How to use from the
Use from the
MLX library
# Download the model from the Hub
pip install huggingface_hub[hf_xet]

huggingface-cli download --local-dir whisper-medium-malayalam-mlx thanveerdev/whisper-medium-malayalam-mlx

Whisper-medium-Malayalam (MLX)

Apple MLX-converted weights for vrclc/Whisper-medium-Malayalam optimized for Apple Silicon.

  • Base model: vrclc/Whisper-medium-Malayalam
  • Format: MLX (weights.safetensors, config.json)
  • Intended runtime: mlx-whisper on Apple Silicon (M-series)

Usage (Python)

import mlx_whisper

result = mlx_whisper.transcribe(
    "/path/to/audio.wav",
    path_or_hf_repo="<this-repo>",
    # Optional decoding controls
    language="ml",               # Malayalam
    task="transcribe",           # or "translate"
    temperature=0.0,
    no_speech_threshold=0.3,
    logprob_threshold=-1.0,
    compression_ratio_threshold=2.4,
)
print(result["text"]) 

Local HTTP server (FastAPI)

With the server at whisper/server_mlx.py from avatar-npm:

export WHISPER_MODEL=<this-repo-or-local-mlx-path>
export WHISPER_LANGUAGE=ml
python server_mlx.py
# POST /transcribe with form field `file`

Notes

  • This repo contains only the MLX weights and config. Tokenization and audio preprocessing are handled by mlx-whisper.
  • If you need the original (non-MLX) model, see vrclc/Whisper-medium-Malayalam.

License

The original model’s license applies. See the upstream repository for details.

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