38 lines
1.3 KiB
Python
38 lines
1.3 KiB
Python
import torch
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from fairseq import checkpoint_utils
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from vencoder.encoder import SpeechEncoder
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class ContentVec768L12(SpeechEncoder):
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def __init__(self, vec_path="pretrain/checkpoint_best_legacy_500.pt", device=None):
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super().__init__()
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print("load model(s) from {}".format(vec_path))
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self.hidden_dim = 768
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models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
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[vec_path],
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suffix="",
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)
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if device is None:
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self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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else:
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self.dev = torch.device(device)
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self.model = models[0].to(self.dev)
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self.model.eval()
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def encoder(self, wav):
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feats = wav
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if feats.dim() == 2: # double channels
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feats = feats.mean(-1)
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assert feats.dim() == 1, feats.dim()
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feats = feats.view(1, -1)
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padding_mask = torch.BoolTensor(feats.shape).fill_(False)
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inputs = {
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"source": feats.to(wav.device),
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"padding_mask": padding_mask.to(wav.device),
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"output_layer": 12, # layer 12
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}
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with torch.no_grad():
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logits = self.model.extract_features(**inputs)
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return logits[0].transpose(1, 2)
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