Refactor the code (#15)

* code refactoring

* Remove reference files

* Update README and CI

* small fixes

* fix style issues

* add style check for CI

* fix style issues

* remove kaldi-native-io

sherpa-onnx/csrc/sherpa-onnx.cc

@@ -0,0 +1,129 @@
+/**
+ * Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+ *
+ * See LICENSE for clarification regarding multiple authors
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <iostream>
+#include <string>
+#include <vector>
+
+#include "kaldi-native-fbank/csrc/online-feature.h"
+#include "sherpa-onnx/csrc/decode.h"
+#include "sherpa-onnx/csrc/rnnt-model.h"
+#include "sherpa-onnx/csrc/symbol-table.h"
+#include "sherpa-onnx/csrc/wave-reader.h"
+
+/** Compute fbank features of the input wave filename.
+ *
+ * @param wav_filename. Path to a mono wave file.
+ * @param expected_sampling_rate  Expected sampling rate of the input wave file.
+ * @param num_frames On return, it contains the number of feature frames.
+ * @return Return the computed feature of shape (num_frames, feature_dim)
+ *         stored in row-major.
+ */
+static std::vector<float> ComputeFeatures(const std::string &wav_filename,
+                                          float expected_sampling_rate,
+                                          int32_t *num_frames) {
+  std::vector<float> samples =
+      sherpa_onnx::ReadWave(wav_filename, expected_sampling_rate);
+
+  float duration = samples.size() / expected_sampling_rate;
+
+  std::cout << "wav filename: " << wav_filename << "\n";
+  std::cout << "wav duration (s): " << duration << "\n";
+
+  knf::FbankOptions opts;
+  opts.frame_opts.dither = 0;
+  opts.frame_opts.snip_edges = false;
+  opts.frame_opts.samp_freq = expected_sampling_rate;
+
+  int32_t feature_dim = 80;
+
+  opts.mel_opts.num_bins = feature_dim;
+
+  knf::OnlineFbank fbank(opts);
+  fbank.AcceptWaveform(expected_sampling_rate, samples.data(), samples.size());
+  fbank.InputFinished();
+
+  *num_frames = fbank.NumFramesReady();
+
+  std::vector<float> features(*num_frames * feature_dim);
+  float *p = features.data();
+
+  for (int32_t i = 0; i != fbank.NumFramesReady(); ++i, p += feature_dim) {
+    const float *f = fbank.GetFrame(i);
+    std::copy(f, f + feature_dim, p);
+  }
+
+  return features;
+}
+
+int main(int32_t argc, char *argv[]) {
+  if (argc < 8 || argc > 9) {
+    const char *usage = R"usage(
+Usage:
+  ./bin/sherpa-onnx \
+    /path/to/tokens.txt \
+    /path/to/encoder.onnx \
+    /path/to/decoder.onnx \
+    /path/to/joiner.onnx \
+    /path/to/joiner_encoder_proj.ncnn.param \
+    /path/to/joiner_decoder_proj.ncnn.param \
+    /path/to/foo.wav [num_threads]
+
+You can download pre-trained models from the following repository:
+https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13
+)usage";
+    std::cerr << usage << "\n";
+
+    return 0;
+  }
+
+  std::string tokens = argv[1];
+  std::string encoder = argv[2];
+  std::string decoder = argv[3];
+  std::string joiner = argv[4];
+  std::string joiner_encoder_proj = argv[5];
+  std::string joiner_decoder_proj = argv[6];
+  std::string wav_filename = argv[7];
+  int32_t num_threads = 4;
+  if (argc == 9) {
+    num_threads = atoi(argv[8]);
+  }
+
+  sherpa_onnx::SymbolTable sym(tokens);
+
+  int32_t num_frames;
+  auto features = ComputeFeatures(wav_filename, 16000, &num_frames);
+  int32_t feature_dim = features.size() / num_frames;
+
+  sherpa_onnx::RnntModel model(encoder, decoder, joiner, joiner_encoder_proj,
+                               joiner_decoder_proj, num_threads);
+  Ort::Value encoder_out =
+      model.RunEncoder(features.data(), num_frames, feature_dim);
+
+  auto hyp = sherpa_onnx::GreedySearch(model, encoder_out);
+
+  std::string text;
+  for (auto i : hyp) {
+    text += sym[i];
+  }
+
+  std::cout << "Recognition result for " << wav_filename << "\n"
+            << text << "\n";
+
+  return 0;
+}