/**
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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 "paraformer.h"
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#include "encode_converter.h"
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#include <cstddef>
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using namespace std;
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namespace funasr {
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Paraformer::Paraformer()
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:use_hotword(false),
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env_(ORT_LOGGING_LEVEL_ERROR, "paraformer"),session_options_{},
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hw_env_(ORT_LOGGING_LEVEL_ERROR, "paraformer_hw"),hw_session_options{} {
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}
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// offline
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void Paraformer::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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// fbank_ = std::make_unique<knf::OnlineFbank>(fbank_opts);
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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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string strName;
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GetInputName(m_session_.get(), strName);
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m_strInputNames.push_back(strName.c_str());
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GetInputName(m_session_.get(), strName,1);
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m_strInputNames.push_back(strName);
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if (use_hotword) {
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GetInputName(m_session_.get(), strName, 2);
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m_strInputNames.push_back(strName);
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}
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size_t numOutputNodes = m_session_->GetOutputCount();
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for(int index=0; index<numOutputNodes; index++){
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GetOutputName(m_session_.get(), strName, index);
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m_strOutputNames.push_back(strName);
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}
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for (auto& item : m_strInputNames)
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m_szInputNames.push_back(item.c_str());
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for (auto& item : m_strOutputNames)
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m_szOutputNames.push_back(item.c_str());
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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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// online
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void Paraformer::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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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 Paraformer::InitAsr(const std::string &am_model, 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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// online
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InitAsr(en_model, de_model, am_cmvn, am_config, 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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string strName;
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GetInputName(m_session_.get(), strName);
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m_strInputNames.push_back(strName.c_str());
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GetInputName(m_session_.get(), strName,1);
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m_strInputNames.push_back(strName);
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if (use_hotword) {
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GetInputName(m_session_.get(), strName, 2);
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m_strInputNames.push_back(strName);
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}
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// support time stamp
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size_t numOutputNodes = m_session_->GetOutputCount();
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for(int index=0; index<numOutputNodes; index++){
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GetOutputName(m_session_.get(), strName, index);
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m_strOutputNames.push_back(strName);
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}
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for (auto& item : m_strInputNames)
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m_szInputNames.push_back(item.c_str());
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for (auto& item : m_strOutputNames)
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m_szOutputNames.push_back(item.c_str());
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}
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void Paraformer::InitLm(const std::string &lm_file,
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const std::string &lm_cfg_file,
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const std::string &lex_file) {
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try {
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lm_ = std::shared_ptr<fst::Fst<fst::StdArc>>(
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fst::Fst<fst::StdArc>::Read(lm_file));
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if (lm_){
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lm_vocab = new Vocab(lm_cfg_file.c_str(), lex_file.c_str());
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LOG(INFO) << "Successfully load lm file " << lm_file;
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}else{
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LOG(ERROR) << "Failed to load lm file " << lm_file;
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}
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} catch (std::exception const &e) {
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LOG(ERROR) << "Error when load lm file: " << e.what();
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exit(0);
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}
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}
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void Paraformer::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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this->asr_sample_rate = frontend_conf["fs"].as<int>();
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YAML::Node lang_conf = config["lang"];
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if (lang_conf.IsDefined()){
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language = lang_conf.as<string>();
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}
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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 Paraformer::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 Paraformer::InitHwCompiler(const std::string &hw_model, int thread_num) {
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hw_session_options.SetIntraOpNumThreads(thread_num);
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hw_session_options.SetGraphOptimizationLevel(ORT_ENABLE_ALL);
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// DisableCpuMemArena can improve performance
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hw_session_options.DisableCpuMemArena();
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try {
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hw_m_session = std::make_unique<Ort::Session>(hw_env_, ORTSTRING(hw_model).c_str(), hw_session_options);
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LOG(INFO) << "Successfully load model from " << hw_model;
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} catch (std::exception const &e) {
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LOG(ERROR) << "Error when load hw compiler onnx model: " << e.what();
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exit(-1);
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}
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string strName;
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GetInputName(hw_m_session.get(), strName);
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hw_m_strInputNames.push_back(strName.c_str());
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//GetInputName(hw_m_session.get(), strName,1);
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//hw_m_strInputNames.push_back(strName);
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GetOutputName(hw_m_session.get(), strName);
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hw_m_strOutputNames.push_back(strName);
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for (auto& item : hw_m_strInputNames)
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hw_m_szInputNames.push_back(item.c_str());
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for (auto& item : hw_m_strOutputNames)
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hw_m_szOutputNames.push_back(item.c_str());
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// if init hotword compiler is called, this is a hotword paraformer model
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use_hotword = true;
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}
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void Paraformer::InitSegDict(const std::string &seg_dict_model) {
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seg_dict = new SegDict(seg_dict_model.c_str());
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}
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Paraformer::~Paraformer()
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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(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 Paraformer::StartUtterance()
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{
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}
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void Paraformer::EndUtterance()
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{
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}
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void Paraformer::Reset()
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{
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}
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void Paraformer::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 Paraformer::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 Paraformer::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 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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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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string Paraformer::BeamSearch(WfstDecoder* &wfst_decoder, float *in, int len, int64_t token_nums)
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{
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return wfst_decoder->Search(in, len, token_nums);
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}
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string Paraformer::FinalizeDecode(WfstDecoder* &wfst_decoder,
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bool is_stamp, std::vector<float> us_alphas, std::vector<float> us_cif_peak)
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{
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return wfst_decoder->FinalizeDecode(is_stamp, us_alphas, us_cif_peak);
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}
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void Paraformer::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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string Paraformer::Forward(float* din, int len, bool input_finished, const std::vector<std::vector<float>> &hw_emb, void* decoder_handle)
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{
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WfstDecoder* wfst_decoder = (WfstDecoder*)decoder_handle;
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int32_t in_feat_dim = fbank_opts_.mel_opts.num_bins;
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std::vector<std::vector<float>> asr_feats;
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FbankKaldi(asr_sample_rate, din, len, asr_feats);
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if(asr_feats.size() == 0){
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return "";
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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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#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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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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std::vector<float> embedding;
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try{
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if (use_hotword) {
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if(hw_emb.size()<=0){
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LOG(ERROR) << "hw_emb is null";
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return "";
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}
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//PrintMat(hw_emb, "input_clas_emb");
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const int64_t hotword_shape[3] = {1, static_cast<int64_t>(hw_emb.size()), static_cast<int64_t>(hw_emb[0].size())};
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embedding.reserve(hw_emb.size() * hw_emb[0].size());
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for (auto item : hw_emb) {
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embedding.insert(embedding.end(), item.begin(), item.end());
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}
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//LOG(INFO) << "hotword shape " << hotword_shape[0] << " " << hotword_shape[1] << " " << hotword_shape[2] << " size " << embedding.size();
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Ort::Value onnx_hw_emb = Ort::Value::CreateTensor<float>(
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m_memoryInfo, embedding.data(), embedding.size(), hotword_shape, 3);
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input_onnx.emplace_back(std::move(onnx_hw_emb));
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}
|
}catch (std::exception const &e)
|
{
|
LOG(ERROR)<<e.what();
|
return "";
|
}
|
|
string result="";
|
try {
|
auto outputTensor = m_session_->Run(Ort::RunOptions{nullptr}, m_szInputNames.data(), input_onnx.data(), input_onnx.size(), m_szOutputNames.data(), m_szOutputNames.size());
|
std::vector<int64_t> outputShape = outputTensor[0].GetTensorTypeAndShapeInfo().GetShape();
|
//LOG(INFO) << "paraformer out shape " << outputShape[0] << " " << outputShape[1] << " " << outputShape[2];
|
|
int64_t outputCount = std::accumulate(outputShape.begin(), outputShape.end(), 1, std::multiplies<int64_t>());
|
float* floatData = outputTensor[0].GetTensorMutableData<float>();
|
auto encoder_out_lens = outputTensor[1].GetTensorMutableData<int64_t>();
|
// timestamp
|
if(outputTensor.size() == 4){
|
std::vector<int64_t> us_alphas_shape = outputTensor[2].GetTensorTypeAndShapeInfo().GetShape();
|
float* us_alphas_data = outputTensor[2].GetTensorMutableData<float>();
|
std::vector<float> us_alphas(us_alphas_shape[1]);
|
for (int i = 0; i < us_alphas_shape[1]; i++) {
|
us_alphas[i] = us_alphas_data[i];
|
}
|
|
std::vector<int64_t> us_peaks_shape = outputTensor[3].GetTensorTypeAndShapeInfo().GetShape();
|
float* us_peaks_data = outputTensor[3].GetTensorMutableData<float>();
|
std::vector<float> us_peaks(us_peaks_shape[1]);
|
for (int i = 0; i < us_peaks_shape[1]; i++) {
|
us_peaks[i] = us_peaks_data[i];
|
}
|
if (lm_ == nullptr) {
|
result = GreedySearch(floatData, *encoder_out_lens, outputShape[2], true, us_alphas, us_peaks);
|
} else {
|
result = BeamSearch(wfst_decoder, floatData, *encoder_out_lens, outputShape[2]);
|
if (input_finished) {
|
result = FinalizeDecode(wfst_decoder, true, us_alphas, us_peaks);
|
}
|
}
|
}else{
|
if (lm_ == nullptr) {
|
result = GreedySearch(floatData, *encoder_out_lens, outputShape[2]);
|
} else {
|
result = BeamSearch(wfst_decoder, floatData, *encoder_out_lens, outputShape[2]);
|
if (input_finished) {
|
result = FinalizeDecode(wfst_decoder);
|
}
|
}
|
}
|
}
|
catch (std::exception const &e)
|
{
|
LOG(ERROR)<<e.what();
|
}
|
|
return result;
|
}
|
|
|
std::vector<std::vector<float>> Paraformer::CompileHotwordEmbedding(std::string &hotwords) {
|
int embedding_dim = encoder_size;
|
std::vector<std::vector<float>> hw_emb;
|
if (!use_hotword) {
|
std::vector<float> vec(embedding_dim, 0);
|
hw_emb.push_back(vec);
|
return hw_emb;
|
}
|
int max_hotword_len = 10;
|
std::vector<int32_t> hotword_matrix;
|
std::vector<int32_t> lengths;
|
int hotword_size = 1;
|
int real_hw_size = 0;
|
if (!hotwords.empty()) {
|
std::vector<std::string> hotword_array = split(hotwords, ' ');
|
hotword_size = hotword_array.size() + 1;
|
hotword_matrix.reserve(hotword_size * max_hotword_len);
|
for (auto hotword : hotword_array) {
|
std::vector<std::string> chars;
|
if (EncodeConverter::IsAllChineseCharactor((const U8CHAR_T*)hotword.c_str(), hotword.size())) {
|
KeepChineseCharacterAndSplit(hotword, chars);
|
} else {
|
// for english
|
std::vector<std::string> words = split(hotword, ' ');
|
for (auto word : words) {
|
std::vector<string> tokens = seg_dict->GetTokensByWord(word);
|
chars.insert(chars.end(), tokens.begin(), tokens.end());
|
}
|
}
|
if(chars.size()==0){
|
continue;
|
}
|
std::vector<int32_t> hw_vector(max_hotword_len, 0);
|
int vector_len = std::min(max_hotword_len, (int)chars.size());
|
int chs_oov = false;
|
for (int i=0; i<vector_len; i++) {
|
hw_vector[i] = phone_set_->String2Id(chars[i]);
|
if(hw_vector[i] == -1){
|
chs_oov = true;
|
break;
|
}
|
}
|
if(chs_oov){
|
LOG(INFO) << "OOV: " << hotword;
|
continue;
|
}
|
LOG(INFO) << hotword;
|
lengths.push_back(vector_len);
|
real_hw_size += 1;
|
hotword_matrix.insert(hotword_matrix.end(), hw_vector.begin(), hw_vector.end());
|
}
|
hotword_size = real_hw_size + 1;
|
}
|
std::vector<int32_t> blank_vec(max_hotword_len, 0);
|
blank_vec[0] = 1;
|
hotword_matrix.insert(hotword_matrix.end(), blank_vec.begin(), blank_vec.end());
|
lengths.push_back(1);
|
|
#ifdef _WIN_X86
|
Ort::MemoryInfo m_memoryInfo = Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU);
|
#else
|
Ort::MemoryInfo m_memoryInfo = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
|
#endif
|
|
const int64_t input_shape_[2] = {hotword_size, max_hotword_len};
|
Ort::Value onnx_hotword = Ort::Value::CreateTensor<int32_t>(m_memoryInfo,
|
(int32_t*)hotword_matrix.data(),
|
hotword_size * max_hotword_len,
|
input_shape_,
|
2);
|
LOG(INFO) << "clas shape " << hotword_size << " " << max_hotword_len << std::endl;
|
|
std::vector<Ort::Value> input_onnx;
|
input_onnx.emplace_back(std::move(onnx_hotword));
|
|
std::vector<std::vector<float>> result;
|
try {
|
auto outputTensor = hw_m_session->Run(Ort::RunOptions{nullptr}, hw_m_szInputNames.data(), input_onnx.data(), input_onnx.size(), hw_m_szOutputNames.data(), hw_m_szOutputNames.size());
|
std::vector<int64_t> outputShape = outputTensor[0].GetTensorTypeAndShapeInfo().GetShape();
|
|
int64_t outputCount = std::accumulate(outputShape.begin(), outputShape.end(), 1, std::multiplies<int64_t>());
|
float* floatData = outputTensor[0].GetTensorMutableData<float>(); // shape [max_hotword_len, hotword_size, dim]
|
// get embedding by real hotword length
|
assert(outputShape[0] == max_hotword_len);
|
assert(outputShape[1] == hotword_size);
|
embedding_dim = outputShape[2];
|
|
for (int j = 0; j < hotword_size; j++)
|
{
|
int start_pos = hotword_size * (lengths[j] - 1) * embedding_dim + j * embedding_dim;
|
std::vector<float> embedding;
|
embedding.insert(embedding.begin(), floatData + start_pos, floatData + start_pos + embedding_dim);
|
result.push_back(embedding);
|
}
|
}
|
catch (std::exception const &e)
|
{
|
LOG(ERROR)<<e.what();
|
}
|
//PrintMat(result, "clas_embedding_output");
|
return result;
|
}
|
|
Vocab* Paraformer::GetVocab()
|
{
|
return vocab;
|
}
|
|
Vocab* Paraformer::GetLmVocab()
|
{
|
return lm_vocab;
|
}
|
|
PhoneSet* Paraformer::GetPhoneSet()
|
{
|
return phone_set_;
|
}
|
|
string Paraformer::Rescoring()
|
{
|
LOG(ERROR)<<"Not Imp!!!!!!";
|
return "";
|
}
|
} // namespace funasr
|
