/**
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* Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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* MIT License (https://opensource.org/licenses/MIT)
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*/
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#ifndef _WIN32
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#include <sys/time.h>
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#else
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#include <win_func.h>
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#endif
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#include <iostream>
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#include <fstream>
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#include <sstream>
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#include <map>
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#include <glog/logging.h>
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#include "funasrruntime.h"
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#include "tclap/CmdLine.h"
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#include "com-define.h"
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#include <unordered_map>
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#include "util.h"
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using namespace std;
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bool is_target_file(const std::string& filename, const std::string target) {
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std::size_t pos = filename.find_last_of(".");
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if (pos == std::string::npos) {
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return false;
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}
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std::string extension = filename.substr(pos + 1);
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return (extension == target);
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}
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void GetValue(TCLAP::ValueArg<std::string>& value_arg, string key, std::map<std::string, std::string>& model_path)
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{
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if (value_arg.isSet()){
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model_path.insert({key, value_arg.getValue()});
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LOG(INFO)<< key << " : " << value_arg.getValue();
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}
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}
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int main(int argc, char** argv)
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{
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google::InitGoogleLogging(argv[0]);
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FLAGS_logtostderr = true;
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TCLAP::CmdLine cmd("funasr-onnx-offline", ' ', "1.0");
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TCLAP::ValueArg<std::string> model_dir("", MODEL_DIR, "the asr model path, which contains model.onnx, config.yaml, am.mvn", true, "", "string");
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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");
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TCLAP::ValueArg<std::string> vad_dir("", VAD_DIR, "the vad model path, which contains model.onnx, vad.yaml, vad.mvn", false, "", "string");
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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");
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TCLAP::ValueArg<std::string> punc_dir("", PUNC_DIR, "the punc model path, which contains model.onnx, punc.yaml", false, "", "string");
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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");
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TCLAP::ValueArg<std::string> lm_dir("", LM_DIR, "the lm model path, which contains compiled models: TLG.fst, config.yaml ", false, "", "string");
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TCLAP::ValueArg<float> global_beam("", GLOB_BEAM, "the decoding beam for beam searching ", false, 3.0, "float");
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TCLAP::ValueArg<float> lattice_beam("", LAT_BEAM, "the lattice generation beam for beam searching ", false, 3.0, "float");
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TCLAP::ValueArg<float> am_scale("", AM_SCALE, "the acoustic scale for beam searching ", false, 10.0, "float");
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TCLAP::ValueArg<std::int32_t> fst_inc_wts("", FST_INC_WTS, "the fst hotwords incremental bias", false, 20, "int32_t");
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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");
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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");
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TCLAP::ValueArg<std::string> hotword("", HOTWORD, "the hotword file, one hotword perline, Format: Hotword Weight (could be: 阿里巴巴 20)", false, "", "string");
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cmd.add(model_dir);
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cmd.add(quantize);
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cmd.add(vad_dir);
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cmd.add(vad_quant);
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cmd.add(punc_dir);
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cmd.add(punc_quant);
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cmd.add(itn_dir);
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cmd.add(lm_dir);
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cmd.add(global_beam);
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cmd.add(lattice_beam);
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cmd.add(am_scale);
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cmd.add(fst_inc_wts);
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cmd.add(wav_path);
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cmd.add(hotword);
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cmd.parse(argc, argv);
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std::map<std::string, std::string> model_path;
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GetValue(model_dir, MODEL_DIR, model_path);
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GetValue(quantize, QUANTIZE, model_path);
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GetValue(vad_dir, VAD_DIR, model_path);
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GetValue(vad_quant, VAD_QUANT, model_path);
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GetValue(punc_dir, PUNC_DIR, model_path);
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GetValue(punc_quant, PUNC_QUANT, model_path);
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GetValue(itn_dir, ITN_DIR, model_path);
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GetValue(lm_dir, LM_DIR, model_path);
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GetValue(wav_path, WAV_PATH, model_path);
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struct timeval start, end;
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gettimeofday(&start, NULL);
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int thread_num = 1;
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FUNASR_HANDLE asr_hanlde=FunOfflineInit(model_path, thread_num);
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if (!asr_hanlde)
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{
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LOG(ERROR) << "FunASR init failed";
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exit(-1);
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}
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float glob_beam = 3.0f;
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float lat_beam = 3.0f;
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float am_sc = 10.0f;
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if (lm_dir.isSet()) {
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glob_beam = global_beam.getValue();
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lat_beam = lattice_beam.getValue();
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am_sc = am_scale.getValue();
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}
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// init wfst decoder
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FUNASR_DEC_HANDLE decoder_handle = FunASRWfstDecoderInit(asr_hanlde, ASR_OFFLINE, glob_beam, lat_beam, am_sc);
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// hotword file
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unordered_map<string, int> hws_map;
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std::string nn_hotwords_ = "";
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std::string hotword_path = hotword.getValue();
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LOG(INFO) << "hotword path: " << hotword_path;
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funasr::ExtractHws(hotword_path, hws_map, nn_hotwords_);
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gettimeofday(&end, NULL);
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long seconds = (end.tv_sec - start.tv_sec);
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long modle_init_micros = ((seconds * 1000000) + end.tv_usec) - (start.tv_usec);
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LOG(INFO) << "Model initialization takes " << (double)modle_init_micros / 1000000 << " s";
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// read wav_path
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vector<string> wav_list;
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vector<string> wav_ids;
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string default_id = "wav_default_id";
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string wav_path_ = model_path.at(WAV_PATH);
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if(is_target_file(wav_path_, "scp")){
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ifstream in(wav_path_);
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if (!in.is_open()) {
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LOG(ERROR) << "Failed to open file: " << model_path.at(WAV_SCP) ;
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return 0;
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}
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string line;
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while(getline(in, line))
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{
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istringstream iss(line);
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string column1, column2;
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iss >> column1 >> column2;
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wav_list.emplace_back(column2);
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wav_ids.emplace_back(column1);
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}
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in.close();
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}else{
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wav_list.emplace_back(wav_path_);
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wav_ids.emplace_back(default_id);
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}
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float snippet_time = 0.0f;
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long taking_micros = 0;
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// load hotwords list and build graph
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FunWfstDecoderLoadHwsRes(decoder_handle, fst_inc_wts.getValue(), hws_map);
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std::vector<std::vector<float>> hotwords_embedding = CompileHotwordEmbedding(asr_hanlde, nn_hotwords_);
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for (int i = 0; i < wav_list.size(); i++) {
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auto& wav_file = wav_list[i];
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auto& wav_id = wav_ids[i];
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gettimeofday(&start, NULL);
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FUNASR_RESULT result=FunOfflineInfer(asr_hanlde, wav_file.c_str(), RASR_NONE, NULL, hotwords_embedding, 16000, false, decoder_handle);
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gettimeofday(&end, NULL);
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seconds = (end.tv_sec - start.tv_sec);
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taking_micros += ((seconds * 1000000) + end.tv_usec) - (start.tv_usec);
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if (result)
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{
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string msg = FunASRGetResult(result, 0);
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LOG(INFO)<< wav_id <<" : "<<msg;
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string stamp = FunASRGetStamp(result);
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if(stamp !=""){
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LOG(INFO)<< wav_id <<" : "<<stamp;
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}
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snippet_time += FunASRGetRetSnippetTime(result);
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FunASRFreeResult(result);
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}
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else
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{
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LOG(ERROR) << ("No return data!\n");
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}
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}
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FunWfstDecoderUnloadHwsRes(decoder_handle);
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LOG(INFO) << "Audio length: " << (double)snippet_time << " s";
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LOG(INFO) << "Model inference takes: " << (double)taking_micros / 1000000 <<" s";
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LOG(INFO) << "Model inference RTF: " << (double)taking_micros/ (snippet_time*1000000);
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FunASRWfstDecoderUninit(decoder_handle);
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FunOfflineUninit(asr_hanlde);
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return 0;
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}
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