whisper large v3 french distil dec16 EU AI Act Compliance Profile

Whisper-Large-V3-French-Distil-Dec16

Whisper-Large-V3-French-Distil represents a series of distilled versions of Whisper-Large-V3-French, achieved by reducing the number of decoder layers from 32 to 16, 8, 4, or 2 and distilling using a large-scale dataset, as outlined in this paper.

The distilled variants reduce memory usage and inference time while maintaining performance (based on the retained number of layers) and mitigating the risk of hallucinations, particularly in long-form transcriptions. Moreover, they can be seamlessly combined with the original Whisper-Large-V3-French model for speculative decoding, resulting in improved inference speed and consistent outputs compared to using the standalone model.

This model has been converted into various formats, facilitating its usage across different libraries, including transformers, openai-whisper, fasterwhisper, whisper.cpp, candle, mlx, etc.

Table of Contents

• Performance
• Usage
  • Hugging Face Pipeline
  • Hugging Face Low-level APIs
  • Speculative Decoding
  • OpenAI Whisper
  • Faster Whisper
  • Whisper.cpp
  • Candle
  • MLX
• Training details
• Acknowledgements

Performance

We evaluated our model on both short and long-form transcriptions, and also tested it on both in-distribution and out-of-distribution datasets to conduct a comprehensive analysis assessing its accuracy, generalizability, and robustness.

Please note that the reported WER is the result after converting numbers to text, removing punctuation (except for apostrophes and hyphens), and converting all characters to lowercase.

All evaluation results on the public datasets can be found [here](htt

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