File size: 5,258 Bytes
4aed451 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 | """Pick a random tar file from each of three HF datasets and extract 6 random
audio samples per dataset.
Datasets:
1. mitermix/audioset-with-grounded-captions (mp3 in audio-dataset-XXXXXX.tar)
2. laion/captioned-ai-music-snippets (mp3 in suno-dataset-XXXXXX.tar)
3. TTS-AGI/majestrino-unified-detailed-captions-temporal
(flac in data/XXXXX.tar)
Output: ./samples/<dataset_short_name>/<key>.<ext> (only the audio file is kept,
the tar archive is deleted afterwards to save disk).
"""
from __future__ import annotations
import os
import random
import shutil
import sys
import tarfile
from pathlib import Path
from huggingface_hub import HfFileSystem, hf_hub_download
ROOT = Path(__file__).resolve().parent.parent
SAMPLES_DIR = ROOT / "samples"
SAMPLES_DIR.mkdir(parents=True, exist_ok=True)
# Reproducibility
random.seed(20260411)
DATASETS = [
{
"repo_id": "mitermix/audioset-with-grounded-captions",
"short": "audioset",
"tar_glob": "*.tar", # tars are at repo root
},
{
"repo_id": "laion/captioned-ai-music-snippets",
"short": "music",
"tar_glob": "*.tar",
},
{
"repo_id": "TTS-AGI/majestrino-unified-detailed-captions-temporal",
"short": "majestrino",
"tar_glob": "data/*.tar", # tars under data/
},
]
AUDIO_EXTS = {".mp3", ".flac", ".wav", ".ogg", ".m4a", ".opus"}
SAMPLES_PER_DATASET = 6
def list_tars(repo_id: str, glob_pat: str) -> list[str]:
"""Return relative tar paths inside the dataset repo."""
fs = HfFileSystem()
prefix = f"datasets/{repo_id}/"
found = fs.glob(prefix + glob_pat)
return [p[len(prefix):] for p in found]
def pick_audio_members(tar_path: Path, k: int) -> list[tarfile.TarInfo]:
with tarfile.open(tar_path, "r") as tf:
members = [m for m in tf.getmembers()
if m.isfile() and Path(m.name).suffix.lower() in AUDIO_EXTS]
if not members:
raise RuntimeError(f"No audio members found in {tar_path}")
if len(members) <= k:
return members
return random.sample(members, k)
def extract_members(tar_path: Path, members: list[tarfile.TarInfo],
out_dir: Path, short: str) -> list[Path]:
out_paths: list[Path] = []
out_dir.mkdir(parents=True, exist_ok=True)
with tarfile.open(tar_path, "r") as tf:
for m in members:
with tf.extractfile(m) as src:
if src is None:
continue
# Flatten and prefix with the dataset short name to avoid
# collisions across tars.
base = Path(m.name).name
stem = Path(base).stem
ext = Path(base).suffix.lower()
out_path = out_dir / f"{short}__{stem}{ext}"
with open(out_path, "wb") as dst:
shutil.copyfileobj(src, dst)
out_paths.append(out_path)
return out_paths
def main() -> int:
manifest_lines = []
for ds in DATASETS:
repo_id = ds["repo_id"]
short = ds["short"]
out_dir = SAMPLES_DIR / short
existing = sorted(out_dir.glob("*")) if out_dir.exists() else []
existing_audio = [p for p in existing if p.suffix.lower() in AUDIO_EXTS]
if len(existing_audio) >= SAMPLES_PER_DATASET:
print(f"[skip] {short}: already have {len(existing_audio)} samples")
for p in existing_audio[:SAMPLES_PER_DATASET]:
manifest_lines.append(f"{short}\t{p}")
continue
print(f"[list] {repo_id}: looking up tar files...")
tars = list_tars(repo_id, ds["tar_glob"])
if not tars:
print(f"[error] No tar files found in {repo_id}")
continue
print(f" found {len(tars)} tar files")
chosen = random.choice(tars)
print(f"[pick] {chosen}")
print(f"[download] {repo_id}::{chosen}")
local_tar = hf_hub_download(
repo_id=repo_id,
filename=chosen,
repo_type="dataset",
cache_dir=str(SAMPLES_DIR / "_cache"),
)
local_tar = Path(local_tar)
print(f" -> {local_tar} ({local_tar.stat().st_size/1e6:.1f} MB)")
print(f"[extract] picking {SAMPLES_PER_DATASET} random audio members")
members = pick_audio_members(local_tar, SAMPLES_PER_DATASET)
out_paths = extract_members(local_tar, members, out_dir, short)
for p in out_paths:
print(f" {p}")
manifest_lines.append(f"{short}\t{p}")
# Reclaim disk: drop the tar and the cache
try:
local_tar.unlink()
# also drop empty parent dirs in the cache
cache_dir = SAMPLES_DIR / "_cache"
if cache_dir.exists():
shutil.rmtree(cache_dir, ignore_errors=True)
except OSError:
pass
manifest_path = SAMPLES_DIR / "manifest.tsv"
manifest_path.write_text("\n".join(manifest_lines) + "\n")
print(f"\nWrote manifest with {len(manifest_lines)} samples to {manifest_path}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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