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-ctc-decoder.h

@@ -0,0 +1,42 @@
+// sherpa-onnx/csrc/offline-ctc-decoder.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_CSRC_OFFLINE_CTC_DECODER_H_
+#define SHERPA_ONNX_CSRC_OFFLINE_CTC_DECODER_H_
+
+#include <vector>
+
+#include "onnxruntime_cxx_api.h"  // NOLINT
+
+namespace sherpa_onnx {
+
+struct OfflineCtcDecoderResult {
+  /// The decoded token IDs
+  std::vector<int64_t> tokens;
+
+  /// timestamps[i] contains the output frame index where tokens[i] is decoded.
+  /// Note: The index is after subsampling
+  std::vector<int32_t> timestamps;
+};
+
+class OfflineCtcDecoder {
+ public:
+  virtual ~OfflineCtcDecoder() = default;
+
+  /** Run CTC decoding given the output from the encoder model.
+   *
+   * @param log_probs A 3-D tensor of shape (N, T, vocab_size) containing
+   *                  lob_probs.
+   * @param log_probs_length A 1-D tensor of shape (N,) containing number
+   *                         of valid frames in log_probs before padding.
+   *
+   * @return Return a vector of size `N` containing the decoded results.
+   */
+  virtual std::vector<OfflineCtcDecoderResult> Decode(
+      Ort::Value log_probs, Ort::Value log_probs_length) = 0;
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_OFFLINE_CTC_DECODER_H_