add cosyvoice code

tools/extract_speech_token.py

@@ -0,0 +1,61 @@
+#!/usr/bin/env python3
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
+#
+# 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.
+import argparse
+import logging
+import torch
+from tqdm import tqdm
+import onnxruntime
+import numpy as np
+import torchaudio
+import whisper
+
+
+def main(args):
+    utt2wav = {}
+    with open('{}/wav.scp'.format(args.dir)) as f:
+        for l in f:
+            l = l.replace('\n', '').split()
+            utt2wav[l[0]] = l[1]
+
+    option = onnxruntime.SessionOptions()
+    option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
+    option.intra_op_num_threads = 1
+    providers = ["CUDAExecutionProvider"]
+    ort_session = onnxruntime.InferenceSession(args.onnx_path, sess_options=option, providers=providers)
+
+    utt2speech_token = {}
+    for utt in tqdm(utt2wav.keys()):
+        audio, sample_rate = torchaudio.load(utt2wav[utt])
+        if sample_rate != 16000:
+            audio = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(audio)
+        if audio.shape[1] / 16000 > 30:
+            logging.warning('do not support extract speech token for audio longer than 30s')
+            speech_token = []
+        else:
+            feat = whisper.log_mel_spectrogram(audio, n_mels=128)
+            speech_token = ort_session.run(None, {ort_session.get_inputs()[0].name: feat.detach().cpu().numpy(),
+                                                  ort_session.get_inputs()[1].name: np.array([feat.shape[2]], dtype=np.int32)})[0].flatten().tolist()
+        utt2speech_token[utt] = speech_token
+    torch.save(utt2speech_token, '{}/utt2speech_token.pt'.format(args.dir))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--dir',
+                        type=str)
+    parser.add_argument('--onnx_path',
+                        type=str)
+    args = parser.parse_args()
+    main(args)