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