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| import onnxruntime | |
| import torch | |
| from vencoder.encoder import SpeechEncoder | |
| class ContentVec256L9_Onnx(SpeechEncoder): | |
| def __init__(self, vec_path="pretrain/vec-256-layer-9.onnx", device=None): | |
| super().__init__() | |
| print("load model(s) from {}".format(vec_path)) | |
| self.hidden_dim = 256 | |
| 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).cpu().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).to(self.dev) | |