| """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() |
|
|
