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.inference_mode(): units = self.model.units(feats) return units.transpose(1,2)