add option parser; add copyright;

lyblsgo

2023-04-25 1d205d340ff5129e457fa462eb5b31b152086339

 funasr/runtime/onnxruntime/src/fsmn-vad.cpp

@@ -1,8 +1,10 @@
/**
 * Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
 * MIT License  (https://opensource.org/licenses/MIT)
*/

#include <fstream>
#include "precomp.h"
//#include "glog/logging.h"


void FsmnVad::InitVad(const std::string &vad_model, const std::string &vad_cmvn, int vad_sample_rate, int vad_silence_duration, int vad_max_len,
                       float vad_speech_noise_thres) {
@@ -34,10 +36,10 @@
        vad_session_ = std::make_shared<Ort::Session>(
                env_, vad_model.c_str(), session_options_);
    } catch (std::exception const &e) {
        //LOG(ERROR) << "Error when load onnx model: " << e.what();
        LOG(ERROR) << "Error when load vad onnx model: " << e.what();
        exit(0);
    }
    //LOG(INFO) << "vad onnx:";
    LOG(INFO) << "vad onnx:";
    GetInputOutputInfo(vad_session_, &vad_in_names_, &vad_out_names_);
}

@@ -59,8 +61,8 @@
            shape << j;
            shape << " ";
        }
        // LOG(INFO) << "\tInput " << i << " : name=" << name.get() << " type=" << type
        //           << " dims=" << shape.str();
        LOG(INFO) << "\tInput " << i << " : name=" << name.get() << " type=" << type
                  << " dims=" << shape.str();
        (*in_names)[i] = name.get();
        name.release();
    }
@@ -78,8 +80,8 @@
            shape << j;
            shape << " ";
        }
        // LOG(INFO) << "\tOutput " << i << " : name=" << name.get() << " type=" << type
        //           << " dims=" << shape.str();
        LOG(INFO) << "\tOutput " << i << " : name=" << name.get() << " type=" << type
                  << " dims=" << shape.str();
        (*out_names)[i] = name.get();
        name.release();
    }
@@ -119,12 +121,12 @@
    // 4. Onnx infer
    std::vector<Ort::Value> vad_ort_outputs;
    try {
        // VLOG(3) << "Start infer";
        VLOG(3) << "Start infer";
        vad_ort_outputs = vad_session_->Run(
                Ort::RunOptions{nullptr}, vad_in_names_.data(), vad_inputs.data(),
                vad_inputs.size(), vad_out_names_.data(), vad_out_names_.size());
    } catch (std::exception const &e) {
        // LOG(ERROR) << e.what();
        LOG(ERROR) << e.what();
        return;
    }

@@ -163,36 +165,41 @@

void FsmnVad::LoadCmvn(const char *filename)
{
    using namespace std;
    ifstream cmvn_stream(filename);
    string line;
    try{
        using namespace std;
        ifstream cmvn_stream(filename);
        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]));
        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;
                }
                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);
                        vars_list.push_back(stof(vars_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);
                    vars_list.push_back(stof(vars_lines[j]));
                }
                continue;
            }
        }
    }catch(std::exception const &e) {
        LOG(ERROR) << "Error when load vad cmvn : " << e.what();
        exit(0);
    }
}