Add C++ runtime for *streaming* faster conformer transducer from NeMo. (#889)
Co-authored-by: sangeet2020 <15uec053@gmail.com>
This commit is contained in:
Sangeet Sagar
441
sherpa-onnx/csrc/online-transducer-nemo-model.cc
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441
sherpa-onnx/csrc/online-transducer-nemo-model.cc
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// sherpa-onnx/csrc/online-transducer-nemo-model.cc
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//
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// Copyright (c) 2024 Xiaomi Corporation
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// Copyright (c) 2024 Sangeet Sagar
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#include "sherpa-onnx/csrc/online-transducer-nemo-model.h"
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#include <assert.h>
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#include <math.h>
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#include <algorithm>
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#include <memory>
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#include <numeric>
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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 "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/text-utils.h"
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#include "sherpa-onnx/csrc/transpose.h"
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#include "sherpa-onnx/csrc/unbind.h"
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namespace sherpa_onnx {
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class OnlineTransducerNeMoModel::Impl {
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public:
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explicit Impl(const OnlineModelConfig &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);
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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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Impl(AAssetManager *mgr, const OnlineModelConfig &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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std::vector<Ort::Value> states) {
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Ort::Value &cache_last_channel = states[0];
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Ort::Value &cache_last_time = states[1];
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Ort::Value &cache_last_channel_len = states[2];
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int32_t batch_size = features.GetTensorTypeAndShapeInfo().GetShape()[0];
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std::array<int64_t, 1> length_shape{batch_size};
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Ort::Value length = Ort::Value::CreateTensor<int64_t>(
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allocator_, length_shape.data(), length_shape.size());
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int64_t *p_length = length.GetTensorMutableData<int64_t>();
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std::fill(p_length, p_length + batch_size, ChunkSize());
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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, 5> inputs = {
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std::move(features), View(&length), std::move(cache_last_channel),
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std::move(cache_last_time), std::move(cache_last_channel_len)};
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auto out =
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encoder_sess_->Run({}, encoder_input_names_ptr_.data(), inputs.data(), inputs.size(),
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encoder_output_names_ptr_.data(), encoder_output_names_ptr_.size());
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// out[0]: logit
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// out[1] logit_length
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// out[2:] states_next
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//
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// we need to remove out[1]
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std::vector<Ort::Value> ans;
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ans.reserve(out.size() - 1);
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for (int32_t i = 0; i != out.size(); ++i) {
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if (i == 1) {
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continue;
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}
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ans.push_back(std::move(out[i]));
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}
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return ans;
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}
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std::pair<Ort::Value, std::vector<Ort::Value>> RunDecoder(
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Ort::Value targets, std::vector<Ort::Value> states) {
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Ort::MemoryInfo memory_info = Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU);
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// Create the tensor with a single int32_t value of 1
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int32_t length_value = 1;
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std::vector<int64_t> length_shape = {1};
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Ort::Value targets_length = Ort::Value::CreateTensor<int32_t>(
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memory_info, &length_value, 1, length_shape.data(), length_shape.size()
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);
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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 (discarded)
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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 =
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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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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 ChunkSize() const { return window_size_; }
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int32_t ChunkShift() const { return chunk_shift_; }
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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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// Return a vector containing 3 tensors
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// - cache_last_channel
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// - cache_last_time_
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// - cache_last_channel_len
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std::vector<Ort::Value> GetInitStates() {
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std::vector<Ort::Value> ans;
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ans.reserve(3);
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ans.push_back(View(&cache_last_channel_));
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ans.push_back(View(&cache_last_time_));
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ans.push_back(View(&cache_last_channel_len_));
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return ans;
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}
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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(window_size_, "window_size");
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SHERPA_ONNX_READ_META_DATA(chunk_shift_, "chunk_shift");
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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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SHERPA_ONNX_READ_META_DATA(cache_last_channel_dim1_,
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"cache_last_channel_dim1");
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SHERPA_ONNX_READ_META_DATA(cache_last_channel_dim2_,
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"cache_last_channel_dim2");
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SHERPA_ONNX_READ_META_DATA(cache_last_channel_dim3_,
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"cache_last_channel_dim3");
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SHERPA_ONNX_READ_META_DATA(cache_last_time_dim1_, "cache_last_time_dim1");
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SHERPA_ONNX_READ_META_DATA(cache_last_time_dim2_, "cache_last_time_dim2");
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SHERPA_ONNX_READ_META_DATA(cache_last_time_dim3_, "cache_last_time_dim3");
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if (normalize_type_ == "NA") {
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normalize_type_ = "";
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}
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InitStates();
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}
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void InitStates() {
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std::array<int64_t, 4> cache_last_channel_shape{1, cache_last_channel_dim1_,
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cache_last_channel_dim2_,
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cache_last_channel_dim3_};
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cache_last_channel_ = Ort::Value::CreateTensor<float>(
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allocator_, cache_last_channel_shape.data(),
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cache_last_channel_shape.size());
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Fill<float>(&cache_last_channel_, 0);
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std::array<int64_t, 4> cache_last_time_shape{
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1, cache_last_time_dim1_, cache_last_time_dim2_, cache_last_time_dim3_};
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cache_last_time_ = Ort::Value::CreateTensor<float>(
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allocator_, cache_last_time_shape.data(), cache_last_time_shape.size());
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Fill<float>(&cache_last_time_, 0);
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int64_t shape = 1;
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cache_last_channel_len_ =
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Ort::Value::CreateTensor<int64_t>(allocator_, &shape, 1);
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cache_last_channel_len_.GetTensorMutableData<int64_t>()[0] = 0;
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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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OnlineModelConfig 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 window_size_;
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int32_t chunk_shift_;
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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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int32_t cache_last_channel_dim1_;
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int32_t cache_last_channel_dim2_;
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int32_t cache_last_channel_dim3_;
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int32_t cache_last_time_dim1_;
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int32_t cache_last_time_dim2_;
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int32_t cache_last_time_dim3_;
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Ort::Value cache_last_channel_{nullptr};
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Ort::Value cache_last_time_{nullptr};
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Ort::Value cache_last_channel_len_{nullptr};
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};
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OnlineTransducerNeMoModel::OnlineTransducerNeMoModel(
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const OnlineModelConfig &config)
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: impl_(std::make_unique<Impl>(config)) {}
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#if __ANDROID_API__ >= 9
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OnlineTransducerNeMoModel::OnlineTransducerNeMoModel(
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AAssetManager *mgr, const OnlineModelConfig &config)
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: impl_(std::make_unique<Impl>(mgr, config)) {}
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#endif
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OnlineTransducerNeMoModel::~OnlineTransducerNeMoModel() = default;
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std::vector<Ort::Value>
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OnlineTransducerNeMoModel::RunEncoder(Ort::Value features,
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std::vector<Ort::Value> states) const {
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return impl_->RunEncoder(std::move(features), std::move(states));
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}
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std::pair<Ort::Value, std::vector<Ort::Value>>
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OnlineTransducerNeMoModel::RunDecoder(Ort::Value targets,
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std::vector<Ort::Value> states) const {
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return impl_->RunDecoder(std::move(targets), std::move(states));
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}
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std::vector<Ort::Value> OnlineTransducerNeMoModel::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 OnlineTransducerNeMoModel::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 OnlineTransducerNeMoModel::ChunkSize() const {
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return impl_->ChunkSize();
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}
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int32_t OnlineTransducerNeMoModel::ChunkShift() const {
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return impl_->ChunkShift();
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}
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int32_t OnlineTransducerNeMoModel::SubsamplingFactor() const {
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return impl_->SubsamplingFactor();
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}
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int32_t OnlineTransducerNeMoModel::VocabSize() const {
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return impl_->VocabSize();
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}
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OrtAllocator *OnlineTransducerNeMoModel::Allocator() const {
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return impl_->Allocator();
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}
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std::string OnlineTransducerNeMoModel::FeatureNormalizationMethod() const {
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return impl_->FeatureNormalizationMethod();
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}
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std::vector<Ort::Value> OnlineTransducerNeMoModel::GetInitStates() const {
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return impl_->GetInitStates();
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}
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} // namespace sherpa_onnx
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