// 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 #include #include #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 Create( const OfflineModelConfig &config); template static std::unique_ptr 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 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; } }; } // namespace sherpa_onnx #endif // SHERPA_ONNX_CSRC_OFFLINE_CTC_MODEL_H_