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)