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