process.py

#!/usr/bin/env python3
"""
Processes the DeepMind LibriSpeech-Long dataset into Parquet files
for Hugging Face Hub compatibility.

This script converts FLAC audio files to WAV format in-memory,
gathers metadata, and saves the data into Parquet files, one for each split.
It also generates the necessary YAML front-matter for the README.md file
on the Hugging Face Hub, ensuring the dataset is correctly displayed,
especially in the Data Studio.

Example Usage:
  python process.py \
    /path/to/source/librispeech-long \
    /path/to/your/cloned-hf-repo

To process only a small subset for testing:
  python process.py \
    /path/to/source/librispeech-long \
    /path/to/your/cloned-hf-repo \
    --limit-speakers
"""
import argparse
import re
import shutil
import subprocess
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, List, Optional, Tuple

import datasets
from datasets import Audio, Dataset, Features, Value
from tqdm import tqdm

# --- Configuration ---

datasets.config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS = 20

# Config name on the Hub.
CONFIG_NAME = "librispeech_long"

# Parquet files will be written under this subfolder in the repo.
OUTPUT_SUBDIR = CONFIG_NAME

# Map source split directory names to the desired Hugging Face Hub split names.
# Using underscores instead of dots is safer for the Hub's Data Studio.
SPLIT_MAP: Dict[str, str] = {
    "dev-clean": "dev_clean",
    "dev-other": "dev_other",
    "test-clean": "test_clean",
    "test-other": "test_other",
}

# Dataset string stored in the "dataset" column of the Parquet file.
DATASET_NAME = "librispeech-long"

# Parquet filename template.
FILENAME_TEMPLATE = "{split}-00000-of-00001.parquet"

# Audio conversion settings.
TARGET_SR = 16000
TARGET_CHANNELS = 1
TARGET_CODEC = "pcm_s16le"

# --- Data Structures ---

@dataclass
class Row:
    """Represents a single row in the dataset."""
    audio_bytes: bytes
    dataset: str
    text: str
    id: str
    audio_length_s: float

# --- Helper Functions ---

def check_ffmpeg() -> None:
    """Checks if ffmpeg and ffprobe are installed and available on PATH."""
    for bin_name in ("ffmpeg", "ffprobe"):
        try:
            subprocess.run(
                [bin_name, "-version"],
                check=True,
                stdout=subprocess.DEVNULL,
                stderr=subprocess.DEVNULL,
            )
        except FileNotFoundError:
            raise RuntimeError(
                f"'{bin_name}' not found on PATH. Please install FFmpeg and retry."
            )

def cleanup_outputs(target_dir: Path, clean: bool) -> None:
    """Removes old Parquet files and temporary directories."""
    if not clean:
        return

    output_path = target_dir / OUTPUT_SUBDIR
    if output_path.exists():
        print(f"[INFO] Cleaning up old output directory: {output_path}")
        shutil.rmtree(output_path)

    # Clean up any old temporary directories from previous runs
    for p in target_dir.glob(".tmp_write_*"):
        try:
            shutil.rmtree(p, ignore_errors=True)
        except Exception:
            pass

def find_first_speaker_dir(split_dir: Path) -> Optional[Path]:
    """Finds the first speaker directory within a split directory."""
    if not split_dir.is_dir():
        return None
    speakers = sorted(p for p in split_dir.iterdir() if p.is_dir())
    return speakers[0] if speakers else None

def collect_flac_pairs(root: Path) -> List[Tuple[Path, Path]]:
    """Collects pairs of FLAC audio files and their corresponding text files."""
    pairs: List[Tuple[Path, Path]] = []
    for flac in sorted(root.rglob("*.flac")):
        txt = flac.with_suffix(".txt")
        if txt.exists():
            pairs.append((flac, txt))
    return pairs

def ffmpeg_flac_to_wav_bytes(flac_path: Path) -> bytes:
    """Converts a FLAC file to WAV format bytes using ffmpeg."""
    cmd = [
        "ffmpeg", "-v", "error", "-i", str(flac_path),
        "-ac", str(TARGET_CHANNELS), "-ar", str(TARGET_SR),
        "-f", "wav", "-acodec", TARGET_CODEC, "pipe:1",
    ]
    proc = subprocess.run(cmd, check=True, capture_output=True)
    return proc.stdout

def ffprobe_duration_seconds(audio_path: Path) -> float:
    """Gets the duration of an audio file in seconds using ffprobe."""
    cmd = [
        "ffprobe", "-v", "error", "-show_entries", "format=duration",
        "-of", "default=noprint_wrappers=1:nokey=1", str(audio_path),
    ]
    proc = subprocess.run(cmd, check=True, capture_output=True, text=True)
    try:
        return float(proc.stdout.strip())
    except (ValueError, TypeError):
        return 0.0

def make_id_from_path(flac_path: Path, split_dir: Path) -> str:
    """Creates a unique ID from the file path, e.g., '1272-128104-0000'."""
    rel_path = flac_path.relative_to(split_dir)
    parts = rel_path.parts
    if len(parts) < 3:
        return flac_path.stem.replace("_", "-")

    speaker, session, stem_with_ext = parts[0], parts[1], parts[-1]
    stem = Path(stem_with_ext).stem

    match = re.match(r"^\d+_(\d+)$", stem)
    utt_id = match.group(1) if match else stem.replace('_', '-')
    return f"{speaker}-{session}-{utt_id}"

def read_text(txt_path: Path) -> str:
    """Reads the text from a transcript file."""
    return txt_path.read_text(encoding="utf-8").strip()

def rows_for_split(source_split_path: Path, limit_speakers: bool) -> List[Row]:
    """Generates a list of Row objects for a given data split."""
    if not source_split_path.exists():
        print(f"[WARN] Source split directory not found: {source_split_path}")
        return []

    if limit_speakers:
        spk_dir = find_first_speaker_dir(source_split_path)
        if spk_dir is None:
            print(f"[WARN] No speaker directories in {source_split_path}")
            return []
        roots_to_process = [spk_dir]
        print(f"[INFO] Using speaker subset for {source_split_path.name}: {spk_dir.name}")
    else:
        roots_to_process = [p for p in source_split_path.iterdir() if p.is_dir()]

    file_pairs = []
    for root in roots_to_process:
        file_pairs.extend(collect_flac_pairs(root))

    rows: List[Row] = []
    for flac_path, txt_path in tqdm(file_pairs, desc=f"{source_split_path.name}: converting", unit="file"):
        rows.append(Row(
            audio_bytes=ffmpeg_flac_to_wav_bytes(flac_path),
            dataset=DATASET_NAME,
            text=read_text(txt_path),
            id=make_id_from_path(flac_path, source_split_path),
            audio_length_s=ffprobe_duration_seconds(flac_path),
        ))

    rows.sort(key=lambda r: r.id)
    return rows

def build_parquet_dataset(rows: List[Row]) -> Dataset:
    """
    Builds a Hugging Face Dataset with the correct Audio feature type.
    This ensures the Parquet file has the right schema (a STRUCT for audio)
    for the Hugging Face Data Studio to interpret it correctly.
    """
    features = Features({
        "audio": Audio(sampling_rate=TARGET_SR, decode=False),
        "dataset": Value("string"),
        "text": Value("string"),
        "id": Value("string"),
        "audio_length_s": Value("float64"),
    })

    data_list = [
        {
            "audio": {"bytes": r.audio_bytes, "path": None},
            "dataset": r.dataset,
            "text": r.text,
            "id": r.id,
            "audio_length_s": r.audio_length_s,
        }
        for r in rows
    ]

    return Dataset.from_list(data_list, features=features)


def write_split(ds: Dataset, out_path: Path) -> Tuple[int, int]:
    """Writes a Dataset split to a Parquet file."""
    out_path.parent.mkdir(parents=True, exist_ok=True)
    if out_path.exists():
        out_path.unlink()
    ds.to_parquet(str(out_path))
    return ds.num_rows, out_path.stat().st_size

def format_yaml_block(stats: Dict[str, Dict[str, float]]) -> str:
    """Generates the YAML front-matter for the README.md file."""
    download_size = sum(int(v["num_bytes"]) for v in stats.values())

    lines = [
        "---",
        "license: cc-by-4.0",
        "dataset_info:",
        f"- config_name: {CONFIG_NAME}",
        "  features:",
        "  - name: audio",
        "    dtype:",
        "      audio:",
        f"        sampling_rate: {TARGET_SR}",
        "  - name: dataset",
        "    dtype: string",
        "  - name: text",
        "    dtype: string",
        "  - name: id",
        "    dtype: string",
        "  - name: audio_length_s",
        "    dtype: float64",
        "  splits:",
    ]

    for hub_split, vals in sorted(stats.items()):
        lines.append(f"  - name: {hub_split}")
        lines.append(f"    num_bytes: {float(vals['num_bytes'])}")
        lines.append(f"    num_examples: {int(vals['num_examples'])}")

    lines.extend([
        f"  download_size: {download_size}",
        f"  dataset_size: {download_size}",
        "configs:",
        f"- config_name: {CONFIG_NAME}",
        "  data_files:",
    ])

    for hub_split in sorted(stats.keys()):
        path_pattern = f"{OUTPUT_SUBDIR}/{hub_split}-*"
        lines.append(f"  - split: {hub_split}")
        lines.append(f"    path: {path_pattern}")

    lines.append("---")
    return "\n".join(lines)

def main() -> None:
    """Main function to run the data processing pipeline."""
    parser = argparse.ArgumentParser(
        description="Process LibriSpeech-Long dataset for Hugging Face Hub."
    )
    parser.add_argument(
        "source_root", type=Path, help="Path to the source data directory (e.g., '.../librispeech-long')."
    )
    parser.add_argument(
        "target_repo_root", type=Path, help="Path to your cloned Hugging Face dataset repository."
    )
    parser.add_argument(
        "--limit-speakers", action="store_true", help="Only process the first speaker per split for quick testing."
    )
    parser.add_argument(
        "--no-clean", dest="clean", action="store_false", help="Do not clean up old output files before running."
    )
    args = parser.parse_args()

    check_ffmpeg()
    cleanup_outputs(args.target_repo_root, args.clean)

    out_dir = args.target_repo_root / OUTPUT_SUBDIR
    out_dir.mkdir(parents=True, exist_ok=True)

    all_stats: Dict[str, Dict[str, float]] = {}

    for src_split, hub_split in SPLIT_MAP.items():
        source_split_path = args.source_root / src_split
        rows = rows_for_split(source_split_path, args.limit_speakers)
        if not rows:
            print(f"[WARN] No rows found for split '{src_split}', skipping.")
            continue

        ds = build_parquet_dataset(rows)
        out_name = FILENAME_TEMPLATE.format(split=hub_split)
        out_path = out_dir / out_name

        print(f"[INFO] Writing {hub_split} -> {out_path}")
        num_examples, num_bytes = write_split(ds, out_path)
        all_stats[hub_split] = {
            "num_examples": num_examples,
            "num_bytes": num_bytes,
        }
        print(f"[INFO] {hub_split}: examples={num_examples}, bytes={num_bytes}")

    print("\n" + "="*60)
    print("=== Paste this YAML at the top of your README.md ===")
    print("="*60)
    print(format_yaml_block(all_stats))
    print("="*60)

if __name__ == "__main__":
    main()