File size: 10,880 Bytes
eca55dc | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 | #!/usr/bin/env python3
from __future__ import annotations
import argparse
import math
from concurrent.futures import Future
from concurrent.futures import ProcessPoolExecutor
from concurrent.futures import as_completed
from pathlib import Path
import numpy as np
import pandas as pd
import torchaudio
from rich.progress import BarColumn
from rich.progress import MofNCompleteColumn
from rich.progress import Progress
from rich.progress import TaskProgressColumn
from rich.progress import TextColumn
from rich.progress import TimeElapsedColumn
from rich.progress import TimeRemainingColumn
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description=(
"Estimate storage required for a WAV-only WebDataset generated from a parquet manifest."
)
)
parser.add_argument(
"parquet_path",
type=Path,
help="Path to parquet file (must include audio path column).",
)
parser.add_argument(
"--audio-column",
type=str,
default="file_path",
help="Column name containing source audio file paths.",
)
parser.add_argument(
"--duration-column",
type=str,
default="duration_sec",
help="Column name containing durations in seconds.",
)
parser.add_argument(
"--workers",
type=int,
default=24,
help="Parallel worker count used to probe missing durations.",
)
parser.add_argument(
"--sample-rate",
type=int,
default=16000,
help="Target WAV sample rate.",
)
parser.add_argument(
"--channels",
type=int,
default=1,
help="Target WAV channel count.",
)
parser.add_argument(
"--bits-per-sample",
type=int,
default=16,
choices=[8, 16, 24, 32],
help="Target WAV PCM bit depth.",
)
parser.add_argument(
"--shard-size-gb",
type=float,
default=1.0,
help="Assumed max size per .tar shard in GB (decimal) for trailer overhead estimate.",
)
parser.add_argument(
"--no-probe-missing",
action="store_true",
help="Fail if duration is missing/invalid instead of probing audio headers.",
)
return parser.parse_args()
def format_bytes(n_bytes: float) -> str:
units = ["B", "KiB", "MiB", "GiB", "TiB", "PiB"]
value = float(n_bytes)
for unit in units:
if value < 1024.0 or unit == units[-1]:
return f"{value:,.2f} {unit}"
value /= 1024.0
return f"{value:,.2f} PiB"
def estimate_shard_count(total_bytes: int, shard_size_gb: float) -> int:
shard_size_bytes = max(1, int(shard_size_gb * 1_000_000_000))
return max(1, math.ceil(total_bytes / shard_size_bytes))
def parse_duration_value(value: object) -> float:
if value is None:
return float("nan")
if isinstance(value, (int, float, np.integer, np.floating)):
return float(value)
if isinstance(value, str):
try:
return float(value)
except ValueError:
return float("nan")
try:
return float(str(value))
except (TypeError, ValueError):
return float("nan")
def probe_duration_sec(audio_path: str) -> float:
try:
info_fn = getattr(torchaudio, "info", None)
if info_fn is None:
return float("nan")
info = info_fn(audio_path)
if info.sample_rate <= 0:
return float("nan")
return float(info.num_frames) / float(info.sample_rate)
except Exception:
return float("nan")
def probe_file_size_bytes(audio_path: str) -> int:
try:
return Path(audio_path).stat().st_size
except Exception:
return -1
def probe_durations_parallel(paths: list[str], workers: int) -> list[float]:
if not paths:
return []
results: list[float] = [float("nan")] * len(paths)
with ProcessPoolExecutor(max_workers=workers) as executor:
future_to_idx: dict[Future[float], int] = {
executor.submit(probe_duration_sec, path): idx
for idx, path in enumerate(paths)
}
with Progress(
TextColumn("[bold cyan]{task.description}"),
BarColumn(),
MofNCompleteColumn(),
TaskProgressColumn(),
TimeRemainingColumn(),
TimeElapsedColumn(),
) as progress:
task_id = progress.add_task("Probing missing durations", total=len(paths))
for future in as_completed(future_to_idx):
idx = future_to_idx[future]
try:
results[idx] = future.result()
except Exception:
results[idx] = float("nan")
progress.advance(task_id, 1)
return results
def probe_file_sizes_parallel(paths: list[str], workers: int) -> list[int]:
if not paths:
return []
results: list[int] = [0] * len(paths)
with ProcessPoolExecutor(max_workers=workers) as executor:
future_to_idx: dict[Future[int], int] = {
executor.submit(probe_file_size_bytes, path): idx
for idx, path in enumerate(paths)
}
with Progress(
TextColumn("[bold cyan]{task.description}"),
BarColumn(),
MofNCompleteColumn(),
TaskProgressColumn(),
TimeRemainingColumn(),
TimeElapsedColumn(),
) as progress:
task_id = progress.add_task("Probing current file sizes", total=len(paths))
for future in as_completed(future_to_idx):
idx = future_to_idx[future]
try:
results[idx] = future.result()
except Exception:
results[idx] = -1
progress.advance(task_id, 1)
return results
def main() -> None:
args = parse_args()
if not args.parquet_path.exists():
raise FileNotFoundError(f"Parquet not found: {args.parquet_path}")
if args.workers < 1:
raise ValueError("--workers must be >= 1")
df = pd.read_parquet(args.parquet_path)
if args.audio_column not in df.columns:
raise ValueError(f"Missing audio column '{args.audio_column}' in parquet.")
if args.duration_column not in df.columns:
raise ValueError(
f"Missing duration column '{args.duration_column}' in parquet. "
"Either add it or adapt the script."
)
durations = np.array(
[parse_duration_value(value) for value in df[args.duration_column].tolist()],
dtype=np.float64,
)
paths = [str(path) for path in df[args.audio_column].tolist()]
n_rows = len(paths)
unique_paths = list(dict.fromkeys(paths))
unique_sizes = probe_file_sizes_parallel(unique_paths, workers=args.workers)
size_by_path = dict(zip(unique_paths, unique_sizes))
row_sizes = np.array([size_by_path[path] for path in paths], dtype=np.int64)
current_row_known_mask = row_sizes >= 0
current_unique_known_mask = np.array(unique_sizes, dtype=np.int64) >= 0
current_rows_total_bytes = int(row_sizes[current_row_known_mask].sum())
current_unique_total_bytes = int(
np.array(unique_sizes, dtype=np.int64)[current_unique_known_mask].sum()
)
current_rows_missing = int((~current_row_known_mask).sum())
current_unique_missing = int((~current_unique_known_mask).sum())
invalid_mask = ~np.isfinite(durations) | (durations <= 0.0)
n_missing = int(invalid_mask.sum())
if n_missing > 0:
if args.no_probe_missing:
raise ValueError(
f"Found {n_missing} rows with missing/invalid durations and --no-probe-missing was set."
)
probe_indices = np.where(invalid_mask)[0].tolist()
probe_paths = [paths[i] for i in probe_indices]
probed = probe_durations_parallel(probe_paths, workers=args.workers)
for i, duration in zip(probe_indices, probed, strict=True):
durations[i] = duration
unresolved_mask = ~np.isfinite(durations) | (durations <= 0.0)
n_unresolved = int(unresolved_mask.sum())
valid_durations = durations[~unresolved_mask]
n_valid = int(valid_durations.shape[0])
if n_valid == 0:
raise RuntimeError("No valid durations available; cannot estimate size.")
bytes_per_sample = args.bits_per_sample // 8
bytes_per_frame = args.channels * bytes_per_sample
frames = np.rint(valid_durations * args.sample_rate).astype(np.int64)
wav_file_bytes = 44 + frames * bytes_per_frame
wav_total_bytes = int(wav_file_bytes.sum())
padded_wav_data = ((wav_file_bytes + 511) // 512) * 512
tar_entry_overhead = 512 * n_valid
tar_data_bytes = int(padded_wav_data.sum())
estimated_shards = estimate_shard_count(wav_total_bytes, args.shard_size_gb)
tar_trailer_overhead = 1024 * estimated_shards
wds_tar_total_bytes = tar_entry_overhead + tar_data_bytes + tar_trailer_overhead
total_duration_sec = float(valid_durations.sum())
total_hours = total_duration_sec / 3600.0
print("=== WebDataset WAV Size Estimate ===")
print(f"Parquet: {args.parquet_path}")
print(f"Rows total: {n_rows:,}")
print(f"Rows valid: {n_valid:,}")
print(f"Rows missing/invalid duration initially: {n_missing:,}")
print(f"Rows unresolved after probe: {n_unresolved:,}")
print()
print("Current dataset volume (before conversion):")
print(
"- Referenced rows total bytes: "
f"{format_bytes(current_rows_total_bytes)} "
f"(missing rows: {current_rows_missing:,})"
)
print(
"- Unique source files total bytes: "
f"{format_bytes(current_unique_total_bytes)} "
f"(missing files: {current_unique_missing:,})"
)
print()
print(
f"Target WAV format: {args.sample_rate} Hz, {args.channels} ch, "
f"PCM {args.bits_per_sample}-bit"
)
print(f"Total audio duration: {total_hours:,.2f} h")
print()
print(f"Estimated WAV bytes (sum of .wav files): {format_bytes(wav_total_bytes)}")
print(f"Estimated WDS TAR bytes (wav-only): {format_bytes(wds_tar_total_bytes)}")
print(
f"Average WAV per sample: {format_bytes(wav_total_bytes / n_valid)}"
)
print(
f"Average WDS per sample: {format_bytes(wds_tar_total_bytes / n_valid)}"
)
print()
print(f"Assumed shard size: {args.shard_size_gb} GB")
print(f"Estimated shard count: {estimated_shards:,}")
print()
print("Shard count quick table (for WAV payload):")
for shard_size_gb in [1.0, 2.0, 5.0, 10.0]:
shard_count = estimate_shard_count(wav_total_bytes, shard_size_gb)
print(f"- {shard_size_gb:>4.1f} GB -> {shard_count:,} shards")
if __name__ == "__main__":
main()
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