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| from vencoder.encoder import SpeechEncoder | |
| import torch | |
| from fairseq import checkpoint_utils | |
| class ContentVec256L9(SpeechEncoder): | |
| def __init__(self,vec_path = "pretrain/checkpoint_best_legacy_500.pt",device=None): | |
| print("load model(s) from {}".format(vec_path)) | |
| models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( | |
| [vec_path], | |
| suffix="", | |
| ) | |
| self.hidden_dim = 256 | |
| if device is None: | |
| self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| else: | |
| self.dev = torch.device(device) | |
| self.model = models[0].to(self.dev) | |
| self.model.eval() | |
| def encoder(self, wav): | |
| feats = wav | |
| if feats.dim() == 2: # double channels | |
| feats = feats.mean(-1) | |
| assert feats.dim() == 1, feats.dim() | |
| feats = feats.view(1, -1) | |
| padding_mask = torch.BoolTensor(feats.shape).fill_(False) | |
| inputs = { | |
| "source": feats.to(wav.device), | |
| "padding_mask": padding_mask.to(wav.device), | |
| "output_layer": 9, # layer 9 | |
| } | |
| with torch.no_grad(): | |
| logits = self.model.extract_features(**inputs) | |
| feats = self.model.final_proj(logits[0]) | |
| return feats.transpose(1, 2) | |