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from vencoder.encoder import SpeechEncoder |
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import torch |
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from fairseq import checkpoint_utils |
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class ContentVec256L9(SpeechEncoder): |
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def __init__(self,vec_path = "pretrain/checkpoint_best_legacy_500.pt",device=None): |
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print("load model(s) from {}".format(vec_path)) |
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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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self.hidden_dim = 256 |
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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: |
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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": 9, |
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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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feats = self.model.final_proj(logits[0]) |
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return feats.transpose(1, 2) |
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