/**
|
* Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
|
* MIT License (https://opensource.org/licenses/MIT)
|
*/
|
|
#ifndef _WIN32
|
#include <sys/time.h>
|
#else
|
#include <win_func.h>
|
#endif
|
|
#include <iostream>
|
#include <fstream>
|
#include <sstream>
|
#include <map>
|
#include <glog/logging.h>
|
#include "util.h"
|
#include "funasrruntime.h"
|
#include "tclap/CmdLine.h"
|
#include "com-define.h"
|
#include "audio.h"
|
|
using namespace std;
|
|
bool is_target_file(const std::string& filename, const std::string target) {
|
std::size_t pos = filename.find_last_of(".");
|
if (pos == std::string::npos) {
|
return false;
|
}
|
std::string extension = filename.substr(pos + 1);
|
return (extension == target);
|
}
|
|
void GetValue(TCLAP::ValueArg<std::string>& value_arg, string key, std::map<std::string, std::string>& model_path)
|
{
|
model_path.insert({key, value_arg.getValue()});
|
LOG(INFO)<< key << " : " << value_arg.getValue();
|
}
|
|
int main(int argc, char** argv)
|
{
|
google::InitGoogleLogging(argv[0]);
|
FLAGS_logtostderr = true;
|
|
TCLAP::CmdLine cmd("funasr-onnx-2pass", ' ', "1.0");
|
TCLAP::ValueArg<std::string> offline_model_dir("", OFFLINE_MODEL_DIR, "the asr offline model path, which contains model.onnx, config.yaml, am.mvn", true, "", "string");
|
TCLAP::ValueArg<std::string> online_model_dir("", ONLINE_MODEL_DIR, "the asr online model path, which contains model.onnx, decoder.onnx, config.yaml, am.mvn", true, "", "string");
|
TCLAP::ValueArg<std::string> quantize("", QUANTIZE, "true (Default), load the model of model.onnx in model_dir. If set true, load the model of model_quant.onnx in model_dir", false, "true", "string");
|
TCLAP::ValueArg<std::string> vad_dir("", VAD_DIR, "the vad online model path, which contains model.onnx, vad.yaml, vad.mvn", false, "", "string");
|
TCLAP::ValueArg<std::string> vad_quant("", VAD_QUANT, "true (Default), load the model of model.onnx in vad_dir. If set true, load the model of model_quant.onnx in vad_dir", false, "true", "string");
|
TCLAP::ValueArg<std::string> punc_dir("", PUNC_DIR, "the punc online model path, which contains model.onnx, punc.yaml", false, "", "string");
|
TCLAP::ValueArg<std::string> punc_quant("", PUNC_QUANT, "true (Default), load the model of model.onnx in punc_dir. If set true, load the model of model_quant.onnx in punc_dir", false, "true", "string");
|
TCLAP::ValueArg<std::string> itn_dir("", ITN_DIR, "the itn model(fst) path, which contains zh_itn_tagger.fst and zh_itn_verbalizer.fst", false, "", "string");
|
TCLAP::ValueArg<std::string> lm_dir("", LM_DIR, "the lm model path, which contains compiled models: TLG.fst, config.yaml, lexicon.txt ", false, "", "string");
|
TCLAP::ValueArg<float> global_beam("", GLOB_BEAM, "the decoding beam for beam searching ", false, 3.0, "float");
|
TCLAP::ValueArg<float> lattice_beam("", LAT_BEAM, "the lattice generation beam for beam searching ", false, 3.0, "float");
|
TCLAP::ValueArg<float> am_scale("", AM_SCALE, "the acoustic scale for beam searching ", false, 10.0, "float");
|
TCLAP::ValueArg<std::int32_t> fst_inc_wts("", FST_INC_WTS, "the fst hotwords incremental bias", false, 20, "int32_t");
|
TCLAP::ValueArg<std::string> asr_mode("", ASR_MODE, "offline, online, 2pass", false, "2pass", "string");
|
TCLAP::ValueArg<std::int32_t> onnx_thread("", "model-thread-num", "onnxruntime SetIntraOpNumThreads", false, 1, "int32_t");
|
|
TCLAP::ValueArg<std::string> wav_path("", WAV_PATH, "the input could be: wav_path, e.g.: asr_example.wav; pcm_path, e.g.: asr_example.pcm; wav.scp, kaldi style wav list (wav_id \t wav_path)", true, "", "string");
|
TCLAP::ValueArg<std::int32_t> audio_fs("", AUDIO_FS, "the sample rate of audio", false, 16000, "int32_t");
|
TCLAP::ValueArg<std::string> hotword("", HOTWORD, "the hotword file, one hotword perline, Format: Hotword Weight (could be: 阿里巴巴 20)", false, "", "string");
|
|
cmd.add(offline_model_dir);
|
cmd.add(online_model_dir);
|
cmd.add(quantize);
|
cmd.add(vad_dir);
|
cmd.add(vad_quant);
|
cmd.add(punc_dir);
|
cmd.add(punc_quant);
|
cmd.add(lm_dir);
|
cmd.add(global_beam);
|
cmd.add(lattice_beam);
|
cmd.add(am_scale);
|
cmd.add(fst_inc_wts);
|
cmd.add(itn_dir);
|
cmd.add(wav_path);
|
cmd.add(audio_fs);
|
cmd.add(asr_mode);
|
cmd.add(onnx_thread);
|
cmd.add(hotword);
|
cmd.parse(argc, argv);
|
|
std::map<std::string, std::string> model_path;
|
GetValue(offline_model_dir, OFFLINE_MODEL_DIR, model_path);
|
GetValue(online_model_dir, ONLINE_MODEL_DIR, model_path);
|
GetValue(quantize, QUANTIZE, model_path);
|
GetValue(vad_dir, VAD_DIR, model_path);
|
GetValue(vad_quant, VAD_QUANT, model_path);
|
GetValue(punc_dir, PUNC_DIR, model_path);
|
GetValue(punc_quant, PUNC_QUANT, model_path);
|
GetValue(lm_dir, LM_DIR, model_path);
|
GetValue(itn_dir, ITN_DIR, model_path);
|
GetValue(wav_path, WAV_PATH, model_path);
|
GetValue(asr_mode, ASR_MODE, model_path);
|
|
struct timeval start, end;
|
gettimeofday(&start, nullptr);
|
int thread_num = onnx_thread.getValue();
|
int asr_mode_ = -1;
|
if(model_path[ASR_MODE] == "offline"){
|
asr_mode_ = 0;
|
}else if(model_path[ASR_MODE] == "online"){
|
asr_mode_ = 1;
|
}else if(model_path[ASR_MODE] == "2pass"){
|
asr_mode_ = 2;
|
}else{
|
LOG(ERROR) << "Wrong asr-mode : " << model_path[ASR_MODE];
|
exit(-1);
|
}
|
FUNASR_HANDLE tpass_handle=FunTpassInit(model_path, thread_num);
|
|
if (!tpass_handle)
|
{
|
LOG(ERROR) << "FunTpassInit init failed";
|
exit(-1);
|
}
|
float glob_beam = 3.0f;
|
float lat_beam = 3.0f;
|
float am_sc = 10.0f;
|
if (lm_dir.isSet()) {
|
glob_beam = global_beam.getValue();
|
lat_beam = lattice_beam.getValue();
|
am_sc = am_scale.getValue();
|
}
|
// init wfst decoder
|
FUNASR_DEC_HANDLE decoder_handle = FunASRWfstDecoderInit(tpass_handle, ASR_TWO_PASS, glob_beam, lat_beam, am_sc);
|
|
gettimeofday(&end, nullptr);
|
long seconds = (end.tv_sec - start.tv_sec);
|
long modle_init_micros = ((seconds * 1000000) + end.tv_usec) - (start.tv_usec);
|
LOG(INFO) << "Model initialization takes " << (double)modle_init_micros / 1000000 << " s";
|
|
// hotword file
|
unordered_map<string, int> hws_map;
|
std::string nn_hotwords_ = "";
|
std::string hotword_path = hotword.getValue();
|
LOG(INFO) << "hotword path: " << hotword_path;
|
funasr::ExtractHws(hotword_path, hws_map, nn_hotwords_);
|
|
// read wav_path
|
vector<string> wav_list;
|
vector<string> wav_ids;
|
string default_id = "wav_default_id";
|
string wav_path_ = model_path.at(WAV_PATH);
|
|
if(is_target_file(wav_path_, "scp")){
|
ifstream in(wav_path_);
|
if (!in.is_open()) {
|
LOG(ERROR) << "Failed to open file: " << model_path.at(WAV_SCP) ;
|
return 0;
|
}
|
string line;
|
while(getline(in, line))
|
{
|
istringstream iss(line);
|
string column1, column2;
|
iss >> column1 >> column2;
|
wav_list.emplace_back(column2);
|
wav_ids.emplace_back(column1);
|
}
|
in.close();
|
}else{
|
wav_list.emplace_back(wav_path_);
|
wav_ids.emplace_back(default_id);
|
}
|
|
// load hotwords list and build graph
|
FunWfstDecoderLoadHwsRes(decoder_handle, fst_inc_wts.getValue(), hws_map);
|
|
std::vector<std::vector<float>> hotwords_embedding = CompileHotwordEmbedding(tpass_handle, nn_hotwords_, ASR_TWO_PASS);
|
// init online features
|
std::vector<int> chunk_size = {5,10,5};
|
FUNASR_HANDLE tpass_online_handle=FunTpassOnlineInit(tpass_handle, chunk_size);
|
float snippet_time = 0.0f;
|
long taking_micros = 0;
|
for (int i = 0; i < wav_list.size(); i++) {
|
auto& wav_file = wav_list[i];
|
auto& wav_id = wav_ids[i];
|
|
int32_t sampling_rate_ = audio_fs.getValue();
|
funasr::Audio audio(1);
|
if(is_target_file(wav_file.c_str(), "wav")){
|
if(!audio.LoadWav2Char(wav_file.c_str(), &sampling_rate_)){
|
LOG(ERROR)<<"Failed to load "<< wav_file;
|
exit(-1);
|
}
|
}else if(is_target_file(wav_file.c_str(), "pcm")){
|
if (!audio.LoadPcmwav2Char(wav_file.c_str(), &sampling_rate_)){
|
LOG(ERROR)<<"Failed to load "<< wav_file;
|
exit(-1);
|
}
|
}else{
|
if (!audio.FfmpegLoad(wav_file.c_str(), true)){
|
LOG(ERROR)<<"Failed to load "<< wav_file;
|
exit(-1);
|
}
|
}
|
char* speech_buff = audio.GetSpeechChar();
|
int buff_len = audio.GetSpeechLen()*2;
|
|
int step = 800*2;
|
bool is_final = false;
|
string online_res="";
|
string tpass_res="";
|
string time_stamp_res="";
|
std::vector<std::vector<string>> punc_cache(2);
|
for (int sample_offset = 0; sample_offset < buff_len; sample_offset += std::min(step, buff_len - sample_offset)) {
|
if (sample_offset + step >= buff_len - 1) {
|
step = buff_len - sample_offset;
|
is_final = true;
|
} else {
|
is_final = false;
|
}
|
gettimeofday(&start, nullptr);
|
FUNASR_RESULT result = FunTpassInferBuffer(tpass_handle, tpass_online_handle,
|
speech_buff+sample_offset, step, punc_cache, is_final, sampling_rate_, "pcm",
|
(ASR_TYPE)asr_mode_, hotwords_embedding, true, decoder_handle);
|
gettimeofday(&end, nullptr);
|
seconds = (end.tv_sec - start.tv_sec);
|
taking_micros += ((seconds * 1000000) + end.tv_usec) - (start.tv_usec);
|
|
if (result)
|
{
|
string online_msg = FunASRGetResult(result, 0);
|
online_res += online_msg;
|
if(online_msg != ""){
|
LOG(INFO)<< wav_id <<" : "<<online_msg;
|
}
|
string tpass_msg = FunASRGetTpassResult(result, 0);
|
tpass_res += tpass_msg;
|
if(tpass_msg != ""){
|
LOG(INFO)<< wav_id <<" offline results : "<<tpass_msg;
|
}
|
string stamp = FunASRGetStamp(result);
|
if(stamp !=""){
|
LOG(INFO) << wav_ids[i] << " time stamp : " << stamp;
|
if(time_stamp_res == ""){
|
time_stamp_res += stamp;
|
}else{
|
time_stamp_res = time_stamp_res.erase(time_stamp_res.length()-1)
|
+ "," + stamp.substr(1);
|
}
|
}
|
snippet_time += FunASRGetRetSnippetTime(result);
|
FunASRFreeResult(result);
|
}
|
}
|
if(asr_mode_==2){
|
LOG(INFO) << wav_id << " Final online results "<<" : "<<online_res;
|
}
|
if(asr_mode_==1){
|
LOG(INFO) << wav_id << " Final online results "<<" : "<<tpass_res;
|
}
|
if(asr_mode_==0 || asr_mode_==2){
|
LOG(INFO) << wav_id << " Final offline results " <<" : "<<tpass_res;
|
if(time_stamp_res!=""){
|
LOG(INFO) << wav_id << " Final timestamp results " <<" : "<<time_stamp_res;
|
}
|
}
|
}
|
|
FunWfstDecoderUnloadHwsRes(decoder_handle);
|
LOG(INFO) << "Audio length: " << (double)snippet_time << " s";
|
LOG(INFO) << "Model inference takes: " << (double)taking_micros / 1000000 <<" s";
|
LOG(INFO) << "Model inference RTF: " << (double)taking_micros/ (snippet_time*1000000);
|
FunASRWfstDecoderUninit(decoder_handle);
|
FunTpassOnlineUninit(tpass_online_handle);
|
FunTpassUninit(tpass_handle);
|
return 0;
|
}
|
