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aee48fb | 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 | """Kaggle notebook body: mine prompt voices from DownFlow/ASMR.
Reuses the setup stages from notebook_eval so the env is identical. Then runs
voice_mining.py, which harvests 20 artists x 6 clips, re-transcribes each crop,
and ranks them by the teacher's CER with that clip as the prompt.
N_ARTISTS=20 N_PER_ARTIST=6 STAGE=harvest # cheap, CPU only
N_ARTISTS=20 N_PER_ARTIST=6 STAGE=probe # GPU, needs candidates.json
"""
import os
import subprocess
import sys
from pathlib import Path
from notebook_eval import (BUNDLE, MODEL, REPO, ROOT, WEIGHTS, pull,
stage_deps, stage_repo)
BUNDLE_REPO = "idefinity/webfm-eval"
def main() -> None:
stage_repo()
stage_deps()
subprocess.run("pip install -q datasets soundfile", shell=True, check=False)
pull(WEIGHTS, MODEL)
pull(BUNDLE_REPO, BUNDLE, repo_type="dataset")
env = dict(os.environ, WORK_ROOT=str(ROOT), STAGE=os.environ.get("STAGE", "all"))
rc = subprocess.run([sys.executable, str(BUNDLE / "kaggle/voice_mining.py")], env=env).returncode
print(f"voice_mining exit={rc}")
print("artifacts: voices/candidates.json, voices/probe.json, voices/best.json")
if __name__ == "__main__":
main()
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