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from vencoder.encoder import SpeechEncoder |
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import torch |
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from vencoder.hubert import hubert_model |
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class HubertSoft(SpeechEncoder): |
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def __init__(self,vec_path = "pretrain/hubert-soft-0d54a1f4.pt",device=None): |
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print("load model(s) from {}".format(vec_path)) |
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hubert_soft = hubert_model.hubert_soft(vec_path) |
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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 = 256 |
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self.model = hubert_soft.to(self.dev) |
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def encoder(self, wav): |
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feats = wav |
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if feats.dim() == 2: |
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feats = feats.mean(-1) |
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assert feats.dim() == 1, feats.dim() |
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feats = feats[None,None,:] |
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with torch.inference_mode(): |
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units = self.model.units(feats) |
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return units.transpose(1,2) |
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