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-greedy-search-decoder.cc

@@ -0,0 +1,54 @@
+// sherpa-onnx/csrc/offline-ctc-greedy-search-decoder.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/offline-ctc-greedy-search-decoder.h"
+
+#include <algorithm>
+#include <utility>
+#include <vector>
+
+#include "sherpa-onnx/csrc/macros.h"
+
+namespace sherpa_onnx {
+
+std::vector<OfflineCtcDecoderResult> OfflineCtcGreedySearchDecoder::Decode(
+    Ort::Value log_probs, Ort::Value log_probs_length) {
+  std::vector<int64_t> shape = log_probs.GetTensorTypeAndShapeInfo().GetShape();
+  int32_t batch_size = static_cast<int32_t>(shape[0]);
+  int32_t num_frames = static_cast<int32_t>(shape[1]);
+  int32_t vocab_size = static_cast<int32_t>(shape[2]);
+
+  const int64_t *p_log_probs_length = log_probs_length.GetTensorData<int64_t>();
+
+  std::vector<OfflineCtcDecoderResult> ans;
+  ans.reserve(batch_size);
+
+  for (int32_t b = 0; b != batch_size; ++b) {
+    const float *p_log_probs =
+        log_probs.GetTensorData<float>() + b * num_frames * vocab_size;
+
+    OfflineCtcDecoderResult r;
+    int64_t prev_id = -1;
+
+    for (int32_t t = 0; t != static_cast<int32_t>(p_log_probs_length[b]); ++t) {
+      auto y = static_cast<int64_t>(std::distance(
+          static_cast<const float *>(p_log_probs),
+          std::max_element(
+              static_cast<const float *>(p_log_probs),
+              static_cast<const float *>(p_log_probs) + vocab_size)));
+      p_log_probs += vocab_size;
+
+      if (y != blank_id_ && y != prev_id) {
+        r.tokens.push_back(y);
+        r.timestamps.push_back(t);
+        prev_id = y;
+      }
+    }  // for (int32_t t = 0; ...)
+
+    ans.push_back(std::move(r));
+  }
+  return ans;
+}
+
+}  // namespace sherpa_onnx