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import logging
import os
from pathlib import Path
from typing import Optional, Union
import pandas as pd
import torch
import torchaudio
from torch.utils.data.dataset import Dataset
from torchvision.transforms import v2
from torio.io import StreamingMediaDecoder
from mmaudio.utils.dist_utils import local_rank
log = logging.getLogger()
_CLIP_SIZE = 384
_CLIP_FPS = 8.0
_SYNC_SIZE = 224
_SYNC_FPS = 25.0
class VGGSound(Dataset):
def __init__(
self,
root: Union[str, Path],
*,
tsv_path: Union[str, Path] = 'sets/vgg3-train.tsv',
sample_rate: int = 16_000,
duration_sec: float = 8.0,
audio_samples: Optional[int] = None,
normalize_audio: bool = False,
):
self.root = Path(root)
self.normalize_audio = normalize_audio
if audio_samples is None:
self.audio_samples = int(sample_rate * duration_sec)
else:
self.audio_samples = audio_samples
effective_duration = audio_samples / sample_rate
# make sure the duration is close enough, within 15ms
assert abs(effective_duration - duration_sec) < 0.015, \
f'audio_samples {audio_samples} does not match duration_sec {duration_sec}'
####videos = sorted(os.listdir(self.root))
####videos = set([Path(v).stem for v in videos]) # remove extensions
####self.labels = {}
####self.videos = []
####missing_videos = []
####
##### read the tsv for subset information
####df_list = pd.read_csv(tsv_path, sep='\t', dtype={'id': str}).to_dict('records')
####for record in df_list:
#### id = record['id']
#### label = record['label']
#### if id in videos:
#### self.labels[id] = label
#### self.videos.append(id)
#### else:
#### missing_videos.append(id)
self.labels = []
self.videos = []
with open(tsv_path, "r") as fr:
for line in fr.readlines():
video_path, label = line.strip().split("\t")
self.labels.append("the sound of " + label)
self.videos.append(video_path)
if local_rank == 0:
####log.info(f'{len(videos)} videos found in {root}')
log.info(f'{len(self.videos)} videos found in {tsv_path}')
####log.info(f'{len(missing_videos)} videos missing in {root}')
self.sample_rate = sample_rate
self.duration_sec = duration_sec
self.expected_audio_length = audio_samples
self.clip_expected_length = int(_CLIP_FPS * self.duration_sec)
self.sync_expected_length = int(_SYNC_FPS * self.duration_sec)
self.clip_transform = v2.Compose([
v2.Resize((_CLIP_SIZE, _CLIP_SIZE), interpolation=v2.InterpolationMode.BICUBIC),
v2.ToImage(),
v2.ToDtype(torch.float32, scale=True),
])
self.sync_transform = v2.Compose([
v2.Resize(_SYNC_SIZE, interpolation=v2.InterpolationMode.BICUBIC),
v2.CenterCrop(_SYNC_SIZE),
v2.ToImage(),
v2.ToDtype(torch.float32, scale=True),
v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
])
self.resampler = {}
def sample(self, idx: int) -> dict[str, torch.Tensor]:
####video_id = self.videos[idx]
####label = self.labels[video_id]
video_path = Path(self.videos[idx]).expanduser()
label = self.labels[idx]
video_id = video_path.stem
####reader = StreamingMediaDecoder(self.root / (video_id + '.mp4'))
reader = StreamingMediaDecoder(video_path)
reader.add_basic_video_stream(
frames_per_chunk=int(_CLIP_FPS * self.duration_sec),
frame_rate=_CLIP_FPS,
format='rgb24',
)
reader.add_basic_video_stream(
frames_per_chunk=int(_SYNC_FPS * self.duration_sec),
frame_rate=_SYNC_FPS,
format='rgb24',
)
reader.add_basic_audio_stream(frames_per_chunk=2**30, )
reader.fill_buffer()
data_chunk = reader.pop_chunks()
clip_chunk = data_chunk[0]
sync_chunk = data_chunk[1]
audio_chunk = data_chunk[2]
if clip_chunk is None:
raise RuntimeError(f'CLIP video returned None {video_id}')
####repeat = self.clip_expected_length // clip_chunk.shape[0] + 2
####clip_chunk = clip_chunk.repeat(repeat,1,1,1)
if clip_chunk.shape[0] < self.clip_expected_length:
raise RuntimeError(
f'CLIP video too short {video_id}, expected {self.clip_expected_length}, got {clip_chunk.shape[0]}'
)
if sync_chunk is None:
raise RuntimeError(f'Sync video returned None {video_id}')
####sync_chunk = sync_chunk.repeat(repeat,1,1,1)
if sync_chunk.shape[0] < self.sync_expected_length:
raise RuntimeError(
f'Sync video too short {video_id}, expected {self.sync_expected_length}, got {sync_chunk.shape[0]}'
)
# process audio
sample_rate = int(reader.get_out_stream_info(2).sample_rate)
audio_chunk = audio_chunk.transpose(0, 1)
audio_chunk = audio_chunk.mean(dim=0) # mono
if self.normalize_audio:
abs_max = audio_chunk.abs().max()
audio_chunk = audio_chunk / abs_max * 0.95
if abs_max <= 1e-6:
raise RuntimeError(f'Audio is silent {video_id}')
# resample
if sample_rate == self.sample_rate:
audio_chunk = audio_chunk
else:
if sample_rate not in self.resampler:
# https://pytorch.org/audio/stable/tutorials/audio_resampling_tutorial.html#kaiser-best
self.resampler[sample_rate] = torchaudio.transforms.Resample(
sample_rate,
self.sample_rate,
lowpass_filter_width=64,
rolloff=0.9475937167399596,
resampling_method='sinc_interp_kaiser',
beta=14.769656459379492,
)
audio_chunk = self.resampler[sample_rate](audio_chunk)
####audio_chunk = audio_chunk.repeat(repeat)
if audio_chunk.shape[0] < self.expected_audio_length:
raise RuntimeError(f'Audio too short {video_id}')
audio_chunk = audio_chunk[:self.expected_audio_length]
# truncate the video
clip_chunk = clip_chunk[:self.clip_expected_length]
if clip_chunk.shape[0] != self.clip_expected_length:
raise RuntimeError(f'CLIP video wrong length {video_id}, '
f'expected {self.clip_expected_length}, '
f'got {clip_chunk.shape[0]}')
clip_chunk = self.clip_transform(clip_chunk)
sync_chunk = sync_chunk[:self.sync_expected_length]
if sync_chunk.shape[0] != self.sync_expected_length:
raise RuntimeError(f'Sync video wrong length {video_id}, '
f'expected {self.sync_expected_length}, '
f'got {sync_chunk.shape[0]}')
sync_chunk = self.sync_transform(sync_chunk)
data = {
'id': video_id,
'caption': label,
'audio': audio_chunk,
'clip_video': clip_chunk,
'sync_video': sync_chunk,
}
return data
def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
try:
return self.sample(idx)
except Exception as e:
log.error(f'Error loading video {self.videos[idx]}: {e}')
return None
def __len__(self):
return len(self.labels)
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