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