Add C++ runtime for non-streaming faster conformer transducer from NeMo. (#854)
This commit is contained in:
Fangjun Kuang
301
sherpa-onnx/csrc/offline-transducer-nemo-model.cc
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301
sherpa-onnx/csrc/offline-transducer-nemo-model.cc
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// sherpa-onnx/csrc/offline-transducer-nemo-model.cc
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//
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// Copyright (c) 2024 Xiaomi Corporation
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#include "sherpa-onnx/csrc/offline-transducer-nemo-model.h"
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#include <algorithm>
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#include <string>
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#include <utility>
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#include <vector>
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#include "sherpa-onnx/csrc/macros.h"
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#include "sherpa-onnx/csrc/offline-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/transpose.h"
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namespace sherpa_onnx {
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class OfflineTransducerNeMoModel::Impl {
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public:
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explicit Impl(const OfflineModelConfig &config)
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: config_(config),
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env_(ORT_LOGGING_LEVEL_WARNING),
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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_filename);
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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_filename);
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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_filename);
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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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Impl(AAssetManager *mgr, const OfflineModelConfig &config)
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: config_(config),
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env_(ORT_LOGGING_LEVEL_WARNING),
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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_filename);
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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_filename);
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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_filename);
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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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std::vector<Ort::Value> RunEncoder(Ort::Value features,
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Ort::Value features_length) {
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// (B, T, C) -> (B, C, T)
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features = Transpose12(allocator_, &features);
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std::array<Ort::Value, 2> encoder_inputs = {std::move(features),
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std::move(features_length)};
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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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return encoder_out;
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}
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std::pair<Ort::Value, std::vector<Ort::Value>> RunDecoder(
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Ort::Value targets, Ort::Value targets_length,
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std::vector<Ort::Value> states) {
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std::vector<Ort::Value> decoder_inputs;
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decoder_inputs.reserve(2 + states.size());
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decoder_inputs.push_back(std::move(targets));
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decoder_inputs.push_back(std::move(targets_length));
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for (auto &s : states) {
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decoder_inputs.push_back(std::move(s));
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}
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auto decoder_out = decoder_sess_->Run(
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{}, decoder_input_names_ptr_.data(), decoder_inputs.data(),
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decoder_inputs.size(), decoder_output_names_ptr_.data(),
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decoder_output_names_ptr_.size());
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std::vector<Ort::Value> states_next;
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states_next.reserve(states.size());
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// decoder_out[0]: decoder_output
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// decoder_out[1]: decoder_output_length
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// decoder_out[2:] states_next
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for (int32_t i = 0; i != states.size(); ++i) {
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states_next.push_back(std::move(decoder_out[i + 2]));
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}
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// we discard decoder_out[1]
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return {std::move(decoder_out[0]), std::move(states_next)};
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}
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Ort::Value RunJoiner(Ort::Value encoder_out, 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 = joiner_sess_->Run({}, joiner_input_names_ptr_.data(),
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joiner_input.data(), joiner_input.size(),
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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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std::vector<Ort::Value> GetDecoderInitStates(int32_t batch_size) const {
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std::array<int64_t, 3> s0_shape{pred_rnn_layers_, batch_size, pred_hidden_};
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Ort::Value s0 = Ort::Value::CreateTensor<float>(allocator_, s0_shape.data(),
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s0_shape.size());
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Fill<float>(&s0, 0);
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std::array<int64_t, 3> s1_shape{pred_rnn_layers_, batch_size, pred_hidden_};
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Ort::Value s1 = Ort::Value::CreateTensor<float>(allocator_, s1_shape.data(),
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s1_shape.size());
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Fill<float>(&s1, 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(s0));
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states.push_back(std::move(s1));
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return states;
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}
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int32_t SubsamplingFactor() const { return subsampling_factor_; }
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int32_t VocabSize() const { return vocab_size_; }
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OrtAllocator *Allocator() const { return allocator_; }
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std::string FeatureNormalizationMethod() const { return normalize_type_; }
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private:
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void InitEncoder(void *model_data, size_t model_data_length) {
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encoder_sess_ = std::make_unique<Ort::Session>(
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env_, model_data, 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\n", 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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// need to increase by 1 since the blank token is not included in computing
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// vocab_size in NeMo.
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vocab_size_ += 1;
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SHERPA_ONNX_READ_META_DATA(subsampling_factor_, "subsampling_factor");
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SHERPA_ONNX_READ_META_DATA_STR(normalize_type_, "normalize_type");
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SHERPA_ONNX_READ_META_DATA(pred_rnn_layers_, "pred_rnn_layers");
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SHERPA_ONNX_READ_META_DATA(pred_hidden_, "pred_hidden");
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if (normalize_type_ == "NA") {
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normalize_type_ = "";
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}
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}
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void InitDecoder(void *model_data, size_t model_data_length) {
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decoder_sess_ = std::make_unique<Ort::Session>(
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env_, model_data, 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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}
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void InitJoiner(void *model_data, size_t model_data_length) {
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joiner_sess_ = std::make_unique<Ort::Session>(
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env_, model_data, 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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}
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private:
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OfflineModelConfig config_;
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Ort::Env env_;
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Ort::SessionOptions sess_opts_;
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Ort::AllocatorWithDefaultOptions allocator_;
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std::unique_ptr<Ort::Session> encoder_sess_;
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std::unique_ptr<Ort::Session> decoder_sess_;
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std::unique_ptr<Ort::Session> joiner_sess_;
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std::vector<std::string> encoder_input_names_;
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std::vector<const char *> encoder_input_names_ptr_;
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std::vector<std::string> encoder_output_names_;
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std::vector<const char *> encoder_output_names_ptr_;
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std::vector<std::string> decoder_input_names_;
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std::vector<const char *> decoder_input_names_ptr_;
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std::vector<std::string> decoder_output_names_;
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std::vector<const char *> decoder_output_names_ptr_;
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std::vector<std::string> joiner_input_names_;
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std::vector<const char *> joiner_input_names_ptr_;
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std::vector<std::string> joiner_output_names_;
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std::vector<const char *> joiner_output_names_ptr_;
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int32_t vocab_size_ = 0;
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int32_t subsampling_factor_ = 8;
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std::string normalize_type_;
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int32_t pred_rnn_layers_ = -1;
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int32_t pred_hidden_ = -1;
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};
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OfflineTransducerNeMoModel::OfflineTransducerNeMoModel(
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const OfflineModelConfig &config)
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: impl_(std::make_unique<Impl>(config)) {}
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#if __ANDROID_API__ >= 9
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OfflineTransducerNeMoModel::OfflineTransducerNeMoModel(
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AAssetManager *mgr, const OfflineModelConfig &config)
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: impl_(std::make_unique<Impl>(mgr, config)) {}
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#endif
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OfflineTransducerNeMoModel::~OfflineTransducerNeMoModel() = default;
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std::vector<Ort::Value> OfflineTransducerNeMoModel::RunEncoder(
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Ort::Value features, Ort::Value features_length) const {
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return impl_->RunEncoder(std::move(features), std::move(features_length));
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}
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std::pair<Ort::Value, std::vector<Ort::Value>>
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OfflineTransducerNeMoModel::RunDecoder(Ort::Value targets,
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Ort::Value targets_length,
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std::vector<Ort::Value> states) const {
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return impl_->RunDecoder(std::move(targets), std::move(targets_length),
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std::move(states));
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}
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std::vector<Ort::Value> OfflineTransducerNeMoModel::GetDecoderInitStates(
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int32_t batch_size) const {
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return impl_->GetDecoderInitStates(batch_size);
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}
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Ort::Value OfflineTransducerNeMoModel::RunJoiner(Ort::Value encoder_out,
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Ort::Value decoder_out) const {
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return impl_->RunJoiner(std::move(encoder_out), std::move(decoder_out));
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}
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int32_t OfflineTransducerNeMoModel::SubsamplingFactor() const {
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return impl_->SubsamplingFactor();
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}
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int32_t OfflineTransducerNeMoModel::VocabSize() const {
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return impl_->VocabSize();
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}
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OrtAllocator *OfflineTransducerNeMoModel::Allocator() const {
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return impl_->Allocator();
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}
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std::string OfflineTransducerNeMoModel::FeatureNormalizationMethod() const {
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return impl_->FeatureNormalizationMethod();
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}
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} // namespace sherpa_onnx
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