FunASR/runtime/onnxruntime/src/paraformer.cpp

/**
 * Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
 * MIT License  (https://opensource.org/licenses/MIT)
*/

#include "precomp.h"
#include "paraformer.h"
#include "encode_converter.h"
#include <cstddef>

using namespace std;
namespace funasr {

Paraformer::Paraformer()
:use_hotword(false),
 env_(ORT_LOGGING_LEVEL_ERROR, "paraformer"),session_options_{},
 hw_env_(ORT_LOGGING_LEVEL_ERROR, "paraformer_hw"),hw_session_options{} {
}

// offline
void Paraformer::InitAsr(const std::string &am_model, const std::string &am_cmvn, const std::string &am_config, int thread_num){
    LoadConfigFromYaml(am_config.c_str());
    // knf options
    fbank_opts_.frame_opts.dither = 0;
    fbank_opts_.mel_opts.num_bins = n_mels;
    fbank_opts_.frame_opts.samp_freq = asr_sample_rate;
    fbank_opts_.frame_opts.window_type = window_type;
    fbank_opts_.frame_opts.frame_shift_ms = frame_shift;
    fbank_opts_.frame_opts.frame_length_ms = frame_length;
    fbank_opts_.energy_floor = 0;
    fbank_opts_.mel_opts.debug_mel = false;
    // fbank_ = std::make_unique<knf::OnlineFbank>(fbank_opts);

    // session_options_.SetInterOpNumThreads(1);
    session_options_.SetIntraOpNumThreads(thread_num);
    session_options_.SetGraphOptimizationLevel(ORT_ENABLE_ALL);
    // DisableCpuMemArena can improve performance
    session_options_.DisableCpuMemArena();

    try {
        m_session_ = std::make_unique<Ort::Session>(env_, ORTSTRING(am_model).c_str(), session_options_);
        LOG(INFO) << "Successfully load model from " << am_model;
    } catch (std::exception const &e) {
        LOG(ERROR) << "Error when load am onnx model: " << e.what();
        exit(-1);
    }

    string strName;
    GetInputName(m_session_.get(), strName);
    m_strInputNames.push_back(strName.c_str());
    GetInputName(m_session_.get(), strName,1);
    m_strInputNames.push_back(strName);
    if (use_hotword) {
        GetInputName(m_session_.get(), strName, 2);
        m_strInputNames.push_back(strName);
    }
    
    size_t numOutputNodes = m_session_->GetOutputCount();
    for(int index=0; index<numOutputNodes; index++){
        GetOutputName(m_session_.get(), strName, index);
        m_strOutputNames.push_back(strName);
    }

    for (auto& item : m_strInputNames)
        m_szInputNames.push_back(item.c_str());
    for (auto& item : m_strOutputNames)
        m_szOutputNames.push_back(item.c_str());
    vocab = new Vocab(am_config.c_str());
	phone_set_ = new PhoneSet(am_config.c_str());
    LoadCmvn(am_cmvn.c_str());
}

// online
void Paraformer::InitAsr(const std::string &en_model, const std::string &de_model, const std::string &am_cmvn, const std::string &am_config, int thread_num){
    
    LoadOnlineConfigFromYaml(am_config.c_str());
    // knf options
    fbank_opts_.frame_opts.dither = 0;
    fbank_opts_.mel_opts.num_bins = n_mels;
    fbank_opts_.frame_opts.samp_freq = asr_sample_rate;
    fbank_opts_.frame_opts.window_type = window_type;
    fbank_opts_.frame_opts.frame_shift_ms = frame_shift;
    fbank_opts_.frame_opts.frame_length_ms = frame_length;
    fbank_opts_.energy_floor = 0;
    fbank_opts_.mel_opts.debug_mel = false;

    // session_options_.SetInterOpNumThreads(1);
    session_options_.SetIntraOpNumThreads(thread_num);
    session_options_.SetGraphOptimizationLevel(ORT_ENABLE_ALL);
    // DisableCpuMemArena can improve performance
    session_options_.DisableCpuMemArena();

    try {
        encoder_session_ = std::make_unique<Ort::Session>(env_, ORTSTRING(en_model).c_str(), session_options_);
        LOG(INFO) << "Successfully load model from " << en_model;
    } catch (std::exception const &e) {
        LOG(ERROR) << "Error when load am encoder model: " << e.what();
        exit(-1);
    }

    try {
        decoder_session_ = std::make_unique<Ort::Session>(env_, ORTSTRING(de_model).c_str(), session_options_);
        LOG(INFO) << "Successfully load model from " << de_model;
    } catch (std::exception const &e) {
        LOG(ERROR) << "Error when load am decoder model: " << e.what();
        exit(-1);
    }

    // encoder
    string strName;
    GetInputName(encoder_session_.get(), strName);
    en_strInputNames.push_back(strName.c_str());
    GetInputName(encoder_session_.get(), strName,1);
    en_strInputNames.push_back(strName);
    
    GetOutputName(encoder_session_.get(), strName);
    en_strOutputNames.push_back(strName);
    GetOutputName(encoder_session_.get(), strName,1);
    en_strOutputNames.push_back(strName);
    GetOutputName(encoder_session_.get(), strName,2);
    en_strOutputNames.push_back(strName);

    for (auto& item : en_strInputNames)
        en_szInputNames_.push_back(item.c_str());
    for (auto& item : en_strOutputNames)
        en_szOutputNames_.push_back(item.c_str());

    // decoder
    int de_input_len = 4 + fsmn_layers;
    int de_out_len = 2 + fsmn_layers;
    for(int i=0;i<de_input_len; i++){
        GetInputName(decoder_session_.get(), strName, i);
        de_strInputNames.push_back(strName.c_str());
    }

    for(int i=0;i<de_out_len; i++){
        GetOutputName(decoder_session_.get(), strName,i);
        de_strOutputNames.push_back(strName);
    }

    for (auto& item : de_strInputNames)
        de_szInputNames_.push_back(item.c_str());
    for (auto& item : de_strOutputNames)
        de_szOutputNames_.push_back(item.c_str());

    vocab = new Vocab(am_config.c_str());
    phone_set_ = new PhoneSet(am_config.c_str());
    LoadCmvn(am_cmvn.c_str());
}

// 2pass
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, int thread_num){
    // online
    InitAsr(en_model, de_model, am_cmvn, am_config, thread_num);

    // offline
    try {
        m_session_ = std::make_unique<Ort::Session>(env_, ORTSTRING(am_model).c_str(), session_options_);
        LOG(INFO) << "Successfully load model from " << am_model;
    } catch (std::exception const &e) {
        LOG(ERROR) << "Error when load am onnx model: " << e.what();
        exit(-1);
    }

    string strName;
    GetInputName(m_session_.get(), strName);
    m_strInputNames.push_back(strName.c_str());
    GetInputName(m_session_.get(), strName,1);
    m_strInputNames.push_back(strName);

    if (use_hotword) {
        GetInputName(m_session_.get(), strName, 2);
        m_strInputNames.push_back(strName);
    }
    
    // support time stamp
    size_t numOutputNodes = m_session_->GetOutputCount();
    for(int index=0; index<numOutputNodes; index++){
        GetOutputName(m_session_.get(), strName, index);
        m_strOutputNames.push_back(strName);
    }

    for (auto& item : m_strInputNames)
        m_szInputNames.push_back(item.c_str());
    for (auto& item : m_strOutputNames)
        m_szOutputNames.push_back(item.c_str());
}

void Paraformer::InitLm(const std::string &lm_file, 
                        const std::string &lm_cfg_file, 
                        const std::string &lex_file) {
    try {
        lm_ = std::shared_ptr<fst::Fst<fst::StdArc>>(
            fst::Fst<fst::StdArc>::Read(lm_file));
        if (lm_){
            if (vocab) { delete vocab; }
            vocab = new Vocab(lm_cfg_file.c_str(), lex_file.c_str());
            LOG(INFO) << "Successfully load lm file " << lm_file;
        }else{
            LOG(ERROR) << "Failed to load lm file " << lm_file;
        }
    } catch (std::exception const &e) {
        LOG(ERROR) << "Error when load lm file: " << e.what();
        exit(0);
    }
}

void Paraformer::LoadConfigFromYaml(const char* filename){

    YAML::Node config;
    try{
        config = YAML::LoadFile(filename);
    }catch(exception const &e){
        LOG(ERROR) << "Error loading file, yaml file error or not exist.";
        exit(-1);
    }

    try{
        YAML::Node frontend_conf = config["frontend_conf"];
        this->asr_sample_rate = frontend_conf["fs"].as<int>();

        YAML::Node lang_conf = config["lang"];
        if (lang_conf.IsDefined()){
            language = lang_conf.as<string>();
        }
    }catch(exception const &e){
        LOG(ERROR) << "Error when load argument from vad config YAML.";
        exit(-1);
    }
}

void Paraformer::LoadOnlineConfigFromYaml(const char* filename){

    YAML::Node config;
    try{
        config = YAML::LoadFile(filename);
    }catch(exception const &e){
        LOG(ERROR) << "Error loading file, yaml file error or not exist.";
        exit(-1);
    }

    try{
        YAML::Node frontend_conf = config["frontend_conf"];
        YAML::Node encoder_conf = config["encoder_conf"];
        YAML::Node decoder_conf = config["decoder_conf"];
        YAML::Node predictor_conf = config["predictor_conf"];

        this->window_type = frontend_conf["window"].as<string>();
        this->n_mels = frontend_conf["n_mels"].as<int>();
        this->frame_length = frontend_conf["frame_length"].as<int>();
        this->frame_shift = frontend_conf["frame_shift"].as<int>();
        this->lfr_m = frontend_conf["lfr_m"].as<int>();
        this->lfr_n = frontend_conf["lfr_n"].as<int>();

        this->encoder_size = encoder_conf["output_size"].as<int>();
        this->fsmn_dims = encoder_conf["output_size"].as<int>();

        this->fsmn_layers = decoder_conf["num_blocks"].as<int>();
        this->fsmn_lorder = decoder_conf["kernel_size"].as<int>()-1;

        this->cif_threshold = predictor_conf["threshold"].as<double>();
        this->tail_alphas = predictor_conf["tail_threshold"].as<double>();

        this->asr_sample_rate = frontend_conf["fs"].as<int>();


    }catch(exception const &e){
        LOG(ERROR) << "Error when load argument from vad config YAML.";
        exit(-1);
    }
}

void Paraformer::InitHwCompiler(const std::string &hw_model, int thread_num) {
    hw_session_options.SetIntraOpNumThreads(thread_num);
    hw_session_options.SetGraphOptimizationLevel(ORT_ENABLE_ALL);
    // DisableCpuMemArena can improve performance
    hw_session_options.DisableCpuMemArena();

    try {
        hw_m_session = std::make_unique<Ort::Session>(hw_env_, ORTSTRING(hw_model).c_str(), hw_session_options);
        LOG(INFO) << "Successfully load model from " << hw_model;
    } catch (std::exception const &e) {
        LOG(ERROR) << "Error when load hw compiler onnx model: " << e.what();
        exit(-1);
    }

    string strName;
    GetInputName(hw_m_session.get(), strName);
    hw_m_strInputNames.push_back(strName.c_str());
    //GetInputName(hw_m_session.get(), strName,1);
    //hw_m_strInputNames.push_back(strName);
    
    GetOutputName(hw_m_session.get(), strName);
    hw_m_strOutputNames.push_back(strName);

    for (auto& item : hw_m_strInputNames)
        hw_m_szInputNames.push_back(item.c_str());
    for (auto& item : hw_m_strOutputNames)
        hw_m_szOutputNames.push_back(item.c_str());
    // if init hotword compiler is called, this is a hotword paraformer model
    use_hotword = true;
}

void Paraformer::InitSegDict(const std::string &seg_dict_model) {
    seg_dict = new SegDict(seg_dict_model.c_str());
}

Paraformer::~Paraformer()
{
    if(vocab){
        delete vocab;
    }
    if(seg_dict){
        delete seg_dict;
    }
    if(phone_set_){
        delete phone_set_;
    }
}

void Paraformer::StartUtterance()
{
}

void Paraformer::EndUtterance()
{
}

void Paraformer::Reset()
{
}

void Paraformer::FbankKaldi(float sample_rate, const float* waves, int len, std::vector<std::vector<float>> &asr_feats) {
    knf::OnlineFbank fbank_(fbank_opts_);
    std::vector<float> buf(len);
    for (int32_t i = 0; i != len; ++i) {
        buf[i] = waves[i] * 32768;
    }
    fbank_.AcceptWaveform(sample_rate, buf.data(), buf.size());

    int32_t frames = fbank_.NumFramesReady();
    for (int32_t i = 0; i != frames; ++i) {
        const float *frame = fbank_.GetFrame(i);
        std::vector<float> frame_vector(frame, frame + fbank_opts_.mel_opts.num_bins);
        asr_feats.emplace_back(frame_vector);
    }
}

void Paraformer::LoadCmvn(const char *filename)
{
    ifstream cmvn_stream(filename);
    if (!cmvn_stream.is_open()) {
        LOG(ERROR) << "Failed to open file: " << filename;
        exit(-1);
    }
    string line;

    while (getline(cmvn_stream, line)) {
        istringstream iss(line);
        vector<string> line_item{istream_iterator<string>{iss}, istream_iterator<string>{}};
        if (line_item[0] == "<AddShift>") {
            getline(cmvn_stream, line);
            istringstream means_lines_stream(line);
            vector<string> means_lines{istream_iterator<string>{means_lines_stream}, istream_iterator<string>{}};
            if (means_lines[0] == "<LearnRateCoef>") {
                for (int j = 3; j < means_lines.size() - 1; j++) {
                    means_list_.push_back(stof(means_lines[j]));
                }
                continue;
            }
        }
        else if (line_item[0] == "<Rescale>") {
            getline(cmvn_stream, line);
            istringstream vars_lines_stream(line);
            vector<string> vars_lines{istream_iterator<string>{vars_lines_stream}, istream_iterator<string>{}};
            if (vars_lines[0] == "<LearnRateCoef>") {
                for (int j = 3; j < vars_lines.size() - 1; j++) {
                    vars_list_.push_back(stof(vars_lines[j])*scale);
                }
                continue;
            }
        }
    }
}

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)
{
    vector<int> hyps;
    int Tmax = n_len;
    for (int i = 0; i < Tmax; i++) {
        int max_idx;
        float max_val;
        FindMax(in + i * token_nums, token_nums, max_val, max_idx);
        hyps.push_back(max_idx);
    }
    if(!is_stamp){
        return vocab->Vector2StringV2(hyps, language);
    }else{
        std::vector<string> char_list;
        std::vector<std::vector<float>> timestamp_list;
        std::string res_str;
        vocab->Vector2String(hyps, char_list);
        std::vector<string> raw_char(char_list);
        TimestampOnnx(us_alphas, us_cif_peak, char_list, res_str, timestamp_list);

        return PostProcess(raw_char, timestamp_list);
    }
}

string Paraformer::BeamSearch(WfstDecoder* &wfst_decoder, float *in, int len, int64_t token_nums)
{
  return wfst_decoder->Search(in, len, token_nums);
}

string Paraformer::FinalizeDecode(WfstDecoder* &wfst_decoder,
                                  bool is_stamp, std::vector<float> us_alphas, std::vector<float> us_cif_peak)
{
  return wfst_decoder->FinalizeDecode(is_stamp, us_alphas, us_cif_peak);
}

void Paraformer::LfrCmvn(std::vector<std::vector<float>> &asr_feats) {

    std::vector<std::vector<float>> out_feats;
    int T = asr_feats.size();
    int T_lrf = ceil(1.0 * T / lfr_n);

    // Pad frames at start(copy first frame)
    for (int i = 0; i < (lfr_m - 1) / 2; i++) {
        asr_feats.insert(asr_feats.begin(), asr_feats[0]);
    }
    // Merge lfr_m frames as one,lfr_n frames per window
    T = T + (lfr_m - 1) / 2;
    std::vector<float> p;
    for (int i = 0; i < T_lrf; i++) {
        if (lfr_m <= T - i * lfr_n) {
            for (int j = 0; j < lfr_m; j++) {
                p.insert(p.end(), asr_feats[i * lfr_n + j].begin(), asr_feats[i * lfr_n + j].end());
            }
            out_feats.emplace_back(p);
            p.clear();
        } else {
            // Fill to lfr_m frames at last window if less than lfr_m frames  (copy last frame)
            int num_padding = lfr_m - (T - i * lfr_n);
            for (int j = 0; j < (asr_feats.size() - i * lfr_n); j++) {
                p.insert(p.end(), asr_feats[i * lfr_n + j].begin(), asr_feats[i * lfr_n + j].end());
            }
            for (int j = 0; j < num_padding; j++) {
                p.insert(p.end(), asr_feats[asr_feats.size() - 1].begin(), asr_feats[asr_feats.size() - 1].end());
            }
            out_feats.emplace_back(p);
            p.clear();
        }
    }
    // Apply cmvn
    for (auto &out_feat: out_feats) {
        for (int j = 0; j < means_list_.size(); j++) {
            out_feat[j] = (out_feat[j] + means_list_[j]) * vars_list_[j];
        }
    }
    asr_feats = out_feats;
}

string Paraformer::Forward(float* din, int len, bool input_finished, const std::vector<std::vector<float>> &hw_emb, void* decoder_handle)
{
    WfstDecoder* wfst_decoder = (WfstDecoder*)decoder_handle;
    int32_t in_feat_dim = fbank_opts_.mel_opts.num_bins;

    std::vector<std::vector<float>> asr_feats;
    FbankKaldi(MODEL_SAMPLE_RATE, din, len, asr_feats);
    if(asr_feats.size() == 0){
      return "";
    }
    LfrCmvn(asr_feats);
    int32_t feat_dim = lfr_m*in_feat_dim;
    int32_t num_frames = asr_feats.size();

    std::vector<float> wav_feats;
    for (const auto &frame_feat: asr_feats) {
        wav_feats.insert(wav_feats.end(), frame_feat.begin(), frame_feat.end());
    }

#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_[3] = {1, num_frames, feat_dim};
    Ort::Value onnx_feats = Ort::Value::CreateTensor<float>(m_memoryInfo,
        wav_feats.data(),
        wav_feats.size(),
        input_shape_,
        3);

    const int64_t paraformer_length_shape[1] = {1};
    std::vector<int32_t> paraformer_length;
    paraformer_length.emplace_back(num_frames);
    Ort::Value onnx_feats_len = Ort::Value::CreateTensor<int32_t>(
          m_memoryInfo, paraformer_length.data(), paraformer_length.size(), paraformer_length_shape, 1);

    std::vector<Ort::Value> input_onnx;
    input_onnx.emplace_back(std::move(onnx_feats));
    input_onnx.emplace_back(std::move(onnx_feats_len));

    std::vector<float> embedding;
    try{
        if (use_hotword) {
            if(hw_emb.size()<=0){
                LOG(ERROR) << "hw_emb is null";
                return "";
            }
            //PrintMat(hw_emb, "input_clas_emb");
            const int64_t hotword_shape[3] = {1, static_cast<int64_t>(hw_emb.size()), static_cast<int64_t>(hw_emb[0].size())};
            embedding.reserve(hw_emb.size() * hw_emb[0].size());
            for (auto item : hw_emb) {
                embedding.insert(embedding.end(), item.begin(), item.end());
            }
            //LOG(INFO) << "hotword shape " << hotword_shape[0] << " " << hotword_shape[1] << " " << hotword_shape[2] << " size " << embedding.size();
            Ort::Value onnx_hw_emb = Ort::Value::CreateTensor<float>(
                m_memoryInfo, embedding.data(), embedding.size(), hotword_shape, 3);

            input_onnx.emplace_back(std::move(onnx_hw_emb));
        }
    }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;
}

PhoneSet* Paraformer::GetPhoneSet()
{
    return phone_set_;
}

string Paraformer::Rescoring()
{
    LOG(ERROR)<<"Not Imp!!!!!!";
    return "";
}
} // namespace funasr