|
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: |
|
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) |
|
|
