online-transducer-nemo-model.h
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// sherpa-onnx/csrc/online-transducer-nemo-model.h
//
// Copyright (c) 2024 Xiaomi Corporation
// Copyright (c) 2024 Sangeet Sagar
#ifndef SHERPA_ONNX_CSRC_ONLINE_TRANSDUCER_NEMO_MODEL_H_
#define SHERPA_ONNX_CSRC_ONLINE_TRANSDUCER_NEMO_MODEL_H_
#include <memory>
#include <string>
#include <utility>
#include <vector>
#include "onnxruntime_cxx_api.h" // NOLINT
#include "sherpa-onnx/csrc/online-model-config.h"
namespace sherpa_onnx {
// see
// https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/models/hybrid_rnnt_ctc_bpe_models.py#L40
// Its decoder is stateful, not stateless.
class OnlineTransducerNeMoModel {
public:
explicit OnlineTransducerNeMoModel(const OnlineModelConfig &config);
template <typename Manager>
OnlineTransducerNeMoModel(Manager *mgr, const OnlineModelConfig &config);
~OnlineTransducerNeMoModel();
// A list of 3 tensors:
// - cache_last_channel
// - cache_last_time
// - cache_last_channel_len
std::vector<Ort::Value> GetEncoderInitStates() const;
// stack encoder states
std::vector<Ort::Value> StackStates(
std::vector<std::vector<Ort::Value>> states) const;
// unstack encoder states
std::vector<std::vector<Ort::Value>> UnStackStates(
std::vector<Ort::Value> states) const;
/** Run the encoder.
*
* @param features A tensor of shape (N, T, C). It is changed in-place.
* @param states It is from GetEncoderInitStates() or returned from this
* method.
*
* @return Return a tuple containing:
* - ans[0]: encoder_out, a tensor of shape (N, encoder_out_dim, T')
* - ans[1:]: contains next states
*/
std::vector<Ort::Value> RunEncoder(
Ort::Value features, std::vector<Ort::Value> states) const; // NOLINT
/** Run the decoder network.
*
* @param targets A int32 tensor of shape (batch_size, 1)
* @param states The states for the decoder model.
* @return Return a vector:
* - ans[0] is the decoder_out (a float tensor)
* - ans[1:] is the next states
*/
std::pair<Ort::Value, std::vector<Ort::Value>> RunDecoder(
Ort::Value targets, std::vector<Ort::Value> states) const;
std::vector<Ort::Value> GetDecoderInitStates() const;
/** Run the joint network.
*
* @param encoder_out Output of the encoder network.
* @param decoder_out Output of the decoder network.
* @return Return a tensor of shape (N, 1, 1, vocab_size) containing logits.
*/
Ort::Value RunJoiner(Ort::Value encoder_out, Ort::Value decoder_out) const;
/** We send this number of feature frames to the encoder at a time. */
int32_t ChunkSize() const;
/** Number of input frames to discard after each call to RunEncoder.
*
* For instance, if we have 30 frames, chunk_size=8, chunk_shift=6.
*
* In the first call of RunEncoder, we use frames 0~7 since chunk_size is 8.
* Then we discard frame 0~5 since chunk_shift is 6.
* In the second call of RunEncoder, we use frames 6~13; and then we discard
* frames 6~11.
* In the third call of RunEncoder, we use frames 12~19; and then we discard
* frames 12~16.
*
* Note: ChunkSize() - ChunkShift() == right context size
*/
int32_t ChunkShift() const;
/** Return the subsampling factor of the model.
*/
int32_t SubsamplingFactor() const;
int32_t VocabSize() const;
/** Return an allocator for allocating memory
*/
OrtAllocator *Allocator() const;
// Possible values:
// - per_feature
// - all_features (not implemented yet)
// - fixed_mean (not implemented)
// - fixed_std (not implemented)
// - or just leave it to empty
// See
// https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/parts/preprocessing/features.py#L59
// for details
std::string FeatureNormalizationMethod() const;
private:
class Impl;
std::unique_ptr<Impl> impl_;
};
} // namespace sherpa_onnx
#endif // SHERPA_ONNX_CSRC_ONLINE_TRANSDUCER_NEMO_MODEL_H_