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| # Copyright (c) 2023 Amphion. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import random | |
| import torch | |
| from torch.nn.utils.rnn import pad_sequence | |
| from utils.data_utils import * | |
| from models.base.base_dataset import ( | |
| BaseOfflineCollator, | |
| BaseOfflineDataset, | |
| BaseTestDataset, | |
| BaseTestCollator, | |
| ) | |
| import librosa | |
| class AutoencoderKLDataset(BaseOfflineDataset): | |
| def __init__(self, cfg, dataset, is_valid=False): | |
| BaseOfflineDataset.__init__(self, cfg, dataset, is_valid=is_valid) | |
| cfg = self.cfg | |
| # utt2melspec | |
| if cfg.preprocess.use_melspec: | |
| self.utt2melspec_path = {} | |
| for utt_info in self.metadata: | |
| dataset = utt_info["Dataset"] | |
| uid = utt_info["Uid"] | |
| utt = "{}_{}".format(dataset, uid) | |
| self.utt2melspec_path[utt] = os.path.join( | |
| cfg.preprocess.processed_dir, | |
| dataset, | |
| cfg.preprocess.melspec_dir, | |
| uid + ".npy", | |
| ) | |
| # utt2wav | |
| if cfg.preprocess.use_wav: | |
| self.utt2wav_path = {} | |
| for utt_info in self.metadata: | |
| dataset = utt_info["Dataset"] | |
| uid = utt_info["Uid"] | |
| utt = "{}_{}".format(dataset, uid) | |
| self.utt2wav_path[utt] = os.path.join( | |
| cfg.preprocess.processed_dir, | |
| dataset, | |
| cfg.preprocess.wav_dir, | |
| uid + ".wav", | |
| ) | |
| def __getitem__(self, index): | |
| # melspec: (n_mels, T) | |
| # wav: (T,) | |
| single_feature = BaseOfflineDataset.__getitem__(self, index) | |
| utt_info = self.metadata[index] | |
| dataset = utt_info["Dataset"] | |
| uid = utt_info["Uid"] | |
| utt = "{}_{}".format(dataset, uid) | |
| if self.cfg.preprocess.use_melspec: | |
| single_feature["melspec"] = np.load(self.utt2melspec_path[utt]) | |
| if self.cfg.preprocess.use_wav: | |
| wav, sr = librosa.load( | |
| self.utt2wav_path[utt], sr=16000 | |
| ) # hard coding for 16KHz... | |
| single_feature["wav"] = wav | |
| return single_feature | |
| def __len__(self): | |
| return len(self.metadata) | |
| def __len__(self): | |
| return len(self.metadata) | |
| class AutoencoderKLCollator(BaseOfflineCollator): | |
| def __init__(self, cfg): | |
| BaseOfflineCollator.__init__(self, cfg) | |
| def __call__(self, batch): | |
| # mel: (B, n_mels, T) | |
| # wav (option): (B, T) | |
| packed_batch_features = dict() | |
| for key in batch[0].keys(): | |
| if key == "melspec": | |
| packed_batch_features["melspec"] = torch.from_numpy( | |
| np.array([b["melspec"][:, :624] for b in batch]) | |
| ) | |
| if key == "wav": | |
| values = [torch.from_numpy(b[key]) for b in batch] | |
| packed_batch_features[key] = pad_sequence( | |
| values, batch_first=True, padding_value=0 | |
| ) | |
| return packed_batch_features | |
| class AutoencoderKLTestDataset(BaseTestDataset): ... | |
| class AutoencoderKLTestCollator(BaseTestCollator): ... | |