from vencoder.encoder import SpeechEncoder import torch from vencoder.whisper.model import Whisper, ModelDimensions from vencoder.whisper.audio import pad_or_trim, log_mel_spectrogram class WhisperPPG(SpeechEncoder): def __init__(self,vec_path = "pretrain/medium.pt",device=None): if device is None: self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu") else: self.dev = torch.device(device) checkpoint = torch.load(vec_path, map_location=device) dims = ModelDimensions(**checkpoint["dims"]) model = Whisper(dims) model.load_state_dict(checkpoint["model_state_dict"]) self.hidden_dim = dims self.model = model.to(self.dev) def encoder(self, wav): audio = wav audln = audio.shape[0] ppgln = audln // 320 audio = pad_or_trim(audio) mel = log_mel_spectrogram(audio).to(self.dev) with torch.no_grad(): ppg = self.model.encoder(mel.unsqueeze(0)).squeeze().data.cpu().float().numpy() ppg = torch.FloatTensor(ppg[:ppgln,]).to(self.dev) return ppg[None,:,:].transpose(1, 2)