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vencoder/ContentVec768L12_Onnx.py

@@ -1 +1,28 @@
+from vencoder.encoder import SpeechEncoder
+import onnxruntime
+import torch
 
+class ContentVec768L12_Onnx(SpeechEncoder):
+    def __init__(self,vec_path = "pretrain/vec-768-layer-12.onnx",device=None):
+        print("load model(s) from {}".format(vec_path))
+        self.hidden_dim = 768
+        if device is None:
+            self.dev = torch.device("cpu")
+        else:
+            self.dev = torch.device(device)
+        if device == 'cpu' or device == torch.device("cpu") or device is None:
+            providers = ['CPUExecutionProvider']
+        elif device == 'cuda' or device == torch.device("cuda"):
+            providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
+        self.model = onnxruntime.InferenceSession(vec_path, providers=providers)
+
+    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)
+        feats = feats.unsqueeze(0).detach().numpy()
+        onnx_input = {self.model.get_inputs()[0].name: feats}
+        logits = self.model.run(None, onnx_input)
+        return torch.tensor(logits[0]).transpose(1, 2)