开源C++智能语音识别库whisper.cpp开发使用入门

whisper.cpp是一个C++编写的轻量级开源智能语音识别库，是基于openai的开源python智能语音模型whisper的移植版本，依赖项少，内存占用低，性能更优，方便作为依赖库集成的到应用程序中提供语音识别功能。

以下基于whisper.cpp的源码利用C++ api来开发实例demo演示读取本地音频文件并转成文字。

项目结构

whispercpp_starter
    - whisper.cpp-v1.5.0
    - src
      |- main.cpp
    - CMakeLists.txt

CMakeLists.txt

cmake_minimum_required(VERSION 3.15)

project(whispercpp_starter)

set(CMAKE_CXX_STANDARD 14)
set(CMAKE_CXX_STANDARD_REQUIRED ON)

add_subdirectory(whisper.cpp-v1.5.0)

include_directories(${CMAKE_CURRENT_SOURCE_DIR}/whisper.cpp-v1.5.0
    ${CMAKE_CURRENT_SOURCE_DIR}/whisper.cpp-v1.5.0/examples
)

file(GLOB SRC
    src/*.h
    src/*.cpp
)

add_executable(${PROJECT_NAME}${SRC})

target_link_libraries(${PROJECT_NAME}
    common
    whisper # remember to copy dll or so to bin folder)

main.cpp

#include<cmath>#include<fstream>#include<cstdio>#include<string>#include<thread>#include<vector>#include<cstring>#include"common.h"#include"whisper.h"#ifdefined(_MSC_VER)#pragmawarning(disable:42444267)// possible loss of data#endif// Terminal color map. 10 colors grouped in ranges [0.0, 0.1, ..., 0.9]// Lowest is red, middle is yellow, highest is green.const std::vector<std::string> k_colors ={"\033[38;5;196m","\033[38;5;202m","\033[38;5;208m","\033[38;5;214m","\033[38;5;220m","\033[38;5;226m","\033[38;5;190m","\033[38;5;154m","\033[38;5;118m","\033[38;5;82m",};//  500 -> 00:05.000// 6000 -> 01:00.000
std::string to_timestamp(int64_t t,bool comma =false){int64_t msec = t *10;int64_t hr = msec /(1000*60*60);
    msec = msec - hr *(1000*60*60);int64_t min = msec /(1000*60);
    msec = msec - min *(1000*60);int64_t sec = msec /1000;
    msec = msec - sec *1000;char buf[32];snprintf(buf,sizeof(buf),"%02d:%02d:%02d%s%03d",(int)hr,(int)min,(int)sec, comma ?",":".",(int)msec);return std::string(buf);}inttimestamp_to_sample(int64_t t,int n_samples){return std::max(0, std::min((int)n_samples -1,(int)((t * WHISPER_SAMPLE_RATE)/100)));}// helper function to replace substringsvoidreplace_all(std::string& s,const std::string& search,const std::string& replace){for(size_t pos =0;; pos += replace.length()){
        pos = s.find(search, pos);if(pos == std::string::npos)break;
        s.erase(pos, search.length());
        s.insert(pos, replace);}}// command-line parametersstructwhisper_params{int32_t n_threads = std::min(4,(int32_t)std::thread::hardware_concurrency());int32_t n_processors =1;int32_t offset_t_ms =0;int32_t offset_n =0;int32_t duration_ms =0;int32_t progress_step =5;int32_t max_context =-1;int32_t max_len =0;int32_t best_of =whisper_full_default_params(WHISPER_SAMPLING_GREEDY).greedy.best_of;int32_t beam_size =whisper_full_default_params(WHISPER_SAMPLING_BEAM_SEARCH).beam_search.beam_size;float word_thold =0.01f;float entropy_thold =2.40f;float logprob_thold =-1.00f;bool speed_up =false;bool debug_mode =false;bool translate =false;bool detect_language =false;bool diarize =false;bool tinydiarize =false;bool split_on_word =false;bool no_fallback =false;bool output_txt =false;bool output_vtt =false;bool output_srt =false;bool output_wts =false;bool output_csv =false;bool output_jsn =false;bool output_jsn_full =false;bool output_lrc =false;bool print_special =false;bool print_colors =false;bool print_progress =false;bool no_timestamps =false;bool log_score =false;bool use_gpu =true;

    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";// [TDRZ] speaker turn string
    std::string tdrz_speaker_turn =" [SPEAKER_TURN]";// TODO: set from command line

    std::string openvino_encode_device ="CPU";

    std::vector<std::string> fname_inp ={};
    std::vector<std::string> fname_out ={};};structwhisper_print_user_data{const whisper_params* params;const std::vector<std::vector<float>>* pcmf32s;int progress_prev;};

std::string estimate_diarization_speaker(std::vector<std::vector<float>> pcmf32s,int64_t t0,int64_t t1,bool id_only =false){
    std::string speaker ="";constint64_t n_samples = pcmf32s[0].size();constint64_t is0 =timestamp_to_sample(t0, n_samples);constint64_t is1 =timestamp_to_sample(t1, n_samples);double energy0 =0.0f;double energy1 =0.0f;for(int64_t j = is0; j < is1; j++){
        energy0 +=fabs(pcmf32s[0][j]);
        energy1 +=fabs(pcmf32s[1][j]);}if(energy0 >1.1* energy1){
        speaker ="0";}elseif(energy1 >1.1* energy0){
        speaker ="1";}else{
        speaker ="?";}//printf("is0 = %lld, is1 = %lld, energy0 = %f, energy1 = %f, speaker = %s\n", is0, is1, energy0, energy1, speaker.c_str());if(!id_only){
        speaker.insert(0,"(speaker ");
        speaker.append(")");}return speaker;}voidwhisper_print_progress_callback(structwhisper_context*/*ctx*/,structwhisper_state*/*state*/,int progress,void* user_data){int progress_step =((whisper_print_user_data*)user_data)->params->progress_step;int* progress_prev =&(((whisper_print_user_data*)user_data)->progress_prev);if(progress >=*progress_prev + progress_step){*progress_prev += progress_step;fprintf(stderr,"%s: progress = %3d%%\n",__func__, progress);}}voidwhisper_print_segment_callback(structwhisper_context* ctx,structwhisper_state*/*state*/,int n_new,void* user_data){constauto& params =*((whisper_print_user_data*)user_data)->params;constauto& pcmf32s =*((whisper_print_user_data*)user_data)->pcmf32s;constint n_segments =whisper_full_n_segments(ctx);

    std::string speaker ="";int64_t t0 =0;int64_t t1 =0;// print the last n_new segmentsconstint s0 = n_segments - n_new;if(s0 ==0){printf("\n");}for(int i = s0; i < n_segments; i++){if(!params.no_timestamps || params.diarize){
            t0 =whisper_full_get_segment_t0(ctx, i);
            t1 =whisper_full_get_segment_t1(ctx, i);}if(!params.no_timestamps){printf("[%s --> %s]  ",to_timestamp(t0).c_str(),to_timestamp(t1).c_str());}if(params.diarize && pcmf32s.size()==2){
            speaker =estimate_diarization_speaker(pcmf32s, t0, t1);}if(params.print_colors){for(int j =0; j <whisper_full_n_tokens(ctx, i);++j){if(params.print_special ==false){const whisper_token id =whisper_full_get_token_id(ctx, i, j);if(id >=whisper_token_eot(ctx)){continue;}}constchar* text =whisper_full_get_token_text(ctx, i, j);constfloat  p =whisper_full_get_token_p(ctx, i, j);constint col = std::max(0, std::min((int)k_colors.size()-1,(int)(std::pow(p,3)*float(k_colors.size()))));printf("%s%s%s%s", speaker.c_str(), k_colors[col].c_str(), text,"\033[0m");}}else{constchar* text =whisper_full_get_segment_text(ctx, i);printf("%s%s", speaker.c_str(), text);}if(params.tinydiarize){if(whisper_full_get_segment_speaker_turn_next(ctx, i)){printf("%s", params.tdrz_speaker_turn.c_str());}}// with timestamps or speakers: each segment on new lineif(!params.no_timestamps || params.diarize){printf("\n");}fflush(stdout);}}booloutput_txt(structwhisper_context* ctx,constchar* fname,const whisper_params& params, std::vector<std::vector<float>> pcmf32s){
    std::ofstream fout(fname);if(!fout.is_open()){fprintf(stderr,"%s: failed to open '%s' for writing\n",__func__, fname);returnfalse;}fprintf(stderr,"%s: saving output to '%s'\n",__func__, fname);constint n_segments =whisper_full_n_segments(ctx);for(int i =0; i < n_segments;++i){constchar* text =whisper_full_get_segment_text(ctx, i);
        std::string speaker ="";if(params.diarize && pcmf32s.size()==2){constint64_t t0 =whisper_full_get_segment_t0(ctx, i);constint64_t t1 =whisper_full_get_segment_t1(ctx, i);
            speaker =estimate_diarization_speaker(pcmf32s, t0, t1);}

        fout << speaker << text <<"\n";}returntrue;}intmain(int argc,char** argv){const std::string model_file_path ="./ggml-base.en.bin";const std::string audio_file_path ="sample.wav";// should be wav 16bit format// set whisper params
    whisper_params params;
    params.model = model_file_path;
    params.fname_inp.emplace_back(audio_file_path);// whisper initstructwhisper_context_params cparams;
    cparams.use_gpu = params.use_gpu;structwhisper_context* ctx =whisper_init_from_file_with_params(params.model.c_str(), cparams);if(ctx ==nullptr){fprintf(stderr,"error: failed to initialize whisper context\n");return3;}// initialize openvino encoder. this has no effect on whisper.cpp builds that don't have OpenVINO configuredwhisper_ctx_init_openvino_encoder(ctx,nullptr, params.openvino_encode_device.c_str(),nullptr);for(int f =0; f <(int)params.fname_inp.size();++f){constauto fname_inp = params.fname_inp[f];constauto fname_out = f <(int)params.fname_out.size()&&!params.fname_out[f].empty()? params.fname_out[f]: params.fname_inp[f];

        std::vector<float> pcmf32;// mono-channel F32 PCM
        std::vector<std::vector<float>> pcmf32s;// stereo-channel F32 PCMif(!read_wav(fname_inp, pcmf32, pcmf32s, params.diarize)){fprintf(stderr,"error: failed to read WAV file '%s'\n", fname_inp.c_str());continue;}// print system information{fprintf(stderr,"\n");fprintf(stderr,"system_info: n_threads = %d / %d | %s\n",
                params.n_threads * params.n_processors, std::thread::hardware_concurrency(),whisper_print_system_info());}// print some info about the processing{fprintf(stderr,"\n");if(!whisper_is_multilingual(ctx)){if(params.language !="en"|| params.translate){
                    params.language ="en";
                    params.translate =false;fprintf(stderr,"%s: WARNING: model is not multilingual, ignoring language and translation options\n",__func__);}}if(params.detect_language){
                params.language ="auto";}fprintf(stderr,"%s: processing '%s' (%d samples, %.1f sec), %d threads, %d processors, %d beams + best of %d, lang = %s, task = %s, %stimestamps = %d ...\n",__func__, fname_inp.c_str(),int(pcmf32.size()),float(pcmf32.size())/ WHISPER_SAMPLE_RATE,
                params.n_threads, params.n_processors, params.beam_size, params.best_of,
                params.language.c_str(),
                params.translate ?"translate":"transcribe",
                params.tinydiarize ?"tdrz = 1, ":"",
                params.no_timestamps ?0:1);fprintf(stderr,"\n");}// run the inference{
            whisper_full_params wparams =whisper_full_default_params(WHISPER_SAMPLING_GREEDY);

            wparams.strategy = params.beam_size >1? WHISPER_SAMPLING_BEAM_SEARCH : WHISPER_SAMPLING_GREEDY;

            wparams.print_realtime =false;
            wparams.print_progress = params.print_progress;
            wparams.print_timestamps =!params.no_timestamps;
            wparams.print_special = params.print_special;
            wparams.translate = params.translate;
            wparams.language = params.language.c_str();
            wparams.detect_language = params.detect_language;
            wparams.n_threads = params.n_threads;
            wparams.n_max_text_ctx = params.max_context >=0? params.max_context : wparams.n_max_text_ctx;
            wparams.offset_ms = params.offset_t_ms;
            wparams.duration_ms = params.duration_ms;

            wparams.token_timestamps = params.output_wts || params.output_jsn_full || params.max_len >0;
            wparams.thold_pt = params.word_thold;
            wparams.max_len = params.output_wts && params.max_len ==0?60: params.max_len;
            wparams.split_on_word = params.split_on_word;

            wparams.speed_up = params.speed_up;
            wparams.debug_mode = params.debug_mode;

            wparams.tdrz_enable = params.tinydiarize;// [TDRZ]

            wparams.initial_prompt = params.prompt.c_str();

            wparams.greedy.best_of = params.best_of;
            wparams.beam_search.beam_size = params.beam_size;

            wparams.temperature_inc = params.no_fallback ?0.0f: wparams.temperature_inc;
            wparams.entropy_thold = params.entropy_thold;
            wparams.logprob_thold = params.logprob_thold;

            whisper_print_user_data user_data ={&params,&pcmf32s,0};// this callback is called on each new segmentif(!wparams.print_realtime){
                wparams.new_segment_callback = whisper_print_segment_callback;
                wparams.new_segment_callback_user_data =&user_data;}if(wparams.print_progress){
                wparams.progress_callback = whisper_print_progress_callback;
                wparams.progress_callback_user_data =&user_data;}// examples for abort mechanism// in examples below, we do not abort the processing, but we could if the flag is set to true// the callback is called before every encoder run - if it returns false, the processing is aborted{staticbool is_aborted =false;// NOTE: this should be atomic to avoid data race

                wparams.encoder_begin_callback =[](structwhisper_context*/*ctx*/,structwhisper_state*/*state*/,void* user_data){bool is_aborted =*(bool*)user_data;return!is_aborted;};
                wparams.encoder_begin_callback_user_data =&is_aborted;}// the callback is called before every computation - if it returns true, the computation is aborted{staticbool is_aborted =false;// NOTE: this should be atomic to avoid data race

                wparams.abort_callback =[](void* user_data){bool is_aborted =*(bool*)user_data;return is_aborted;};
                wparams.abort_callback_user_data =&is_aborted;}if(whisper_full_parallel(ctx, wparams, pcmf32.data(), pcmf32.size(), params.n_processors)!=0){fprintf(stderr,"%s: failed to process audio\n", argv[0]);return10;}}// output stuff{printf("\n");// output to text fileif(params.output_txt){constauto fname_txt = fname_out +".txt";output_txt(ctx, fname_txt.c_str(), params, pcmf32s);}}}// whisper releasewhisper_print_timings(ctx);whisper_free(ctx);return0;}

注：

• whisper支持的模型文件需要自己去下载
• whisper.cpp编译可以配置多种类型的增强选项，比如支持CPU/GPU加速，数据计算加速库
• whisper.cpp的编译cmake文件做了少量改动，方便集成到项目，具体可参看demo

源码

whispercpp_starter

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