/**
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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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#include "precomp.h"
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#include "sensevoice-small.h"
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#include <cstddef>
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using namespace std;
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namespace funasr {
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SenseVoiceSmall::SenseVoiceSmall()
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:use_hotword(false),
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env_(ORT_LOGGING_LEVEL_ERROR, "sensevoice"),session_options_{} {
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}
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// offline
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void SenseVoiceSmall::InitAsr(const std::string &am_model, const std::string &am_cmvn, const std::string &am_config, const std::string &token_file, int thread_num){
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LoadConfigFromYaml(am_config.c_str());
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// knf options
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fbank_opts_.frame_opts.dither = 0;
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fbank_opts_.mel_opts.num_bins = n_mels;
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fbank_opts_.frame_opts.samp_freq = asr_sample_rate;
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fbank_opts_.frame_opts.window_type = window_type;
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fbank_opts_.frame_opts.frame_shift_ms = frame_shift;
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fbank_opts_.frame_opts.frame_length_ms = frame_length;
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fbank_opts_.energy_floor = 0;
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fbank_opts_.mel_opts.debug_mel = false;
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// session_options_.SetInterOpNumThreads(1);
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session_options_.SetIntraOpNumThreads(thread_num);
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session_options_.SetGraphOptimizationLevel(ORT_ENABLE_ALL);
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// DisableCpuMemArena can improve performance
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session_options_.DisableCpuMemArena();
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try {
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m_session_ = std::make_unique<Ort::Session>(env_, ORTSTRING(am_model).c_str(), session_options_);
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LOG(INFO) << "Successfully load model from " << am_model;
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} catch (std::exception const &e) {
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LOG(ERROR) << "Error when load am onnx model: " << e.what();
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exit(-1);
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}
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GetInputNames(m_session_.get(), m_strInputNames, m_szInputNames);
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GetOutputNames(m_session_.get(), m_strOutputNames, m_szOutputNames);
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vocab = new Vocab(token_file.c_str());
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LoadCmvn(am_cmvn.c_str());
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}
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// online
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void SenseVoiceSmall::InitAsr(const std::string &en_model, const std::string &de_model, const std::string &am_cmvn, const std::string &am_config, const std::string &token_file, int thread_num){
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LoadOnlineConfigFromYaml(am_config.c_str());
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// knf options
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fbank_opts_.frame_opts.dither = 0;
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fbank_opts_.mel_opts.num_bins = n_mels;
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fbank_opts_.frame_opts.samp_freq = asr_sample_rate;
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fbank_opts_.frame_opts.window_type = window_type;
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fbank_opts_.frame_opts.frame_shift_ms = frame_shift;
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fbank_opts_.frame_opts.frame_length_ms = frame_length;
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fbank_opts_.energy_floor = 0;
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fbank_opts_.mel_opts.debug_mel = false;
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// session_options_.SetInterOpNumThreads(1);
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session_options_.SetIntraOpNumThreads(thread_num);
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session_options_.SetGraphOptimizationLevel(ORT_ENABLE_ALL);
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// DisableCpuMemArena can improve performance
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session_options_.DisableCpuMemArena();
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try {
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encoder_session_ = std::make_unique<Ort::Session>(env_, ORTSTRING(en_model).c_str(), session_options_);
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LOG(INFO) << "Successfully load model from " << en_model;
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} catch (std::exception const &e) {
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LOG(ERROR) << "Error when load am encoder model: " << e.what();
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exit(-1);
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}
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try {
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decoder_session_ = std::make_unique<Ort::Session>(env_, ORTSTRING(de_model).c_str(), session_options_);
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LOG(INFO) << "Successfully load model from " << de_model;
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} catch (std::exception const &e) {
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LOG(ERROR) << "Error when load am decoder model: " << e.what();
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exit(-1);
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}
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// encoder
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string strName;
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GetInputName(encoder_session_.get(), strName);
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en_strInputNames.push_back(strName.c_str());
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GetInputName(encoder_session_.get(), strName,1);
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en_strInputNames.push_back(strName);
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GetOutputName(encoder_session_.get(), strName);
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en_strOutputNames.push_back(strName);
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GetOutputName(encoder_session_.get(), strName,1);
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en_strOutputNames.push_back(strName);
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GetOutputName(encoder_session_.get(), strName,2);
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en_strOutputNames.push_back(strName);
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for (auto& item : en_strInputNames)
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en_szInputNames_.push_back(item.c_str());
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for (auto& item : en_strOutputNames)
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en_szOutputNames_.push_back(item.c_str());
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// decoder
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int de_input_len = 4 + fsmn_layers;
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int de_out_len = 2 + fsmn_layers;
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for(int i=0;i<de_input_len; i++){
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GetInputName(decoder_session_.get(), strName, i);
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de_strInputNames.push_back(strName.c_str());
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}
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for(int i=0;i<de_out_len; i++){
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GetOutputName(decoder_session_.get(), strName,i);
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de_strOutputNames.push_back(strName);
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}
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for (auto& item : de_strInputNames)
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de_szInputNames_.push_back(item.c_str());
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for (auto& item : de_strOutputNames)
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de_szOutputNames_.push_back(item.c_str());
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online_vocab = new Vocab(token_file.c_str());
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phone_set_ = new PhoneSet(token_file.c_str());
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LoadCmvn(am_cmvn.c_str());
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}
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// 2pass
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void SenseVoiceSmall::InitAsr(const std::string &am_model, const std::string &en_model, const std::string &de_model,
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const std::string &am_cmvn, const std::string &am_config, const std::string &token_file, const std::string &online_token_file, int thread_num){
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// online
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InitAsr(en_model, de_model, am_cmvn, am_config, online_token_file, thread_num);
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// offline
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try {
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m_session_ = std::make_unique<Ort::Session>(env_, ORTSTRING(am_model).c_str(), session_options_);
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LOG(INFO) << "Successfully load model from " << am_model;
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} catch (std::exception const &e) {
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LOG(ERROR) << "Error when load am onnx model: " << e.what();
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exit(-1);
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}
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GetInputNames(m_session_.get(), m_strInputNames, m_szInputNames);
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GetOutputNames(m_session_.get(), m_strOutputNames, m_szOutputNames);
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vocab = new Vocab(token_file.c_str());
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}
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void SenseVoiceSmall::LoadOnlineConfigFromYaml(const char* filename){
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YAML::Node config;
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try{
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config = YAML::LoadFile(filename);
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}catch(exception const &e){
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LOG(ERROR) << "Error loading file, yaml file error or not exist.";
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exit(-1);
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}
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try{
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YAML::Node frontend_conf = config["frontend_conf"];
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YAML::Node encoder_conf = config["encoder_conf"];
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YAML::Node decoder_conf = config["decoder_conf"];
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YAML::Node predictor_conf = config["predictor_conf"];
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this->window_type = frontend_conf["window"].as<string>();
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this->n_mels = frontend_conf["n_mels"].as<int>();
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this->frame_length = frontend_conf["frame_length"].as<int>();
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this->frame_shift = frontend_conf["frame_shift"].as<int>();
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this->lfr_m = frontend_conf["lfr_m"].as<int>();
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this->lfr_n = frontend_conf["lfr_n"].as<int>();
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this->encoder_size = encoder_conf["output_size"].as<int>();
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this->fsmn_dims = encoder_conf["output_size"].as<int>();
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this->fsmn_layers = decoder_conf["num_blocks"].as<int>();
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this->fsmn_lorder = decoder_conf["kernel_size"].as<int>()-1;
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this->cif_threshold = predictor_conf["threshold"].as<double>();
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this->tail_alphas = predictor_conf["tail_threshold"].as<double>();
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this->asr_sample_rate = frontend_conf["fs"].as<int>();
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}catch(exception const &e){
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LOG(ERROR) << "Error when load argument from vad config YAML.";
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exit(-1);
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}
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}
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void SenseVoiceSmall::LoadConfigFromYaml(const char* filename){
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YAML::Node config;
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try{
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config = YAML::LoadFile(filename);
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}catch(exception const &e){
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LOG(ERROR) << "Error loading file, yaml file error or not exist.";
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exit(-1);
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}
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try{
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YAML::Node frontend_conf = config["frontend_conf"];
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YAML::Node encoder_conf = config["encoder_conf"];
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this->window_type = frontend_conf["window"].as<string>();
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this->n_mels = frontend_conf["n_mels"].as<int>();
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this->frame_length = frontend_conf["frame_length"].as<int>();
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this->frame_shift = frontend_conf["frame_shift"].as<int>();
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this->lfr_m = frontend_conf["lfr_m"].as<int>();
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this->lfr_n = frontend_conf["lfr_n"].as<int>();
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this->encoder_size = encoder_conf["output_size"].as<int>();
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this->fsmn_dims = encoder_conf["output_size"].as<int>();
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this->asr_sample_rate = frontend_conf["fs"].as<int>();
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}catch(exception const &e){
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LOG(ERROR) << "Error when load argument from vad config YAML.";
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exit(-1);
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}
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}
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SenseVoiceSmall::~SenseVoiceSmall()
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{
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if(vocab){
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delete vocab;
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}
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if(online_vocab){
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delete online_vocab;
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}
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if(lm_vocab){
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delete lm_vocab;
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}
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if(seg_dict){
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delete seg_dict;
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}
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if(phone_set_){
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delete phone_set_;
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}
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}
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void SenseVoiceSmall::StartUtterance()
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{
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}
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void SenseVoiceSmall::EndUtterance()
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{
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}
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void SenseVoiceSmall::Reset()
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{
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}
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void SenseVoiceSmall::FbankKaldi(float sample_rate, const float* waves, int len, std::vector<std::vector<float>> &asr_feats) {
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knf::OnlineFbank fbank_(fbank_opts_);
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std::vector<float> buf(len);
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for (int32_t i = 0; i != len; ++i) {
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buf[i] = waves[i] * 32768;
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}
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fbank_.AcceptWaveform(sample_rate, buf.data(), buf.size());
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int32_t frames = fbank_.NumFramesReady();
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for (int32_t i = 0; i != frames; ++i) {
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const float *frame = fbank_.GetFrame(i);
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std::vector<float> frame_vector(frame, frame + fbank_opts_.mel_opts.num_bins);
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asr_feats.emplace_back(frame_vector);
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}
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}
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void SenseVoiceSmall::LoadCmvn(const char *filename)
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{
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ifstream cmvn_stream(filename);
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if (!cmvn_stream.is_open()) {
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LOG(ERROR) << "Failed to open file: " << filename;
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exit(-1);
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}
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string line;
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while (getline(cmvn_stream, line)) {
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istringstream iss(line);
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vector<string> line_item{istream_iterator<string>{iss}, istream_iterator<string>{}};
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if (line_item[0] == "<AddShift>") {
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getline(cmvn_stream, line);
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istringstream means_lines_stream(line);
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vector<string> means_lines{istream_iterator<string>{means_lines_stream}, istream_iterator<string>{}};
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if (means_lines[0] == "<LearnRateCoef>") {
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for (int j = 3; j < means_lines.size() - 1; j++) {
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means_list_.push_back(stof(means_lines[j]));
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}
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continue;
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}
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}
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else if (line_item[0] == "<Rescale>") {
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getline(cmvn_stream, line);
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istringstream vars_lines_stream(line);
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vector<string> vars_lines{istream_iterator<string>{vars_lines_stream}, istream_iterator<string>{}};
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if (vars_lines[0] == "<LearnRateCoef>") {
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for (int j = 3; j < vars_lines.size() - 1; j++) {
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vars_list_.push_back(stof(vars_lines[j])*scale);
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}
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continue;
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}
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}
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}
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}
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string SenseVoiceSmall::CTCSearch(float * in, std::vector<int32_t> paraformer_length, std::vector<int64_t> outputShape)
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{
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std::string unicodeChar = "▁";
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int32_t vocab_size = outputShape[2];
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std::vector<int64_t> tokens;
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std::string text="";
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int32_t prev_id = -1;
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for (int32_t t = 0; t != paraformer_length[0]; ++t) {
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auto y = std::distance(
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static_cast<const float *>(in),
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std::max_element(
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static_cast<const float *>(in),
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static_cast<const float *>(in) + vocab_size));
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in += vocab_size;
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if (y != blank_id && y != prev_id) {
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tokens.push_back(y);
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}
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prev_id = y;
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}
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string str_lang = "";
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string str_emo = "";
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string str_event = "";
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string str_itn = "";
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if(tokens.size() >=3){
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str_lang = vocab->Id2String(tokens[0]);
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str_emo = vocab->Id2String(tokens[1]);
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str_event = vocab->Id2String(tokens[2]);
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str_itn = vocab->Id2String(tokens[3]);
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}
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for(int32_t i = 4; i < tokens.size(); ++i){
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string word = vocab->Id2String(tokens[i]);
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size_t found = word.find(unicodeChar);
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if(found != std::string::npos){
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text += " " + word.substr(3);
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}else{
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text += word;
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}
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}
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if(str_itn == "<|withitn|>"){
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if(str_lang == "<|zh|>"){
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text += "。";
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}else{
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text += ".";
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}
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}
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return str_lang + str_emo + str_event + " " + text;
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}
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string SenseVoiceSmall::GreedySearch(float * in, int n_len, int64_t token_nums, bool is_stamp, std::vector<float> us_alphas, std::vector<float> us_cif_peak)
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{
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vector<int> hyps;
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int Tmax = n_len;
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for (int i = 0; i < Tmax; i++) {
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int max_idx;
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float max_val;
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FindMax(in + i * token_nums, token_nums, max_val, max_idx);
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hyps.push_back(max_idx);
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}
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if(!is_stamp){
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return online_vocab->Vector2StringV2(hyps, language);
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}else{
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std::vector<string> char_list;
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std::vector<std::vector<float>> timestamp_list;
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std::string res_str;
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online_vocab->Vector2String(hyps, char_list);
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std::vector<string> raw_char(char_list);
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TimestampOnnx(us_alphas, us_cif_peak, char_list, res_str, timestamp_list);
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return PostProcess(raw_char, timestamp_list);
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}
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}
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void SenseVoiceSmall::LfrCmvn(std::vector<std::vector<float>> &asr_feats) {
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std::vector<std::vector<float>> out_feats;
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int T = asr_feats.size();
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int T_lrf = ceil(1.0 * T / lfr_n);
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// Pad frames at start(copy first frame)
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for (int i = 0; i < (lfr_m - 1) / 2; i++) {
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asr_feats.insert(asr_feats.begin(), asr_feats[0]);
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}
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// Merge lfr_m frames as one,lfr_n frames per window
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T = T + (lfr_m - 1) / 2;
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std::vector<float> p;
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for (int i = 0; i < T_lrf; i++) {
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if (lfr_m <= T - i * lfr_n) {
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for (int j = 0; j < lfr_m; j++) {
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p.insert(p.end(), asr_feats[i * lfr_n + j].begin(), asr_feats[i * lfr_n + j].end());
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}
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out_feats.emplace_back(p);
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p.clear();
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} else {
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// Fill to lfr_m frames at last window if less than lfr_m frames (copy last frame)
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int num_padding = lfr_m - (T - i * lfr_n);
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for (int j = 0; j < (asr_feats.size() - i * lfr_n); j++) {
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p.insert(p.end(), asr_feats[i * lfr_n + j].begin(), asr_feats[i * lfr_n + j].end());
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}
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for (int j = 0; j < num_padding; j++) {
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p.insert(p.end(), asr_feats[asr_feats.size() - 1].begin(), asr_feats[asr_feats.size() - 1].end());
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}
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out_feats.emplace_back(p);
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p.clear();
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}
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}
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// Apply cmvn
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for (auto &out_feat: out_feats) {
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for (int j = 0; j < means_list_.size(); j++) {
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out_feat[j] = (out_feat[j] + means_list_[j]) * vars_list_[j];
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}
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}
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asr_feats = out_feats;
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}
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std::vector<std::vector<float>> SenseVoiceSmall::CompileHotwordEmbedding(std::string &hotwords) {
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int embedding_dim = encoder_size;
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std::vector<std::vector<float>> hw_emb;
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std::vector<float> vec(embedding_dim, 0);
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hw_emb.push_back(vec);
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return hw_emb;
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}
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std::vector<std::string> SenseVoiceSmall::Forward(float** din, int* len, bool input_finished, std::string svs_lang, bool svs_itn, int batch_in)
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{
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std::vector<std::string> results;
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string result="";
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int32_t in_feat_dim = fbank_opts_.mel_opts.num_bins;
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if(batch_in != 1){
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results.push_back(result);
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return results;
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}
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std::vector<std::vector<float>> asr_feats;
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FbankKaldi(asr_sample_rate, din[0], len[0], asr_feats);
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if(asr_feats.size() == 0){
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results.push_back(result);
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return results;
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}
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LfrCmvn(asr_feats);
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int32_t feat_dim = lfr_m*in_feat_dim;
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int32_t num_frames = asr_feats.size();
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std::vector<float> wav_feats;
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for (const auto &frame_feat: asr_feats) {
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wav_feats.insert(wav_feats.end(), frame_feat.begin(), frame_feat.end());
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}
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//lid textnorm
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int svs_lid = 0;
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int svs_itnid = 15;
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if(lid_map.find(svs_lang) != lid_map.end()){
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svs_lid = lid_map[svs_lang];
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}
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if(svs_itn){
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svs_itnid = 14;
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}
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#ifdef _WIN_X86
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Ort::MemoryInfo m_memoryInfo = Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU);
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#else
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Ort::MemoryInfo m_memoryInfo = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
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#endif
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const int64_t input_shape_[3] = {1, num_frames, feat_dim};
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Ort::Value onnx_feats = Ort::Value::CreateTensor<float>(m_memoryInfo,
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wav_feats.data(),
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wav_feats.size(),
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input_shape_,
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3);
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const int64_t paraformer_length_shape[1] = {1};
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std::vector<int32_t> paraformer_length;
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paraformer_length.emplace_back(num_frames);
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Ort::Value onnx_feats_len = Ort::Value::CreateTensor<int32_t>(
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m_memoryInfo, paraformer_length.data(), paraformer_length.size(), paraformer_length_shape, 1);
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const int64_t lid_shape[1] = {1};
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std::vector<int32_t> lid_length;
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lid_length.emplace_back(svs_lid);
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Ort::Value onnx_lid = Ort::Value::CreateTensor<int32_t>(
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m_memoryInfo, lid_length.data(), lid_length.size(), lid_shape, 1);
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const int64_t textnorm_shape[1] = {1};
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std::vector<int32_t> textnorm_length;
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textnorm_length.emplace_back(svs_itnid);
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Ort::Value onnx_itn = Ort::Value::CreateTensor<int32_t>(
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m_memoryInfo, textnorm_length.data(), textnorm_length.size(), textnorm_shape, 1);
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std::vector<Ort::Value> input_onnx;
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input_onnx.emplace_back(std::move(onnx_feats));
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input_onnx.emplace_back(std::move(onnx_feats_len));
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input_onnx.emplace_back(std::move(onnx_lid));
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input_onnx.emplace_back(std::move(onnx_itn));
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try {
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auto outputTensor = m_session_->Run(Ort::RunOptions{nullptr}, m_szInputNames.data(), input_onnx.data(), input_onnx.size(), m_szOutputNames.data(), m_szOutputNames.size());
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float* floatData = outputTensor[0].GetTensorMutableData<float>();
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std::vector<int64_t> outputShape = outputTensor[0].GetTensorTypeAndShapeInfo().GetShape();
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result = CTCSearch(floatData, paraformer_length, outputShape);
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}
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catch (std::exception const &e)
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{
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LOG(ERROR)<<e.what();
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}
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results.push_back(result);
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return results;
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}
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string SenseVoiceSmall::Rescoring()
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{
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LOG(ERROR)<<"Not Imp!!!!!!";
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return "";
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}
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} // namespace funasr
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