/**
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* Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights
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* Reserved. MIT License (https://opensource.org/licenses/MIT)
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*/
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/* 2022-2023 by zhaomingwork */
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#include "websocket-server.h"
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#ifdef _WIN32
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#include "win_func.h"
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#else
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#include <unistd.h>
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#endif
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#include <fstream>
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#include "util.h"
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// hotwords
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std::unordered_map<std::string, int> hws_map_;
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int fst_inc_wts_=20;
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float global_beam_, lattice_beam_, am_scale_;
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using namespace std;
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void GetValue(TCLAP::ValueArg<std::string>& value_arg, string key,
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std::map<std::string, std::string>& model_path) {
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model_path.insert({key, value_arg.getValue()});
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LOG(INFO) << key << " : " << value_arg.getValue();
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}
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int main(int argc, char* argv[]) {
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#ifdef _WIN32
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#include <windows.h>
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SetConsoleOutputCP(65001);
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#endif
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try {
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google::InitGoogleLogging(argv[0]);
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FLAGS_logtostderr = true;
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std::string offline_version = "";
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#ifdef _WIN32
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offline_version = "0.1.0";
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#endif
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TCLAP::CmdLine cmd("funasr-wss-server", ' ', offline_version);
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TCLAP::ValueArg<std::string> download_model_dir(
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"", "download-model-dir",
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"Download model from Modelscope to download_model_dir",
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false, "/workspace/models", "string");
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TCLAP::ValueArg<std::string> model_dir(
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"", MODEL_DIR,
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"default: /workspace/models/asr, the asr model path, which contains model_quant.onnx, config.yaml, am.mvn",
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false, "/workspace/models/asr", "string");
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TCLAP::ValueArg<std::string> model_revision(
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"", "model-revision",
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"ASR model revision",
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false, "v1.2.1", "string");
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TCLAP::ValueArg<std::string> quantize(
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"", QUANTIZE,
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"true (Default), load the model of model_quant.onnx in model_dir. If set "
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"false, load the model of model.onnx in model_dir",
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false, "true", "string");
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TCLAP::ValueArg<std::string> vad_dir(
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"", VAD_DIR,
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"default: /workspace/models/vad, the vad model path, which contains model_quant.onnx, vad.yaml, vad.mvn",
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false, "/workspace/models/vad", "string");
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TCLAP::ValueArg<std::string> vad_revision(
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"", "vad-revision",
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"VAD model revision",
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false, "v1.2.0", "string");
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TCLAP::ValueArg<std::string> vad_quant(
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"", VAD_QUANT,
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"true (Default), load the model of model_quant.onnx in vad_dir. If set "
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"false, load the model of model.onnx in vad_dir",
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false, "true", "string");
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TCLAP::ValueArg<std::string> punc_dir(
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"", PUNC_DIR,
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"default: /workspace/models/punc, the punc model path, which contains model_quant.onnx, punc.yaml",
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false, "/workspace/models/punc",
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"string");
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TCLAP::ValueArg<std::string> punc_revision(
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"", "punc-revision",
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"PUNC model revision",
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false, "v1.1.7", "string");
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TCLAP::ValueArg<std::string> punc_quant(
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"", PUNC_QUANT,
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"true (Default), load the model of model_quant.onnx in punc_dir. If set "
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"false, load the model of model.onnx in punc_dir",
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false, "true", "string");
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TCLAP::ValueArg<std::string> itn_dir(
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"", ITN_DIR,
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"default: thuduj12/fst_itn_zh, the itn model path, which contains "
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"zh_itn_tagger.fst, zh_itn_verbalizer.fst",
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false, "thuduj12/fst_itn_zh", "string");
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TCLAP::ValueArg<std::string> itn_revision(
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"", "itn-revision", "ITN model revision", false, "v1.0.1", "string");
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TCLAP::ValueArg<std::string> listen_ip("", "listen-ip", "listen ip", false,
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"0.0.0.0", "string");
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TCLAP::ValueArg<int> port("", "port", "port", false, 10095, "int");
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TCLAP::ValueArg<int> io_thread_num("", "io-thread-num", "io thread num",
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false, 2, "int");
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TCLAP::ValueArg<int> decoder_thread_num(
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"", "decoder-thread-num", "decoder thread num", false, 8, "int");
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TCLAP::ValueArg<int> model_thread_num("", "model-thread-num",
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"model thread num", false, 1, "int");
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TCLAP::ValueArg<std::string> certfile("", "certfile",
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"default: ../../../ssl_key/server.crt, path of certficate for WSS connection. if it is empty, it will be in WS mode.",
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false, "../../../ssl_key/server.crt", "string");
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TCLAP::ValueArg<std::string> keyfile("", "keyfile",
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"default: ../../../ssl_key/server.key, path of keyfile for WSS connection",
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false, "../../../ssl_key/server.key", "string");
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TCLAP::ValueArg<float> global_beam("", GLOB_BEAM, "the decoding beam for beam searching ", false, 3.0, "float");
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TCLAP::ValueArg<float> lattice_beam("", LAT_BEAM, "the lattice generation beam for beam searching ", false, 3.0, "float");
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TCLAP::ValueArg<float> am_scale("", AM_SCALE, "the acoustic scale for beam searching ", false, 10.0, "float");
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TCLAP::ValueArg<std::string> lm_dir("", LM_DIR,
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"the LM model path, which contains compiled models: TLG.fst, config.yaml ", false, "damo/speech_ngram_lm_zh-cn-ai-wesp-fst", "string");
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TCLAP::ValueArg<std::string> lm_revision(
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"", "lm-revision", "LM model revision", false, "v1.0.2", "string");
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TCLAP::ValueArg<std::string> hotword("", HOTWORD,
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"the hotword file, one hotword perline, Format: Hotword Weight (could be: 阿里巴巴 20)",
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false, "/workspace/resources/hotwords.txt", "string");
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TCLAP::ValueArg<std::int32_t> fst_inc_wts("", FST_INC_WTS,
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"the fst hotwords incremental bias", false, 20, "int32_t");
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TCLAP::SwitchArg use_gpu("", USE_GPU, "Whether to use GPU, default is false", false);
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// add file
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cmd.add(hotword);
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cmd.add(fst_inc_wts);
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cmd.add(global_beam);
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cmd.add(lattice_beam);
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cmd.add(am_scale);
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cmd.add(certfile);
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cmd.add(keyfile);
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cmd.add(download_model_dir);
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cmd.add(model_dir);
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cmd.add(model_revision);
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cmd.add(quantize);
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cmd.add(vad_dir);
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cmd.add(vad_revision);
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cmd.add(vad_quant);
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cmd.add(punc_dir);
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cmd.add(punc_revision);
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cmd.add(punc_quant);
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cmd.add(itn_dir);
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cmd.add(itn_revision);
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cmd.add(lm_dir);
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cmd.add(lm_revision);
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cmd.add(listen_ip);
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cmd.add(port);
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cmd.add(io_thread_num);
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cmd.add(decoder_thread_num);
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cmd.add(model_thread_num);
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cmd.add(use_gpu);
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cmd.parse(argc, argv);
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std::map<std::string, std::string> model_path;
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GetValue(model_dir, MODEL_DIR, model_path);
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GetValue(quantize, QUANTIZE, model_path);
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GetValue(vad_dir, VAD_DIR, model_path);
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GetValue(vad_quant, VAD_QUANT, model_path);
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GetValue(punc_dir, PUNC_DIR, model_path);
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GetValue(punc_quant, PUNC_QUANT, model_path);
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GetValue(itn_dir, ITN_DIR, model_path);
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GetValue(lm_dir, LM_DIR, model_path);
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GetValue(hotword, HOTWORD, model_path);
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GetValue(model_revision, "model-revision", model_path);
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GetValue(vad_revision, "vad-revision", model_path);
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GetValue(punc_revision, "punc-revision", model_path);
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GetValue(itn_revision, "itn-revision", model_path);
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GetValue(lm_revision, "lm-revision", model_path);
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global_beam_ = global_beam.getValue();
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lattice_beam_ = lattice_beam.getValue();
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am_scale_ = am_scale.getValue();
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bool use_gpu_ = use_gpu.getValue();
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// Download model form Modelscope
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try{
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std::string s_download_model_dir = download_model_dir.getValue();
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std::string s_vad_path = model_path[VAD_DIR];
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std::string s_vad_quant = model_path[VAD_QUANT];
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std::string s_asr_path = model_path[MODEL_DIR];
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std::string s_asr_quant = model_path[QUANTIZE];
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std::string s_punc_path = model_path[PUNC_DIR];
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std::string s_punc_quant = model_path[PUNC_QUANT];
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std::string s_itn_path = model_path[ITN_DIR];
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std::string s_lm_path = model_path[LM_DIR];
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std::string python_cmd = "python -m funasr.download.runtime_sdk_download_tool --type onnx --quantize True ";
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if(vad_dir.isSet() && !s_vad_path.empty()){
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std::string python_cmd_vad;
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std::string down_vad_path;
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std::string down_vad_model;
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if (access(s_vad_path.c_str(), F_OK) == 0){
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// local
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python_cmd_vad = python_cmd + " --model-name " + s_vad_path + " --export-dir ./ " + " --model_revision " + model_path["vad-revision"];
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down_vad_path = s_vad_path;
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}else{
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// modelscope
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LOG(INFO) << "Download model: " << s_vad_path << " from modelscope: ";
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python_cmd_vad = python_cmd + " --model-name " + s_vad_path + " --export-dir " + s_download_model_dir + " --model_revision " + model_path["vad-revision"];
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down_vad_path = s_download_model_dir+"/"+s_vad_path;
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}
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int ret = system(python_cmd_vad.c_str());
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if(ret !=0){
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LOG(INFO) << "Failed to download model from modelscope. If you set local vad model path, you can ignore the errors.";
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}
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down_vad_model = down_vad_path+"/model_quant.onnx";
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if(s_vad_quant=="false" || s_vad_quant=="False" || s_vad_quant=="FALSE"){
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down_vad_model = down_vad_path+"/model.onnx";
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}
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if (access(down_vad_model.c_str(), F_OK) != 0){
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LOG(ERROR) << down_vad_model << " do not exists.";
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exit(-1);
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}else{
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model_path[VAD_DIR]=down_vad_path;
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LOG(INFO) << "Set " << VAD_DIR << " : " << model_path[VAD_DIR];
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}
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}else{
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LOG(INFO) << "VAD model is not set, use default.";
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}
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if(model_dir.isSet() && !s_asr_path.empty()){
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std::string python_cmd_asr;
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std::string down_asr_path;
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std::string down_asr_model;
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// modify model-revision by model name
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size_t found = s_asr_path.find("speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404");
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if (found != std::string::npos) {
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model_path["model-revision"]="v1.2.4";
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}
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found = s_asr_path.find("speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404");
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if (found != std::string::npos) {
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model_path["model-revision"]="v1.0.5";
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}
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found = s_asr_path.find("speech_paraformer-large_asr_nat-en-16k-common-vocab10020");
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if (found != std::string::npos) {
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model_path["model-revision"]="v1.0.0";
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s_itn_path="";
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s_lm_path="";
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}
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if (access(s_asr_path.c_str(), F_OK) == 0){
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// local
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python_cmd_asr = python_cmd + " --model-name " + s_asr_path + " --export-dir ./ " + " --model_revision " + model_path["model-revision"];
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down_asr_path = s_asr_path;
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}else{
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// modelscope
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LOG(INFO) << "Download model: " << s_asr_path << " from modelscope: ";
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python_cmd_asr = python_cmd + " --model-name " + s_asr_path + " --export-dir " + s_download_model_dir + " --model_revision " + model_path["model-revision"];
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down_asr_path = s_download_model_dir+"/"+s_asr_path;
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}
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int ret = system(python_cmd_asr.c_str());
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if(ret !=0){
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LOG(INFO) << "Failed to download model from modelscope. If you set local asr model path, you can ignore the errors.";
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}
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down_asr_model = down_asr_path+"/model_quant.onnx";
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if(s_asr_quant=="false" || s_asr_quant=="False" || s_asr_quant=="FALSE"){
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down_asr_model = down_asr_path+"/model.onnx";
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}
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if (access(down_asr_model.c_str(), F_OK) != 0){
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LOG(ERROR) << down_asr_model << " do not exists.";
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exit(-1);
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}else{
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model_path[MODEL_DIR]=down_asr_path;
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LOG(INFO) << "Set " << MODEL_DIR << " : " << model_path[MODEL_DIR];
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}
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}else{
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LOG(INFO) << "ASR model is not set, use default.";
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}
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if (!s_itn_path.empty()) {
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std::string python_cmd_itn;
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std::string down_itn_path;
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std::string down_itn_model;
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if (access(s_itn_path.c_str(), F_OK) == 0) {
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// local
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python_cmd_itn = python_cmd + " --model-name " + s_itn_path +
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" --export-dir ./ " + " --model_revision " +
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model_path["itn-revision"] + " --export False ";
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down_itn_path = s_itn_path;
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} else {
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// modelscope
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LOG(INFO) << "Download model: " << s_itn_path
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<< " from modelscope : ";
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python_cmd_itn = python_cmd + " --model-name " +
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s_itn_path +
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" --export-dir " + s_download_model_dir +
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" --model_revision " + model_path["itn-revision"]
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+ " --export False ";
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down_itn_path =
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s_download_model_dir +
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"/" + s_itn_path;
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}
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int ret = system(python_cmd_itn.c_str());
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if (ret != 0) {
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LOG(INFO) << "Failed to download model from modelscope. If you set local itn model path, you can ignore the errors.";
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}
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down_itn_model = down_itn_path + "/zh_itn_tagger.fst";
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if (access(down_itn_model.c_str(), F_OK) != 0) {
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LOG(ERROR) << down_itn_model << " do not exists.";
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exit(-1);
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} else {
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model_path[ITN_DIR] = down_itn_path;
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LOG(INFO) << "Set " << ITN_DIR << " : " << model_path[ITN_DIR];
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}
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} else {
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LOG(INFO) << "ITN model is not set, not executed.";
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}
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if (!s_lm_path.empty() && s_lm_path != "NONE" && s_lm_path != "none") {
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std::string python_cmd_lm;
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std::string down_lm_path;
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std::string down_lm_model;
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if (access(s_lm_path.c_str(), F_OK) == 0) {
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// local
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python_cmd_lm = python_cmd + " --model-name " + s_lm_path +
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" --export-dir ./ " + " --model_revision " +
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model_path["lm-revision"] + " --export False ";
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down_lm_path = s_lm_path;
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} else {
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// modelscope
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LOG(INFO) << "Download model: " << s_lm_path
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<< " from modelscope : ";
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python_cmd_lm = python_cmd + " --model-name " +
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s_lm_path +
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" --export-dir " + s_download_model_dir +
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" --model_revision " + model_path["lm-revision"]
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+ " --export False ";
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down_lm_path =
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s_download_model_dir +
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"/" + s_lm_path;
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}
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int ret = system(python_cmd_lm.c_str());
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if (ret != 0) {
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LOG(INFO) << "Failed to download model from modelscope. If you set local lm model path, you can ignore the errors.";
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}
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down_lm_model = down_lm_path + "/TLG.fst";
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if (access(down_lm_model.c_str(), F_OK) != 0) {
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LOG(ERROR) << down_lm_model << " do not exists.";
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exit(-1);
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} else {
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model_path[LM_DIR] = down_lm_path;
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LOG(INFO) << "Set " << LM_DIR << " : " << model_path[LM_DIR];
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}
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} else {
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LOG(INFO) << "LM model is not set, not executed.";
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model_path[LM_DIR] = "";
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}
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if(punc_dir.isSet() && !s_punc_path.empty()){
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std::string python_cmd_punc;
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std::string down_punc_path;
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std::string down_punc_model;
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if (access(s_punc_path.c_str(), F_OK) == 0){
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// local
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python_cmd_punc = python_cmd + " --model-name " + s_punc_path + " --export-dir ./ " + " --model_revision " + model_path["punc-revision"];
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down_punc_path = s_punc_path;
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}else{
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// modelscope
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LOG(INFO) << "Download model: " << s_punc_path << " from modelscope: ";
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python_cmd_punc = python_cmd + " --model-name " + s_punc_path + " --export-dir " + s_download_model_dir + " --model_revision " + model_path["punc-revision"];
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down_punc_path = s_download_model_dir+"/"+s_punc_path;
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}
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int ret = system(python_cmd_punc.c_str());
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if(ret !=0){
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LOG(INFO) << "Failed to download model from modelscope. If you set local punc model path, you can ignore the errors.";
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}
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down_punc_model = down_punc_path+"/model_quant.onnx";
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if(s_punc_quant=="false" || s_punc_quant=="False" || s_punc_quant=="FALSE"){
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down_punc_model = down_punc_path+"/model.onnx";
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}
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if (access(down_punc_model.c_str(), F_OK) != 0){
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LOG(ERROR) << down_punc_model << " do not exists.";
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exit(-1);
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}else{
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model_path[PUNC_DIR]=down_punc_path;
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LOG(INFO) << "Set " << PUNC_DIR << " : " << model_path[PUNC_DIR];
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}
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}else{
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LOG(INFO) << "PUNC model is not set, use default.";
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}
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} catch (std::exception const& e) {
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LOG(ERROR) << "Error: " << e.what();
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}
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std::string s_listen_ip = listen_ip.getValue();
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int s_port = port.getValue();
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int s_io_thread_num = io_thread_num.getValue();
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int s_decoder_thread_num = decoder_thread_num.getValue();
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int s_model_thread_num = model_thread_num.getValue();
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asio::io_context io_decoder; // context for decoding
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asio::io_context io_server; // context for server
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std::vector<std::thread> decoder_threads;
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std::string s_certfile = certfile.getValue();
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std::string s_keyfile = keyfile.getValue();
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// hotword file
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std::string hotword_path;
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hotword_path = model_path.at(HOTWORD);
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fst_inc_wts_ = fst_inc_wts.getValue();
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LOG(INFO) << "hotword path: " << hotword_path;
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funasr::ExtractHws(hotword_path, hws_map_);
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bool is_ssl = false;
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if (!s_certfile.empty() && access(s_certfile.c_str(), F_OK) == 0) {
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is_ssl = true;
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}
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auto conn_guard = asio::make_work_guard(
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io_decoder); // make sure threads can wait in the queue
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auto server_guard = asio::make_work_guard(
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io_server); // make sure threads can wait in the queue
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// create threads pool
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for (int32_t i = 0; i < s_decoder_thread_num; ++i) {
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decoder_threads.emplace_back([&io_decoder]() { io_decoder.run(); });
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}
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server server_; // server for websocket
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wss_server wss_server_;
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server* server = nullptr;
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wss_server* wss_server = nullptr;
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if (is_ssl) {
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LOG(INFO)<< "SSL is opened!";
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wss_server_.init_asio(&io_server); // init asio
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wss_server_.set_reuse_addr(
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true); // reuse address as we create multiple threads
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// list on port for accept
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wss_server_.listen(asio::ip::address::from_string(s_listen_ip), s_port);
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wss_server = &wss_server_;
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} else {
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LOG(INFO)<< "SSL is closed!";
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server_.init_asio(&io_server); // init asio
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server_.set_reuse_addr(
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true); // reuse address as we create multiple threads
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// list on port for accept
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server_.listen(asio::ip::address::from_string(s_listen_ip), s_port);
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server = &server_;
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}
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WebSocketServer websocket_srv(
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io_decoder, is_ssl, server, wss_server, s_certfile,
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s_keyfile); // websocket server for asr engine
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websocket_srv.initAsr(model_path, s_model_thread_num, use_gpu_); // init asr model
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LOG(INFO) << "decoder-thread-num: " << s_decoder_thread_num;
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LOG(INFO) << "io-thread-num: " << s_io_thread_num;
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LOG(INFO) << "model-thread-num: " << s_model_thread_num;
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LOG(INFO) << "asr model init finished. listen on port:" << s_port;
|
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// 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;
|
}
|
