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add paraformer-torch

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雾聪 2024-03-13 14:15:10 +08:00
parent c522408653
commit 7675a2a0ba
8 changed files with 463 additions and 10 deletions
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2
runtime/onnxruntime/include/funasrruntime.h
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@ -96,7 +96,7 @@ _FUNASRAPI void CTTransformerFreeResult(FUNASR_RESULT result);
_FUNASRAPI void CTTransformerUninit(FUNASR_HANDLE handle);
//OfflineStream
_FUNASRAPI FUNASR_HANDLE FunOfflineInit(std::map<std::string, std::string>& model_path, int thread_num);
_FUNASRAPI FUNASR_HANDLE FunOfflineInit(std::map<std::string, std::string>& model_path, int thread_num, bool use_gpu=false);
_FUNASRAPI void FunOfflineReset(FUNASR_HANDLE handle, FUNASR_DEC_HANDLE dec_handle=nullptr);
// buffer
_FUNASRAPI FUNASR_RESULT FunOfflineInferBuffer(FUNASR_HANDLE handle, const char* sz_buf, int n_len,

4
runtime/onnxruntime/include/offline-stream.h
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@ -14,7 +14,7 @@
namespace funasr {
class OfflineStream {
public:
OfflineStream(std::map<std::string, std::string>& model_path, int thread_num);
OfflineStream(std::map<std::string, std::string>& model_path, int thread_num, bool use_gpu=false);
~OfflineStream(){};
std::unique_ptr<VadModel> vad_handle= nullptr;
@ -33,6 +33,6 @@ class OfflineStream {
bool use_itn=false;
};
OfflineStream *CreateOfflineStream(std::map<std::string, std::string>& model_path, int thread_num=1);
OfflineStream *CreateOfflineStream(std::map<std::string, std::string>& model_path, int thread_num=1, bool use_gpu=false);
} // namespace funasr
#endif

6
runtime/onnxruntime/src/CMakeLists.txt
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@ -25,7 +25,11 @@ else()
include_directories(${FFMPEG_DIR}/include)
endif()
if(GPU)
set(TORCH_DEPS torch torch_cuda torch_cpu c10 c10_cuda torch_blade ral_base_context)
endif()
#message("CXX_FLAGS "${CMAKE_CXX_FLAGS})
include_directories(${CMAKE_SOURCE_DIR}/include)
include_directories(${CMAKE_SOURCE_DIR}/third_party)
target_link_libraries(funasr PUBLIC onnxruntime ${EXTRA_LIBS})
target_link_libraries(funasr PUBLIC onnxruntime ${EXTRA_LIBS} ${TORCH_DEPS})

4
runtime/onnxruntime/src/funasrruntime.cpp
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@ -33,9 +33,9 @@
return mm;
}
_FUNASRAPI FUNASR_HANDLE FunOfflineInit(std::map<std::string, std::string>& model_path, int thread_num)
_FUNASRAPI FUNASR_HANDLE FunOfflineInit(std::map<std::string, std::string>& model_path, int thread_num, bool use_gpu)
{
funasr::OfflineStream* mm = funasr::CreateOfflineStream(model_path, thread_num);
funasr::OfflineStream* mm = funasr::CreateOfflineStream(model_path, thread_num, use_gpu);
return mm;
}

13
runtime/onnxruntime/src/offline-stream.cpp
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@ -1,7 +1,7 @@
#include "precomp.h"
namespace funasr {
OfflineStream::OfflineStream(std::map<std::string, std::string>& model_path, int thread_num)
OfflineStream::OfflineStream(std::map<std::string, std::string>& model_path, int thread_num, bool use_gpu)
{
// VAD model
if(model_path.find(VAD_DIR) != model_path.end()){
@ -35,7 +35,12 @@ OfflineStream::OfflineStream(std::map<std::string, std::string>& model_path, int
string hw_compile_model_path;
string seg_dict_path;
asr_handle = make_unique<Paraformer>();
if(use_gpu){
asr_handle = make_unique<ParaformerTorch>();
}else{
asr_handle = make_unique<Paraformer>();
}
bool enable_hotword = false;
hw_compile_model_path = PathAppend(model_path.at(MODEL_DIR), MODEL_EB_NAME);
seg_dict_path = PathAppend(model_path.at(MODEL_DIR), MODEL_SEG_DICT);
@ -115,10 +120,10 @@ OfflineStream::OfflineStream(std::map<std::string, std::string>& model_path, int
#endif
}
OfflineStream *CreateOfflineStream(std::map<std::string, std::string>& model_path, int thread_num)
OfflineStream *CreateOfflineStream(std::map<std::string, std::string>& model_path, int thread_num, bool use_gpu)
{
OfflineStream *mm;
mm = new OfflineStream(model_path, thread_num);
mm = new OfflineStream(model_path, thread_num, use_gpu);
return mm;
}

351
runtime/onnxruntime/src/paraformer-torch.cpp Normal file
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@ -0,0 +1,351 @@
/**
* Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
* MIT License (https://opensource.org/licenses/MIT)
*/
#include "precomp.h"
#include "paraformer-torch.h"
#include "encode_converter.h"
#include <cstddef>
using namespace std;
namespace funasr {
ParaformerTorch::ParaformerTorch()
:use_hotword(false){
}
// offline
void ParaformerTorch::InitAsr(const std::string &am_model, const std::string &am_cmvn, const std::string &am_config, 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;
vocab = new Vocab(am_config.c_str());
phone_set_ = new PhoneSet(am_config.c_str());
LoadCmvn(am_cmvn.c_str());
torch::DeviceType device = at::kCPU;
#ifdef USE_GPU
if (!torch::cuda::is_available()) {
LOG(ERROR) << "CUDA is not available! Please check your GPU settings";
exit(-1);
} else {
LOG(INFO) << "CUDA available! Running on GPU";
device = at::kCUDA;
}
#endif
#ifdef USE_IPEX
torch::jit::setTensorExprFuserEnabled(false);
#endif
torch::jit::script::Module model = torch::jit::load(am_model, device);
model_ = std::make_shared<TorchModule>(std::move(model));
}
void ParaformerTorch::InitLm(const std::string &lm_file,
const std::string &lm_cfg_file,
const std::string &lex_file) {
try {
lm_ = std::shared_ptr<fst::Fst<fst::StdArc>>(
fst::Fst<fst::StdArc>::Read(lm_file));
if (lm_){
lm_vocab = new Vocab(lm_cfg_file.c_str(), lex_file.c_str());
LOG(INFO) << "Successfully load lm file " << lm_file;
}else{
LOG(ERROR) << "Failed to load lm file " << lm_file;
}
} catch (std::exception const &e) {
LOG(ERROR) << "Error when load lm file: " << e.what();
exit(0);
}
}
void ParaformerTorch::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"];
this->asr_sample_rate = frontend_conf["fs"].as<int>();
YAML::Node lang_conf = config["lang"];
if (lang_conf.IsDefined()){
language = lang_conf.as<string>();
}
}catch(exception const &e){
LOG(ERROR) << "Error when load argument from vad config YAML.";
exit(-1);
}
}
void ParaformerTorch::InitHwCompiler(const std::string &hw_model, int thread_num) {
// TODO
use_hotword = true;
}
void ParaformerTorch::InitSegDict(const std::string &seg_dict_model) {
seg_dict = new SegDict(seg_dict_model.c_str());
}
ParaformerTorch::~ParaformerTorch()
{
if(vocab){
delete vocab;
}
if(lm_vocab){
delete lm_vocab;
}
if(seg_dict){
delete seg_dict;
}
if(phone_set_){
delete phone_set_;
}
}
void ParaformerTorch::StartUtterance()
{
}
void ParaformerTorch::EndUtterance()
{
}
void ParaformerTorch::Reset()
{
}
void ParaformerTorch::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 ParaformerTorch::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 ParaformerTorch::GreedySearch(float * in, int n_len, int64_t token_nums, bool is_stamp, std::vector<float> us_alphas, std::vector<float> us_cif_peak)
{
vector<int> hyps;
int Tmax = n_len;
for (int i = 0; i < Tmax; i++) {
int max_idx;
float max_val;
FindMax(in + i * token_nums, token_nums, max_val, max_idx);
hyps.push_back(max_idx);
}
if(!is_stamp){
return vocab->Vector2StringV2(hyps, language);
}else{
std::vector<string> char_list;
std::vector<std::vector<float>> timestamp_list;
std::string res_str;
vocab->Vector2String(hyps, char_list);
std::vector<string> raw_char(char_list);
TimestampOnnx(us_alphas, us_cif_peak, char_list, res_str, timestamp_list);
return PostProcess(raw_char, timestamp_list);
}
}
string ParaformerTorch::BeamSearch(WfstDecoder* &wfst_decoder, float *in, int len, int64_t token_nums)
{
return wfst_decoder->Search(in, len, token_nums);
}
string ParaformerTorch::FinalizeDecode(WfstDecoder* &wfst_decoder,
bool is_stamp, std::vector<float> us_alphas, std::vector<float> us_cif_peak)
{
return wfst_decoder->FinalizeDecode(is_stamp, us_alphas, us_cif_peak);
}
void ParaformerTorch::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;
}
string ParaformerTorch::Forward(float* din, int len, bool input_finished, const std::vector<std::vector<float>> &hw_emb, void* decoder_handle)
{
WfstDecoder* wfst_decoder = (WfstDecoder*)decoder_handle;
int32_t in_feat_dim = fbank_opts_.mel_opts.num_bins;
std::vector<std::vector<float>> asr_feats;
FbankKaldi(asr_sample_rate, din, len, asr_feats);
if(asr_feats.size() == 0){
return "";
}
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());
}
std::vector<int32_t> paraformer_length;
paraformer_length.emplace_back(num_frames);
torch::NoGradGuard no_grad;
torch::Tensor feats =
torch::from_blob(wav_feats.data(),
{1, num_frames, feat_dim}, torch::kFloat).contiguous();
torch::Tensor feat_lens = torch::from_blob(paraformer_length.data(),
{1}, torch::kInt32);
// 2. forward
#ifdef USE_GPU
feats = feats.to(at::kCUDA);
feat_lens = feat_lens.to(at::kCUDA);
#endif
std::vector<torch::jit::IValue> inputs = {feats, feat_lens};
string result="";
try {
auto outputs = model_->forward(inputs).toTuple()->elements();
torch::Tensor am_scores;
torch::Tensor valid_token_lens;
#ifdef USE_GPU
am_scores = outputs[0].toTensor().to(at::kCPU);
valid_token_lens = outputs[1].toTensor().to(at::kCPU);
#else
am_scores = outputs[0].toTensor();
valid_token_lens = outputs[1].toTensor();
#endif
if (lm_ == nullptr) {
result = GreedySearch(am_scores[0].data_ptr<float>(), valid_token_lens[0].item<int>(), am_scores.size(2));
} else {
result = BeamSearch(wfst_decoder, am_scores[0].data_ptr<float>(), valid_token_lens[0].item<int>(), am_scores.size(2));
if (input_finished) {
result = FinalizeDecode(wfst_decoder);
}
}
}
catch (std::exception const &e)
{
LOG(ERROR)<<e.what();
}
return result;
}
std::vector<std::vector<float>> ParaformerTorch::CompileHotwordEmbedding(std::string &hotwords) {
std::vector<std::vector<float>> result;
return result;
}
Vocab* ParaformerTorch::GetVocab()
{
return vocab;
}
Vocab* ParaformerTorch::GetLmVocab()
{
return lm_vocab;
}
PhoneSet* ParaformerTorch::GetPhoneSet()
{
return phone_set_;
}
string ParaformerTorch::Rescoring()
{
LOG(ERROR)<<"Not Imp!!!!!!";
return "";
}
} // namespace funasr

92
runtime/onnxruntime/src/paraformer-torch.h Normal file
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@ -0,0 +1,92 @@
/**
* Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
* MIT License (https://opensource.org/licenses/MIT)
*/
#pragma once
#include <torch/serialize.h>
#include <torch/script.h>
#include <torch/torch.h>
#include <torch/csrc/jit/passes/tensorexpr_fuser.h>
#include "precomp.h"
#include "fst/fstlib.h"
#include "fst/symbol-table.h"
#include "bias-lm.h"
#include "phone-set.h"
namespace funasr {
class ParaformerTorch : public Model {
/**
* Author: Speech Lab of DAMO Academy, Alibaba Group
* Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
* https://arxiv.org/pdf/2206.08317.pdf
*/
private:
Vocab* vocab = nullptr;
Vocab* lm_vocab = nullptr;
SegDict* seg_dict = nullptr;
PhoneSet* phone_set_ = nullptr;
//const float scale = 22.6274169979695;
const float scale = 1.0;
void LoadConfigFromYaml(const char* filename);
void LoadCmvn(const char *filename);
void LfrCmvn(std::vector<std::vector<float>> &asr_feats);
using TorchModule = torch::jit::script::Module;
std::shared_ptr<TorchModule> model_ = nullptr;
std::vector<torch::Tensor> encoder_outs_;
bool use_hotword;
public:
ParaformerTorch();
~ParaformerTorch();
void InitAsr(const std::string &am_model, const std::string &am_cmvn, const std::string &am_config, 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);
string Forward(float* din, int len, bool input_finished=true, const std::vector<std::vector<float>> &hw_emb={{0.0}}, void* wfst_decoder=nullptr);
string GreedySearch( float* in, int n_len, int64_t token_nums,
bool is_stamp=false, std::vector<float> us_alphas={0}, std::vector<float> us_cif_peak={0});
string Rescoring();
string GetLang(){return language;};
int GetAsrSampleRate() { return asr_sample_rate; };
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::string language="zh-cn";
// 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;
float cif_threshold = 1.0;
float tail_alphas = 0.45;
int asr_sample_rate = MODEL_SAMPLE_RATE;
};
} // namespace funasr

1
runtime/onnxruntime/src/precomp.h
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@ -64,6 +64,7 @@ using namespace std;
#include "seg_dict.h"
#include "resample.h"
#include "paraformer.h"
#include "paraformer-torch.h"
#include "paraformer-online.h"
#include "offline-stream.h"
#include "tpass-stream.h"