcpy(nthreads)
}
suspend fun benchGgmlMulMat(nthreads: Int): String = withContext(scope.coroutineContext) {
return@withContext WhisperLib.benchGgmlMulMat(nthreads)
}
suspend fun release() = withContext(scope.coroutineContext) {
if (ptr != 0L) {
WhisperLib.freeContext(ptr)
@ -74,10 +66,6 @@ class WhisperContext private constructor(private var ptr: Long) {
}
return WhisperContext(ptr)
}
fun getSystemInfo(): String {
return WhisperLib.getSystemInfo()
}
}
}
@ -86,7 +74,6 @@ private class WhisperLib {
init {
Log.d(LOG_TAG, "Primary ABI: ${Build.SUPPORTED_ABIS[0]}")
var loadVfpv4 = false
var loadV8fp16 = false
if (isArmEabiV7a()) {
// armeabi-v7a needs runtime detection support
val cpuInfo = cpuInfo()
@ -97,24 +84,11 @@ private class WhisperLib {
loadVfpv4 = true
}
}
} else if (isArmEabiV8a()) {
// ARMv8.2a needs runtime detection support
val cpuInfo = cpuInfo()
cpuInfo?.let {
Log.d(LOG_TAG, "CPU info: $cpuInfo")
if (cpuInfo.contains("fphp")) {
Log.d(LOG_TAG, "CPU supports fp16 arithmetic")
loadV8fp16 = true
}
}
}
if (loadVfpv4) {
Log.d(LOG_TAG, "Loading libwhisper_vfpv4.so")
System.loadLibrary("whisper_vfpv4")
} else if (loadV8fp16) {
Log.d(LOG_TAG, "Loading libwhisper_v8fp16_va.so")
System.loadLibrary("whisper_v8fp16_va")
} else {
Log.d(LOG_TAG, "Loading libwhisper.so")
System.loadLibrary("whisper")
@ -129,9 +103,6 @@ private class WhisperLib {
external fun fullTranscribe(contextPtr: Long, audioData: FloatArray)
external fun getTextSegmentCount(contextPtr: Long): Int
external fun getTextSegment(contextPtr: Long, index: Int): String
external fun getSystemInfo(): String
external fun benchMemcpy(nthread: Int): String
external fun benchGgmlMulMat(nthread: Int): String
}
}
@ -139,10 +110,6 @@ private fun isArmEabiV7a(): Boolean {
return Build.SUPPORTED_ABIS[0].equals("armeabi-v7a")
}
private fun isArmEabiV8a(): Boolean {
return Build.SUPPORTED_ABIS[0].equals("arm64-v8a")
}
private fun cpuInfo(): String? {
return try {
File("/proc/cpuinfo").inputStream().bufferedReader().use {

View File

@ -12,15 +12,4 @@ ifeq ($(TARGET_ARCH_ABI),armeabi-v7a)
# https://android.googlesource.com/platform/ndk/+/master/sources/android/cpufeatures/cpu-features.h
LOCAL_CFLAGS += -mfpu=neon-vfpv4
include $(BUILD_SHARED_LIBRARY)
endif
ifeq ($(TARGET_ARCH_ABI),arm64-v8a)
include $(CLEAR_VARS)
LOCAL_MODULE := libwhisper_v8fp16_va
include $(LOCAL_PATH)/Whisper.mk
# Allow building NEON FMA code.
# https://android.googlesource.com/platform/ndk/+/master/sources/android/cpufeatures/cpu-features.h
LOCAL_CFLAGS += -march=armv8.2-a+fp16
include $(BUILD_SHARED_LIBRARY)
endif
endif

View File

@ -6,7 +6,6 @@
#include <sys/sysinfo.h>
#include <string.h>
#include "whisper.h"
#include "ggml.h"
#define UNUSED(x) (void)(x)
#define TAG "JNI"
@ -214,30 +213,4 @@ Java_com_whispercppdemo_whisper_WhisperLib_00024Companion_getTextSegment(
const char *text = whisper_full_get_segment_text(context, index);
jstring string = (*env)->NewStringUTF(env, text);
return string;
}
JNIEXPORT jstring JNICALL
Java_com_whispercppdemo_whisper_WhisperLib_00024Companion_getSystemInfo(
JNIEnv *env, jobject thiz
) {
UNUSED(thiz);
const char *sysinfo = whisper_print_system_info();
jstring string = (*env)->NewStringUTF(env, sysinfo);
return string;
}
JNIEXPORT jstring JNICALL
Java_com_whispercppdemo_whisper_WhisperLib_00024Companion_benchMemcpy(JNIEnv *env, jobject thiz,
jint n_threads) {
UNUSED(thiz);
const char *bench_ggml_memcpy = whisper_bench_memcpy_str(n_threads);
jstring string = (*env)->NewStringUTF(env, bench_ggml_memcpy);
}
JNIEXPORT jstring JNICALL
Java_com_whispercppdemo_whisper_WhisperLib_00024Companion_benchGgmlMulMat(JNIEnv *env, jobject thiz,
jint n_threads) {
UNUSED(thiz);
const char *bench_ggml_mul_mat = whisper_bench_ggml_mul_mat_str(n_threads);
jstring string = (*env)->NewStringUTF(env, bench_ggml_mul_mat);
}
}

View File

@ -24,5 +24,3 @@ Also, don't forget to add the `-DGGML_USE_ACCELERATE` compiler flag in Build Pha
This can significantly improve the performance of the transcription:
<img width="1072" alt="image" src="https://user-images.githubusercontent.com/1991296/208511239-8d7cdbd1-aa48-41b5-becd-ca288d53cc07.png">
In this project, it also added `-O3 -DNDEBUG` to `Other C Flags`, but adding flags to app proj is not ideal in real world (applies to all C/C++ files), consider splitting xcodeproj in workspace in your own project.

View File

@ -296,10 +296,6 @@
IPHONEOS_DEPLOYMENT_TARGET = 16.0;
MTL_ENABLE_DEBUG_INFO = NO;
MTL_FAST_MATH = YES;
OTHER_CFLAGS = (
"-O3",
"-DNDEBUG",
);
SDKROOT = iphoneos;
VALIDATE_PRODUCT = YES;
};

View File

@ -7,9 +7,8 @@ To use:
2. Add the model to "whisper.swiftui.demo/Resources/models" via Xcode.
3. Select a sample audio file (for example, [jfk.wav](https://github.com/ggerganov/whisper.cpp/raw/master/samples/jfk.wav)).
4. Add the model to "whisper.swiftui.demo/Resources/samples" via Xcode.
5. Select the "Release" [^2] build configuration under "Run", then deploy and run to your device.
5. Select the "release" build configuration under "Run", then deploy and run to your device.
[^1]: I recommend the tiny, base or small models for running on an iOS device.
[^2]: The `Release` build can boost performance of transcription. In this project, it also added `-O3 -DNDEBUG` to `Other C Flags`, but adding flags to app proj is not ideal in real world (applies to all C/C++ files), consider splitting xcodeproj in workspace in your own project.
![image](https://user-images.githubusercontent.com/1991296/212539216-0aef65e4-f882-480a-8358-0f816838fd52.png)

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@ -430,10 +430,6 @@
LLVM_LTO = YES;
MACOSX_DEPLOYMENT_TARGET = 13.0;
MARKETING_VERSION = 1.0;
OTHER_CFLAGS = (
"-O3",
"-DNDEBUG",
);
PRODUCT_BUNDLE_IDENTIFIER = com.whispercppdemo.WhisperCppDemo;
PRODUCT_NAME = "$(TARGET_NAME)";
SDKROOT = auto;

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@ -67,6 +67,23 @@ msg() {
echo >&2 -e "${1-}"
}
################################################################################
# create a temporary directory to work in
# set the temp_dir and temp_filename variables
################################################################################
temp_dir="$(mktemp -d ${SCRIPT_DIR}/tmp.XXXXXX)";
temp_filename="${temp_dir}/yt-dlp-filename";
################################################################################
# for now we only take one argument
# TODO: a for loop
################################################################################
source_url="${1}"
title_name="";
cleanup() {
local -r clean_me="${1}";
@ -128,20 +145,6 @@ fi
check_requirements;
################################################################################
# create a temporary directory to work in
# set the temp_dir and temp_filename variables
################################################################################
temp_dir="$(mktemp -d ${SCRIPT_DIR}/tmp.XXXXXX)";
temp_filename="${temp_dir}/yt-dlp-filename";
################################################################################
# for now we only take one argument
# TODO: a for loop
################################################################################
source_url="${1}"
title_name="";
msg "Downloading VOD...";
################################################################################
@ -196,6 +199,6 @@ ffmpeg -i "${temp_dir}/${title_name}.vod.mp4" \
-c:s mov_text \
-y "${title_name}-res.mp4";
#cleanup "${temp_dir}";
cleanup "${temp_dir}";
msg "Done! Your finished file is ready: ${title_name}-res.mp4";

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@ -1,70 +0,0 @@
# Benchmark word-level timestamps for different models
#
# This script takes two arguments
# - an audio file
# - [optional] path to a font file
# I'm using "/usr/share/fonts/truetype/freefont/FreeMono.ttf" on Ubuntu
if [ -z "$1" ]; then
echo "Usage: $0 <audio file> [font file]"
exit 1
fi
#TODO: Make this a command line parameter
#models="base small large"
#models="tiny.en tiny base.en base small.en small medium.en medium large-v1 large"
models="tiny.en base.en small.en medium.en large"
DURATION=$(ffprobe -i $1 -show_entries format=duration -v quiet -of csv="p=0")
DURATION=$(printf "%.2f" $DURATION)
echo "Input file duration: ${DURATION}s"
for model in $models; do
echo "Running $model"
COMMAND="./main -m models/ggml-$model.bin -owts -f $1 -of $1.$model"
if [ ! -z "$2" ]; then
COMMAND="$COMMAND -fp $2"
fi
#TODO: Surface errors better
# TIMEFMT is for zsh, TIMEFORMAT is for bash
EXECTIME=$({ TIMEFMT="%E";TIMEFORMAT=%E; time $COMMAND >/dev/null 2>&1; } 2>&1)
# Slightly different formats between zsh and bash
if [ "${EXECTIME: -1}" == "s" ]; then
EXECTIME=${EXECTIME::-1}
fi
RATIO=$(echo "$DURATION / $EXECTIME" | bc -l)
RATIO=$(printf "%.2f" $RATIO)
echo "Execution time: ${EXECTIME}s (${RATIO}x realtime)"
# If the file already exists, delete it
if [ -f $1.mp4 ]; then
rm $1.mp4
fi
bash $1.$model.wts >/dev/null 2>&1
mv $1.mp4 $1.$model.mp4
ffmpeg -y -f lavfi -i color=c=black:s=1200x50:d=$DURATION -vf "drawtext=fontfile=$2:fontsize=36:x=10:y=(h-text_h)/2:text='ggml-$model - ${EXECTIME}s (${RATIO}x realtime)':fontcolor=lightgrey" $1.$model.info.mp4 >/dev/null 2>&1
done
COMMAND="ffmpeg -y"
for model in $models; do
COMMAND="$COMMAND -i $1.$model.info.mp4 -i $1.$model.mp4"
done
COMMAND="$COMMAND -filter_complex \""
COUNT=0
for model in $models; do
COMMAND="$COMMAND[${COUNT}:v][$(($COUNT+1)):v]"
COUNT=$((COUNT+2))
done
COMMAND="$COMMAND vstack=inputs=${COUNT}[v]\" -map \"[v]\" -map 1:a $1.all.mp4 >/dev/null 2>&1"
echo $COMMAND
# Run the command
eval $COMMAND

2864
ggml.c

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27
ggml.h
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@ -198,8 +198,6 @@ struct ggml_object;
struct ggml_context;
enum ggml_type {
GGML_TYPE_Q4_0,
GGML_TYPE_Q4_1,
GGML_TYPE_I8,
GGML_TYPE_I16,
GGML_TYPE_I32,
@ -228,9 +226,7 @@ enum ggml_op {
GGML_OP_STEP,
GGML_OP_RELU,
GGML_OP_GELU,
GGML_OP_SILU,
GGML_OP_NORM, // normalize
GGML_OP_RMS_NORM,
GGML_OP_MUL_MAT,
@ -330,10 +326,7 @@ void ggml_print_objects(const struct ggml_context * ctx);
int ggml_nelements(const struct ggml_tensor * tensor);
size_t ggml_nbytes (const struct ggml_tensor * tensor);
int ggml_blck_size (enum ggml_type type);
size_t ggml_type_size (enum ggml_type type); // size in bytes for all elements in a block
float ggml_type_sizef(enum ggml_type type); // ggml_type_size()/ggml_blck_size() as float
size_t ggml_type_size (enum ggml_type type);
size_t ggml_element_size(const struct ggml_tensor * tensor);
struct ggml_context * ggml_init(struct ggml_init_params params);
@ -343,9 +336,6 @@ size_t ggml_used_mem(const struct ggml_context * ctx);
size_t ggml_set_scratch(struct ggml_context * ctx, struct ggml_scratch scratch);
bool ggml_mlock_supported(void);
bool ggml_mlock(struct ggml_context * ctx, char ** err_p);
struct ggml_tensor * ggml_new_tensor(
struct ggml_context * ctx,
enum ggml_type type,
@ -476,20 +466,12 @@ struct ggml_tensor * ggml_gelu(
struct ggml_context * ctx,
struct ggml_tensor * a);
struct ggml_tensor * ggml_silu(
struct ggml_context * ctx,
struct ggml_tensor * a);
// normalize along rows
// TODO: eps is hardcoded to 1e-5 for now
struct ggml_tensor * ggml_norm(
struct ggml_context * ctx,
struct ggml_tensor * a);
struct ggml_tensor * ggml_rms_norm(
struct ggml_context * ctx,
struct ggml_tensor * a);
// A: m rows, n columns
// B: p rows, n columns (i.e. we transpose it internally)
// result is m columns, p rows
@ -744,13 +726,6 @@ enum ggml_opt_result ggml_opt(
struct ggml_opt_params params,
struct ggml_tensor * f);
//
// quantization
//
size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int qk, int64_t * hist);
size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int qk, int64_t * hist);
//
// system info
//

View File

@ -6,7 +6,7 @@ using the [convert-pt-to-ggml.py](convert-pt-to-ggml.py) script. You can either
the `ggml` files yourself using the conversion script, or you can use the [download-ggml-model.sh](download-ggml-model.sh)
script to download the already converted models. Currently, they are hosted on the following locations:
- https://huggingface.co/ggerganov/whisper.cpp
- https://huggingface.co/datasets/ggerganov/whisper.cpp
- https://ggml.ggerganov.com
Sample usage:
@ -23,7 +23,7 @@ You can now use it like this:
A third option to obtain the model files is to download them from Hugging Face:
https://huggingface.co/ggerganov/whisper.cpp/tree/main
https://huggingface.co/datasets/ggerganov/whisper.cpp/tree/main
## Available models

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@ -79,11 +79,11 @@ dir_model = sys.argv[1]
dir_whisper = sys.argv[2]
dir_out = sys.argv[3]
with open(dir_model + "/vocab.json", "r", encoding="utf8") as f:
with open(dir_model + "/vocab.json", "r") as f:
encoder = json.load(f)
with open(dir_model + "/added_tokens.json", "r", encoding="utf8") as f:
with open(dir_model + "/added_tokens.json", "r") as f:
encoder_added = json.load(f)
with open(dir_model + "/config.json", "r", encoding="utf8") as f:
with open(dir_model + "/config.json", "r") as f:
hparams = json.load(f)
model = WhisperForConditionalGeneration.from_pretrained(dir_model)

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@ -1,82 +0,0 @@
#!/bin/bash
# This script downloads Whisper model files that have already been converted to Core ML format.
# This way you don't have to convert them yourself.
src="https://huggingface.co/datasets/ggerganov/whisper.cpp-coreml"
pfx="resolve/main/ggml"
# get the path of this script
function get_script_path() {
if [ -x "$(command -v realpath)" ]; then
echo "$(dirname $(realpath $0))"
else
local ret="$(cd -- "$(dirname "$0")" >/dev/null 2>&1 ; pwd -P)"
echo "$ret"
fi
}
models_path="$(get_script_path)"
# Whisper models
models=( "tiny.en" "tiny" "base.en" "base" "small.en" "small" "medium.en" "medium" "large-v1" "large" )
# list available models
function list_models {
printf "\n"
printf " Available models:"
for model in "${models[@]}"; do
printf " $model"
done
printf "\n\n"
}
if [ "$#" -ne 1 ]; then
printf "Usage: $0 <model>\n"
list_models
exit 1
fi
model=$1
if [[ ! " ${models[@]} " =~ " ${model} " ]]; then
printf "Invalid model: $model\n"
list_models
exit 1
fi
# download Core ML model
printf "Downloading Core ML model $model from '$src' ...\n"
cd $models_path
if [ -f "ggml-$model.mlmodel" ]; then
printf "Model $model already exists. Skipping download.\n"
exit 0
fi
if [ -x "$(command -v wget)" ]; then
wget --quiet --show-progress -O ggml-$model.mlmodel $src/$pfx-$model.mlmodel
elif [ -x "$(command -v curl)" ]; then
curl -L --output ggml-$model.mlmodel $src/$pfx-$model.mlmodel
else
printf "Either wget or curl is required to download models.\n"
exit 1
fi
if [ $? -ne 0 ]; then
printf "Failed to download Core ML model $model \n"
printf "Please try again later or download the original Whisper model files and convert them yourself.\n"
exit 1
fi
printf "Done! Model '$model' saved in 'models/ggml-$model.mlmodel'\n"
printf "Run the following command to compile it:\n\n"
printf " $ xcrun coremlc compile ./models/ggml-$model.mlmodel ./models\n\n"
printf "You can now use it like this:\n\n"
printf " $ ./main -m models/ggml-$model.bin -f samples/jfk.wav\n"
printf "\n"

View File

@ -40,7 +40,7 @@ if exist "ggml-%model%.bin" (
goto :eof
)
PowerShell -NoProfile -ExecutionPolicy Bypass -Command "Invoke-WebRequest -Uri https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-%model%.bin -OutFile ggml-%model%.bin"
PowerShell -NoProfile -ExecutionPolicy Bypass -Command "Invoke-WebRequest -Uri https://huggingface.co/datasets/ggerganov/whisper.cpp/resolve/main/ggml-%model%.bin -OutFile ggml-%model%.bin"
if %ERRORLEVEL% neq 0 (
echo Failed to download ggml model %model%

View File

@ -6,7 +6,7 @@
#src="https://ggml.ggerganov.com"
#pfx="ggml-model-whisper"
src="https://huggingface.co/ggerganov/whisper.cpp"
src="https://huggingface.co/datasets/ggerganov/whisper.cpp"
pfx="resolve/main/ggml"
# get the path of this script

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185
whisper.h
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@ -66,7 +66,6 @@ extern "C" {
//
struct whisper_context;
struct whisper_state;
typedef int whisper_token;
@ -102,20 +101,11 @@ extern "C" {
WHISPER_API struct whisper_context * whisper_init_from_buffer(void * buffer, size_t buffer_size);
WHISPER_API struct whisper_context * whisper_init(struct whisper_model_loader * loader);
// These are the same as the above, but the internal state of the context is not allocated automatically
// It is the responsibility of the caller to allocate the state using whisper_init_state() (#523)
WHISPER_API struct whisper_context * whisper_init_from_file_no_state(const char * path_model);
WHISPER_API struct whisper_context * whisper_init_from_buffer_no_state(void * buffer, size_t buffer_size);
WHISPER_API struct whisper_context * whisper_init_no_state(struct whisper_model_loader * loader);
WHISPER_API struct whisper_state * whisper_init_state(struct whisper_context * ctx);
// Frees all allocated memory
WHISPER_API void whisper_free (struct whisper_context * ctx);
WHISPER_API void whisper_free_state(struct whisper_state * state);
// Frees all memory allocated by the model.
WHISPER_API void whisper_free(struct whisper_context * ctx);
// Convert RAW PCM audio to log mel spectrogram.
// The resulting spectrogram is stored inside the default state of the provided whisper context.
// The resulting spectrogram is stored inside the provided whisper context.
// Returns 0 on success
WHISPER_API int whisper_pcm_to_mel(
struct whisper_context * ctx,
@ -123,30 +113,17 @@ extern "C" {
int n_samples,
int n_threads);
WHISPER_API int whisper_pcm_to_mel_with_state(
struct whisper_context * ctx,
struct whisper_state * state,
const float * samples,
int n_samples,
int n_threads);
// 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.
// 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 provided whisper context.
// Returns 0 on success
WHISPER_API int whisper_pcm_to_mel_phase_vocoder(
struct whisper_context * ctx,
const float * samples,
int n_samples,
int n_threads);
struct whisper_context* ctx,
const float* samples,
int n_samples,
int n_threads);
WHISPER_API int whisper_pcm_to_mel_phase_vocoder_with_state(
struct whisper_context * ctx,
struct whisper_state * state,
const 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.
// This can be used to set a custom log mel spectrogram inside the provided whisper context.
// Use this instead of whisper_pcm_to_mel() if you want to provide your own log mel spectrogram.
// n_mel must be 80
// Returns 0 on success
@ -156,14 +133,7 @@ extern "C" {
int n_len,
int n_mel);
WHISPER_API int whisper_set_mel_with_state(
struct whisper_context * ctx,
struct whisper_state * state,
const 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.
// Run the Whisper encoder on the log mel spectrogram stored inside the provided whisper context.
// Make sure to call whisper_pcm_to_mel() or whisper_set_mel() first.
// offset can be used to specify the offset of the first frame in the spectrogram.
// Returns 0 on success
@ -172,12 +142,6 @@ extern "C" {
int offset,
int n_threads);
WHISPER_API int whisper_encode_with_state(
struct whisper_context * ctx,
struct whisper_state * 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.
@ -191,14 +155,6 @@ extern "C" {
int n_past,
int n_threads);
WHISPER_API int whisper_decode_with_state(
struct whisper_context * ctx,
struct whisper_state * state,
const whisper_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
@ -234,44 +190,20 @@ extern "C" {
int n_threads,
float * lang_probs);
WHISPER_API int whisper_lang_auto_detect_with_state(
struct whisper_context * ctx,
struct whisper_state * state,
int offset_ms,
int n_threads,
float * lang_probs);
WHISPER_API int whisper_n_len (struct whisper_context * ctx); // mel length
WHISPER_API int whisper_n_len_from_state(struct whisper_state * state); // mel length
WHISPER_API int whisper_n_vocab (struct whisper_context * ctx);
WHISPER_API int whisper_n_text_ctx (struct whisper_context * ctx);
WHISPER_API int whisper_n_audio_ctx (struct whisper_context * ctx);
WHISPER_API int whisper_is_multilingual (struct whisper_context * ctx);
WHISPER_API int whisper_model_n_vocab (struct whisper_context * ctx);
WHISPER_API int whisper_model_n_audio_ctx (struct whisper_context * ctx);
WHISPER_API int whisper_model_n_audio_state(struct whisper_context * ctx);
WHISPER_API int whisper_model_n_audio_head (struct whisper_context * ctx);
WHISPER_API int whisper_model_n_audio_layer(struct whisper_context * ctx);
WHISPER_API int whisper_model_n_text_ctx (struct whisper_context * ctx);
WHISPER_API int whisper_model_n_text_state (struct whisper_context * ctx);
WHISPER_API int whisper_model_n_text_head (struct whisper_context * ctx);
WHISPER_API int whisper_model_n_text_layer (struct whisper_context * ctx);
WHISPER_API int whisper_model_n_mels (struct whisper_context * ctx);
WHISPER_API int whisper_model_f16 (struct whisper_context * ctx);
WHISPER_API int whisper_model_type (struct whisper_context * ctx);
WHISPER_API int whisper_n_len (struct whisper_context * ctx); // mel length
WHISPER_API int whisper_n_vocab (struct whisper_context * ctx);
WHISPER_API int whisper_n_text_ctx (struct whisper_context * ctx);
WHISPER_API int whisper_n_audio_ctx (struct whisper_context * ctx);
WHISPER_API int whisper_is_multilingual(struct whisper_context * 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
WHISPER_API float * whisper_get_logits (struct whisper_context * ctx);
WHISPER_API float * whisper_get_logits_from_state(struct whisper_state * state);
WHISPER_API float * whisper_get_logits(struct whisper_context * ctx);
// Token Id -> String. Uses the vocabulary in the provided context
WHISPER_API const char * whisper_token_to_str(struct whisper_context * ctx, whisper_token token);
WHISPER_API const char * whisper_model_type_readable(struct whisper_context * ctx);
// Special tokens
WHISPER_API whisper_token whisper_token_eot (struct whisper_context * ctx);
@ -286,7 +218,7 @@ extern "C" {
WHISPER_API whisper_token whisper_token_translate (void);
WHISPER_API whisper_token whisper_token_transcribe(void);
// Performance information from the default state.
// Performance information
WHISPER_API void whisper_print_timings(struct whisper_context * ctx);
WHISPER_API void whisper_reset_timings(struct whisper_context * ctx);
@ -304,23 +236,12 @@ extern "C" {
// Text segment callback
// Called on every newly generated text segment
// Use the whisper_full_...() functions to obtain the text segments
typedef void (*whisper_new_segment_callback)(struct whisper_context * ctx, struct whisper_state * state, int n_new, void * user_data);
typedef void (*whisper_new_segment_callback)(struct whisper_context * ctx, int n_new, void * user_data);
// Encoder begin callback
// If not NULL, called before the encoder starts
// If it returns false, the computation is aborted
typedef bool (*whisper_encoder_begin_callback)(struct whisper_context * ctx, struct whisper_state * state, void * user_data);
// Logits filter callback
// Can be used to modify the logits before sampling
// If not NULL, called after applying temperature to logits
typedef void (*whisper_logits_filter_callback)(
struct whisper_context * ctx,
struct whisper_state * state,
const whisper_token_data * tokens,
int n_tokens,
float * logits,
void * user_data);
typedef bool (*whisper_encoder_begin_callback)(struct whisper_context * ctx, void * user_data);
// Parameters for the whisper_full() function
// If you chnage the order or add new parameters, make sure to update the default values in whisper.cpp:
@ -394,16 +315,11 @@ extern "C" {
// called each time before the encoder starts
whisper_encoder_begin_callback encoder_begin_callback;
void * encoder_begin_callback_user_data;
// called by each decoder to filter obtained logits
whisper_logits_filter_callback logits_filter_callback;
void * logits_filter_callback_user_data;
};
WHISPER_API struct whisper_full_params whisper_full_default_params(enum whisper_sampling_strategy strategy);
// 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.
WHISPER_API int whisper_full(
struct whisper_context * ctx,
@ -411,16 +327,7 @@ extern "C" {
const float * samples,
int n_samples);
WHISPER_API int whisper_full_with_state(
struct whisper_context * ctx,
struct whisper_state * state,
struct whisper_full_params params,
const 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.
// Split the input audio in chunks and process each chunk separately using whisper_full()
// It seems this approach can offer some speedup in some cases.
// However, the transcription accuracy can be worse at the beginning and end of each chunk.
WHISPER_API int whisper_full_parallel(
@ -430,56 +337,40 @@ extern "C" {
int n_samples,
int n_processors);
// Number of generated text segments
// Number of generated text segments.
// A segment can be a few words, a sentence, or even a paragraph.
WHISPER_API int whisper_full_n_segments (struct whisper_context * ctx);
WHISPER_API int whisper_full_n_segments_from_state(struct whisper_state * state);
WHISPER_API int whisper_full_n_segments(struct whisper_context * ctx);
// Language id associated with the context's default state
// Language id associated with the current context
WHISPER_API int whisper_full_lang_id(struct whisper_context * ctx);
// Language id associated with the provided state
WHISPER_API int whisper_full_lang_id_from_state(struct whisper_state * state);
// Get the start and end time of the specified segment.
WHISPER_API int64_t whisper_full_get_segment_t0(struct whisper_context * ctx, int i_segment);
WHISPER_API int64_t whisper_full_get_segment_t1(struct whisper_context * ctx, int i_segment);
// Get the start and end time of the specified segment
WHISPER_API int64_t whisper_full_get_segment_t0 (struct whisper_context * ctx, int i_segment);
WHISPER_API int64_t whisper_full_get_segment_t0_from_state(struct whisper_state * state, int i_segment);
// Get the text of the specified segment.
WHISPER_API const char * whisper_full_get_segment_text(struct whisper_context * ctx, int i_segment);
WHISPER_API int64_t whisper_full_get_segment_t1 (struct whisper_context * ctx, int i_segment);
WHISPER_API int64_t whisper_full_get_segment_t1_from_state(struct whisper_state * state, int i_segment);
// Get number of tokens in the specified segment.
WHISPER_API int whisper_full_n_tokens(struct whisper_context * ctx, int i_segment);
// Get the text of the specified segment
WHISPER_API const char * whisper_full_get_segment_text (struct whisper_context * ctx, int i_segment);
WHISPER_API const char * whisper_full_get_segment_text_from_state(struct whisper_state * state, int i_segment);
// Get the token text of the specified token in the specified segment.
WHISPER_API const char * whisper_full_get_token_text(struct whisper_context * ctx, int i_segment, int i_token);
WHISPER_API whisper_token whisper_full_get_token_id (struct whisper_context * ctx, int i_segment, int i_token);
// Get number of tokens in the specified segment
WHISPER_API int whisper_full_n_tokens (struct whisper_context * ctx, int i_segment);
WHISPER_API int whisper_full_n_tokens_from_state(struct whisper_state * state, int i_segment);
// Get the token text of the specified token in the specified segment
WHISPER_API const char * whisper_full_get_token_text (struct whisper_context * ctx, int i_segment, int i_token);
WHISPER_API const char * whisper_full_get_token_text_from_state(struct whisper_context * ctx, struct whisper_state * state, int i_segment, int i_token);
WHISPER_API whisper_token whisper_full_get_token_id (struct whisper_context * ctx, int i_segment, int i_token);
WHISPER_API whisper_token whisper_full_get_token_id_from_state(struct whisper_state * state, int i_segment, int i_token);
// Get token data for the specified token in the specified segment
// Get token data for the specified token in the specified segment.
// This contains probabilities, timestamps, etc.
WHISPER_API whisper_token_data whisper_full_get_token_data (struct whisper_context * ctx, int i_segment, int i_token);
WHISPER_API whisper_token_data whisper_full_get_token_data_from_state(struct whisper_state * state, int i_segment, int i_token);
WHISPER_API whisper_token_data whisper_full_get_token_data(struct whisper_context * ctx, int i_segment, int i_token);
// Get the probability of the specified token in the specified segment
WHISPER_API float whisper_full_get_token_p (struct whisper_context * ctx, int i_segment, int i_token);
WHISPER_API float whisper_full_get_token_p_from_state(struct whisper_state * state, int i_segment, int i_token);
// Get the probability of the specified token in the specified segment.
WHISPER_API float whisper_full_get_token_p(struct whisper_context * ctx, int i_segment, int i_token);
////////////////////////////////////////////////////////////////////////////
// Temporary helpers needed for exposing ggml interface
WHISPER_API int whisper_bench_memcpy(int n_threads);
WHISPER_API const char * whisper_bench_memcpy_str(int n_threads);
WHISPER_API int whisper_bench_ggml_mul_mat(int n_threads);
WHISPER_API const char * whisper_bench_ggml_mul_mat_str(int n_threads);
#ifdef __cplusplus
}