chore: vendor selva_core from jnwnlee/selva@d7d40a9
Pure PyTorch SelVA source for SelvaModelLoader/FeatureExtractor/Sampler nodes. Imports rewritten from selva.* to selva_core.*. mel_converter.py: replaced librosa.filters.mel with pure-numpy implementation to avoid librosa→numba→NumPy version incompatibility in some ComfyUI environments. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
@@ -0,0 +1,194 @@
|
||||
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 selva_core.data.av_utils import normalize_video_chunk
|
||||
from selva_core.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',
|
||||
audio_required: bool = True,
|
||||
sample_rate: int = 16_000,
|
||||
duration_sec: float = 8.0,
|
||||
audio_samples: Optional[int] = None,
|
||||
normalize_audio: bool = False,
|
||||
clip_video_required: bool = True,
|
||||
):
|
||||
self.root = Path(root)
|
||||
self.audio_required = audio_required
|
||||
if audio_required:
|
||||
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}'
|
||||
self.clip_video_required = clip_video_required
|
||||
|
||||
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)
|
||||
|
||||
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
|
||||
|
||||
if audio_required:
|
||||
self.expected_audio_length = self.audio_samples
|
||||
self.sync_expected_length = int(_SYNC_FPS * self.duration_sec)
|
||||
if clip_video_required:
|
||||
self.clip_expected_length = int(_CLIP_FPS * self.duration_sec)
|
||||
|
||||
self.sync_transform = v2.Compose([
|
||||
v2.Resize((_SYNC_SIZE, _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]),
|
||||
])
|
||||
|
||||
if clip_video_required:
|
||||
self.clip_transform = v2.Compose([
|
||||
v2.Resize((_CLIP_SIZE, _CLIP_SIZE), interpolation=v2.InterpolationMode.BICUBIC),
|
||||
v2.ToImage(),
|
||||
v2.ToDtype(torch.float32, scale=True),
|
||||
])
|
||||
if audio_required:
|
||||
self.resampler = {}
|
||||
|
||||
def sample(self, idx: int) -> dict[str, torch.Tensor]:
|
||||
video_id = self.videos[idx]
|
||||
|
||||
label = self.labels[video_id]
|
||||
|
||||
reader = StreamingMediaDecoder(self.root / (video_id + '.mp4'))
|
||||
reader.add_basic_video_stream(
|
||||
frames_per_chunk=int(_SYNC_FPS * self.duration_sec),
|
||||
frame_rate=_SYNC_FPS,
|
||||
format='rgb24',
|
||||
)
|
||||
if self.audio_required:
|
||||
reader.add_basic_audio_stream(frames_per_chunk=2**30, )
|
||||
if self.clip_video_required:
|
||||
reader.add_basic_video_stream(
|
||||
frames_per_chunk=int(_CLIP_FPS * self.duration_sec),
|
||||
frame_rate=_CLIP_FPS,
|
||||
format='rgb24',
|
||||
)
|
||||
|
||||
reader.fill_buffer()
|
||||
data_chunk = reader.pop_chunks()
|
||||
|
||||
sync_chunk = data_chunk[0]
|
||||
if sync_chunk is None:
|
||||
raise RuntimeError(f'Sync video returned None {video_id}')
|
||||
sync_chunk = normalize_video_chunk(sync_chunk, self.sync_expected_length,
|
||||
n_tolerance_frame=3, desc=video_id)
|
||||
sync_chunk = self.sync_transform(sync_chunk)
|
||||
|
||||
if self.audio_required:
|
||||
audio_chunk = data_chunk[1]
|
||||
|
||||
if self.clip_video_required:
|
||||
clip_chunk = data_chunk[2 if self.audio_required else 1]
|
||||
if clip_chunk is None:
|
||||
raise RuntimeError(f'CLIP video returned None {video_id}')
|
||||
clip_chunk = normalize_video_chunk(clip_chunk, self.clip_expected_length,
|
||||
n_tolerance_frame=1, desc=video_id)
|
||||
clip_chunk = self.clip_transform(clip_chunk)
|
||||
|
||||
# process audio
|
||||
if self.audio_required:
|
||||
sample_rate = int(reader.get_out_stream_info(1).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 * (0.95 / abs_max)
|
||||
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)
|
||||
|
||||
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]
|
||||
|
||||
data = {
|
||||
'id': video_id,
|
||||
'caption': label,
|
||||
'sync_video': sync_chunk,
|
||||
}
|
||||
|
||||
if self.audio_required:
|
||||
data['audio'] = audio_chunk
|
||||
if self.clip_video_required:
|
||||
data['clip_video'] = clip_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)
|
||||
@@ -0,0 +1,129 @@
|
||||
import logging
|
||||
import os
|
||||
from pathlib import Path
|
||||
from typing import Union
|
||||
|
||||
import open_clip
|
||||
import pandas as pd
|
||||
import torch
|
||||
import torchaudio
|
||||
from torch.utils.data.dataset import Dataset
|
||||
|
||||
log = logging.getLogger()
|
||||
|
||||
|
||||
class WavTextClipsDataset(Dataset):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
root: Union[str, Path],
|
||||
*,
|
||||
captions_tsv: Union[str, Path],
|
||||
clips_tsv: Union[str, Path],
|
||||
sample_rate: int,
|
||||
num_samples: int,
|
||||
normalize_audio: bool = False,
|
||||
reject_silent: bool = False,
|
||||
tokenizer_id: str = 'ViT-H-14-378-quickgelu',
|
||||
):
|
||||
self.root = Path(root)
|
||||
self.sample_rate = sample_rate
|
||||
self.num_samples = num_samples
|
||||
self.normalize_audio = normalize_audio
|
||||
self.reject_silent = reject_silent
|
||||
self.tokenizer = open_clip.get_tokenizer(tokenizer_id)
|
||||
|
||||
audios = sorted(os.listdir(self.root))
|
||||
audios = set([
|
||||
Path(audio).stem for audio in audios
|
||||
if audio.endswith('.wav') or audio.endswith('.flac')
|
||||
])
|
||||
self.captions = {}
|
||||
|
||||
# read the caption tsv
|
||||
df_list = pd.read_csv(captions_tsv, sep='\t', dtype={'id': str}).to_dict('records')
|
||||
for record in df_list:
|
||||
id = record['id']
|
||||
caption = record['caption']
|
||||
self.captions[id] = caption
|
||||
|
||||
# read the clip tsv
|
||||
df_list = pd.read_csv(clips_tsv, sep='\t', dtype={
|
||||
'id': str,
|
||||
'name': str
|
||||
}).to_dict('records')
|
||||
self.clips = []
|
||||
for record in df_list:
|
||||
record['id'] = record['id']
|
||||
record['name'] = record['name']
|
||||
id = record['id']
|
||||
name = record['name']
|
||||
record['caption'] = self.captions[name]
|
||||
self.clips.append(record)
|
||||
|
||||
log.info(f'Found {len(self.clips)} audio files in {self.root}')
|
||||
|
||||
self.resampler = {}
|
||||
|
||||
def __getitem__(self, idx: int) -> torch.Tensor:
|
||||
try:
|
||||
clip = self.clips[idx]
|
||||
audio_name = clip['name']
|
||||
audio_id = clip['id']
|
||||
caption = clip['caption']
|
||||
start_sample = clip['start_sample']
|
||||
end_sample = clip['end_sample']
|
||||
|
||||
audio_path = self.root / f'{audio_name}.flac'
|
||||
if not audio_path.exists():
|
||||
audio_path = self.root / f'{audio_name}.wav'
|
||||
assert audio_path.exists()
|
||||
|
||||
audio_chunk, sample_rate = torchaudio.load(audio_path)
|
||||
audio_chunk = audio_chunk.mean(dim=0) # mono
|
||||
abs_max = audio_chunk.abs().max()
|
||||
if self.normalize_audio:
|
||||
audio_chunk = audio_chunk / abs_max * 0.95
|
||||
|
||||
if self.reject_silent and abs_max < 1e-6:
|
||||
log.warning(f'Rejecting silent audio')
|
||||
return None
|
||||
|
||||
audio_chunk = audio_chunk[start_sample:end_sample]
|
||||
|
||||
# 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[0] < self.num_samples:
|
||||
raise ValueError('Audio is too short')
|
||||
audio_chunk = audio_chunk[:self.num_samples]
|
||||
|
||||
tokens = self.tokenizer([caption])[0]
|
||||
|
||||
output = {
|
||||
'waveform': audio_chunk,
|
||||
'id': audio_id,
|
||||
'caption': caption,
|
||||
'tokens': tokens,
|
||||
}
|
||||
|
||||
return output
|
||||
except Exception as e:
|
||||
log.error(f'Error reading {audio_path}: {e}')
|
||||
return None
|
||||
|
||||
def __len__(self):
|
||||
return len(self.clips)
|
||||
Reference in New Issue
Block a user