| import torch | |
| from resemblyzer import VoiceEncoder | |
| class ResemblyzerVoiceEncoder: | |
| def __init__(self, device) -> None: | |
| self.model = VoiceEncoder(device) | |
| def __call__(self, audio: torch.Tensor): | |
| if audio.ndimension() == 1: | |
| return torch.tensor(self.model.embed_utterance(audio.numpy())).float().cpu() | |
| else: | |
| e = torch.stack([torch.tensor(self.model.embed_utterance(audio[i,:].numpy())).float().cpu() | |
| for i in range(audio.shape[0])]) | |
| return e | |