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#!/usr/bin/env -S uv run --script
# /// script
# requires-python = ">=3.10"
# dependencies = [
# "mlx-whisper",
# "pyannote.audio",
# "torch",
# "torchaudio",
# ]
# ///
"""Transcribe audio with mlx-whisper, optionally diarize with pyannote. Outputs JSON."""
import argparse
import json
import sys
from pathlib import Path
import mlx_whisper
def transcribe(audio_path: str, model: str) -> dict:
"""Run mlx-whisper transcription, returning segments with timestamps."""
return mlx_whisper.transcribe(
audio_path,
path_or_hf_repo=model,
word_timestamps=True,
verbose=False,
)
def diarize(audio_path: str, hf_token: str):
"""Run pyannote speaker diarization."""
import torch
from pyannote.audio import Pipeline
pipeline = Pipeline.from_pretrained(
"pyannote/speaker-diarization-3.1",
token=hf_token,
)
if torch.backends.mps.is_available():
pipeline.to(torch.device("mps"))
elif torch.cuda.is_available():
pipeline.to(torch.device("cuda"))
return pipeline(audio_path)
def assign_speakers(segments: list[dict], diarization) -> list[dict]:
"""Assign a speaker label to each whisper segment based on diarization overlap."""
labeled = []
for seg in segments:
seg_start = seg["start"]
seg_end = seg["end"]
# Find the diarization speaker with the most overlap.
best_speaker = None
best_overlap = 0.0
for turn, _, speaker in diarization.itertracks(yield_label=True):
overlap_start = max(seg_start, turn.start)
overlap_end = min(seg_end, turn.end)
overlap = max(0.0, overlap_end - overlap_start)
if overlap > best_overlap:
best_overlap = overlap
best_speaker = speaker
labeled.append({**seg, "speaker": best_speaker or "UNKNOWN"})
return labeled
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("audio", help="Path to audio file")
parser.add_argument("output", help="Path to write JSON transcript")
parser.add_argument(
"--model",
default="mlx-community/whisper-large-v3-turbo",
help="MLX whisper model (default: mlx-community/whisper-large-v3-turbo)",
)
parser.add_argument(
"--diarize",
action="store_true",
help="Run pyannote speaker diarization (requires --hf-token)",
)
parser.add_argument(
"--hf-token",
default=None,
help="Hugging Face token for pyannote (default: $HUGGING_FACE_TOKEN)",
)
args = parser.parse_args()
import os
audio = args.audio
if not Path(audio).exists():
print(f"Audio file not found: {audio}", file=sys.stderr)
sys.exit(1)
print(f"Transcribing {audio}...", file=sys.stderr)
result = transcribe(audio, args.model)
segments = result["segments"]
if args.diarize:
hf_token = args.hf_token or os.environ.get("HUGGING_FACE_TOKEN")
if not hf_token:
print("Set --hf-token or $HUGGING_FACE_TOKEN for diarization.", file=sys.stderr)
sys.exit(1)
print("Diarizing...", file=sys.stderr)
diarization_result = diarize(audio, hf_token)
segments = assign_speakers(segments, diarization_result)
output = {
"text": result["text"],
"language": result.get("language"),
"segments": segments,
}
Path(args.output).write_text(
json.dumps(output, ensure_ascii=False, indent=2),
encoding="utf-8",
)
print(f"Done. Wrote {len(segments)} segments to {args.output}.", file=sys.stderr)
if __name__ == "__main__":
main()