whispermlx 3.9.0

Time-Accurate Automatic Speech Recognition using Whisper.

whisper(ml)x

Fast, accurate speech recognition on Apple Silicon — powered by MLX.

A fork of WhisperX with the inference backend replaced by mlx-whisper, running natively on Apple Silicon via MLX. Word-level timestamps, speaker diarization, and VAD are all retained.

• ⚡️ MLX inference — runs on Apple Silicon GPU via unified memory
• 🎯 Word-level timestamps via wav2vec2 forced alignment
• 👥 Speaker diarization via pyannote-audio
• 🗣️ VAD preprocessing via pyannote or silero

Installation

pip install whispermlx

Or with uv:

uv add whispermlx

Usage

CLI

# Auto-downloads mlx-community/whisper-large-v3-mlx on first run
whispermlx audio.mp3 --model large-v3

# With speaker diarization
whispermlx audio.mp3 --model large-v3 --diarize --hf_token YOUR_TOKEN

# Use any mlx-community model directly
whispermlx audio.mp3 --model mlx-community/whisper-large-v3-turbo

Python

import whispermlx

# Short name — auto-maps to mlx-community/whisper-large-v3-mlx
model = whispermlx.load_model("large-v3", device="cpu")
result = model.transcribe("audio.mp3")
print(result["segments"])

# With alignment
model_a, metadata = whispermlx.load_align_model(language_code=result["language"], device="cpu")
result = whispermlx.align(result["segments"], model_a, metadata, "audio.mp3", device="cpu")

# With diarization
from whispermlx.diarize import DiarizationPipeline
diarize_model = DiarizationPipeline(token="YOUR_HF_TOKEN", device="cpu")
diarize_segments = diarize_model("audio.mp3")
result = whispermlx.assign_word_speakers(diarize_segments, result)

Model Names

Short names are automatically mapped to their mlx-community equivalents. Full HF repo IDs also work.

Short name	HF repo
tiny, base, small, medium	mlx-community/whisper-{name}-mlx
large-v3	mlx-community/whisper-large-v3-mlx
large-v3-turbo / turbo	mlx-community/whisper-large-v3-turbo

Speaker Diarization

Requires a Hugging Face access token and acceptance of the pyannote speaker-diarization-community-1 model agreement.

Acknowledgements

Built on top of WhisperX by Max Bain et al., mlx-whisper, pyannote-audio, and OpenAI Whisper.

@article{bain2022whisperx,
  title={WhisperX: Time-Accurate Speech Transcription of Long-Form Audio},
  author={Bain, Max and Huh, Jaesung and Han, Tengda and Zisserman, Andrew},
  journal={INTERSPEECH 2023},
  year={2023}
}