/** * Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. * MIT License (https://opensource.org/licenses/MIT) */ #ifndef _WIN32 #include #else #include #endif #include #include #include #include #include #include #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& value_arg, string key, std::map& model_path) { if (value_arg.isSet()){ model_path.insert({key, value_arg.getValue()}); LOG(INFO)<< key << " : " << value_arg.getValue(); } } void print_segs(vector>* vec, string &wav_id) { if((*vec).size() == 0){ return; } string seg_out=wav_id + ": ["; for (int i = 0; i < vec->size(); i++) { vector inner_vec = (*vec)[i]; if(inner_vec.size() == 0){ continue; } seg_out += "["; for (int j = 0; j < inner_vec.size(); j++) { seg_out += to_string(inner_vec[j]); if (j != inner_vec.size() - 1) { seg_out += ","; } } seg_out += "]"; if (i != vec->size() - 1) { seg_out += ","; } } seg_out += "]"; LOG(INFO)< model_dir("", MODEL_DIR, "the vad model path, which contains model.onnx, vad.yaml, vad.mvn", true, "", "string"); TCLAP::ValueArg quantize("", QUANTIZE, "false (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 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 audio_fs("", AUDIO_FS, "the sample rate of audio", false, 16000, "int32_t"); cmd.add(model_dir); cmd.add(quantize); cmd.add(wav_path); cmd.add(audio_fs); cmd.parse(argc, argv); std::map model_path; GetValue(model_dir, MODEL_DIR, model_path); GetValue(quantize, QUANTIZE, model_path); GetValue(wav_path, WAV_PATH, model_path); struct timeval start, end; gettimeofday(&start, nullptr); int thread_num = 1; FUNASR_HANDLE vad_hanlde=FsmnVadInit(model_path, thread_num); if (!vad_hanlde) { LOG(ERROR) << "FunVad init failed"; exit(-1); } 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"; // read wav_path vector wav_list; vector wav_ids; string default_id = "wav_default_id"; string wav_path_ = model_path.at(WAV_PATH); if(is_target_file(wav_path_, "wav") || is_target_file(wav_path_, "pcm")){ wav_list.emplace_back(wav_path_); wav_ids.emplace_back(default_id); } else 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{ LOG(ERROR)<<"Please check the wav extension!"; exit(-1); } // init online features FUNASR_HANDLE online_hanlde=FsmnVadOnlineInit(vad_hanlde); 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{ LOG(ERROR)<<"Wrong wav extension"; exit(-1); } char* speech_buff = audio.GetSpeechChar(); int buff_len = audio.GetSpeechLen()*2; int step = 800*2; bool is_final = false; 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 = FsmnVadInferBuffer(online_hanlde, speech_buff+sample_offset, step, nullptr, is_final, sampling_rate_); gettimeofday(&end, nullptr); seconds = (end.tv_sec - start.tv_sec); taking_micros += ((seconds * 1000000) + end.tv_usec) - (start.tv_usec); if (result) { vector>* vad_segments = FsmnVadGetResult(result, 0); print_segs(vad_segments, wav_id); snippet_time += FsmnVadGetRetSnippetTime(result); FsmnVadFreeResult(result); } else { LOG(ERROR) << ("No return data!\n"); } } } 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); FsmnVadUninit(online_hanlde); FsmnVadUninit(vad_hanlde); return 0; }