111 lines
3.5 KiB
C++
111 lines
3.5 KiB
C++
// sherpa-onnx/csrc/offline-transducer-model.h
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//
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// Copyright (c) 2023 Xiaomi Corporation
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#ifndef SHERPA_ONNX_CSRC_OFFLINE_TRANSDUCER_MODEL_H_
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#define SHERPA_ONNX_CSRC_OFFLINE_TRANSDUCER_MODEL_H_
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#include <memory>
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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 "onnxruntime_cxx_api.h" // NOLINT
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#include "sherpa-onnx/csrc/hypothesis.h"
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#include "sherpa-onnx/csrc/offline-model-config.h"
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namespace sherpa_onnx {
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struct OfflineTransducerDecoderResult;
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class OfflineTransducerModel {
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public:
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explicit OfflineTransducerModel(const OfflineModelConfig &config);
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#if __ANDROID_API__ >= 9
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OfflineTransducerModel(AAssetManager *mgr, const OfflineModelConfig &config);
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#endif
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~OfflineTransducerModel();
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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 pair 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::pair<Ort::Value, Ort::Value> RunEncoder(Ort::Value features,
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Ort::Value features_length);
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/** Run the decoder network.
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*
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* Caution: We assume there are no recurrent connections in the decoder and
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* the decoder is stateless. See
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* https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/pruned_transducer_stateless2/decoder.py
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* for an example
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*
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* @param decoder_input It is usually of shape (N, context_size)
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* @return Return a tensor of shape (N, decoder_dim).
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*/
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Ort::Value RunDecoder(Ort::Value decoder_input);
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/** Run the joint network.
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*
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* @param encoder_out Output of the encoder network. A tensor of shape
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* (N, joiner_dim).
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* @param decoder_out Output of the decoder network. A tensor of shape
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* (N, joiner_dim).
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* @return Return a tensor of shape (N, vocab_size). In icefall, the last
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* last layer of the joint network is `nn.Linear`,
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* not `nn.LogSoftmax`.
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*/
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Ort::Value RunJoiner(Ort::Value encoder_out, Ort::Value decoder_out);
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/** Return the vocabulary size of the model
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*/
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int32_t VocabSize() const;
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/** Return the context_size of the decoder model.
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*/
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int32_t ContextSize() 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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/** Return an allocator for allocating memory
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*/
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OrtAllocator *Allocator() const;
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/** Build decoder_input from the current results.
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*
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* @param results Current decoded results.
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* @param end_index We only use results[0:end_index] to build
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* the decoder_input. results[end_index] is not used.
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* @return Return a tensor of shape (results.size(), ContextSize())
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*/
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Ort::Value BuildDecoderInput(
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const std::vector<OfflineTransducerDecoderResult> &results,
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int32_t end_index) const;
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Ort::Value BuildDecoderInput(const std::vector<Hypothesis> &results,
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int32_t end_index) 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_MODEL_H_
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