FunASR/runtime/websocket/bin/funasr-wss-server.cpp

/**
 * Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights
 * Reserved. MIT License  (https://opensource.org/licenses/MIT)
 */
/* 2022-2023 by zhaomingwork */

#include "websocket-server.h"
#ifdef _WIN32
#include "win_func.h"
#else
#include <unistd.h>
#endif
#include <fstream>
#include "util.h"

// hotwords
std::unordered_map<std::string, int> hws_map_;
int fst_inc_wts_=20;
float global_beam_, lattice_beam_, am_scale_;

using namespace std;
void GetValue(TCLAP::ValueArg<std::string>& value_arg, string key,
              std::map<std::string, std::string>& model_path) {
    model_path.insert({key, value_arg.getValue()});
    LOG(INFO) << key << " : " << value_arg.getValue();
}

int main(int argc, char* argv[]) {
  try {

    google::InitGoogleLogging(argv[0]);
    FLAGS_logtostderr = true;
    std::string offline_version = "";
#ifdef _WIN32
    offline_version = "0.1.0";
#endif
    TCLAP::CmdLine cmd("funasr-wss-server", ' ', offline_version);
    TCLAP::ValueArg<std::string> download_model_dir(
        "", "download-model-dir",
        "Download model from Modelscope to download_model_dir",
        false, "/workspace/models", "string");
    TCLAP::ValueArg<std::string> model_dir(
        "", MODEL_DIR,
        "default: /workspace/models/asr, the asr model path, which contains model_quant.onnx, config.yaml, am.mvn",
        false, "/workspace/models/asr", "string");
    TCLAP::ValueArg<std::string> model_revision(
        "", "model-revision",
        "ASR model revision",
        false, "v1.2.1", "string");
    TCLAP::ValueArg<std::string> quantize(
        "", QUANTIZE,
        "true (Default), load the model of model_quant.onnx in model_dir. If set "
        "false, load the model of model.onnx in model_dir",
        false, "true", "string");
    TCLAP::ValueArg<std::string> vad_dir(
        "", VAD_DIR,
        "default: /workspace/models/vad, the vad model path, which contains model_quant.onnx, vad.yaml, vad.mvn",
        false, "/workspace/models/vad", "string");
    TCLAP::ValueArg<std::string> vad_revision(
        "", "vad-revision",
        "VAD model revision",
        false, "v1.2.0", "string");
    TCLAP::ValueArg<std::string> vad_quant(
        "", VAD_QUANT,
        "true (Default), load the model of model_quant.onnx in vad_dir. If set "
        "false, load the model of model.onnx in vad_dir",
        false, "true", "string");
    TCLAP::ValueArg<std::string> punc_dir(
        "", PUNC_DIR,
        "default: /workspace/models/punc, the punc model path, which contains model_quant.onnx, punc.yaml", 
        false, "/workspace/models/punc",
        "string");
    TCLAP::ValueArg<std::string> punc_revision(
        "", "punc-revision",
        "PUNC model revision",
        false, "v1.1.7", "string");
    TCLAP::ValueArg<std::string> punc_quant(
        "", PUNC_QUANT,
        "true (Default), load the model of model_quant.onnx in punc_dir. If set "
        "false, load the model of model.onnx in punc_dir",
        false, "true", "string");
    TCLAP::ValueArg<std::string> itn_dir(
        "", ITN_DIR,
        "default: thuduj12/fst_itn_zh, the itn model path, which contains "
        "zh_itn_tagger.fst, zh_itn_verbalizer.fst",
        false, "thuduj12/fst_itn_zh", "string");
    TCLAP::ValueArg<std::string> itn_revision(
        "", "itn-revision", "ITN model revision", false, "v1.0.1", "string");

    TCLAP::ValueArg<std::string> listen_ip("", "listen-ip", "listen ip", false,
                                           "0.0.0.0", "string");
    TCLAP::ValueArg<int> port("", "port", "port", false, 10095, "int");
    TCLAP::ValueArg<int> io_thread_num("", "io-thread-num", "io thread num",
                                       false, 2, "int");
    TCLAP::ValueArg<int> decoder_thread_num(
        "", "decoder-thread-num", "decoder thread num", false, 8, "int");
    TCLAP::ValueArg<int> model_thread_num("", "model-thread-num",
                                          "model thread num", false, 1, "int");

    TCLAP::ValueArg<std::string> certfile("", "certfile", 
        "default: ../../../ssl_key/server.crt, path of certficate for WSS connection. if it is empty, it will be in WS mode.",
        false, "../../../ssl_key/server.crt", "string");
    TCLAP::ValueArg<std::string> keyfile("", "keyfile", 
        "default: ../../../ssl_key/server.key, path of keyfile for WSS connection", 
        false, "../../../ssl_key/server.key", "string");

    TCLAP::ValueArg<float>    global_beam("", GLOB_BEAM, "the decoding beam for beam searching ", false, 3.0, "float");
    TCLAP::ValueArg<float>    lattice_beam("", LAT_BEAM, "the lattice generation beam for beam searching ", false, 3.0, "float");
    TCLAP::ValueArg<float>    am_scale("", AM_SCALE, "the acoustic scale for beam searching ", false, 10.0, "float");

    TCLAP::ValueArg<std::string> lm_dir("", LM_DIR,
        "the LM model path, which contains compiled models: TLG.fst, config.yaml ", false, "damo/speech_ngram_lm_zh-cn-ai-wesp-fst", "string");
    TCLAP::ValueArg<std::string> lm_revision(
        "", "lm-revision", "LM model revision", false, "v1.0.2", "string");
    TCLAP::ValueArg<std::string> hotword("", HOTWORD,
        "the hotword file, one hotword perline, Format: Hotword Weight (could be: 阿里巴巴 20)", 
        false, "/workspace/resources/hotwords.txt", "string");
    TCLAP::ValueArg<std::int32_t> fst_inc_wts("", FST_INC_WTS, 
        "the fst hotwords incremental bias", false, 20, "int32_t");

    // add file
    cmd.add(hotword);
    cmd.add(fst_inc_wts);
    cmd.add(global_beam);
    cmd.add(lattice_beam);
    cmd.add(am_scale);

    cmd.add(certfile);
    cmd.add(keyfile);
    cmd.add(download_model_dir);
    cmd.add(model_dir);
    cmd.add(model_revision);
    cmd.add(quantize);
    cmd.add(vad_dir);
    cmd.add(vad_revision);
    cmd.add(vad_quant);
    cmd.add(punc_dir);
    cmd.add(punc_revision);
    cmd.add(punc_quant);
    cmd.add(itn_dir);
    cmd.add(itn_revision);
    cmd.add(lm_dir);
    cmd.add(lm_revision);

    cmd.add(listen_ip);
    cmd.add(port);
    cmd.add(io_thread_num);
    cmd.add(decoder_thread_num);
    cmd.add(model_thread_num);
    cmd.parse(argc, argv);

    std::map<std::string, std::string> model_path;
    GetValue(model_dir, MODEL_DIR, model_path);
    GetValue(quantize, QUANTIZE, model_path);
    GetValue(vad_dir, VAD_DIR, model_path);
    GetValue(vad_quant, VAD_QUANT, model_path);
    GetValue(punc_dir, PUNC_DIR, model_path);
    GetValue(punc_quant, PUNC_QUANT, model_path);
    GetValue(itn_dir, ITN_DIR, model_path);
    GetValue(lm_dir, LM_DIR, model_path);
    GetValue(hotword, HOTWORD, model_path);

    GetValue(model_revision, "model-revision", model_path);
    GetValue(vad_revision, "vad-revision", model_path);
    GetValue(punc_revision, "punc-revision", model_path);
    GetValue(itn_revision, "itn-revision", model_path);
    GetValue(lm_revision, "lm-revision", model_path);

    global_beam_ = global_beam.getValue();
    lattice_beam_ = lattice_beam.getValue();
    am_scale_ = am_scale.getValue();

    // Download model form Modelscope
    try{
        std::string s_download_model_dir = download_model_dir.getValue();

        std::string s_vad_path = model_path[VAD_DIR];
        std::string s_vad_quant = model_path[VAD_QUANT];
        std::string s_asr_path = model_path[MODEL_DIR];
        std::string s_asr_quant = model_path[QUANTIZE];
        std::string s_punc_path = model_path[PUNC_DIR];
        std::string s_punc_quant = model_path[PUNC_QUANT];
        std::string s_itn_path = model_path[ITN_DIR];
        std::string s_lm_path = model_path[LM_DIR];

        std::string python_cmd = "python -m funasr.utils.runtime_sdk_download_tool --type onnx --quantize True ";

        if(vad_dir.isSet() && !s_vad_path.empty()){
            std::string python_cmd_vad;
            std::string down_vad_path;
            std::string down_vad_model;  

            if (access(s_vad_path.c_str(), F_OK) == 0){
                // local
                python_cmd_vad = python_cmd + " --model-name " + s_vad_path + " --export-dir ./ " + " --model_revision " + model_path["vad-revision"];
                down_vad_path  = s_vad_path;
            }else{
                // modelscope
                LOG(INFO) << "Download model: " <<  s_vad_path << " from modelscope: ";
                python_cmd_vad = python_cmd + " --model-name " + s_vad_path + " --export-dir " + s_download_model_dir + " --model_revision " + model_path["vad-revision"];
                down_vad_path  = s_download_model_dir+"/"+s_vad_path;
            }
                
            int ret = system(python_cmd_vad.c_str());
            if(ret !=0){
                LOG(INFO) << "Failed to download model from modelscope. If you set local vad model path, you can ignore the errors.";
            }
            down_vad_model = down_vad_path+"/model_quant.onnx";
            if(s_vad_quant=="false" || s_vad_quant=="False" || s_vad_quant=="FALSE"){
                down_vad_model = down_vad_path+"/model.onnx";
            }

            if (access(down_vad_model.c_str(), F_OK) != 0){
                LOG(ERROR) << down_vad_model << " do not exists."; 
                exit(-1);
            }else{
                model_path[VAD_DIR]=down_vad_path;
                LOG(INFO) << "Set " << VAD_DIR << " : " << model_path[VAD_DIR];
            }
        }else{
            LOG(INFO) << "VAD model is not set, use default.";
        }

        if(model_dir.isSet() && !s_asr_path.empty()){
            std::string python_cmd_asr;
            std::string down_asr_path;
            std::string down_asr_model;

            // modify model-revision by model name
            size_t found = s_asr_path.find("speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404");
            if (found != std::string::npos) {
                model_path["model-revision"]="v1.2.4";
            }

            found = s_asr_path.find("speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404");
            if (found != std::string::npos) {
                model_path["model-revision"]="v1.0.5";
            }

            found = s_asr_path.find("speech_paraformer-large_asr_nat-en-16k-common-vocab10020");
            if (found != std::string::npos) {
                model_path["model-revision"]="v1.0.0";
                s_itn_path="";
                s_lm_path="";
            }

            if (access(s_asr_path.c_str(), F_OK) == 0){
                // local
                python_cmd_asr = python_cmd + " --model-name " + s_asr_path + " --export-dir ./ " + " --model_revision " + model_path["model-revision"];
                down_asr_path  = s_asr_path;
            }else{
                // modelscope
                LOG(INFO) << "Download model: " <<  s_asr_path << " from modelscope: ";
                python_cmd_asr = python_cmd + " --model-name " + s_asr_path + " --export-dir " + s_download_model_dir + " --model_revision " + model_path["model-revision"];
                down_asr_path  = s_download_model_dir+"/"+s_asr_path;
            }
                
            int ret = system(python_cmd_asr.c_str());
            if(ret !=0){
                LOG(INFO) << "Failed to download model from modelscope. If you set local asr model path, you can ignore the errors.";
            }
            down_asr_model = down_asr_path+"/model_quant.onnx";
            if(s_asr_quant=="false" || s_asr_quant=="False" || s_asr_quant=="FALSE"){
                down_asr_model = down_asr_path+"/model.onnx";
            }

            if (access(down_asr_model.c_str(), F_OK) != 0){
                LOG(ERROR) << down_asr_model << " do not exists.";
                exit(-1);
            }else{
                model_path[MODEL_DIR]=down_asr_path;
                LOG(INFO) << "Set " << MODEL_DIR << " : " << model_path[MODEL_DIR];
            }
        }else{
            LOG(INFO) << "ASR model is not set, use default.";
        }

        if (!s_itn_path.empty()) {
            std::string python_cmd_itn;
            std::string down_itn_path;
            std::string down_itn_model;

            if (access(s_itn_path.c_str(), F_OK) == 0) {
                // local
                python_cmd_itn = python_cmd + " --model-name " + s_itn_path +
                                    " --export-dir ./ " + " --model_revision " +
                                    model_path["itn-revision"] + " --export False ";
                down_itn_path = s_itn_path;
            } else {
                // modelscope
                LOG(INFO) << "Download model: " << s_itn_path
                            << " from modelscope : "; 
                python_cmd_itn = python_cmd + " --model-name " +
                        s_itn_path +
                        " --export-dir " + s_download_model_dir +
                        " --model_revision " + model_path["itn-revision"]
                        + " --export False "; 
                down_itn_path  =
                        s_download_model_dir +
                        "/" + s_itn_path;
            }

            int ret = system(python_cmd_itn.c_str());
            if (ret != 0) {
                LOG(INFO) << "Failed to download model from modelscope. If you set local itn model path, you can ignore the errors.";
            }
            down_itn_model = down_itn_path + "/zh_itn_tagger.fst";

            if (access(down_itn_model.c_str(), F_OK) != 0) {
                LOG(ERROR) << down_itn_model << " do not exists.";
                exit(-1);
            } else {
                model_path[ITN_DIR] = down_itn_path;
                LOG(INFO) << "Set " << ITN_DIR << " : " << model_path[ITN_DIR];
            }
        } else {
            LOG(INFO) << "ITN model is not set, not executed.";
        }

        if (!s_lm_path.empty() && s_lm_path != "NONE" && s_lm_path != "none") {
            std::string python_cmd_lm;
            std::string down_lm_path;
            std::string down_lm_model;

            if (access(s_lm_path.c_str(), F_OK) == 0) {
                // local
                python_cmd_lm = python_cmd + " --model-name " + s_lm_path +
                                    " --export-dir ./ " + " --model_revision " +
                                    model_path["lm-revision"] + " --export False ";
                down_lm_path = s_lm_path;
            } else {
                // modelscope
                LOG(INFO) << "Download model: " << s_lm_path
                            << " from modelscope : "; 
                python_cmd_lm = python_cmd + " --model-name " +
                        s_lm_path +
                        " --export-dir " + s_download_model_dir +
                        " --model_revision " + model_path["lm-revision"]
                        + " --export False "; 
                down_lm_path  =
                        s_download_model_dir +
                        "/" + s_lm_path;
            }

            int ret = system(python_cmd_lm.c_str());
            if (ret != 0) {
                LOG(INFO) << "Failed to download model from modelscope. If you set local lm model path, you can ignore the errors.";
            }
            down_lm_model = down_lm_path + "/TLG.fst";

            if (access(down_lm_model.c_str(), F_OK) != 0) {
                LOG(ERROR) << down_lm_model << " do not exists.";
                exit(-1);
            } else {
                model_path[LM_DIR] = down_lm_path;
                LOG(INFO) << "Set " << LM_DIR << " : " << model_path[LM_DIR];
            }
        } else {
            LOG(INFO) << "LM model is not set, not executed.";
            model_path[LM_DIR] = "";
        }

        if(punc_dir.isSet() && !s_punc_path.empty()){
            std::string python_cmd_punc;
            std::string down_punc_path;
            std::string down_punc_model;  

            if (access(s_punc_path.c_str(), F_OK) == 0){
                // local
                python_cmd_punc = python_cmd + " --model-name " + s_punc_path + " --export-dir ./ " + " --model_revision " + model_path["punc-revision"];
                down_punc_path  = s_punc_path;
            }else{
                // modelscope
                LOG(INFO) << "Download model: " <<  s_punc_path << " from modelscope: ";
                python_cmd_punc = python_cmd + " --model-name " + s_punc_path + " --export-dir " + s_download_model_dir + " --model_revision " + model_path["punc-revision"];
                down_punc_path  = s_download_model_dir+"/"+s_punc_path;
            }
                
            int ret = system(python_cmd_punc.c_str());
            if(ret !=0){
                LOG(INFO) << "Failed to download model from modelscope. If you set local punc model path, you can ignore the errors.";
            }
            down_punc_model = down_punc_path+"/model_quant.onnx";
            if(s_punc_quant=="false" || s_punc_quant=="False" || s_punc_quant=="FALSE"){
                down_punc_model = down_punc_path+"/model.onnx";
            }

            if (access(down_punc_model.c_str(), F_OK) != 0){
                LOG(ERROR) << down_punc_model << " do not exists."; 
                exit(-1);
            }else{
                model_path[PUNC_DIR]=down_punc_path;
                LOG(INFO) << "Set " << PUNC_DIR << " : " << model_path[PUNC_DIR];
            }
        }else{
            LOG(INFO) << "PUNC model is not set, use default.";
        }

    } catch (std::exception const& e) {
        LOG(ERROR) << "Error: " << e.what();
    }

    std::string s_listen_ip = listen_ip.getValue();
    int s_port = port.getValue();
    int s_io_thread_num = io_thread_num.getValue();
    int s_decoder_thread_num = decoder_thread_num.getValue();

    int s_model_thread_num = model_thread_num.getValue();

    asio::io_context io_decoder;  // context for decoding
    asio::io_context io_server;   // context for server

    std::vector<std::thread> decoder_threads;

    std::string s_certfile = certfile.getValue();
    std::string s_keyfile = keyfile.getValue();
    
    // hotword file
    std::string hotword_path;
    hotword_path = model_path.at(HOTWORD);
    fst_inc_wts_ = fst_inc_wts.getValue();
    LOG(INFO) << "hotword path: " << hotword_path;
    funasr::ExtractHws(hotword_path, hws_map_);

    bool is_ssl = false;
    if (!s_certfile.empty() && access(s_certfile.c_str(), F_OK) == 0) {
      is_ssl = true;
    }

    auto conn_guard = asio::make_work_guard(
        io_decoder);  // make sure threads can wait in the queue
    auto server_guard = asio::make_work_guard(
        io_server);  // make sure threads can wait in the queue
    // create threads pool
    for (int32_t i = 0; i < s_decoder_thread_num; ++i) {
      decoder_threads.emplace_back([&io_decoder]() { io_decoder.run(); });
    }

    server server_;  // server for websocket
    wss_server wss_server_;
    server* server = nullptr;
    wss_server* wss_server = nullptr;
    if (is_ssl) {
      LOG(INFO)<< "SSL is opened!";
      wss_server_.init_asio(&io_server);  // init asio
      wss_server_.set_reuse_addr(
          true);  // reuse address as we create multiple threads

      // list on port for accept
      wss_server_.listen(asio::ip::address::from_string(s_listen_ip), s_port);
      wss_server = &wss_server_;
    } else {
      LOG(INFO)<< "SSL is closed!";
      server_.init_asio(&io_server);  // init asio
      server_.set_reuse_addr(
          true);  // reuse address as we create multiple threads

      // list on port for accept
      server_.listen(asio::ip::address::from_string(s_listen_ip), s_port);
      server = &server_;
    }


    WebSocketServer websocket_srv(
        io_decoder, is_ssl, server, wss_server, s_certfile,
        s_keyfile);  // websocket server for asr engine
    websocket_srv.initAsr(model_path, s_model_thread_num);  // init asr model

    LOG(INFO) << "decoder-thread-num: " << s_decoder_thread_num;
    LOG(INFO) << "io-thread-num: " << s_io_thread_num;
    LOG(INFO) << "model-thread-num: " << s_model_thread_num;
    LOG(INFO) << "asr model init finished. listen on port:" << s_port;

    // Start the ASIO network io_service run loop
    std::vector<std::thread> ts;
    // create threads for io network
    for (size_t i = 0; i < s_io_thread_num; i++) {
      ts.emplace_back([&io_server]() { io_server.run(); });
    }
    // wait for theads
    for (size_t i = 0; i < s_io_thread_num; i++) {
      ts[i].join();
    }

    // wait for theads
    for (auto& t : decoder_threads) {
      t.join();
    }

  } catch (std::exception const& e) {
    LOG(ERROR) << "Error: " << e.what();
  }

  return 0;
}