Local transcription and speaker diarization with senko and parakeet-mlx.

Takes an audio or video file, transcribes the speech, optionally identifies who spoke when, and produces a speaker-attributed transcript. Any format ffmpeg can read (mp4, mp3, m4a, webm, etc.) is automatically converted to WAV for processing.

Diarization uses CoreML for hardware-accelerated inference on Apple Silicon. Models download automatically on first run (no account needed).

• macOS with Apple Silicon (parakeet-mlx requires MLX)
• Python 3.13+
• uv (used to run parakeet-mlx)
• ffmpeg (all input is normalized to 16kHz mono WAV): brew install ffmpeg

uv sync

scribe meeting.wav
scribe recording.mp4
scribe podcast.mp3

Writes meeting.txt / recording.txt / podcast.txt:

SPEAKER_00: Thank you for joining. Let's start with the agenda.

SPEAKER_01: Sure, I wanted to discuss the roadmap first.

scribe meeting.wav --no-diarize

scribe meeting.wav -o transcript.txt      # custom output path
scribe meeting.wav -o -                   # write to stdout
scribe meeting.wav --format json          # JSON with timestamps
scribe meeting.wav --format json -o -     # JSON to stdout
scribe meeting.wav --no-diarize           # transcript without speakers

scribe meeting.wav --format json

Writes meeting.json with timestamps and speaker attribution:

{
  "speakers": ["SPEAKER_00", "SPEAKER_01"],
  "segments": [
    {
      "speaker": "SPEAKER_00",
      "text": "Thank you for joining.",
      "start": 0.531,
      "end": 2.874
    }
  ]
}

uv sync --dev
uv run pytest -q
uv run ruff check

MIT