60 lines
1.8 KiB
C++
60 lines
1.8 KiB
C++
// sherpa-onnx/csrc/offline-ctc-model.h
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//
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// Copyright (c) 2022-2023 Xiaomi Corporation
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#ifndef SHERPA_ONNX_CSRC_OFFLINE_CTC_MODEL_H_
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#define SHERPA_ONNX_CSRC_OFFLINE_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 "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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class OfflineCtcModel {
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public:
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virtual ~OfflineCtcModel() = default;
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static std::unique_ptr<OfflineCtcModel> Create(
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const OfflineModelConfig &config);
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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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virtual std::pair<Ort::Value, Ort::Value> Forward(
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Ort::Value features, Ort::Value features_length) = 0;
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/** Return the vocabulary size of the model
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*/
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virtual int32_t VocabSize() const = 0;
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/** SubsamplingFactor of the model
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*
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* For NeMo Citrinet, the subsampling factor is usually 4.
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* For NeMo Conformer CTC, the subsampling factor is usually 8.
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*/
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virtual int32_t SubsamplingFactor() const { return 1; }
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/** Return an allocator for allocating memory
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*/
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virtual OrtAllocator *Allocator() const = 0;
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/** For some models, e.g., those from NeMo, they require some preprocessing
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* for the features.
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*/
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virtual std::string FeatureNormalizationMethod() const { return {}; }
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};
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} // namespace sherpa_onnx
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#endif // SHERPA_ONNX_CSRC_OFFLINE_CTC_MODEL_H_
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