30 lines
1.0 KiB
Python
30 lines
1.0 KiB
Python
import torch
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from vencoder.dphubert.model import wav2vec2_model
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from vencoder.encoder import SpeechEncoder
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class DPHubert(SpeechEncoder):
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def __init__(self, vec_path="pretrain/DPHuBERT-sp0.75.pth", device=None):
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super().__init__()
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print("load model(s) from {}".format(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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ckpt = torch.load(vec_path)
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self.hidden_dim = 768
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self.model = wav2vec2_model(**ckpt["config"]).to(self.dev)
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self.model.load_state_dict(ckpt["state_dict"], strict=False)
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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[None, :]
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with torch.no_grad():
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with torch.inference_mode():
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units = self.model(feats)[0]
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return units.transpose(1,2)
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