33 lines
1.2 KiB
Python
33 lines
1.2 KiB
Python
import torch
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from vencoder.encoder import SpeechEncoder
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from vencoder.wavlm.WavLM import WavLM, WavLMConfig
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class WavLMBasePlus(SpeechEncoder):
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def __init__(self, vec_path="pretrain/WavLM-Base+.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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checkpoint = torch.load(vec_path)
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self.cfg = WavLMConfig(checkpoint['cfg'])
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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.hidden_dim = self.cfg.encoder_embed_dim
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self.model = WavLM(self.cfg)
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self.model.load_state_dict(checkpoint['model'])
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self.model.to(self.dev).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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if self.cfg.normalize:
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feats = torch.nn.functional.layer_norm(feats, feats.shape)
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with torch.no_grad():
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with torch.inference_mode():
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units = self.model.extract_features(feats[None, :])[0]
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return units.transpose(1, 2)
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