79c2ce5dd4
* Refactor online recognizer. Make it easier to support other streaming models. Note that it is a breaking change for the Python API. `sherpa_onnx.OnlineRecognizer()` used before should be replaced by `sherpa_onnx.OnlineRecognizer.from_transducer()`.
142 lines
4.8 KiB
C++
142 lines
4.8 KiB
C++
// sherpa-onnx/csrc/online-transducer-model.h
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//
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// Copyright (c) 2023 Xiaomi Corporation
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#ifndef SHERPA_ONNX_CSRC_ONLINE_TRANSDUCER_MODEL_H_
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#define SHERPA_ONNX_CSRC_ONLINE_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/online-model-config.h"
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#include "sherpa-onnx/csrc/online-transducer-decoder.h"
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#include "sherpa-onnx/csrc/online-transducer-model-config.h"
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namespace sherpa_onnx {
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struct OnlineTransducerDecoderResult;
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class OnlineTransducerModel {
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public:
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virtual ~OnlineTransducerModel() = default;
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static std::unique_ptr<OnlineTransducerModel> Create(
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const OnlineModelConfig &config);
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#if __ANDROID_API__ >= 9
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static std::unique_ptr<OnlineTransducerModel> Create(
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AAssetManager *mgr, const OnlineModelConfig &config);
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#endif
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/** Stack a list of individual states into a batch.
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*
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* It is the inverse operation of `UnStackStates`.
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*
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* @param states states[i] contains the state for the i-th utterance.
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* @return Return a single value representing the batched state.
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*/
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virtual std::vector<Ort::Value> StackStates(
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const std::vector<std::vector<Ort::Value>> &states) const = 0;
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/** Unstack a batch state into a list of individual states.
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*
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* It is the inverse operation of `StackStates`.
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*
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* @param states A batched state.
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* @return ans[i] contains the state for the i-th utterance.
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*/
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virtual std::vector<std::vector<Ort::Value>> UnStackStates(
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const std::vector<Ort::Value> &states) const = 0;
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/** Get the initial encoder states.
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*
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* @return Return the initial encoder state.
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*/
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virtual std::vector<Ort::Value> GetEncoderInitStates() = 0;
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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 states Encoder state of the previous chunk. It is changed in-place.
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* @param processed_frames Processed frames before subsampling. It is a 1-D
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* tensor with data type int64_t.
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*
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* @return Return a tuple containing:
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* - encoder_out, a tensor of shape (N, T', encoder_out_dim)
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* - next_states Encoder state for the next chunk.
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*/
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virtual std::pair<Ort::Value, std::vector<Ort::Value>> RunEncoder(
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Ort::Value features, std::vector<Ort::Value> states,
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Ort::Value processed_frames) = 0; // NOLINT
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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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virtual Ort::Value RunDecoder(Ort::Value decoder_input) = 0;
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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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virtual Ort::Value RunJoiner(Ort::Value encoder_out,
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Ort::Value decoder_out) = 0;
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/** If we are using a stateless decoder and if it contains a
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* Conv1D, this function returns the kernel size of the convolution layer.
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*/
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virtual int32_t ContextSize() const = 0;
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/** We send this number of feature frames to the encoder at a time. */
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virtual int32_t ChunkSize() const = 0;
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/** Number of input frames to discard after each call to RunEncoder.
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*
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* For instance, if we have 30 frames, chunk_size=8, chunk_shift=6.
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*
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* In the first call of RunEncoder, we use frames 0~7 since chunk_size is 8.
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* Then we discard frame 0~5 since chunk_shift is 6.
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* In the second call of RunEncoder, we use frames 6~13; and then we discard
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* frames 6~11.
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* In the third call of RunEncoder, we use frames 12~19; and then we discard
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* frames 12~16.
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*
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* Note: ChunkSize() - ChunkShift() == right context size
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*/
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virtual int32_t ChunkShift() const = 0;
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virtual int32_t VocabSize() const = 0;
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virtual int32_t SubsamplingFactor() const { return 4; }
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virtual OrtAllocator *Allocator() = 0;
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Ort::Value BuildDecoderInput(
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const std::vector<OnlineTransducerDecoderResult> &results);
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Ort::Value BuildDecoderInput(const std::vector<Hypothesis> &hyps);
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};
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} // namespace sherpa_onnx
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#endif // SHERPA_ONNX_CSRC_ONLINE_TRANSDUCER_MODEL_H_
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