Mv folder

utils.py

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+import torch
+import tempfile
+import torchaudio
+from typing import List
+
+torchaudio.set_audio_backend("soundfile")  # switch backend
+
+
+def read_batch(audio_paths: List[str]):
+    return [read_audio(audio_path)
+            for audio_path
+            in audio_paths]
+
+
+def split_into_batches(lst: List[str],
+                       batch_size: int = 10):
+    return [lst[i:i + batch_size]
+            for i in
+            range(0, len(lst), batch_size)]
+
+
+def read_audio(path: str,
+               target_sr: int = 16000):
+
+    assert torchaudio.get_audio_backend() == 'soundfile'
+    wav, sr = torchaudio.load(path)
+
+    if wav.size(0) > 1:
+        wav = wav.mean(dim=0, keepdim=True)
+
+    if sr != target_sr:
+        transform = torchaudio.transforms.Resample(orig_freq=sr,
+                                                   new_freq=target_sr)
+        wav = transform(wav)
+        sr = target_sr
+
+    assert sr == target_sr
+    return wav.squeeze(0)
+
+
+def prepare_model_input(batch: List[torch.Tensor],
+                        device=torch.device('cpu')):
+    max_seqlength = max(max([len(_) for _ in batch]), 12800)
+    inputs = torch.zeros(len(batch), max_seqlength)
+    for i, wav in enumerate(batch):
+        inputs[i, :len(wav)].copy_(wav)
+    inputs = inputs.to(device)
+    return inputs
+
+
+def init_jit_model(model_url: str,
+                   device: torch.device = torch.device('cpu')):
+    torch.set_grad_enabled(False)
+    with tempfile.NamedTemporaryFile('wb', suffix='.model') as f:
+        torch.hub.download_url_to_file(model_url,
+                                       f.name,
+                                       progress=True)
+        model = torch.jit.load(f.name, map_location=device)
+        model.eval()
+    return model