microWakeWord-Trainer-Nvidia-Docker/cli/setup_audioset

#!/bin/bash
set -euo pipefail

PROGPATH=$(realpath "$0")
PROGDIR=$(dirname "${PROGPATH}")

source "${PROGDIR}/shell.functions"

if [ "${HELP}" == "true" ] ; then
    cat <<EOF >&2
Usage: $0 [ --cleanup-archives ] [ --cleanup-input-files ] [ --data-dir=<data_dir> ]

    --cleanup-archives    : Automatically clean up any downloaded archvies after
                            extraction.
    --cleanup-intermediate-files
                          : Automatically clean up the intermediate files after they've
                          : converted to 16k.
    <data_dir>            : Path to the data directory.
                          : Default: ${DATA_DIR}

EOF
    exit 1
fi

mkdir -p "${DATA_DIR}/training_datasets/downloads" || :
cd "${DATA_DIR}/training_datasets"

echo "***** Checking audioset *****"

AUDIO_URL="https://huggingface.co/datasets/agkphysics/AudioSet/resolve"
AUDIO_DIR="./audioset"
mkdir -p "${AUDIO_DIR}"
AUDIO16K_DIR="./audioset_16k"
mkdir -p "${AUDIO16K_DIR}"
AUDIO_FILECOUNT="./downloads/audioset_filecount"
AUDIO_IN_GLOB="*.flac"

declare -A filecounts
for i in {0..9} ; do
    fname="bal_train0${i}.tar"
    filecounts[${fname}]=0
done

get_filecounts filecounts "${AUDIO_FILECOUNT}"


REV_CANDIDATES=(
    "6762f044d1c88619c7f2006486036192128fb07e"
    "0049167e89f259a010c3f070fe3666d9e5242836"
    "ceb9eaaa7844c9ad7351e659c84a572e376ad06d"
    "main"
)

TAR_PATTERNS=(
    "data/bal_train0"
    "data/bal_train/bal_train0"
)

find_rev() {
    for rev in "${REV_CANDIDATES[@]}" ; do
        for pattern in "${TAR_PATTERNS[@]}" ; do
            url="https://huggingface.co/datasets/agkphysics/AudioSet/resolve/${rev}/${pattern}0.tar"
            curl -I -L --fail -s "${url}" > /dev/null && echo "${rev},${pattern}"
        done
    done
    echo ""
}

converter() {
    source ${DATA_DIR}/.venv/bin/activate
    python - "${AUDIO_DIR}" "${AUDIO16K_DIR}" <<-EOF
import os, sys, subprocess, scipy.io.wavfile, numpy as np
from pathlib import Path
import soundfile as sf
import librosa
from tqdm import tqdm

def write_wav(dst: Path, data: np.ndarray, sr: int):
    x = np.clip(data, -1.0, 1.0)
    scipy.io.wavfile.write(dst, sr, (x * 32767).astype(np.int16))

audioset_dir = Path(sys.argv[1])
audioset_out = Path(sys.argv[2])

# convert FLAC → 16k mono WAV
flacs = list(audioset_dir.rglob("*.flac"))
print(f"   FLAC files: {len(flacs)}")
audioset_bad = []
ok = 0
for p in tqdm(flacs, desc="   AudioSet→WAV (resample 16k mono)"):
    try:
        outfile = Path(audioset_out / (p.stem + ".wav"))
        if outfile.exists():
            continue
        y, _ = librosa.load(p, sr=16000, mono=True)
        if y.size == 0:
            raise ValueError("empty audio")
        write_wav(outfile, y, 16000)
        ok += 1
    except Exception as e:
        audioset_bad.append(f"{p}:{e}")

if audioset_bad:
    (audioset_out / "audioset_corrupted_files.log").write_text("\n".join(audioset_bad))
print(f"   AudioSet complete ({ok} ok, {len(audioset_bad)} failed)")
EOF
}

expected_filecount=$(get_total_filecount filecounts)
actual_filecount=$(find "${AUDIO16K_DIR}" -name "*.wav" 2>/dev/null | wc -l) || :
write_filecount=false

if [ "${actual_filecount}" -ne 0 ] && [ "${actual_filecount}" -eq "${expected_filecount}" ] ; then
    echo "   Existing Audioset valid"
else
    dl=$(find_rev)
    [ -n "$dl" ] || { echo "   Could not locate an AudioSet revision with FLAC tarballs still present on HF." ; exit 1 ; }
    rev=${dl%%,*}
    pattern=${dl##*,}
    echo "   Checking 10 tarballs"
    for i in {0..9} ; do
        fname="downloads/bal_train0${i}.tar"
        if [ ! -f "${fname}" ] ; then
            echo "   Downloading bal_train0${i}.tar"
            url="${AUDIO_URL}/${rev}/${pattern}${i}.tar"
            curl -L -s --fail "${url}" -o "${fname}" || { echo "Could not fetch ${fname} at rev ${rev}; continuing." ; continue ; }
        fi

        tarball_filecount=$(tar -tvf "${fname}" | wc -l )
        filecounts["bal_train0${i}.tar"]=${tarball_filecount}
        write_filecount=true

        echo "   Untarring bal_train0${i}.tar"
        tar -xf "${fname}" -C "${AUDIO_DIR}"
        if "${CLEANUP_ARCHIVES}" && [ -f "${fname}" ] ; then
            echo "   Cleaning up bal_train0${i}.tar"
            rm -rf "${fname}"
        fi
    done
    rm -rf "${AUDIO16K_DIR}/audioset_corrupted_files.log" || :
    converter
    if [ -f "${AUDIO16K_DIR}/audioset_corrupted_files.log" ] ; then
        failed=$(cat "${AUDIO16K_DIR}/audioset_corrupted_files.log" | wc -l)
        filecounts[failed]=-${failed}
    fi
    expected_filecount=$(get_total_filecount filecounts)
    actual_filecount=$(find ${AUDIO16K_DIR} -name "*.wav" 2>/dev/null | wc -l) || :
    if [ "${actual_filecount}" -ne "${expected_filecount}" ] ; then
        echo "   Converted file count(${actual_filecount}) != expected file count(${expected_filecount})" >&2
        exit 1
    fi
fi

if ${write_filecount} ; then
    write_filecounts filecounts "${AUDIO_FILECOUNT}"
fi

if "${CLEANUP_ARCHIVES}" ; then
    for i in {0..9} ; do
        fname="downloads/bal_train0${i}.tar"
        if [ -f "${fname}" ] ; then
            echo "   Cleaning up bal_train0${i}.tar"
            rm -rf "${fname}"
        fi
    done
fi

if "${CLEANUP_INTERMEDIATE_FILES}" && [ -d "${AUDIO_DIR}" ] ; then
    echo "   Cleaning up ${AUDIO_DIR}"
    rm -rf "${AUDIO_DIR}"
fi

echo "   Audioset complete"
exit 0