Support non-streaming WeNet CTC models. (#426)

sherpa-onnx/csrc/offline-wenet-ctc-model.h

@@ -0,0 +1,79 @@
+// sherpa-onnx/csrc/offline-wenet-ctc-model.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+#ifndef SHERPA_ONNX_CSRC_OFFLINE_WENET_CTC_MODEL_H_
+#define SHERPA_ONNX_CSRC_OFFLINE_WENET_CTC_MODEL_H_
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+#if __ANDROID_API__ >= 9
+#include "android/asset_manager.h"
+#include "android/asset_manager_jni.h"
+#endif
+
+#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 CTC model from WeNet.
+ *
+ * See
+ * https://github.com/k2-fsa/sherpa-onnx/blob/master/scripts/wenet/export-onnx.py
+ * https://github.com/k2-fsa/sherpa-onnx/blob/master/scripts/wenet/test-onnx.py
+ * https://github.com/k2-fsa/sherpa-onnx/blob/master/scripts/wenet/run.sh
+ *
+ */
+class OfflineWenetCtcModel : public OfflineCtcModel {
+ public:
+  explicit OfflineWenetCtcModel(const OfflineModelConfig &config);
+
+#if __ANDROID_API__ >= 9
+  OfflineWenetCtcModel(AAssetManager *mgr, const OfflineModelConfig &config);
+#endif
+
+  ~OfflineWenetCtcModel() override;
+
+  /** 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
+   */
+  std::vector<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;
+
+  // WeNet CTC models do not support batch size > 1
+  bool SupportBatchProcessing() const override { return false; }
+
+ private:
+  class Impl;
+  std::unique_ptr<Impl> impl_;
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_OFFLINE_WENET_CTC_MODEL_H_