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from vencoder.encoder import SpeechEncoder |
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import onnxruntime |
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import torch |
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class ContentVec256L12_Onnx(SpeechEncoder): |
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def __init__(self,vec_path = "pretrain/vec-256-layer-12.onnx",device=None): |
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print("load model(s) from {}".format(vec_path)) |
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self.hidden_dim = 256 |
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if device is None: |
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self.dev = torch.device("cpu") |
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else: |
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self.dev = torch.device(device) |
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if device == 'cpu' or device == torch.device("cpu") or device is None: |
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providers = ['CPUExecutionProvider'] |
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elif device == 'cuda' or device == torch.device("cuda"): |
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providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] |
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self.model = onnxruntime.InferenceSession(vec_path, providers=providers) |
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def encoder(self, wav): |
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feats = wav |
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if feats.dim() == 2: |
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feats = feats.mean(-1) |
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assert feats.dim() == 1, feats.dim() |
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feats = feats.view(1, -1) |
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feats = feats.unsqueeze(0).cpu().detach().numpy() |
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onnx_input = {self.model.get_inputs()[0].name: feats} |
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logits = self.model.run(None, onnx_input) |
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return torch.tensor(logits[0]).transpose(1, 2).to(self.dev) |
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