99 lines
3.2 KiB
C++
99 lines
3.2 KiB
C++
// sherpa-onnx/csrc/offline-transducer-nemo-model.h
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//
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// Copyright (c) 2024 Xiaomi Corporation
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#ifndef SHERPA_ONNX_CSRC_OFFLINE_TRANSDUCER_NEMO_MODEL_H_
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#define SHERPA_ONNX_CSRC_OFFLINE_TRANSDUCER_NEMO_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-model-config.h"
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namespace sherpa_onnx {
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// see
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// https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/models/hybrid_rnnt_ctc_bpe_models.py#L40
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// Its decoder is stateful, not stateless.
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class OfflineTransducerNeMoModel {
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public:
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explicit OfflineTransducerNeMoModel(const OfflineModelConfig &config);
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template <typename Manager>
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OfflineTransducerNeMoModel(Manager *mgr, const OfflineModelConfig &config);
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~OfflineTransducerNeMoModel();
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/** Run the encoder.
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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 vector containing:
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* - encoder_out: A 3-D tensor of shape (N, T', encoder_dim)
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* - encoder_out_length: A 1-D tensor of shape (N,) containing number
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* of frames in `encoder_out` before padding.
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*/
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std::vector<Ort::Value> RunEncoder(Ort::Value features,
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Ort::Value features_length) const;
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/** Run the decoder network.
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*
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* @param targets A int32 tensor of shape (batch_size, 1)
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* @param targets_length A int32 tensor of shape (batch_size,)
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* @param states The states for the decoder model.
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* @return Return a vector:
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* - ans[0] is the decoder_out (a float tensor)
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* - ans[1] is the decoder_out_length (a int32 tensor)
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* - ans[2:] is the states_next
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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) const;
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std::vector<Ort::Value> GetDecoderInitStates(int32_t batch_size) const;
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/** Run the joint network.
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*
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* @param encoder_out Output of the encoder network.
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* @param decoder_out Output of the decoder network.
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* @return Return a tensor of shape (N, 1, 1, vocab_size) containing logits.
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*/
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Ort::Value RunJoiner(Ort::Value encoder_out, Ort::Value decoder_out) const;
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/** Return the subsampling factor of the model.
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*/
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int32_t SubsamplingFactor() const;
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int32_t VocabSize() const;
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/** Return an allocator for allocating memory
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*/
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OrtAllocator *Allocator() const;
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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;
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bool IsGigaAM() const;
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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_TRANSDUCER_NEMO_MODEL_H_
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