Merge pull request #404 from FunAudioLLM/dev/lyuxiang.lx

Dev/lyuxiang.lx

tools/extract_embedding.py

@@ -13,14 +13,50 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import argparse
+from concurrent.futures import ThreadPoolExecutor, as_completed
+import onnxruntime
 import torch
 import torchaudio
-from tqdm import tqdm
-import onnxruntime
 import torchaudio.compliance.kaldi as kaldi
+from tqdm import tqdm
+
+
+def single_job(utt):
+    audio, sample_rate = torchaudio.load(utt2wav[utt])
+    if sample_rate != 16000:
+        audio = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(audio)
+    feat = kaldi.fbank(audio,
+                       num_mel_bins=80,
+                       dither=0,
+                       sample_frequency=16000)
+    feat = feat - feat.mean(dim=0, keepdim=True)
+    embedding = ort_session.run(None, {ort_session.get_inputs()[0].name: feat.unsqueeze(dim=0).cpu().numpy()})[0].flatten().tolist()
+    return utt, embedding
 
 
 def main(args):
+    all_task = [executor.submit(single_job, utt) for utt in utt2wav.keys()]
+    utt2embedding, spk2embedding = {}, {}
+    for future in tqdm(as_completed(all_task)):
+        utt, embedding = future.result()
+        utt2embedding[utt] = embedding
+        spk = utt2spk[utt]
+        if spk not in spk2embedding:
+            spk2embedding[spk] = []
+        spk2embedding[spk].append(embedding)
+    for k, v in spk2embedding.items():
+        spk2embedding[k] = torch.tensor(v).mean(dim=0).tolist()
+    torch.save(utt2embedding, "{}/utt2embedding.pt".format(args.dir))
+    torch.save(spk2embedding, "{}/spk2embedding.pt".format(args.dir))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--dir", type=str)
+    parser.add_argument("--onnx_path", type=str)
+    parser.add_argument("--num_thread", type=int, default=8)
+    args = parser.parse_args()
+
     utt2wav, utt2spk = {}, {}
     with open('{}/wav.scp'.format(args.dir)) as f:
         for l in f:
@@ -36,35 +72,6 @@ def main(args):
     option.intra_op_num_threads = 1
     providers = ["CPUExecutionProvider"]
     ort_session = onnxruntime.InferenceSession(args.onnx_path, sess_options=option, providers=providers)
+    executor = ThreadPoolExecutor(max_workers=args.num_thread)
 
-    utt2embedding, spk2embedding = {}, {}
-    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)
-        feat = kaldi.fbank(audio,
-                           num_mel_bins=80,
-                           dither=0,
-                           sample_frequency=16000)
-        feat = feat - feat.mean(dim=0, keepdim=True)
-        embedding = ort_session.run(None, {ort_session.get_inputs()[0].name: feat.unsqueeze(dim=0).cpu().numpy()})[0].flatten().tolist()
-        utt2embedding[utt] = embedding
-        spk = utt2spk[utt]
-        if spk not in spk2embedding:
-            spk2embedding[spk] = []
-        spk2embedding[spk].append(embedding)
-    for k, v in spk2embedding.items():
-        spk2embedding[k] = torch.tensor(v).mean(dim=0).tolist()
-
-    torch.save(utt2embedding, '{}/utt2embedding.pt'.format(args.dir))
-    torch.save(spk2embedding, '{}/spk2embedding.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)

tools/extract_speech_token.py

@@ -13,6 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import argparse
+from concurrent.futures import ThreadPoolExecutor, as_completed
 import logging
 import torch
 from tqdm import tqdm
@@ -22,7 +23,36 @@ import torchaudio
 import whisper
 
 
+def single_job(utt):
+    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()
+    return utt, speech_token
+
+
 def main(args):
+    all_task = [executor.submit(single_job, utt) for utt in utt2wav.keys()]
+    utt2speech_token = {}
+    for future in tqdm(as_completed(all_task)):
+        utt, speech_token = future.result()
+        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)
+    parser.add_argument("--num_thread", type=int, default=8)
+    args = parser.parse_args()
+
     utt2wav = {}
     with open('{}/wav.scp'.format(args.dir)) as f:
         for l in f:
@@ -34,28 +64,6 @@ def main(args):
     option.intra_op_num_threads = 1
     providers = ["CUDAExecutionProvider"]
     ort_session = onnxruntime.InferenceSession(args.onnx_path, sess_options=option, providers=providers)
+    executor = ThreadPoolExecutor(max_workers=args.num_thread)
 
-    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)