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 by Alec Radford et al from OpenAI. The original code repository can be found here. Update: following the release of the paper, the Whisper authors announced a large v2 model trained for 2.5x more epochs with regularization. This large v2 model surpasses the performance of the large model, with no architecture changes. Thus, it is recommended that the large v2 model is used in place of the original large model. 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. Model details Whisper is a Transformer based encoder decoder model, also referred to as a sequence to sequence model. It was trained on 680k hours of labelled speech data annotated using large scale weak supervision. The models were trained on either English only data o…
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