Created
          November 26, 2022 17:09 
        
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    get_data_to_buffer with f0 processing
  
        
  
    
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  | def get_data_to_buffer(train_config): | |
| buffer = list() | |
| text = process_text(train_config.data_path) | |
| audio_files = sorted(Path(train_config.audio_path).iterdir()) | |
| hop_length = 256 | |
| frame_period = hop_length / 22_050 * 1000 | |
| start = time.perf_counter() | |
| for i, file in tqdm(zip(range(len(text)), audio_files)): | |
| mel_gt_name = os.path.join( | |
| train_config.mel_ground_truth, "ljspeech-mel-%05d.npy" % (i+1)) | |
| mel_gt_target = np.load(mel_gt_name) | |
| duration = np.load(os.path.join( | |
| train_config.alignment_path, str(i)+".npy")) | |
| character = text[i][0:len(text[i])-1] | |
| character = np.array( | |
| text_to_sequence(character, train_config.text_cleaners)) | |
| wav, sr = librosa.load(file, sr=None, dtype=np.float64) | |
| f0, t = pw.dio(wav, sr, frame_period=frame_period) | |
| assert f0.shape[0] == mel_gt_target.shape[0] | |
| character = torch.from_numpy(character) | |
| duration = torch.from_numpy(duration) | |
| mel_gt_target = torch.from_numpy(mel_gt_target) | |
| f0 = torch.from_numpy(f0) | |
| buffer.append({"text": character, "duration": duration, | |
| "mel_target": mel_gt_target, "f0": f0}) | |
| end = time.perf_counter() | |
| print("cost {:.2f}s to load all data into buffer.".format(end-start)) | |
| return buffer | 
  
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