xuning / sherpaonnx

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Fangjun Kuang
Committed by GitHub
082f230d 1 parent 3f472a99

Fix nemo streaming transducer greedy search (#944)

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正在显示 18 个修改的文件 包含 274 行增加 和 244 行删除
... ... @@ -16,6 +16,45 @@ echo "PATH: $PATH"
which $EXE
log "------------------------------------------------------------"
log "Run NeMo transducer (English)"
log "------------------------------------------------------------"
repo_url=https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-nemo-streaming-fast-conformer-transducer-en-80ms.tar.bz2
curl -SL -O $repo_url
tar xvf sherpa-onnx-nemo-streaming-fast-conformer-transducer-en-80ms.tar.bz2
rm sherpa-onnx-nemo-streaming-fast-conformer-transducer-en-80ms.tar.bz2
repo=sherpa-onnx-nemo-streaming-fast-conformer-transducer-en-80ms
log "Start testing ${repo_url}"
waves=(
$repo/test_wavs/0.wav
$repo/test_wavs/1.wav
$repo/test_wavs/8k.wav
)
for wave in ${waves[@]}; do
time $EXE \
--tokens=$repo/tokens.txt \
--encoder=$repo/encoder.onnx \
--decoder=$repo/decoder.onnx \
--joiner=$repo/joiner.onnx \
--num-threads=2 \
$wave
done
time $EXE \
--tokens=$repo/tokens.txt \
--encoder=$repo/encoder.onnx \
--decoder=$repo/decoder.onnx \
--joiner=$repo/joiner.onnx \
--num-threads=2 \
$repo/test_wavs/0.wav \
$repo/test_wavs/1.wav \
$repo/test_wavs/8k.wav
rm -rf $repo
log "------------------------------------------------------------"
log "Run LSTM transducer (English)"
log "------------------------------------------------------------"
... ...
... ... @@ -196,7 +196,6 @@ jobs:
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/csukuangfj/sherpa-onnx-libs huggingface
cd huggingface
git lfs pull
mkdir -p aarch64
cp -v ../sherpa-onnx-*-shared.tar.bz2 ./aarch64
... ...
... ... @@ -187,7 +187,6 @@ jobs:
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/csukuangfj/sherpa-onnx-libs huggingface
cd huggingface
git lfs pull
mkdir -p aarch64
cp -v ../sherpa-onnx-*-static.tar.bz2 ./aarch64
... ...
... ... @@ -124,7 +124,6 @@ jobs:
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/csukuangfj/sherpa-onnx-libs huggingface
cd huggingface
git lfs pull
cp -v ../sherpa-onnx-*-android.tar.bz2 ./
... ...
... ... @@ -209,7 +209,6 @@ jobs:
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/csukuangfj/sherpa-onnx-libs huggingface
cd huggingface
git lfs pull
mkdir -p arm32
cp -v ../sherpa-onnx-*.tar.bz2 ./arm32
... ...
... ... @@ -138,7 +138,6 @@ jobs:
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/csukuangfj/sherpa-onnx-libs huggingface
cd huggingface
git lfs pull
cp -v ../sherpa-onnx-*.tar.bz2 ./
... ...
... ... @@ -242,7 +242,6 @@ jobs:
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/csukuangfj/sherpa-onnx-libs huggingface
cd huggingface
git lfs pull
mkdir -p riscv64
cp -v ../sherpa-onnx-*-shared.tar.bz2 ./riscv64
... ...
... ... @@ -219,7 +219,6 @@ jobs:
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/csukuangfj/sherpa-onnx-libs huggingface
cd huggingface
git lfs pull
mkdir -p win64
cp -v ../sherpa-onnx-*.tar.bz2 ./win64
... ...
... ... @@ -221,7 +221,6 @@ jobs:
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/csukuangfj/sherpa-onnx-libs huggingface
cd huggingface
git lfs pull
mkdir -p win32
cp -v ../sherpa-onnx-*.tar.bz2 ./win32
... ...
... ... @@ -14,19 +14,18 @@ namespace sherpa_onnx {
std::unique_ptr<OnlineRecognizerImpl> OnlineRecognizerImpl::Create(
const OnlineRecognizerConfig &config) {
if (!config.model_config.transducer.encoder.empty()) {
Ort::Env env(ORT_LOGGING_LEVEL_WARNING);
auto decoder_model = ReadFile(config.model_config.transducer.decoder);
auto sess = std::make_unique<Ort::Session>(env, decoder_model.data(), decoder_model.size(), Ort::SessionOptions{});
auto sess = std::make_unique<Ort::Session>(
env, decoder_model.data(), decoder_model.size(), Ort::SessionOptions{});
size_t node_count = sess->GetOutputCount();
if (node_count == 1) {
return std::make_unique<OnlineRecognizerTransducerImpl>(config);
} else {
SHERPA_ONNX_LOGE("Running streaming Nemo transducer model");
return std::make_unique<OnlineRecognizerTransducerNeMoImpl>(config);
}
}
... ... @@ -52,7 +51,8 @@ std::unique_ptr<OnlineRecognizerImpl> OnlineRecognizerImpl::Create(
Ort::Env env(ORT_LOGGING_LEVEL_WARNING);
auto decoder_model = ReadFile(mgr, config.model_config.transducer.decoder);
auto sess = std::make_unique<Ort::Session>(env, decoder_model.data(), decoder_model.size(), Ort::SessionOptions{});
auto sess = std::make_unique<Ort::Session>(
env, decoder_model.data(), decoder_model.size(), Ort::SessionOptions{});
size_t node_count = sess->GetOutputCount();
... ...
... ... @@ -35,18 +35,15 @@
namespace sherpa_onnx {
static OnlineRecognizerResult Convert(const OnlineTransducerDecoderResult &src,
OnlineRecognizerResult Convert(const OnlineTransducerDecoderResult &src,
const SymbolTable &sym_table,
float frame_shift_ms,
int32_t subsampling_factor,
int32_t segment,
int32_t frames_since_start) {
float frame_shift_ms, int32_t subsampling_factor,
int32_t segment, int32_t frames_since_start) {
OnlineRecognizerResult r;
r.tokens.reserve(src.tokens.size());
r.timestamps.reserve(src.tokens.size());
for (auto i : src.tokens) {
if (i == -1) continue;
auto sym = sym_table[i];
r.text.append(sym);
... ...
... ... @@ -6,6 +6,7 @@
#ifndef SHERPA_ONNX_CSRC_ONLINE_RECOGNIZER_TRANSDUCER_NEMO_IMPL_H_
#define SHERPA_ONNX_CSRC_ONLINE_RECOGNIZER_TRANSDUCER_NEMO_IMPL_H_
#include <algorithm>
#include <fstream>
#include <ios>
#include <memory>
... ... @@ -32,13 +33,10 @@
namespace sherpa_onnx {
// defined in ./online-recognizer-transducer-impl.h
// static may or may not be here? TODDOs
static OnlineRecognizerResult Convert(const OnlineTransducerDecoderResult &src,
OnlineRecognizerResult Convert(const OnlineTransducerDecoderResult &src,
const SymbolTable &sym_table,
float frame_shift_ms,
int32_t subsampling_factor,
int32_t segment,
int32_t frames_since_start);
float frame_shift_ms, int32_t subsampling_factor,
int32_t segment, int32_t frames_since_start);
class OnlineRecognizerTransducerNeMoImpl : public OnlineRecognizerImpl {
public:
... ... @@ -47,8 +45,8 @@ class OnlineRecognizerTransducerNeMoImpl : public OnlineRecognizerImpl {
: config_(config),
symbol_table_(config.model_config.tokens),
endpoint_(config_.endpoint_config),
model_(std::make_unique<OnlineTransducerNeMoModel>(
config.model_config)) {
model_(
std::make_unique<OnlineTransducerNeMoModel>(config.model_config)) {
if (config.decoding_method == "greedy_search") {
decoder_ = std::make_unique<OnlineTransducerGreedySearchNeMoDecoder>(
model_.get(), config_.blank_penalty);
... ... @@ -83,7 +81,6 @@ class OnlineRecognizerTransducerNeMoImpl : public OnlineRecognizerImpl {
std::unique_ptr<OnlineStream> CreateStream() const override {
auto stream = std::make_unique<OnlineStream>(config_.feat_config);
stream->SetStates(model_->GetInitStates());
InitOnlineStream(stream.get());
return stream;
}
... ... @@ -94,14 +91,12 @@ class OnlineRecognizerTransducerNeMoImpl : public OnlineRecognizerImpl {
}
OnlineRecognizerResult GetResult(OnlineStream *s) const override {
OnlineTransducerDecoderResult decoder_result = s->GetResult();
decoder_->StripLeadingBlanks(&decoder_result);
// TODO(fangjun): Remember to change these constants if needed
int32_t frame_shift_ms = 10;
int32_t subsampling_factor = 8;
return Convert(decoder_result, symbol_table_, frame_shift_ms, subsampling_factor,
s->GetCurrentSegment(), s->GetNumFramesSinceStart());
int32_t subsampling_factor = model_->SubsamplingFactor();
return Convert(s->GetResult(), symbol_table_, frame_shift_ms,
subsampling_factor, s->GetCurrentSegment(),
s->GetNumFramesSinceStart());
}
bool IsEndpoint(OnlineStream *s) const override {
... ... @@ -114,8 +109,8 @@ class OnlineRecognizerTransducerNeMoImpl : public OnlineRecognizerImpl {
// frame shift is 10 milliseconds
float frame_shift_in_seconds = 0.01;
// subsampling factor is 8
int32_t trailing_silence_frames = s->GetResult().num_trailing_blanks * 8;
int32_t trailing_silence_frames =
s->GetResult().num_trailing_blanks * model_->SubsamplingFactor();
return endpoint_.IsEndpoint(num_processed_frames, trailing_silence_frames,
frame_shift_in_seconds);
... ... @@ -126,19 +121,16 @@ class OnlineRecognizerTransducerNeMoImpl : public OnlineRecognizerImpl {
// segment is incremented only when the last
// result is not empty
const auto &r = s->GetResult();
if (!r.tokens.empty() && r.tokens.back() != 0) {
if (!r.tokens.empty()) {
s->GetCurrentSegment() += 1;
}
}
// we keep the decoder_out
decoder_->UpdateDecoderOut(&s->GetResult());
Ort::Value decoder_out = std::move(s->GetResult().decoder_out);
s->SetResult({});
auto r = decoder_->GetEmptyResult();
s->SetStates(model_->GetEncoderInitStates());
s->SetResult(r);
s->GetResult().decoder_out = std::move(decoder_out);
s->SetNeMoDecoderStates(model_->GetDecoderInitStates());
// Note: We only update counters. The underlying audio samples
// are not discarded.
... ... @@ -151,7 +143,6 @@ class OnlineRecognizerTransducerNeMoImpl : public OnlineRecognizerImpl {
int32_t feature_dim = ss[0]->FeatureDim();
std::vector<OnlineTransducerDecoderResult> result(n);
std::vector<float> features_vec(n * chunk_size * feature_dim);
std::vector<std::vector<Ort::Value>> encoder_states(n);
... ... @@ -166,9 +157,7 @@ class OnlineRecognizerTransducerNeMoImpl : public OnlineRecognizerImpl {
std::copy(features.begin(), features.end(),
features_vec.data() + i * chunk_size * feature_dim);
result[i] = std::move(ss[i]->GetResult());
encoder_states[i] = std::move(ss[i]->GetStates());
}
auto memory_info =
... ... @@ -180,8 +169,7 @@ class OnlineRecognizerTransducerNeMoImpl : public OnlineRecognizerImpl {
features_vec.size(), x_shape.data(),
x_shape.size());
// Batch size is 1
auto states = std::move(encoder_states[0]);
auto states = model_->StackStates(std::move(encoder_states));
int32_t num_states = states.size(); // num_states = 3
auto t = model_->RunEncoder(std::move(x), std::move(states));
// t[0] encoder_out, float tensor, (batch_size, dim, T)
... ... @@ -194,28 +182,22 @@ class OnlineRecognizerTransducerNeMoImpl : public OnlineRecognizerImpl {
out_states.push_back(std::move(t[k]));
}
Ort::Value encoder_out = Transpose12(model_->Allocator(), &t[0]);
// defined in online-transducer-greedy-search-nemo-decoder.h
// get intial states of decoder.
std::vector<Ort::Value> &decoder_states = ss[0]->GetNeMoDecoderStates();
// Subsequent decoder states (for each chunks) are updated inside the Decode method.
// This returns the decoder state from the LAST chunk. We probably dont need it. So we can discard it.
decoder_states = decoder_->Decode(std::move(encoder_out),
std::move(decoder_states),
&result, ss, n);
auto unstacked_states = model_->UnStackStates(std::move(out_states));
for (int32_t i = 0; i != n; ++i) {
ss[i]->SetStates(std::move(unstacked_states[i]));
}
ss[0]->SetResult(result[0]);
Ort::Value encoder_out = Transpose12(model_->Allocator(), &t[0]);
ss[0]->SetStates(std::move(out_states));
decoder_->Decode(std::move(encoder_out), ss, n);
}
void InitOnlineStream(OnlineStream *stream) const {
auto r = decoder_->GetEmptyResult();
// set encoder states
stream->SetStates(model_->GetEncoderInitStates());
stream->SetResult(r);
stream->SetNeMoDecoderStates(model_->GetDecoderInitStates(1));
// set decoder states
stream->SetNeMoDecoderStates(model_->GetDecoderInitStates());
}
private:
... ... @@ -250,7 +232,6 @@ class OnlineRecognizerTransducerNeMoImpl : public OnlineRecognizerImpl {
symbol_table_.NumSymbols(), vocab_size);
exit(-1);
}
}
private:
... ... @@ -259,7 +240,6 @@ class OnlineRecognizerTransducerNeMoImpl : public OnlineRecognizerImpl {
std::unique_ptr<OnlineTransducerNeMoModel> model_;
std::unique_ptr<OnlineTransducerGreedySearchNeMoDecoder> decoder_;
Endpoint endpoint_;
};
} // namespace sherpa_onnx
... ...
... ... @@ -225,7 +225,8 @@ std::vector<Ort::Value> &OnlineStream::GetStates() {
return impl_->GetStates();
}
void OnlineStream::SetNeMoDecoderStates(std::vector<Ort::Value> decoder_states) {
void OnlineStream::SetNeMoDecoderStates(
std::vector<Ort::Value> decoder_states) {
return impl_->SetNeMoDecoderStates(std::move(decoder_states));
}
... ...
... ... @@ -10,96 +10,57 @@
#include <utility>
#include "sherpa-onnx/csrc/macros.h"
#include "sherpa-onnx/csrc/online-stream.h"
#include "sherpa-onnx/csrc/onnx-utils.h"
namespace sherpa_onnx {
static std::pair<Ort::Value, Ort::Value> BuildDecoderInput(
int32_t token, OrtAllocator *allocator) {
static Ort::Value BuildDecoderInput(int32_t token, OrtAllocator *allocator) {
std::array<int64_t, 2> shape{1, 1};
Ort::Value decoder_input =
Ort::Value::CreateTensor<int32_t>(allocator, shape.data(), shape.size());
std::array<int64_t, 1> length_shape{1};
Ort::Value decoder_input_length = Ort::Value::CreateTensor<int32_t>(
allocator, length_shape.data(), length_shape.size());
int32_t *p = decoder_input.GetTensorMutableData<int32_t>();
int32_t *p_length = decoder_input_length.GetTensorMutableData<int32_t>();
p[0] = token;
p_length[0] = 1;
return {std::move(decoder_input), std::move(decoder_input_length)};
}
OnlineTransducerDecoderResult
OnlineTransducerGreedySearchNeMoDecoder::GetEmptyResult() const {
int32_t context_size = 8;
int32_t blank_id = 0; // always 0
OnlineTransducerDecoderResult r;
r.tokens.resize(context_size, -1);
r.tokens.back() = blank_id;
return r;
}
static void UpdateCachedDecoderOut(
OrtAllocator *allocator, const Ort::Value *decoder_out,
std::vector<OnlineTransducerDecoderResult> *result) {
std::vector<int64_t> shape =
decoder_out->GetTensorTypeAndShapeInfo().GetShape();
auto memory_info =
Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
std::array<int64_t, 2> v_shape{1, shape[1]};
const float *src = decoder_out->GetTensorData<float>();
for (auto &r : *result) {
if (!r.decoder_out) {
r.decoder_out = Ort::Value::CreateTensor<float>(allocator, v_shape.data(),
v_shape.size());
}
float *dst = r.decoder_out.GetTensorMutableData<float>();
std::copy(src, src + shape[1], dst);
src += shape[1];
}
return decoder_input;
}
std::vector<Ort::Value> DecodeOne(
const float *encoder_out, int32_t num_rows, int32_t num_cols,
OnlineTransducerNeMoModel *model, float blank_penalty,
std::vector<Ort::Value>& decoder_states,
std::vector<OnlineTransducerDecoderResult> *result) {
static void DecodeOne(const float *encoder_out, int32_t num_rows,
int32_t num_cols, OnlineTransducerNeMoModel *model,
float blank_penalty, OnlineStream *s) {
auto memory_info =
Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
// OnlineTransducerDecoderResult result;
int32_t vocab_size = model->VocabSize();
int32_t blank_id = vocab_size - 1;
auto &r = (*result)[0];
auto &r = s->GetResult();
Ort::Value decoder_out{nullptr};
auto decoder_input_pair = BuildDecoderInput(blank_id, model->Allocator());
// decoder_input_pair[0]: decoder_input
// decoder_input_pair[1]: decoder_input_length (discarded)
auto decoder_input = BuildDecoderInput(
r.tokens.empty() ? blank_id : r.tokens.back(), model->Allocator());
std::vector<Ort::Value> &last_decoder_states = s->GetNeMoDecoderStates();
std::vector<Ort::Value> tmp_decoder_states;
tmp_decoder_states.reserve(last_decoder_states.size());
for (auto &v : last_decoder_states) {
tmp_decoder_states.push_back(View(&v));
}
// decoder_output_pair.second returns the next decoder state
std::pair<Ort::Value, std::vector<Ort::Value>> decoder_output_pair =
model->RunDecoder(std::move(decoder_input_pair.first),
std::move(decoder_states)); // here decoder_states = {len=0, cap=0}. But decoder_output_pair= {first, second: {len=2, cap=2}} // ATTN
model->RunDecoder(std::move(decoder_input),
std::move(tmp_decoder_states));
std::array<int64_t, 3> encoder_shape{1, num_cols, 1};
decoder_states = std::move(decoder_output_pair.second);
bool emitted = false;
// TODO: Inside this loop, I need to framewise decoding.
for (int32_t t = 0; t != num_rows; ++t) {
Ort::Value cur_encoder_out = Ort::Value::CreateTensor(
memory_info, const_cast<float *>(encoder_out) + t * num_cols, num_cols,
... ... @@ -117,82 +78,52 @@ std::vector<Ort::Value> DecodeOne(
static_cast<const float *>(p_logit),
std::max_element(static_cast<const float *>(p_logit),
static_cast<const float *>(p_logit) + vocab_size)));
SHERPA_ONNX_LOGE("y=%d", y);
if (y != blank_id) {
emitted = true;
r.tokens.push_back(y);
r.timestamps.push_back(t + r.frame_offset);
r.num_trailing_blanks = 0;
decoder_input_pair = BuildDecoderInput(y, model->Allocator());
decoder_input = BuildDecoderInput(y, model->Allocator());
// last decoder state becomes the current state for the first chunk
decoder_output_pair =
model->RunDecoder(std::move(decoder_input_pair.first),
std::move(decoder_states));
// Update the decoder states for the next chunk
decoder_states = std::move(decoder_output_pair.second);
decoder_output_pair = model->RunDecoder(
std::move(decoder_input), std::move(decoder_output_pair.second));
} else {
++r.num_trailing_blanks;
}
}
decoder_out = std::move(decoder_output_pair.first);
// UpdateCachedDecoderOut(model->Allocator(), &decoder_out, result);
// Update frame_offset
for (auto &r : *result) {
r.frame_offset += num_rows;
if (emitted) {
s->SetNeMoDecoderStates(std::move(decoder_output_pair.second));
}
return std::move(decoder_states);
r.frame_offset += num_rows;
}
std::vector<Ort::Value> OnlineTransducerGreedySearchNeMoDecoder::Decode(
Ort::Value encoder_out,
std::vector<Ort::Value> decoder_states,
std::vector<OnlineTransducerDecoderResult> *result,
OnlineStream ** /*ss = nullptr*/, int32_t /*n= 0*/) {
void OnlineTransducerGreedySearchNeMoDecoder::Decode(Ort::Value encoder_out,
OnlineStream **ss,
int32_t n) const {
auto shape = encoder_out.GetTensorTypeAndShapeInfo().GetShape();
int32_t batch_size = static_cast<int32_t>(shape[0]); // bs = 1
if (shape[0] != result->size()) {
SHERPA_ONNX_LOGE(
"Size mismatch! encoder_out.size(0) %d, result.size(0): %d",
static_cast<int32_t>(shape[0]),
static_cast<int32_t>(result->size()));
if (batch_size != n) {
SHERPA_ONNX_LOGE("Size mismatch! encoder_out.size(0) %d, n: %d",
static_cast<int32_t>(shape[0]), n);
exit(-1);
}
int32_t batch_size = static_cast<int32_t>(shape[0]); // bs = 1
int32_t dim1 = static_cast<int32_t>(shape[1]); // 2
int32_t dim2 = static_cast<int32_t>(shape[2]); // 512
// Define and initialize encoder_out_length
Ort::MemoryInfo memory_info = Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU);
int64_t length_value = 1;
std::vector<int64_t> length_shape = {1};
int32_t dim1 = static_cast<int32_t>(shape[1]); // T
int32_t dim2 = static_cast<int32_t>(shape[2]); // encoder_out_dim
Ort::Value encoder_out_length = Ort::Value::CreateTensor<int64_t>(
memory_info, &length_value, 1, length_shape.data(), length_shape.size()
);
const int64_t *p_length = encoder_out_length.GetTensorData<int64_t>();
const float *p = encoder_out.GetTensorData<float>();
// std::vector<OnlineTransducerDecoderResult> ans(batch_size);
for (int32_t i = 0; i != batch_size; ++i) {
const float *this_p = p + dim1 * dim2 * i;
int32_t this_len = p_length[i];
// outputs the decoder state from last chunk.
auto last_decoder_states = DecodeOne(this_p, this_len, dim2, model_, blank_penalty_, decoder_states, result);
// ans[i] = decode_result_pair.first;
decoder_states = std::move(last_decoder_states);
DecodeOne(this_p, dim1, dim2, model_, blank_penalty_, ss[i]);
}
return decoder_states;
}
} // namespace sherpa_onnx
... ...
... ... @@ -7,27 +7,22 @@
#define SHERPA_ONNX_CSRC_ONLINE_TRANSDUCER_GREEDY_SEARCH_NEMO_DECODER_H_
#include <vector>
#include "sherpa-onnx/csrc/online-transducer-decoder.h"
#include "sherpa-onnx/csrc/online-transducer-nemo-model.h"
namespace sherpa_onnx {
class OnlineStream;
class OnlineTransducerGreedySearchNeMoDecoder {
public:
OnlineTransducerGreedySearchNeMoDecoder(OnlineTransducerNeMoModel *model,
float blank_penalty)
: model_(model),
blank_penalty_(blank_penalty) {}
: model_(model), blank_penalty_(blank_penalty) {}
OnlineTransducerDecoderResult GetEmptyResult() const;
void UpdateDecoderOut(OnlineTransducerDecoderResult *result) {}
void StripLeadingBlanks(OnlineTransducerDecoderResult * /*r*/) const {}
std::vector<Ort::Value> Decode(
Ort::Value encoder_out,
std::vector<Ort::Value> decoder_states,
std::vector<OnlineTransducerDecoderResult> *result,
OnlineStream **ss = nullptr, int32_t n = 0);
// @param n number of elements in ss
void Decode(Ort::Value encoder_out, OnlineStream **ss, int32_t n) const;
private:
OnlineTransducerNeMoModel *model_; // Not owned
... ... @@ -37,4 +32,3 @@ class OnlineTransducerGreedySearchNeMoDecoder {
} // namespace sherpa_onnx
#endif // SHERPA_ONNX_CSRC_ONLINE_TRANSDUCER_GREEDY_SEARCH_NEMO_DECODER_H_
... ...
... ... @@ -102,8 +102,8 @@ class OnlineTransducerNeMoModel::Impl {
std::move(features), View(&length), std::move(cache_last_channel),
std::move(cache_last_time), std::move(cache_last_channel_len)};
auto out =
encoder_sess_->Run({}, encoder_input_names_ptr_.data(), inputs.data(), inputs.size(),
auto out = encoder_sess_->Run(
{}, encoder_input_names_ptr_.data(), inputs.data(), inputs.size(),
encoder_output_names_ptr_.data(), encoder_output_names_ptr_.size());
// out[0]: logit
// out[1] logit_length
... ... @@ -127,16 +127,18 @@ class OnlineTransducerNeMoModel::Impl {
std::pair<Ort::Value, std::vector<Ort::Value>> RunDecoder(
Ort::Value targets, std::vector<Ort::Value> states) {
Ort::MemoryInfo memory_info =
Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU);
Ort::MemoryInfo memory_info = Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU);
auto shape = targets.GetTensorTypeAndShapeInfo().GetShape();
int32_t batch_size = static_cast<int32_t>(shape[0]);
// Create the tensor with a single int32_t value of 1
int32_t length_value = 1;
std::vector<int64_t> length_shape = {1};
std::vector<int64_t> length_shape = {batch_size};
std::vector<int32_t> length_value(batch_size, 1);
Ort::Value targets_length = Ort::Value::CreateTensor<int32_t>(
memory_info, &length_value, 1, length_shape.data(), length_shape.size()
);
memory_info, length_value.data(), batch_size, length_shape.data(),
length_shape.size());
std::vector<Ort::Value> decoder_inputs;
decoder_inputs.reserve(2 + states.size());
... ... @@ -171,35 +173,21 @@ class OnlineTransducerNeMoModel::Impl {
Ort::Value RunJoiner(Ort::Value encoder_out, Ort::Value decoder_out) {
std::array<Ort::Value, 2> joiner_input = {std::move(encoder_out),
std::move(decoder_out)};
auto logit =
joiner_sess_->Run({}, joiner_input_names_ptr_.data(), joiner_input.data(),
joiner_input.size(), joiner_output_names_ptr_.data(),
auto logit = joiner_sess_->Run({}, joiner_input_names_ptr_.data(),
joiner_input.data(), joiner_input.size(),
joiner_output_names_ptr_.data(),
joiner_output_names_ptr_.size());
return std::move(logit[0]);
}
std::vector<Ort::Value> GetDecoderInitStates(int32_t batch_size) const {
std::array<int64_t, 3> s0_shape{pred_rnn_layers_, batch_size, pred_hidden_};
Ort::Value s0 = Ort::Value::CreateTensor<float>(allocator_, s0_shape.data(),
s0_shape.size());
Fill<float>(&s0, 0);
std::array<int64_t, 3> s1_shape{pred_rnn_layers_, batch_size, pred_hidden_};
Ort::Value s1 = Ort::Value::CreateTensor<float>(allocator_, s1_shape.data(),
s1_shape.size());
Fill<float>(&s1, 0);
std::vector<Ort::Value> states;
}
states.reserve(2);
states.push_back(std::move(s0));
states.push_back(std::move(s1));
std::vector<Ort::Value> GetDecoderInitStates() {
std::vector<Ort::Value> ans;
ans.reserve(2);
ans.push_back(View(&lstm0_));
ans.push_back(View(&lstm1_));
return states;
return ans;
}
int32_t ChunkSize() const { return window_size_; }
... ... @@ -218,7 +206,7 @@ class OnlineTransducerNeMoModel::Impl {
// - cache_last_channel
// - cache_last_time_
// - cache_last_channel_len
std::vector<Ort::Value> GetInitStates() {
std::vector<Ort::Value> GetEncoderInitStates() {
std::vector<Ort::Value> ans;
ans.reserve(3);
ans.push_back(View(&cache_last_channel_));
... ... @@ -228,7 +216,75 @@ class OnlineTransducerNeMoModel::Impl {
return ans;
}
private:
std::vector<Ort::Value> StackStates(
std::vector<std::vector<Ort::Value>> states) const {
int32_t batch_size = static_cast<int32_t>(states.size());
if (batch_size == 1) {
return std::move(states[0]);
}
std::vector<Ort::Value> ans;
// stack cache_last_channel
std::vector<const Ort::Value *> buf(batch_size);
// there are 3 states to be stacked
for (int32_t i = 0; i != 3; ++i) {
buf.clear();
buf.reserve(batch_size);
for (int32_t b = 0; b != batch_size; ++b) {
assert(states[b].size() == 3);
buf.push_back(&states[b][i]);
}
Ort::Value c{nullptr};
if (i == 2) {
c = Cat<int64_t>(allocator_, buf, 0);
} else {
c = Cat(allocator_, buf, 0);
}
ans.push_back(std::move(c));
}
return ans;
}
std::vector<std::vector<Ort::Value>> UnStackStates(
std::vector<Ort::Value> states) const {
assert(states.size() == 3);
std::vector<std::vector<Ort::Value>> ans;
auto shape = states[0].GetTensorTypeAndShapeInfo().GetShape();
int32_t batch_size = shape[0];
ans.resize(batch_size);
if (batch_size == 1) {
ans[0] = std::move(states);
return ans;
}
for (int32_t i = 0; i != 3; ++i) {
std::vector<Ort::Value> v;
if (i == 2) {
v = Unbind<int64_t>(allocator_, &states[i], 0);
} else {
v = Unbind(allocator_, &states[i], 0);
}
assert(v.size() == batch_size);
for (int32_t b = 0; b != batch_size; ++b) {
ans[b].push_back(std::move(v[b]));
}
}
return ans;
}
private:
void InitEncoder(void *model_data, size_t model_data_length) {
encoder_sess_ = std::make_unique<Ort::Session>(
env_, model_data, model_data_length, sess_opts_);
... ... @@ -276,10 +332,10 @@ private:
normalize_type_ = "";
}
InitStates();
InitEncoderStates();
}
void InitStates() {
void InitEncoderStates() {
std::array<int64_t, 4> cache_last_channel_shape{1, cache_last_channel_dim1_,
cache_last_channel_dim2_,
cache_last_channel_dim3_};
... ... @@ -314,6 +370,24 @@ private:
GetOutputNames(decoder_sess_.get(), &decoder_output_names_,
&decoder_output_names_ptr_);
InitDecoderStates();
}
void InitDecoderStates() {
int32_t batch_size = 1;
std::array<int64_t, 3> s0_shape{pred_rnn_layers_, batch_size, pred_hidden_};
lstm0_ = Ort::Value::CreateTensor<float>(allocator_, s0_shape.data(),
s0_shape.size());
Fill<float>(&lstm0_, 0);
std::array<int64_t, 3> s1_shape{pred_rnn_layers_, batch_size, pred_hidden_};
lstm1_ = Ort::Value::CreateTensor<float>(allocator_, s1_shape.data(),
s1_shape.size());
Fill<float>(&lstm1_, 0);
}
void InitJoiner(void *model_data, size_t model_data_length) {
... ... @@ -363,6 +437,7 @@ private:
int32_t pred_rnn_layers_ = -1;
int32_t pred_hidden_ = -1;
// encoder states
int32_t cache_last_channel_dim1_;
int32_t cache_last_channel_dim2_;
int32_t cache_last_channel_dim3_;
... ... @@ -370,9 +445,14 @@ private:
int32_t cache_last_time_dim2_;
int32_t cache_last_time_dim3_;
// init encoder states
Ort::Value cache_last_channel_{nullptr};
Ort::Value cache_last_time_{nullptr};
Ort::Value cache_last_channel_len_{nullptr};
// init decoder states
Ort::Value lstm0_{nullptr};
Ort::Value lstm1_{nullptr};
};
OnlineTransducerNeMoModel::OnlineTransducerNeMoModel(
... ... @@ -387,9 +467,8 @@ OnlineTransducerNeMoModel::OnlineTransducerNeMoModel(
OnlineTransducerNeMoModel::~OnlineTransducerNeMoModel() = default;
std::vector<Ort::Value>
OnlineTransducerNeMoModel::RunEncoder(Ort::Value features,
std::vector<Ort::Value> states) const {
std::vector<Ort::Value> OnlineTransducerNeMoModel::RunEncoder(
Ort::Value features, std::vector<Ort::Value> states) const {
return impl_->RunEncoder(std::move(features), std::move(states));
}
... ... @@ -399,9 +478,9 @@ OnlineTransducerNeMoModel::RunDecoder(Ort::Value targets,
return impl_->RunDecoder(std::move(targets), std::move(states));
}
std::vector<Ort::Value> OnlineTransducerNeMoModel::GetDecoderInitStates(
int32_t batch_size) const {
return impl_->GetDecoderInitStates(batch_size);
std::vector<Ort::Value> OnlineTransducerNeMoModel::GetDecoderInitStates()
const {
return impl_->GetDecoderInitStates();
}
Ort::Value OnlineTransducerNeMoModel::RunJoiner(Ort::Value encoder_out,
... ... @@ -409,14 +488,13 @@ Ort::Value OnlineTransducerNeMoModel::RunJoiner(Ort::Value encoder_out,
return impl_->RunJoiner(std::move(encoder_out), std::move(decoder_out));
}
int32_t OnlineTransducerNeMoModel::ChunkSize() const {
return impl_->ChunkSize();
}
}
int32_t OnlineTransducerNeMoModel::ChunkShift() const {
return impl_->ChunkShift();
}
}
int32_t OnlineTransducerNeMoModel::SubsamplingFactor() const {
return impl_->SubsamplingFactor();
... ... @@ -434,8 +512,19 @@ std::string OnlineTransducerNeMoModel::FeatureNormalizationMethod() const {
return impl_->FeatureNormalizationMethod();
}
std::vector<Ort::Value> OnlineTransducerNeMoModel::GetInitStates() const {
return impl_->GetInitStates();
std::vector<Ort::Value> OnlineTransducerNeMoModel::GetEncoderInitStates()
const {
return impl_->GetEncoderInitStates();
}
std::vector<Ort::Value> OnlineTransducerNeMoModel::StackStates(
std::vector<std::vector<Ort::Value>> states) const {
return impl_->StackStates(std::move(states));
}
std::vector<std::vector<Ort::Value>> OnlineTransducerNeMoModel::UnStackStates(
std::vector<Ort::Value> states) const {
return impl_->UnStackStates(std::move(states));
}
} // namespace sherpa_onnx
... ...
... ... @@ -38,15 +38,24 @@ class OnlineTransducerNeMoModel {
// - cache_last_channel
// - cache_last_time
// - cache_last_channel_len
std::vector<Ort::Value> GetInitStates() const;
std::vector<Ort::Value> GetEncoderInitStates() const;
// stack encoder states
std::vector<Ort::Value> StackStates(
std::vector<std::vector<Ort::Value>> states) const;
// unstack encoder states
std::vector<std::vector<Ort::Value>> UnStackStates(
std::vector<Ort::Value> states) const;
/** Run the encoder.
*
* @param features A tensor of shape (N, T, C). It is changed in-place.
* @param states It is from GetInitStates() or returned from this method.
* @param states It is from GetEncoderInitStates() or returned from this
* method.
*
* @return Return a tuple containing:
* - ans[0]: encoder_out, a tensor of shape (N, T', encoder_out_dim)
* - ans[0]: encoder_out, a tensor of shape (N, encoder_out_dim, T')
* - ans[1:]: contains next states
*/
std::vector<Ort::Value> RunEncoder(
... ... @@ -63,7 +72,7 @@ class OnlineTransducerNeMoModel {
std::pair<Ort::Value, std::vector<Ort::Value>> RunDecoder(
Ort::Value targets, std::vector<Ort::Value> states) const;
std::vector<Ort::Value> GetDecoderInitStates(int32_t batch_size) const;
std::vector<Ort::Value> GetDecoderInitStates() const;
/** Run the joint network.
*
... ... @@ -71,9 +80,7 @@ class OnlineTransducerNeMoModel {
* @param decoder_out Output of the decoder network.
* @return Return a tensor of shape (N, 1, 1, vocab_size) containing logits.
*/
Ort::Value RunJoiner(Ort::Value encoder_out,
Ort::Value decoder_out) const;
Ort::Value RunJoiner(Ort::Value encoder_out, Ort::Value decoder_out) const;
/** We send this number of feature frames to the encoder at a time. */
int32_t ChunkSize() const;
... ... @@ -117,7 +124,7 @@ class OnlineTransducerNeMoModel {
private:
class Impl;
std::unique_ptr<Impl> impl_;
};
};
} // namespace sherpa_onnx
... ...