Update ContentVec768L12_Onnx.py

vencoder/ContentVec768L12_Onnx.py

@@ -1,34 +1 @@
-from vencoder.encoder import SpeechEncoder
-import torch
-from fairseq import checkpoint_utils
 
-class ContentVec768L12(SpeechEncoder):
-    def __init__(self,vec_path = "pretrain/checkpoint_best_legacy_500.pt",device=None):
-        print("load model(s) from {}".format(vec_path))
-        self.hidden_dim = 768
-        models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-          [vec_path],
-          suffix="",
-        )
-        if device is None:
-            self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        else:
-            self.dev = torch.device(device)
-        self.model = models[0].to(self.dev)
-        self.model.eval()
-
-    def encoder(self, wav):
-        feats = wav
-        if feats.dim() == 2:  # double channels
-          feats = feats.mean(-1)
-        assert feats.dim() == 1, feats.dim()
-        feats = feats.view(1, -1)
-        padding_mask = torch.BoolTensor(feats.shape).fill_(False)
-        inputs = {
-          "source": feats.to(wav.device),
-          "padding_mask": padding_mask.to(wav.device),
-          "output_layer": 12,  # layer 12
-        }
-        with torch.no_grad():
-          logits = self.model.extract_features(**inputs)
-        return logits[0].transpose(1, 2)