import torch def get_model(): import whisper model = whisper.load_model("small", device='cpu') return model.encoder def get_content(model=None, wav_16k_tensor=None): from whisper import log_mel_spectrogram, pad_or_trim dev = next(model.parameters()).device mel = log_mel_spectrogram(wav_16k_tensor).to(dev)[:, :3000] # if torch.cuda.is_available(): # mel = mel.to(torch.float16) feature_len = mel.shape[-1] // 2 assert mel.shape[-1] < 3000, "输入音频过长,只允许输入30以内音频" with torch.no_grad(): feature = model(pad_or_trim(mel, 3000).unsqueeze(0))[:1, :feature_len, :].transpose(1,2) return feature