34 lines
1.1 KiB
Python
34 lines
1.1 KiB
Python
import onnxruntime
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import torch
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from vencoder.encoder import SpeechEncoder
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class HubertSoft_Onnx(SpeechEncoder):
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def __init__(self, vec_path="pretrain/hubert-soft.onnx", device=None):
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super().__init__()
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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 == 'cuda' or device == torch.device("cuda"):
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providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
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else:
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providers = ['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: # double channels
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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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