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add sensevoice-small

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雾聪 2024-09-25 23:44:42 +08:00
parent 3e44172c8b
commit aa72e0ca5f
6 changed files with 503 additions and 1 deletions
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3
runtime/onnxruntime/include/com-define.h
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@ -36,6 +36,9 @@ namespace funasr {
#define HOTWORD_SEP " "
#define AUDIO_FS "audio-fs"
#define MODEL_PARA "Paraformer"
#define MODEL_SVS "SenseVoiceSmall"
// #define VAD_MODEL_PATH "vad-model"
// #define VAD_CMVN_PATH "vad-cmvn"
// #define VAD_CONFIG_PATH "vad-config"

3
runtime/onnxruntime/include/funasrruntime.h
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@ -101,7 +101,8 @@ _FUNASRAPI void FunOfflineReset(FUNASR_HANDLE handle, FUNASR_DEC_HANDLE
// buffer
_FUNASRAPI FUNASR_RESULT FunOfflineInferBuffer(FUNASR_HANDLE handle, const char* sz_buf, int n_len,
FUNASR_MODE mode, QM_CALLBACK fn_callback, const std::vector<std::vector<float>> &hw_emb,
int sampling_rate=16000, std::string wav_format="pcm", bool itn=true, FUNASR_DEC_HANDLE dec_handle=nullptr);
int sampling_rate=16000, std::string wav_format="pcm", bool itn=true, FUNASR_DEC_HANDLE dec_handle=nullptr,
std::string svs_lang="auto", bool svs_itn=true);
// file, support wav & pcm
_FUNASRAPI FUNASR_RESULT FunOfflineInfer(FUNASR_HANDLE handle, const char* sz_filename, FUNASR_MODE mode,
QM_CALLBACK fn_callback, const std::vector<std::vector<float>> &hw_emb,

2
runtime/onnxruntime/include/model.h
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@ -24,6 +24,8 @@ class Model {
virtual std::string Forward(float *din, int len, bool input_finished, const std::vector<std::vector<float>> &hw_emb={{0.0}}, void* wfst_decoder=nullptr){return "";};
virtual std::vector<std::string> Forward(float** din, int* len, bool input_finished, const std::vector<std::vector<float>> &hw_emb={{0.0}}, void* wfst_decoder=nullptr, int batch_in=1)
{return std::vector<string>();};
virtual std::vector<std::string> Forward(float** din, int* len, bool input_finished, std::string svs_lang="auto", bool svs_itn=false, int batch_in=1)
{return std::vector<string>();};
virtual std::string Rescoring() = 0;
virtual void InitHwCompiler(const std::string &hw_model, int thread_num){};
virtual void InitSegDict(const std::string &seg_dict_model){};

3
runtime/onnxruntime/include/offline-stream.h
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@ -9,6 +9,7 @@
#include "vad-model.h"
#if !defined(__APPLE__)
#include "itn-model.h"
#include "com-define.h"
#endif
namespace funasr {
@ -26,11 +27,13 @@ class OfflineStream {
bool UseVad(){return use_vad;};
bool UsePunc(){return use_punc;};
bool UseITN(){return use_itn;};
std::string GetModelType(){return model_type;};
private:
bool use_vad=false;
bool use_punc=false;
bool use_itn=false;
std::string model_type = MODEL_PARA;
};
OfflineStream *CreateOfflineStream(std::map<std::string, std::string>& model_path, int thread_num=1, bool use_gpu=false, int batch_size=1);

377
runtime/onnxruntime/src/sensevoice-small.cpp Normal file
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@ -0,0 +1,377 @@
/**
* Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
* MIT License (https://opensource.org/licenses/MIT)
*/
#include "precomp.h"
#include "sensevoice-small.h"
#include <cstddef>
using namespace std;
namespace funasr {
SenseVoiceSmall::SenseVoiceSmall()
:use_hotword(false),
env_(ORT_LOGGING_LEVEL_ERROR, "sensevoice"),session_options_{} {
}
// offline
void SenseVoiceSmall::InitAsr(const std::string &am_model, const std::string &am_cmvn, const std::string &am_config, const std::string &token_file, 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;
// 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);
GetInputName(m_session_.get(), strName,2);
m_strInputNames.push_back(strName);
GetInputName(m_session_.get(), strName,3);
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(token_file.c_str());
LoadCmvn(am_cmvn.c_str());
}
void SenseVoiceSmall::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"];
YAML::Node encoder_conf = config["encoder_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->asr_sample_rate = frontend_conf["fs"].as<int>();
}catch(exception const &e){
LOG(ERROR) << "Error when load argument from vad config YAML.";
exit(-1);
}
}
SenseVoiceSmall::~SenseVoiceSmall()
{
if(vocab){
delete vocab;
}
if(lm_vocab){
delete lm_vocab;
}
if(seg_dict){
delete seg_dict;
}
if(phone_set_){
delete phone_set_;
}
}
void SenseVoiceSmall::StartUtterance()
{
}
void SenseVoiceSmall::EndUtterance()
{
}
void SenseVoiceSmall::Reset()
{
}
void SenseVoiceSmall::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 SenseVoiceSmall::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 SenseVoiceSmall::CTCSearch(float * in, std::vector<int32_t> paraformer_length, std::vector<int64_t> outputShape)
{
std::string unicodeChar = "▁";
int32_t vocab_size = outputShape[2];
std::vector<int64_t> tokens;
std::string text="";
int32_t prev_id = -1;
for (int32_t t = 0; t != paraformer_length[0]; ++t) {
auto y = std::distance(
static_cast<const float *>(in),
std::max_element(
static_cast<const float *>(in),
static_cast<const float *>(in) + vocab_size));
in += vocab_size;
if (y != blank_id && y != prev_id) {
tokens.push_back(y);
}
prev_id = y;
}
string str_lang = "";
string str_emo = "";
string str_event = "";
string str_itn = "";
if(tokens.size() >=3){
str_lang = vocab->Id2String(tokens[0]);
str_emo = vocab->Id2String(tokens[1]);
str_event = vocab->Id2String(tokens[2]);
str_itn = vocab->Id2String(tokens[3]);
}
for(int32_t i = 4; i < tokens.size(); ++i){
string word = vocab->Id2String(tokens[i]);
size_t found = word.find(unicodeChar);
if(found != std::string::npos){
text += " " + word.substr(3);
}else{
text += word;
}
}
if(str_itn == "<|withitn|>"){
if(str_lang == "<|zh|>"){
text += "。";
}else{
text += ".";
}
}
return str_lang + str_emo + str_event + " " + text;
}
void SenseVoiceSmall::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;
}
std::vector<std::vector<float>> SenseVoiceSmall::CompileHotwordEmbedding(std::string &hotwords) {
int embedding_dim = encoder_size;
std::vector<std::vector<float>> hw_emb;
std::vector<float> vec(embedding_dim, 0);
hw_emb.push_back(vec);
return hw_emb;
}
std::vector<std::string> SenseVoiceSmall::Forward(float** din, int* len, bool input_finished, std::string svs_lang, bool svs_itn, int batch_in)
{
std::vector<std::string> results;
string result="";
int32_t in_feat_dim = fbank_opts_.mel_opts.num_bins;
if(batch_in != 1){
results.push_back(result);
return results;
}
std::vector<std::vector<float>> asr_feats;
FbankKaldi(asr_sample_rate, din[0], len[0], asr_feats);
if(asr_feats.size() == 0){
results.push_back(result);
return results;
}
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());
}
//lid textnorm
int svs_lid = 0;
int svs_itnid = 15;
if(lid_map.find(svs_lang) != lid_map.end()){
svs_lid = lid_map[svs_lang];
}
if(svs_itn){
svs_itnid = 14;
}
#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);
const int64_t lid_shape[1] = {1};
std::vector<int32_t> lid_length;
lid_length.emplace_back(svs_lid);
Ort::Value onnx_lid = Ort::Value::CreateTensor<int32_t>(
m_memoryInfo, lid_length.data(), lid_length.size(), lid_shape, 1);
const int64_t textnorm_shape[1] = {1};
std::vector<int32_t> textnorm_length;
textnorm_length.emplace_back(svs_itnid);
Ort::Value onnx_itn = Ort::Value::CreateTensor<int32_t>(
m_memoryInfo, textnorm_length.data(), textnorm_length.size(), textnorm_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));
input_onnx.emplace_back(std::move(onnx_lid));
input_onnx.emplace_back(std::move(onnx_itn));
try {
auto outputTensor = m_session_->Run(Ort::RunOptions{nullptr}, m_szInputNames.data(), input_onnx.data(), input_onnx.size(), m_szOutputNames.data(), m_szOutputNames.size());
float* floatData = outputTensor[0].GetTensorMutableData<float>();
std::vector<int64_t> outputShape = outputTensor[0].GetTensorTypeAndShapeInfo().GetShape();
result = CTCSearch(floatData, paraformer_length, outputShape);
}
catch (std::exception const &e)
{
LOG(ERROR)<<e.what();
}
results.push_back(result);
return results;
}
string SenseVoiceSmall::Rescoring()
{
LOG(ERROR)<<"Not Imp!!!!!!";
return "";
}
} // namespace funasr

116
runtime/onnxruntime/src/sensevoice-small.h Normal file
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@ -0,0 +1,116 @@
/**
* Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
* MIT License (https://opensource.org/licenses/MIT)
*/
#pragma once
#include "precomp.h"
#include "phone-set.h"
namespace funasr {
class SenseVoiceSmall : public Model {
private:
Vocab* vocab = nullptr;
Vocab* lm_vocab = nullptr;
SegDict* seg_dict = nullptr;
PhoneSet* phone_set_ = nullptr;
const float scale = 1.0;
void LoadConfigFromYaml(const char* filename);
void LoadCmvn(const char *filename);
void LfrCmvn(std::vector<std::vector<float>> &asr_feats);
std::shared_ptr<Ort::Session> hw_m_session = nullptr;
Ort::Env hw_env_;
Ort::SessionOptions hw_session_options;
vector<string> hw_m_strInputNames, hw_m_strOutputNames;
vector<const char*> hw_m_szInputNames;
vector<const char*> hw_m_szOutputNames;
bool use_hotword;
public:
SenseVoiceSmall();
~SenseVoiceSmall();
void InitAsr(const std::string &am_model, const std::string &am_cmvn, const std::string &am_config, const std::string &token_file, int thread_num);
// online
// void InitAsr(const std::string &en_model, const std::string &de_model, const std::string &am_cmvn, const std::string &am_config, const std::string &token_file, int thread_num);
// 2pass
// void 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, const std::string &token_file, int thread_num);
// void InitHwCompiler(const std::string &hw_model, int thread_num);
// void InitSegDict(const std::string &seg_dict_model);
std::vector<std::vector<float>> CompileHotwordEmbedding(std::string &hotwords);
void Reset();
void FbankKaldi(float sample_rate, const float* waves, int len, std::vector<std::vector<float>> &asr_feats);
std::vector<std::string> Forward(float** din, int* len, bool input_finished=true, std::string svs_lang="auto", bool svs_itn=true, int batch_in=1);
string CTCSearch( float * in, std::vector<int32_t> paraformer_length, std::vector<int64_t> outputShape);
string Rescoring();
string GetLang(){return language;};
int GetAsrSampleRate() { return asr_sample_rate; };
int GetBatchSize() {return batch_size_;};
void StartUtterance();
void EndUtterance();
// void InitLm(const std::string &lm_file, const std::string &lm_cfg_file, const std::string &lex_file);
// string BeamSearch(WfstDecoder* &wfst_decoder, float* in, int n_len, int64_t token_nums);
// string FinalizeDecode(WfstDecoder* &wfst_decoder,
// bool is_stamp=false, std::vector<float> us_alphas={0}, std::vector<float> us_cif_peak={0});
// Vocab* GetVocab();
// Vocab* GetLmVocab();
// PhoneSet* GetPhoneSet();
knf::FbankOptions fbank_opts_;
vector<float> means_list_;
vector<float> vars_list_;
int lfr_m = PARA_LFR_M;
int lfr_n = PARA_LFR_N;
// paraformer-offline
std::shared_ptr<Ort::Session> m_session_ = nullptr;
Ort::Env env_;
Ort::SessionOptions session_options_;
vector<string> m_strInputNames, m_strOutputNames;
vector<const char*> m_szInputNames;
vector<const char*> m_szOutputNames;
std::string language="zh-cn";
// paraformer-online
std::shared_ptr<Ort::Session> encoder_session_ = nullptr;
std::shared_ptr<Ort::Session> decoder_session_ = nullptr;
vector<string> en_strInputNames, en_strOutputNames;
vector<const char*> en_szInputNames_;
vector<const char*> en_szOutputNames_;
vector<string> de_strInputNames, de_strOutputNames;
vector<const char*> de_szInputNames_;
vector<const char*> de_szOutputNames_;
// lm
std::shared_ptr<fst::Fst<fst::StdArc>> lm_ = nullptr;
string window_type = "hamming";
int frame_length = 25;
int frame_shift = 10;
int n_mels = 80;
int encoder_size = 512;
int fsmn_layers = 16;
int fsmn_lorder = 10;
int fsmn_dims = 512;
int asr_sample_rate = MODEL_SAMPLE_RATE;
int batch_size_ = 1;
int blank_id = 0;
//dict
std::map<std::string, int> lid_map = {
{"auto", 0},
{"zh", 3},
{"en", 4},
{"yue", 7},
{"ja", 11},
{"ko", 12},
{"nospeech", 13}
};
};
} // namespace funasr