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.
76 lines
2.4 KiB
C++
76 lines
2.4 KiB
C++
// sherpa-onnx/csrc/offline-nemo-enc-dec-ctc-model.h
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//
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// Copyright (c) 2023 Xiaomi Corporation
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#ifndef SHERPA_ONNX_CSRC_OFFLINE_NEMO_ENC_DEC_CTC_MODEL_H_
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#define SHERPA_ONNX_CSRC_OFFLINE_NEMO_ENC_DEC_CTC_MODEL_H_
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#include <memory>
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#include <string>
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#include <utility>
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#include <vector>
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#include "onnxruntime_cxx_api.h" // NOLINT
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#include "sherpa-onnx/csrc/offline-ctc-model.h"
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#include "sherpa-onnx/csrc/offline-model-config.h"
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namespace sherpa_onnx {
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/** This class implements the EncDecCTCModelBPE model from NeMo.
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*
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* See
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* https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/models/ctc_bpe_models.py
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* https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/models/ctc_models.py
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*/
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class OfflineNemoEncDecCtcModel : public OfflineCtcModel {
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public:
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explicit OfflineNemoEncDecCtcModel(const OfflineModelConfig &config);
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~OfflineNemoEncDecCtcModel() override;
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/** Run the forward method of the model.
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*
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* @param features A tensor of shape (N, T, C). It is changed in-place.
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* @param features_length A 1-D tensor of shape (N,) containing number of
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* valid frames in `features` before padding.
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* Its dtype is int64_t.
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*
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* @return Return a pair containing:
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* - log_probs: A 3-D tensor of shape (N, T', vocab_size).
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* - log_probs_length A 1-D tensor of shape (N,). Its dtype is int64_t
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*/
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std::pair<Ort::Value, Ort::Value> Forward(
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Ort::Value features, Ort::Value features_length) override;
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/** Return the vocabulary size of the model
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*/
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int32_t VocabSize() const override;
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/** SubsamplingFactor of the model
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*
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* For Citrinet, the subsampling factor is usually 4.
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* For Conformer CTC, the subsampling factor is usually 8.
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*/
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int32_t SubsamplingFactor() const override;
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/** Return an allocator for allocating memory
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*/
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OrtAllocator *Allocator() const override;
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// Possible values:
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// - per_feature
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// - all_features (not implemented yet)
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// - fixed_mean (not implemented)
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// - fixed_std (not implemented)
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// - or just leave it to empty
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// See
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// https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/parts/preprocessing/features.py#L59
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// for details
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std::string FeatureNormalizationMethod() const override;
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private:
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class Impl;
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std::unique_ptr<Impl> impl_;
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};
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} // namespace sherpa_onnx
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#endif // SHERPA_ONNX_CSRC_OFFLINE_NEMO_ENC_DEC_CTC_MODEL_H_
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