offline-ctc-model.h
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// sherpa-onnx/csrc/offline-ctc-model.h
//
// Copyright (c) 2022-2023 Xiaomi Corporation
#ifndef SHERPA_ONNX_CSRC_OFFLINE_CTC_MODEL_H_
#define SHERPA_ONNX_CSRC_OFFLINE_CTC_MODEL_H_
#include <memory>
#include <string>
#include <vector>
#include "onnxruntime_cxx_api.h" // NOLINT
#include "sherpa-onnx/csrc/offline-model-config.h"
namespace sherpa_onnx {
class OfflineCtcModel {
public:
virtual ~OfflineCtcModel() = default;
static std::unique_ptr<OfflineCtcModel> Create(
const OfflineModelConfig &config);
template <typename Manager>
static std::unique_ptr<OfflineCtcModel> Create(
Manager *mgr, const OfflineModelConfig &config);
/** Run the forward method of the model.
*
* @param features A tensor of shape (N, T, C).
* @param features_length A 1-D tensor of shape (N,) containing number of
* valid frames in `features` before padding.
* Its dtype is int64_t.
*
* @return Return a vector containing:
* - log_probs: A 3-D tensor of shape (N, T', vocab_size).
* - log_probs_length A 1-D tensor of shape (N,). Its dtype is int64_t
*/
virtual std::vector<Ort::Value> Forward(Ort::Value features,
Ort::Value features_length) = 0;
/** Return the vocabulary size of the model
*/
virtual int32_t VocabSize() const = 0;
/** SubsamplingFactor of the model
*
* For NeMo Citrinet, the subsampling factor is usually 4.
* For NeMo Conformer CTC, the subsampling factor is usually 8.
*/
virtual int32_t SubsamplingFactor() const { return 1; }
/** Return an allocator for allocating memory
*/
virtual OrtAllocator *Allocator() const = 0;
/** For some models, e.g., those from NeMo, they require some preprocessing
* for the features.
*/
virtual std::string FeatureNormalizationMethod() const { return {}; }
// Return true if the model supports batch size > 1
virtual bool SupportBatchProcessing() const { return true; }
// return true for models from https://github.com/salute-developers/GigaAM
// return false otherwise
virtual bool IsGigaAM() const { return false; }
// For Dolphin models, they use global CMVN
virtual void NormalizeFeatures(float *features, int32_t num_frames,
int32_t feat_dim) const {}
};
} // namespace sherpa_onnx
#endif // SHERPA_ONNX_CSRC_OFFLINE_CTC_MODEL_H_