80060c276d
Please see https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/nemo/index.html for a list of pre-trained CTC models from NeMo.
132 lines
4.1 KiB
C++
132 lines
4.1 KiB
C++
// sherpa-onnx/csrc/offline-nemo-enc-dec-ctc-model.cc
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//
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// Copyright (c) 2023 Xiaomi Corporation
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#include "sherpa-onnx/csrc/offline-nemo-enc-dec-ctc-model.h"
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#include "sherpa-onnx/csrc/macros.h"
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#include "sherpa-onnx/csrc/onnx-utils.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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namespace sherpa_onnx {
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class OfflineNemoEncDecCtcModel::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_ERROR),
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sess_opts_{},
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allocator_{} {
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sess_opts_.SetIntraOpNumThreads(config_.num_threads);
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sess_opts_.SetInterOpNumThreads(config_.num_threads);
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Init();
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}
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std::pair<Ort::Value, Ort::Value> Forward(Ort::Value features,
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Ort::Value features_length) {
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std::vector<int64_t> shape =
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features_length.GetTensorTypeAndShapeInfo().GetShape();
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Ort::Value out_features_length = Ort::Value::CreateTensor<int64_t>(
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allocator_, shape.data(), shape.size());
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const int64_t *src = features_length.GetTensorData<int64_t>();
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int64_t *dst = out_features_length.GetTensorMutableData<int64_t>();
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for (int64_t i = 0; i != shape[0]; ++i) {
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dst[i] = src[i] / subsampling_factor_;
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}
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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> inputs = {std::move(features),
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std::move(features_length)};
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auto out =
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sess_->Run({}, input_names_ptr_.data(), inputs.data(), inputs.size(),
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output_names_ptr_.data(), output_names_ptr_.size());
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return {std::move(out[0]), std::move(out_features_length)};
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}
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int32_t VocabSize() const { return vocab_size_; }
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int32_t SubsamplingFactor() const { return subsampling_factor_; }
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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 Init() {
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auto buf = ReadFile(config_.nemo_ctc.model);
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sess_ = std::make_unique<Ort::Session>(env_, buf.data(), buf.size(),
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sess_opts_);
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GetInputNames(sess_.get(), &input_names_, &input_names_ptr_);
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GetOutputNames(sess_.get(), &output_names_, &output_names_ptr_);
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// get meta data
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Ort::ModelMetadata meta_data = sess_->GetModelMetadata();
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if (config_.debug) {
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std::ostringstream os;
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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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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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}
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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> sess_;
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std::vector<std::string> input_names_;
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std::vector<const char *> input_names_ptr_;
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std::vector<std::string> output_names_;
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std::vector<const char *> output_names_ptr_;
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int32_t vocab_size_ = 0;
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int32_t subsampling_factor_ = 0;
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std::string normalize_type_;
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};
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OfflineNemoEncDecCtcModel::OfflineNemoEncDecCtcModel(
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const OfflineModelConfig &config)
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: impl_(std::make_unique<Impl>(config)) {}
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OfflineNemoEncDecCtcModel::~OfflineNemoEncDecCtcModel() = default;
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std::pair<Ort::Value, Ort::Value> OfflineNemoEncDecCtcModel::Forward(
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Ort::Value features, Ort::Value features_length) {
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return impl_->Forward(std::move(features), std::move(features_length));
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}
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int32_t OfflineNemoEncDecCtcModel::VocabSize() const {
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return impl_->VocabSize();
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}
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int32_t OfflineNemoEncDecCtcModel::SubsamplingFactor() const {
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return impl_->SubsamplingFactor();
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}
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OrtAllocator *OfflineNemoEncDecCtcModel::Allocator() const {
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return impl_->Allocator();
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}
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std::string OfflineNemoEncDecCtcModel::FeatureNormalizationMethod() const {
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return impl_->FeatureNormalizationMethod();
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}
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} // namespace sherpa_onnx
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