import sys,os sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) import numpy as np import argparse import torch from whisper.model import Whisper, ModelDimensions from whisper.audio import load_audio, pad_or_trim, log_mel_spectrogram def load_model(path) -> Whisper: device = "cuda" if torch.cuda.is_available() else "cpu" checkpoint = torch.load(path, map_location=device) dims = ModelDimensions(**checkpoint["dims"]) model = Whisper(dims) model.load_state_dict(checkpoint["model_state_dict"]) return model.to(device) def pred_ppg(whisper: Whisper, wavPath, ppgPath): audio = load_audio(wavPath) audln = audio.shape[0] ppg_a = [] idx_s = 0 while (idx_s + 25 * 16000 < audln): short = audio[idx_s:idx_s + 25 * 16000] idx_s = idx_s + 25 * 16000 ppgln = 25 * 16000 // 320 # short = pad_or_trim(short) mel = log_mel_spectrogram(short).to(whisper.device) with torch.no_grad(): ppg = whisper.encoder(mel.unsqueeze(0)).squeeze().data.cpu().float().numpy() ppg = ppg[:ppgln,] # [length, dim=1024] ppg_a.extend(ppg) if (idx_s < audln): short = audio[idx_s:audln] ppgln = (audln - idx_s) // 320 # short = pad_or_trim(short) mel = log_mel_spectrogram(short).to(whisper.device) with torch.no_grad(): ppg = whisper.encoder(mel.unsqueeze(0)).squeeze().data.cpu().float().numpy() ppg = ppg[:ppgln,] # [length, dim=1024] ppg_a.extend(ppg) np.save(ppgPath, ppg_a, allow_pickle=False) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.description = 'please enter embed parameter ...' parser.add_argument("-w", "--wav", help="wav", dest="wav") parser.add_argument("-p", "--ppg", help="ppg", dest="ppg") args = parser.parse_args() print(args.wav) print(args.ppg) wavPath = args.wav ppgPath = args.ppg whisper = load_model(os.path.join("whisper_pretrain", "medium.pt")) pred_ppg(whisper, wavPath, ppgPath)