From b454a1054fadbff0ee963944ff42f66b98317582 Mon Sep 17 00:00:00 2001
From: Yabin Li <wucong.lyb@alibaba-inc.com>
Date: 星期二, 08 八月 2023 11:17:43 +0800
Subject: [PATCH] update online runtime, including vad-online, paraformer-online, punc-online,2pass (#815)
---
funasr/runtime/onnxruntime/src/paraformer.cpp | 218 ++++++++++++++++++++++++++++++++++++++++++++---------
1 files changed, 179 insertions(+), 39 deletions(-)
diff --git a/funasr/runtime/onnxruntime/src/paraformer.cpp b/funasr/runtime/onnxruntime/src/paraformer.cpp
index b605fff..ef2a182 100644
--- a/funasr/runtime/onnxruntime/src/paraformer.cpp
+++ b/funasr/runtime/onnxruntime/src/paraformer.cpp
@@ -10,29 +10,30 @@
namespace funasr {
Paraformer::Paraformer()
-:env_(ORT_LOGGING_LEVEL_ERROR, "paraformer"),session_options{}{
+:env_(ORT_LOGGING_LEVEL_ERROR, "paraformer"),session_options_{}{
}
+// offline
void Paraformer::InitAsr(const std::string &am_model, const std::string &am_cmvn, const std::string &am_config, int thread_num){
// knf options
- fbank_opts.frame_opts.dither = 0;
- fbank_opts.mel_opts.num_bins = 80;
- fbank_opts.frame_opts.samp_freq = MODEL_SAMPLE_RATE;
- fbank_opts.frame_opts.window_type = "hamming";
- fbank_opts.frame_opts.frame_shift_ms = 10;
- fbank_opts.frame_opts.frame_length_ms = 25;
- fbank_opts.energy_floor = 0;
- fbank_opts.mel_opts.debug_mel = false;
+ fbank_opts_.frame_opts.dither = 0;
+ fbank_opts_.mel_opts.num_bins = n_mels;
+ fbank_opts_.frame_opts.samp_freq = MODEL_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);
+ // session_options_.SetInterOpNumThreads(1);
+ session_options_.SetIntraOpNumThreads(thread_num);
+ session_options_.SetGraphOptimizationLevel(ORT_ENABLE_ALL);
// DisableCpuMemArena can improve performance
- session_options.DisableCpuMemArena();
+ session_options_.DisableCpuMemArena();
try {
- m_session = std::make_unique<Ort::Session>(env_, am_model.c_str(), session_options);
+ m_session_ = std::make_unique<Ort::Session>(env_, 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();
@@ -40,14 +41,14 @@
}
string strName;
- GetInputName(m_session.get(), strName);
+ GetInputName(m_session_.get(), strName);
m_strInputNames.push_back(strName.c_str());
- GetInputName(m_session.get(), strName,1);
+ GetInputName(m_session_.get(), strName,1);
m_strInputNames.push_back(strName);
- GetOutputName(m_session.get(), strName);
+ GetOutputName(m_session_.get(), strName);
m_strOutputNames.push_back(strName);
- GetOutputName(m_session.get(), strName,1);
+ GetOutputName(m_session_.get(), strName,1);
m_strOutputNames.push_back(strName);
for (auto& item : m_strInputNames)
@@ -56,6 +57,152 @@
m_szOutputNames.push_back(item.c_str());
vocab = new Vocab(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 = MODEL_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_, 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(0);
+ }
+
+ try {
+ decoder_session_ = std::make_unique<Ort::Session>(env_, 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(0);
+ }
+
+ // 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());
+ 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_, 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(0);
+ }
+
+ 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);
+
+ GetOutputName(m_session_.get(), strName);
+ m_strOutputNames.push_back(strName);
+ GetOutputName(m_session_.get(), strName,1);
+ 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::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>();
+
+ }catch(exception const &e){
+ LOG(ERROR) << "Error when load argument from vad config YAML.";
+ exit(-1);
+ }
}
Paraformer::~Paraformer()
@@ -69,7 +216,7 @@
}
vector<float> Paraformer::FbankKaldi(float sample_rate, const float* waves, int len) {
- knf::OnlineFbank fbank_(fbank_opts);
+ knf::OnlineFbank fbank_(fbank_opts_);
std::vector<float> buf(len);
for (int32_t i = 0; i != len; ++i) {
buf[i] = waves[i] * 32768;
@@ -77,7 +224,7 @@
fbank_.AcceptWaveform(sample_rate, buf.data(), buf.size());
//fbank_->InputFinished();
int32_t frames = fbank_.NumFramesReady();
- int32_t feature_dim = fbank_opts.mel_opts.num_bins;
+ int32_t feature_dim = fbank_opts_.mel_opts.num_bins;
vector<float> features(frames * feature_dim);
float *p = features.data();
@@ -108,7 +255,7 @@
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]));
+ means_list_.push_back(stof(means_lines[j]));
}
continue;
}
@@ -119,7 +266,7 @@
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])*scale);
}
continue;
}
@@ -143,11 +290,11 @@
vector<float> Paraformer::ApplyLfr(const std::vector<float> &in)
{
- int32_t in_feat_dim = fbank_opts.mel_opts.num_bins;
+ int32_t in_feat_dim = fbank_opts_.mel_opts.num_bins;
int32_t in_num_frames = in.size() / in_feat_dim;
int32_t out_num_frames =
- (in_num_frames - lfr_window_size) / lfr_window_shift + 1;
- int32_t out_feat_dim = in_feat_dim * lfr_window_size;
+ (in_num_frames - lfr_m) / lfr_n + 1;
+ int32_t out_feat_dim = in_feat_dim * lfr_m;
std::vector<float> out(out_num_frames * out_feat_dim);
@@ -158,7 +305,7 @@
std::copy(p_in, p_in + out_feat_dim, p_out);
p_out += out_feat_dim;
- p_in += lfr_window_shift * in_feat_dim;
+ p_in += lfr_n * in_feat_dim;
}
return out;
@@ -166,29 +313,29 @@
void Paraformer::ApplyCmvn(std::vector<float> *v)
{
- int32_t dim = means_list.size();
+ int32_t dim = means_list_.size();
int32_t num_frames = v->size() / dim;
float *p = v->data();
for (int32_t i = 0; i != num_frames; ++i) {
for (int32_t k = 0; k != dim; ++k) {
- p[k] = (p[k] + means_list[k]) * vars_list[k];
+ p[k] = (p[k] + means_list_[k]) * vars_list_[k];
}
p += dim;
}
}
-string Paraformer::Forward(float* din, int len, int flag)
+string Paraformer::Forward(float* din, int len, bool input_finished)
{
- int32_t in_feat_dim = fbank_opts.mel_opts.num_bins;
+ int32_t in_feat_dim = fbank_opts_.mel_opts.num_bins;
std::vector<float> wav_feats = FbankKaldi(MODEL_SAMPLE_RATE, din, len);
wav_feats = ApplyLfr(wav_feats);
ApplyCmvn(&wav_feats);
- int32_t feat_dim = lfr_window_size*in_feat_dim;
+ int32_t feat_dim = lfr_m*in_feat_dim;
int32_t num_frames = wav_feats.size() / feat_dim;
#ifdef _WIN_X86
@@ -216,7 +363,7 @@
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());
+ 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();
int64_t outputCount = std::accumulate(outputShape.begin(), outputShape.end(), 1, std::multiplies<int64_t>());
@@ -230,13 +377,6 @@
}
return result;
-}
-
-string Paraformer::ForwardChunk(float* din, int len, int flag)
-{
-
- LOG(ERROR)<<"Not Imp!!!!!!";
- return "";
}
string Paraformer::Rescoring()
--
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