 Whisper  Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours  of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains **without** the need  for fine-tuning.  Whisper was proposed in the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356)  by Alec Radford et al from OpenAI. The original code repository can be found [here](https://github.com/openai/whisper).  <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">   <p><b>Update:</b> following the release of the paper, the Whisper authors announced a <a href="ttps://huggingface.co/openai/whisper-large-v2"> large-v2</a> model trained for 2.5x more epochs with regularization. This <a href="ttps://huggingface.co/openai/whisper-large-v2"> large-v2</a> model surpasses the performance of the large model, with no architecture changes. Thus, it is recommended that the <a href="ttps://huggingface.co/openai/whisper-large-v2"> large-v2</a> model is used in-place of the original large model. </p> </div>   **Disclaimer**: Content for this model card has partly been written by the Hugging Face team, and parts of it were  copied and pasted from the original model card.  