Instructions to use devkubnet/whisper-th-distill-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use devkubnet/whisper-th-distill-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download devkubnet/whisper-th-distill-mlx --local-dir whisper-th-distill-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Thai Whisper (distil large-v3) β MLX
An mlx-whisper conversion of
biodatlab/distill-whisper-th-large-v3
(MIT) for Apple Silicon, made for Devkub Edit Kit. All credit for the model goes to biodatlab; their
original model card is in ORIGINAL_MODEL_CARD.md.
Files
| File | |
|---|---|
config.json, weights.safetensors |
mlx-whisper model (float16) |
alignment_heads.json |
word-timing heads from the source generation_config.json β mlx-whisper's loader ignores them, so apply after loading: model.set_alignment_heads(np.array(heads)) |
devkub.json |
decoding hint used by Devkub Edit Kit |
Decoding note
The model was fine-tuned without timestamp tokens, so Whisper's usual 30-second walk drops the end of each window. Transcribe speech-trimmed pieces of β€ 20 s (cut at pauses, no long trailing silence) one at a time. On four real Thai clips this gave a character error rate of about 3% versus 4.5% for whisper-large-v3-turbo, with matching word timestamps.
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Hardware compatibility
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Model tree for devkubnet/whisper-th-distill-mlx
Base model
biodatlab/distill-whisper-th-large-v3