Automatic Speech Recognition
MLX
Russian
whisper
whisper-large-v3
speech-recognition
russian
apple-silicon
Instructions to use valtu4a/whisper-large-v3-russian-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use valtu4a/whisper-large-v3-russian-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir whisper-large-v3-russian-mlx valtu4a/whisper-large-v3-russian-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Whisper Large V3 โ Russian (MLX)
Whisper Large V3 converted to MLX format for Apple Silicon (M1/M2/M3/M4).
This model is optimized for Russian speech transcription.
Usage
import mlx_whisper
result = mlx_whisper.transcribe(
"audio.wav",
path_or_hf_repo="valtu4a/whisper-large-v3-russian-mlx",
language="ru",
word_timestamps=True,
)
print(result["text"])
Recommended Parameters
{
"language": "ru",
"word_timestamps": True,
"condition_on_previous_text": False,
"temperature": 0,
"compression_ratio_threshold": 2.4,
"logprob_threshold": -1.0,
"no_speech_threshold": 0.6,
"hallucination_silence_threshold": 2
}
Requirements
- Apple Silicon (M1 or newer)
- macOS 13.3+
- Python 3.9+
pip install mlx-whisper
Performance
| Chip | RTF (Real Time Factor) |
|---|---|
| M1 | ~0.15 |
| M2 | ~0.12 |
| M3 | ~0.10 |
RTF < 1 means transcription runs faster than real time.
Base Model
Converted from antony66/whisper-large-v3-russian โ a Whisper Large V3 fine-tuned on Russian speech data.
Acknowledgements
Special thanks to antony66 for fine-tuning and open-sourcing whisper-large-v3-russian, which served as the base for this MLX conversion.
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