Spaces:
Running
Running
| import logging | |
| from pathlib import Path | |
| from typing import Union | |
| import pandas as pd | |
| import torch | |
| from tensordict import TensorDict | |
| from torch.utils.data.dataset import Dataset | |
| from mmaudio.utils.dist_utils import local_rank | |
| log = logging.getLogger() | |
| class ExtractedAudio(Dataset): | |
| def __init__( | |
| self, | |
| tsv_path: Union[str, Path], | |
| *, | |
| premade_mmap_dir: Union[str, Path], | |
| data_dim: dict[str, int], | |
| ): | |
| super().__init__() | |
| self.data_dim = data_dim | |
| self.df_list = pd.read_csv(tsv_path, sep='\t').to_dict('records') | |
| self.ids = [str(d['id']) for d in self.df_list] | |
| log.info(f'Loading precomputed mmap from {premade_mmap_dir}') | |
| # load precomputed memory mapped tensors | |
| premade_mmap_dir = Path(premade_mmap_dir) | |
| td = TensorDict.load_memmap(premade_mmap_dir) | |
| log.info(f'Loaded precomputed mmap from {premade_mmap_dir}') | |
| self.mean = td['mean'] | |
| self.std = td['std'] | |
| self.text_features = td['text_features'] | |
| log.info(f'Loaded {len(self)} samples from {premade_mmap_dir}.') | |
| log.info(f'Loaded mean: {self.mean.shape}.') | |
| log.info(f'Loaded std: {self.std.shape}.') | |
| log.info(f'Loaded text features: {self.text_features.shape}.') | |
| assert self.mean.shape[1] == self.data_dim['latent_seq_len'], \ | |
| f'{self.mean.shape[1]} != {self.data_dim["latent_seq_len"]}' | |
| assert self.std.shape[1] == self.data_dim['latent_seq_len'], \ | |
| f'{self.std.shape[1]} != {self.data_dim["latent_seq_len"]}' | |
| assert self.text_features.shape[1] == self.data_dim['text_seq_len'], \ | |
| f'{self.text_features.shape[1]} != {self.data_dim["text_seq_len"]}' | |
| assert self.text_features.shape[-1] == self.data_dim['text_dim'], \ | |
| f'{self.text_features.shape[-1]} != {self.data_dim["text_dim"]}' | |
| self.fake_clip_features = torch.zeros(self.data_dim['clip_seq_len'], | |
| self.data_dim['clip_dim']) | |
| self.fake_sync_features = torch.zeros(self.data_dim['sync_seq_len'], | |
| self.data_dim['sync_dim']) | |
| self.video_exist = torch.tensor(0, dtype=torch.bool) | |
| self.text_exist = torch.tensor(1, dtype=torch.bool) | |
| def compute_latent_stats(self) -> tuple[torch.Tensor, torch.Tensor]: | |
| latents = self.mean | |
| return latents.mean(dim=(0, 1)), latents.std(dim=(0, 1)) | |
| def get_memory_mapped_tensor(self) -> TensorDict: | |
| td = TensorDict({ | |
| 'mean': self.mean, | |
| 'std': self.std, | |
| 'text_features': self.text_features, | |
| }) | |
| return td | |
| def __getitem__(self, idx: int) -> dict[str, torch.Tensor]: | |
| data = { | |
| 'id': str(self.df_list[idx]['id']), | |
| 'a_mean': self.mean[idx], | |
| 'a_std': self.std[idx], | |
| 'clip_features': self.fake_clip_features, | |
| 'sync_features': self.fake_sync_features, | |
| 'text_features': self.text_features[idx], | |
| 'caption': self.df_list[idx]['caption'], | |
| 'video_exist': self.video_exist, | |
| 'text_exist': self.text_exist, | |
| } | |
| return data | |
| def __len__(self): | |
| return len(self.ids) | |