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inference.py — CLI wrapper around IntelligibilityPredictor
=============================================================
IntelligibilityPredictor now lives in lyric_intelligibility_model.py
alongside the model itself, so you can use it directly as a library:
from lyric_intelligibility_model import IntelligibilityPredictor
predictor = IntelligibilityPredictor.from_pretrained(
"your-username/lyric-intelligibility-whisper"
)
result = predictor.predict("path/to/song.wav") # or a directory
This script is just a command-line entry point around that class, writing
results to a CSV file instead of printing them.
Usage
-----
python inference.py --repo_id your-username/lyric-intelligibility-whisper \
--audio path/to/song.wav
# Writes results.csv with one row: song,<score>
# Score every audio file in a directory (top level only, not recursive):
python inference.py --repo_id your-username/lyric-intelligibility-whisper \
--audio path/to/songs_dir
# Writes results.csv with one row per file: <stem>,<score>
# Custom output path:
python inference.py --repo_id your-username/lyric-intelligibility-whisper \
--audio path/to/songs_dir --output scores.csv
# Stereo audio downmixed to mono instead of better-ear left/right max:
python inference.py --repo_id your-username/lyric-intelligibility-whisper \
--audio path/to/song.wav --no-better-ear
# Scores scaled to [0, 100] instead of [0, 1]:
python inference.py --repo_id your-username/lyric-intelligibility-whisper \
--audio path/to/song.wav --max-100
"""
from __future__ import annotations
import argparse
import csv
from pathlib import Path
from lyric_intelligibility_model import IntelligibilityPredictor
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--repo_id",
required=True,
help="Hugging Face Hub repo id, e.g. your-username/lyric-intelligibility-whisper",
)
parser.add_argument(
"--audio",
required=True,
help="Path to an audio file, OR a directory — every audio file at "
"the top level of the directory will be scored (not recursive).",
)
parser.add_argument("--revision", default=None, help="Optional Hub revision/branch")
parser.add_argument(
"--device", default=None, help="cuda / cpu (default: auto-detect)"
)
parser.add_argument(
"--better-ear",
dest="better_ear",
action=argparse.BooleanOptionalAction,
default=True,
help="Stereo handling: --better-ear (default) scores left/right "
"separately and keeps the max; --no-better-ear downmixes to mono "
"first and scores once. No effect on mono audio.",
)
parser.add_argument(
"--output",
default="results.csv",
help="CSV file to write results to (default: results.csv)",
)
parser.add_argument(
"--max-100",
dest="max_100",
action="store_true",
help="Save scores scaled to [0, 100] instead of [0, 1] (default: [0, 1])",
)
args = parser.parse_args()
predictor = IntelligibilityPredictor.from_pretrained(
args.repo_id, revision=args.revision, device=args.device
)
result = predictor.predict(args.audio, better_ear=args.better_ear)
def _scaled(score):
if score == "ERROR":
return score
return score * 100 if args.max_100 else score
if Path(args.audio).is_dir():
rows = [
(Path(filename).stem, _scaled(r.get("score", "ERROR")))
for filename, r in result.items()
]
else:
rows = [(Path(args.audio).stem, _scaled(result.get("score", "ERROR")))]
with open(args.output, "w", newline="") as f:
writer = csv.writer(f)
writer.writerows(rows)
print(f"Wrote {len(rows)} result(s) to {args.output}")
if __name__ == "__main__":
main() |