kyr0/crisperwhisper-unsloth-mlx-8b

CrisperWhisper Unsloth (MLX, FP8)

This repo provides the CrisperWhisper model converted to MLX and quantized to FP8 for fast on-device ASR on Apple Silicon.

This model works exceptionally well for scenarios where word-level precision is desired. Instead of grammatically correct sentences, this model is fine-tuned for word-by-word transcriptions - which is exactly what you probably want to use for interviews, or Alexa-like home automation applications.

Huge credit to Laurin from nyra.health and Daniel + Michael from Unsloth for the heavy lifting. Free to use for non-commercial use only!

See Laurin's original paper for more details.

Base model: unsloth/CrisperWhisper (Torch) → converted via mlx-examples/whisper/convert.py. :contentReference[oaicite:0]{index=0}

What’s inside

• weights.safetensors — MLX FP8 weights
• config.json — MLX Whisper config

Usage (recommended: auto-download from Hugging Face)

mlx_whisper supports Hugging Face repo IDs in path_or_hf_repo, and will download automatically. :contentReference[oaicite:1]{index=1}

from mlx_whisper import transcribe

out = transcribe(
    "audio.wav",
    path_or_hf_repo="kyr0/crisperwhisper-unsloth-mlx-8b",
)
print(out["text"])

Usage (local path)

If you already have a local MLX folder, point path_or_hf_repo to it:

from mlx_whisper import transcribe

out = transcribe(
    "audio.wav",
    path_or_hf_repo="./mlx_models/crisperwhisper-unsloth-mlx-8b",
)
print(out["text"])

Live Transcription!

Please follow me on Github - I'm working on a local, private live transcription system for Apple Silicon: kyr0's crispr-live-mlx