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Zero
File size: 6,205 Bytes
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import os
from pathlib import Path
from typing import Optional, Union
from PIL import Image
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 torchvision.utils import save_image
from transformers import AutoProcessor
import torch.nn.functional as F
import numpy as np
import logging
log = logging.getLogger()
_CLIP_SIZE = 224
_CLIP_FPS = 8.0
_SYNC_SIZE = 224
_SYNC_FPS = 25.0
class Audio_Text(Dataset):
def __init__(
self,
root: Union[str, Path],
*,
tsv_path: Union[str, Path] = 'dataset/vggsound/split_txt/train_caption.csv',
sample_rate: int = 44_100,
duration_sec: float = 9.0,
audio_samples: Optional[int] = 397312,
normalize_audio: bool = False,
start_row: Optional[int] = None,
end_row: Optional[int] = None,
save_dir: str = 'data/vggsound/video_latents_text/train'
):
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
videos = []
self.labels = []
self.videos = []
self.cots = []
missing_videos = []
# read the tsv for subset information
df_list = pd.read_csv(tsv_path, sep=',', dtype={'id': str}).to_dict('records')
# 控制处理的行范围
if start_row is not None and end_row is not None:
df_list = df_list[start_row:end_row]
for record in df_list:
id = record['id']
if os.path.exists(f'{save_dir}/{id}.pth'): continue
label = record['caption']
# if id in videos:
self.labels.append(label)
# print(label,'debug1!!!!!!!!!')
self.cots.append(record['caption_cot'])
# self.labels[id] = label
self.videos.append(id)
# else:
# missing_videos.append(id)
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 = self.audio_samples
self.resampler = {}
def sample(self, idx: int):
video_id = self.videos[idx]
label = self.labels[idx]
cot = self.cots[idx]
audio_path = os.path.join(self.root, f'{video_id}.wav')
if not os.path.exists(audio_path):
audio_path = os.path.join(self.root, f'{video_id}.flac')
if not os.path.exists(audio_path):
raise RuntimeError(f'Audio is not exist {audio_path}')
audio_chunk, sample_rate = torchaudio.load(audio_path)
if len(audio_chunk.shape) != 2:
raise RuntimeError(f'error audio shape {video_id}')
abs_max = audio_chunk[0].abs().max()
if abs_max <= 1e-6:
if audio_chunk.shape[0] > 1 and audio_chunk[1].abs().max() > 1e-6:
audio_chunk = audio_chunk[1:2]
else:
raise RuntimeError(f'Audio is silent {video_id}')
# ensure the stereo audio
if audio_chunk.shape[0] < 2:
audio_chunk = audio_chunk.repeat(2, 1)
elif audio_chunk.shape[0] > 2:
audio_chunk = audio_chunk[:2]
# 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)
if audio_chunk.shape[1] < self.expected_audio_length:
# zero-padding audio
padding_length = self.expected_audio_length - audio_chunk.shape[1]
# 创建 padding 张量，大小为 [batch_size, padding_length]，值为0
padding = torch.zeros(audio_chunk.shape[0], padding_length)
# 将原始音频和 padding 沿第 1 维度拼接在一起
audio_chunk = torch.cat((audio_chunk, padding), dim=1)
# raise RuntimeError(f'Audio too short {video_id}')
audio_chunk = audio_chunk[:,:self.expected_audio_length]
assert audio_chunk.shape == (2, 397312), f'error shape:{video_id},{audio_chunk.shape}'
# print(label,'debug2!!!!!!!!!')
data = {
'id': video_id,
'caption': label,
'caption_cot': cot,
'audio': audio_chunk,
}
return data
def __getitem__(self, idx: int):
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)
# dataset = VGGSound(
# root="data/vggsound/video/train",
# tsv_path="data/vggsound/split_txt/temp.csv",
# sample_rate=44100,
# duration_sec=9.0,
# audio_samples=397312,
# start_row=0,
# end_row=None,
# save_dir="data/vggsound/video_224_latents_text/train"
# )
# dataset[0] |