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