online-lstm-transducer-model.cc
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// sherpa-onnx/csrc/online-lstm-transducer-model.cc
//
// Copyright (c) 2023 Xiaomi Corporation
#include "sherpa-onnx/csrc/online-lstm-transducer-model.h"
#include <algorithm>
#include <cassert>
#include <memory>
#include <sstream>
#include <string>
#include <utility>
#include <vector>
#if __ANDROID_API__ >= 9
#include "android/asset_manager.h"
#include "android/asset_manager_jni.h"
#endif
#if __OHOS__
#include "rawfile/raw_file_manager.h"
#endif
#include "onnxruntime_cxx_api.h" // NOLINT
#include "sherpa-onnx/csrc/cat.h"
#include "sherpa-onnx/csrc/file-utils.h"
#include "sherpa-onnx/csrc/macros.h"
#include "sherpa-onnx/csrc/online-transducer-decoder.h"
#include "sherpa-onnx/csrc/onnx-utils.h"
#include "sherpa-onnx/csrc/session.h"
#include "sherpa-onnx/csrc/unbind.h"
namespace sherpa_onnx {
OnlineLstmTransducerModel::OnlineLstmTransducerModel(
const OnlineModelConfig &config)
: env_(ORT_LOGGING_LEVEL_ERROR),
config_(config),
sess_opts_(GetSessionOptions(config)),
allocator_{} {
{
auto buf = ReadFile(config.transducer.encoder);
InitEncoder(buf.data(), buf.size());
}
{
auto buf = ReadFile(config.transducer.decoder);
InitDecoder(buf.data(), buf.size());
}
{
auto buf = ReadFile(config.transducer.joiner);
InitJoiner(buf.data(), buf.size());
}
}
template <typename Manager>
OnlineLstmTransducerModel::OnlineLstmTransducerModel(
Manager *mgr, const OnlineModelConfig &config)
: env_(ORT_LOGGING_LEVEL_ERROR),
config_(config),
sess_opts_(GetSessionOptions(config)),
allocator_{} {
{
auto buf = ReadFile(mgr, config.transducer.encoder);
InitEncoder(buf.data(), buf.size());
}
{
auto buf = ReadFile(mgr, config.transducer.decoder);
InitDecoder(buf.data(), buf.size());
}
{
auto buf = ReadFile(mgr, config.transducer.joiner);
InitJoiner(buf.data(), buf.size());
}
}
void OnlineLstmTransducerModel::InitEncoder(void *model_data,
size_t model_data_length) {
encoder_sess_ = std::make_unique<Ort::Session>(env_, model_data,
model_data_length, sess_opts_);
GetInputNames(encoder_sess_.get(), &encoder_input_names_,
&encoder_input_names_ptr_);
GetOutputNames(encoder_sess_.get(), &encoder_output_names_,
&encoder_output_names_ptr_);
// get meta data
Ort::ModelMetadata meta_data = encoder_sess_->GetModelMetadata();
if (config_.debug) {
std::ostringstream os;
os << "---encoder---\n";
PrintModelMetadata(os, meta_data);
#if __OHOS__
SHERPA_ONNX_LOGE("%{public}s", os.str().c_str());
#else
SHERPA_ONNX_LOGE("%s", os.str().c_str());
#endif
}
Ort::AllocatorWithDefaultOptions allocator; // used in the macro below
SHERPA_ONNX_READ_META_DATA(num_encoder_layers_, "num_encoder_layers");
SHERPA_ONNX_READ_META_DATA(T_, "T");
SHERPA_ONNX_READ_META_DATA(decode_chunk_len_, "decode_chunk_len");
SHERPA_ONNX_READ_META_DATA(rnn_hidden_size_, "rnn_hidden_size");
SHERPA_ONNX_READ_META_DATA(d_model_, "d_model");
}
void OnlineLstmTransducerModel::InitDecoder(void *model_data,
size_t model_data_length) {
decoder_sess_ = std::make_unique<Ort::Session>(env_, model_data,
model_data_length, sess_opts_);
GetInputNames(decoder_sess_.get(), &decoder_input_names_,
&decoder_input_names_ptr_);
GetOutputNames(decoder_sess_.get(), &decoder_output_names_,
&decoder_output_names_ptr_);
// get meta data
Ort::ModelMetadata meta_data = decoder_sess_->GetModelMetadata();
if (config_.debug) {
std::ostringstream os;
os << "---decoder---\n";
PrintModelMetadata(os, meta_data);
SHERPA_ONNX_LOGE("%s", os.str().c_str());
}
Ort::AllocatorWithDefaultOptions allocator; // used in the macro below
SHERPA_ONNX_READ_META_DATA(vocab_size_, "vocab_size");
SHERPA_ONNX_READ_META_DATA(context_size_, "context_size");
}
void OnlineLstmTransducerModel::InitJoiner(void *model_data,
size_t model_data_length) {
joiner_sess_ = std::make_unique<Ort::Session>(env_, model_data,
model_data_length, sess_opts_);
GetInputNames(joiner_sess_.get(), &joiner_input_names_,
&joiner_input_names_ptr_);
GetOutputNames(joiner_sess_.get(), &joiner_output_names_,
&joiner_output_names_ptr_);
// get meta data
Ort::ModelMetadata meta_data = joiner_sess_->GetModelMetadata();
if (config_.debug) {
std::ostringstream os;
os << "---joiner---\n";
PrintModelMetadata(os, meta_data);
SHERPA_ONNX_LOGE("%s", os.str().c_str());
}
}
std::vector<Ort::Value> OnlineLstmTransducerModel::StackStates(
const std::vector<std::vector<Ort::Value>> &states) const {
int32_t batch_size = static_cast<int32_t>(states.size());
std::vector<const Ort::Value *> h_buf(batch_size);
std::vector<const Ort::Value *> c_buf(batch_size);
for (int32_t i = 0; i != batch_size; ++i) {
assert(states[i].size() == 2);
h_buf[i] = &states[i][0];
c_buf[i] = &states[i][1];
}
auto allocator = const_cast<OnlineLstmTransducerModel *>(this)->allocator_;
Ort::Value h = Cat(allocator, h_buf, 1);
Ort::Value c = Cat(allocator, c_buf, 1);
std::vector<Ort::Value> ans;
ans.reserve(2);
ans.push_back(std::move(h));
ans.push_back(std::move(c));
return ans;
}
std::vector<std::vector<Ort::Value>> OnlineLstmTransducerModel::UnStackStates(
const std::vector<Ort::Value> &states) const {
int32_t batch_size = states[0].GetTensorTypeAndShapeInfo().GetShape()[1];
assert(states.size() == 2);
std::vector<std::vector<Ort::Value>> ans(batch_size);
auto allocator = const_cast<OnlineLstmTransducerModel *>(this)->allocator_;
std::vector<Ort::Value> h_vec = Unbind(allocator, &states[0], 1);
std::vector<Ort::Value> c_vec = Unbind(allocator, &states[1], 1);
assert(h_vec.size() == batch_size);
assert(c_vec.size() == batch_size);
for (int32_t i = 0; i != batch_size; ++i) {
ans[i].push_back(std::move(h_vec[i]));
ans[i].push_back(std::move(c_vec[i]));
}
return ans;
}
std::vector<Ort::Value> OnlineLstmTransducerModel::GetEncoderInitStates() {
// Please see
// https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/lstm_transducer_stateless2/export-onnx.py#L185
// for details
constexpr int32_t kBatchSize = 1;
std::array<int64_t, 3> h_shape{num_encoder_layers_, kBatchSize, d_model_};
Ort::Value h = Ort::Value::CreateTensor<float>(allocator_, h_shape.data(),
h_shape.size());
Fill<float>(&h, 0);
std::array<int64_t, 3> c_shape{num_encoder_layers_, kBatchSize,
rnn_hidden_size_};
Ort::Value c = Ort::Value::CreateTensor<float>(allocator_, c_shape.data(),
c_shape.size());
Fill<float>(&c, 0);
std::vector<Ort::Value> states;
states.reserve(2);
states.push_back(std::move(h));
states.push_back(std::move(c));
return states;
}
std::pair<Ort::Value, std::vector<Ort::Value>>
OnlineLstmTransducerModel::RunEncoder(Ort::Value features,
std::vector<Ort::Value> states,
Ort::Value /* processed_frames */) {
std::array<Ort::Value, 3> encoder_inputs = {
std::move(features), std::move(states[0]), std::move(states[1])};
auto encoder_out = encoder_sess_->Run(
{}, encoder_input_names_ptr_.data(), encoder_inputs.data(),
encoder_inputs.size(), encoder_output_names_ptr_.data(),
encoder_output_names_ptr_.size());
std::vector<Ort::Value> next_states;
next_states.reserve(2);
next_states.push_back(std::move(encoder_out[1]));
next_states.push_back(std::move(encoder_out[2]));
return {std::move(encoder_out[0]), std::move(next_states)};
}
Ort::Value OnlineLstmTransducerModel::RunDecoder(Ort::Value decoder_input) {
auto decoder_out = decoder_sess_->Run(
{}, decoder_input_names_ptr_.data(), &decoder_input, 1,
decoder_output_names_ptr_.data(), decoder_output_names_ptr_.size());
return std::move(decoder_out[0]);
}
Ort::Value OnlineLstmTransducerModel::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(),
joiner_output_names_ptr_.size());
return std::move(logit[0]);
}
#if __ANDROID_API__ >= 9
template OnlineLstmTransducerModel::OnlineLstmTransducerModel(
AAssetManager *mgr, const OnlineModelConfig &config);
#endif
#if __OHOS__
template OnlineLstmTransducerModel::OnlineLstmTransducerModel(
NativeResourceManager *mgr, const OnlineModelConfig &config);
#endif
} // namespace sherpa_onnx