Begin to support CTC models (#119)

Please see https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/nemo/index.html for a list of pre-trained CTC models from NeMo.

sherpa-onnx/csrc/offline-nemo-enc-dec-ctc-model.h

@@ -0,0 +1,75 @@
+// sherpa-onnx/csrc/offline-nemo-enc-dec-ctc-model.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+#ifndef SHERPA_ONNX_CSRC_OFFLINE_NEMO_ENC_DEC_CTC_MODEL_H_
+#define SHERPA_ONNX_CSRC_OFFLINE_NEMO_ENC_DEC_CTC_MODEL_H_
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "onnxruntime_cxx_api.h"  // NOLINT
+#include "sherpa-onnx/csrc/offline-ctc-model.h"
+#include "sherpa-onnx/csrc/offline-model-config.h"
+
+namespace sherpa_onnx {
+
+/** This class implements the EncDecCTCModelBPE model from NeMo.
+ *
+ * See
+ * https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/models/ctc_bpe_models.py
+ * https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/models/ctc_models.py
+ */
+class OfflineNemoEncDecCtcModel : public OfflineCtcModel {
+ public:
+  explicit OfflineNemoEncDecCtcModel(const OfflineModelConfig &config);
+  ~OfflineNemoEncDecCtcModel() override;
+
+  /** Run the forward method of the model.
+   *
+   * @param features  A tensor of shape (N, T, C). It is changed in-place.
+   * @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 pair 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
+   */
+  std::pair<Ort::Value, Ort::Value> Forward(
+      Ort::Value features, Ort::Value features_length) override;
+
+  /** Return the vocabulary size of the model
+   */
+  int32_t VocabSize() const override;
+
+  /** SubsamplingFactor of the model
+   *
+   * For Citrinet, the subsampling factor is usually 4.
+   * For Conformer CTC, the subsampling factor is usually 8.
+   */
+  int32_t SubsamplingFactor() const override;
+
+  /** Return an allocator for allocating memory
+   */
+  OrtAllocator *Allocator() const override;
+
+  // 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 override;
+
+ private:
+  class Impl;
+  std::unique_ptr<Impl> impl_;
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_OFFLINE_NEMO_ENC_DEC_CTC_MODEL_H_