stuff and more stuff
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DeepBeepMeep
0
postprocessing/mmaudio/data/extraction/__init__.py
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postprocessing/mmaudio/data/extraction/__init__.py
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postprocessing/mmaudio/data/extraction/vgg_sound.py
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postprocessing/mmaudio/data/extraction/vgg_sound.py
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import logging
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import os
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from pathlib import Path
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from typing import Optional, Union
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import pandas as pd
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import torch
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import torchaudio
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from torch.utils.data.dataset import Dataset
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from torchvision.transforms import v2
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from torio.io import StreamingMediaDecoder
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from ...utils.dist_utils import local_rank
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log = logging.getLogger()
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_CLIP_SIZE = 384
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_CLIP_FPS = 8.0
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_SYNC_SIZE = 224
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_SYNC_FPS = 25.0
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class VGGSound(Dataset):
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def __init__(
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self,
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root: Union[str, Path],
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*,
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tsv_path: Union[str, Path] = 'sets/vgg3-train.tsv',
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sample_rate: int = 16_000,
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duration_sec: float = 8.0,
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audio_samples: Optional[int] = None,
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normalize_audio: bool = False,
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):
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self.root = Path(root)
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self.normalize_audio = normalize_audio
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if audio_samples is None:
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self.audio_samples = int(sample_rate * duration_sec)
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else:
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self.audio_samples = audio_samples
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effective_duration = audio_samples / sample_rate
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# make sure the duration is close enough, within 15ms
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assert abs(effective_duration - duration_sec) < 0.015, \
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f'audio_samples {audio_samples} does not match duration_sec {duration_sec}'
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videos = sorted(os.listdir(self.root))
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videos = set([Path(v).stem for v in videos]) # remove extensions
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self.labels = {}
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self.videos = []
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missing_videos = []
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# read the tsv for subset information
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df_list = pd.read_csv(tsv_path, sep='\t', dtype={'id': str}).to_dict('records')
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for record in df_list:
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id = record['id']
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label = record['label']
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if id in videos:
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self.labels[id] = label
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self.videos.append(id)
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else:
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missing_videos.append(id)
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if local_rank == 0:
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log.info(f'{len(videos)} videos found in {root}')
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log.info(f'{len(self.videos)} videos found in {tsv_path}')
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log.info(f'{len(missing_videos)} videos missing in {root}')
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self.sample_rate = sample_rate
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self.duration_sec = duration_sec
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self.expected_audio_length = audio_samples
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self.clip_expected_length = int(_CLIP_FPS * self.duration_sec)
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self.sync_expected_length = int(_SYNC_FPS * self.duration_sec)
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self.clip_transform = v2.Compose([
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v2.Resize((_CLIP_SIZE, _CLIP_SIZE), interpolation=v2.InterpolationMode.BICUBIC),
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v2.ToImage(),
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v2.ToDtype(torch.float32, scale=True),
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])
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self.sync_transform = v2.Compose([
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v2.Resize(_SYNC_SIZE, interpolation=v2.InterpolationMode.BICUBIC),
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v2.CenterCrop(_SYNC_SIZE),
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v2.ToImage(),
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v2.ToDtype(torch.float32, scale=True),
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v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
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])
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self.resampler = {}
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def sample(self, idx: int) -> dict[str, torch.Tensor]:
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video_id = self.videos[idx]
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label = self.labels[video_id]
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reader = StreamingMediaDecoder(self.root / (video_id + '.mp4'))
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reader.add_basic_video_stream(
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frames_per_chunk=int(_CLIP_FPS * self.duration_sec),
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frame_rate=_CLIP_FPS,
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format='rgb24',
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)
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reader.add_basic_video_stream(
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frames_per_chunk=int(_SYNC_FPS * self.duration_sec),
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frame_rate=_SYNC_FPS,
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format='rgb24',
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)
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reader.add_basic_audio_stream(frames_per_chunk=2**30, )
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reader.fill_buffer()
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data_chunk = reader.pop_chunks()
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clip_chunk = data_chunk[0]
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sync_chunk = data_chunk[1]
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audio_chunk = data_chunk[2]
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if clip_chunk is None:
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raise RuntimeError(f'CLIP video returned None {video_id}')
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if clip_chunk.shape[0] < self.clip_expected_length:
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raise RuntimeError(
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f'CLIP video too short {video_id}, expected {self.clip_expected_length}, got {clip_chunk.shape[0]}'
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)
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if sync_chunk is None:
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raise RuntimeError(f'Sync video returned None {video_id}')
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if sync_chunk.shape[0] < self.sync_expected_length:
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raise RuntimeError(
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f'Sync video too short {video_id}, expected {self.sync_expected_length}, got {sync_chunk.shape[0]}'
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)
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# process audio
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sample_rate = int(reader.get_out_stream_info(2).sample_rate)
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audio_chunk = audio_chunk.transpose(0, 1)
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audio_chunk = audio_chunk.mean(dim=0) # mono
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if self.normalize_audio:
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abs_max = audio_chunk.abs().max()
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audio_chunk = audio_chunk / abs_max * 0.95
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if abs_max <= 1e-6:
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raise RuntimeError(f'Audio is silent {video_id}')
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# resample
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if sample_rate == self.sample_rate:
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audio_chunk = audio_chunk
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else:
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if sample_rate not in self.resampler:
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# https://pytorch.org/audio/stable/tutorials/audio_resampling_tutorial.html#kaiser-best
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self.resampler[sample_rate] = torchaudio.transforms.Resample(
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sample_rate,
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self.sample_rate,
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lowpass_filter_width=64,
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rolloff=0.9475937167399596,
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resampling_method='sinc_interp_kaiser',
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beta=14.769656459379492,
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)
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audio_chunk = self.resampler[sample_rate](audio_chunk)
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if audio_chunk.shape[0] < self.expected_audio_length:
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raise RuntimeError(f'Audio too short {video_id}')
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audio_chunk = audio_chunk[:self.expected_audio_length]
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# truncate the video
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clip_chunk = clip_chunk[:self.clip_expected_length]
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if clip_chunk.shape[0] != self.clip_expected_length:
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raise RuntimeError(f'CLIP video wrong length {video_id}, '
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f'expected {self.clip_expected_length}, '
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f'got {clip_chunk.shape[0]}')
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clip_chunk = self.clip_transform(clip_chunk)
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sync_chunk = sync_chunk[:self.sync_expected_length]
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if sync_chunk.shape[0] != self.sync_expected_length:
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raise RuntimeError(f'Sync video wrong length {video_id}, '
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f'expected {self.sync_expected_length}, '
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f'got {sync_chunk.shape[0]}')
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sync_chunk = self.sync_transform(sync_chunk)
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data = {
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'id': video_id,
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'caption': label,
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'audio': audio_chunk,
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'clip_video': clip_chunk,
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'sync_video': sync_chunk,
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}
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return data
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def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
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try:
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return self.sample(idx)
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except Exception as e:
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log.error(f'Error loading video {self.videos[idx]}: {e}')
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return None
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def __len__(self):
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return len(self.labels)
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132
postprocessing/mmaudio/data/extraction/wav_dataset.py
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postprocessing/mmaudio/data/extraction/wav_dataset.py
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import logging
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import os
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from pathlib import Path
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from typing import Union
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import open_clip
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import pandas as pd
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import torch
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import torchaudio
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from torch.utils.data.dataset import Dataset
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log = logging.getLogger()
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class WavTextClipsDataset(Dataset):
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def __init__(
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self,
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root: Union[str, Path],
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*,
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captions_tsv: Union[str, Path],
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clips_tsv: Union[str, Path],
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sample_rate: int,
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num_samples: int,
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normalize_audio: bool = False,
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reject_silent: bool = False,
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tokenizer_id: str = 'ViT-H-14-378-quickgelu',
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):
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self.root = Path(root)
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self.sample_rate = sample_rate
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self.num_samples = num_samples
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self.normalize_audio = normalize_audio
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self.reject_silent = reject_silent
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self.tokenizer = open_clip.get_tokenizer(tokenizer_id)
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audios = sorted(os.listdir(self.root))
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audios = set([
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Path(audio).stem for audio in audios
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if audio.endswith('.wav') or audio.endswith('.flac')
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])
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self.captions = {}
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# read the caption tsv
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df_list = pd.read_csv(captions_tsv, sep='\t', dtype={'id': str}).to_dict('records')
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for record in df_list:
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id = record['id']
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caption = record['caption']
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self.captions[id] = caption
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# read the clip tsv
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df_list = pd.read_csv(clips_tsv, sep='\t', dtype={
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'id': str,
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'name': str
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}).to_dict('records')
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self.clips = []
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for record in df_list:
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record['id'] = record['id']
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record['name'] = record['name']
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id = record['id']
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name = record['name']
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if name not in self.captions:
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log.warning(f'Audio {name} not found in {captions_tsv}')
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continue
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record['caption'] = self.captions[name]
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self.clips.append(record)
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log.info(f'Found {len(self.clips)} audio files in {self.root}')
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self.resampler = {}
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def __getitem__(self, idx: int) -> torch.Tensor:
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try:
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clip = self.clips[idx]
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audio_name = clip['name']
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audio_id = clip['id']
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caption = clip['caption']
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start_sample = clip['start_sample']
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end_sample = clip['end_sample']
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audio_path = self.root / f'{audio_name}.flac'
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if not audio_path.exists():
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audio_path = self.root / f'{audio_name}.wav'
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assert audio_path.exists()
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audio_chunk, sample_rate = torchaudio.load(audio_path)
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audio_chunk = audio_chunk.mean(dim=0) # mono
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abs_max = audio_chunk.abs().max()
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if self.normalize_audio:
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audio_chunk = audio_chunk / abs_max * 0.95
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if self.reject_silent and abs_max < 1e-6:
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log.warning(f'Rejecting silent audio')
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return None
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audio_chunk = audio_chunk[start_sample:end_sample]
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# resample
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if sample_rate == self.sample_rate:
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audio_chunk = audio_chunk
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else:
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if sample_rate not in self.resampler:
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# https://pytorch.org/audio/stable/tutorials/audio_resampling_tutorial.html#kaiser-best
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self.resampler[sample_rate] = torchaudio.transforms.Resample(
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sample_rate,
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self.sample_rate,
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lowpass_filter_width=64,
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rolloff=0.9475937167399596,
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resampling_method='sinc_interp_kaiser',
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beta=14.769656459379492,
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)
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audio_chunk = self.resampler[sample_rate](audio_chunk)
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if audio_chunk.shape[0] < self.num_samples:
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raise ValueError('Audio is too short')
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audio_chunk = audio_chunk[:self.num_samples]
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tokens = self.tokenizer([caption])[0]
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output = {
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'waveform': audio_chunk,
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'id': audio_id,
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'caption': caption,
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'tokens': tokens,
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}
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return output
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except Exception as e:
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log.error(f'Error reading {audio_path}: {e}')
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return None
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def __len__(self):
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return len(self.clips)
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