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| import os | |
| import torch | |
| import torchaudio | |
| from torch.utils.data import Dataset | |
| from utils.audio import LogMelSpectrogram | |
| from config import MelConfig | |
| class VocosDataset(Dataset): | |
| def __init__(self, filelist_path, segment_size: int, mel_config: MelConfig): | |
| self.filelist_path = filelist_path | |
| self.segment_size = segment_size | |
| self.mel_extractor = LogMelSpectrogram(mel_config) | |
| self.filelist = self._load_filelist(filelist_path) | |
| def _load_filelist(self, filelist_path): | |
| with open(filelist_path, 'r', encoding='utf-8') as f: | |
| filelist = [line.strip() for line in f if os.path.exists(line.strip())] | |
| return filelist | |
| def __len__(self): | |
| return len(self.filelist) | |
| def __getitem__(self, idx): | |
| audio, _ = torchaudio.load(self.filelist[idx]) | |
| # select a random segment from the audio file | |
| # audio is validated in the preprocess stage, so we skip checking sample_rate and padding short audio | |
| start_index = torch.randint(0, audio.size(-1) - self.segment_size + 1, (1,)).item() | |
| audio = audio[:, start_index:start_index + self.segment_size] # shape: [1, segment_size] | |
| mel = self.mel_extractor(audio).squeeze(0) # shape: [n_mels, segment_size // hop_length] | |
| return audio, mel |