Whisper Whisper is a state of the art model for automatic speech recognition (ASR) and speech translation, proposed in the paper Robust Speech Recognition via Large Scale Weak Supervision by Alec Radford et al. from OpenAI. Trained on 5M hours of labeled data, Whisper demonstrates a strong ability to generalise to many datasets and domains in a zero shot setting. Whisper large v3 has the same architecture as the previous large and large v2 models, except for the following minor differences: 1. The spectrogram input uses 128 Mel frequency bins instead of 80 2. A new language token for Cantonese The Whisper large v3 model was trained on 1 million hours of weakly labeled audio and 4 million hours of pseudo labeled audio collected using Whisper large v2 . The model was trained for 2.0 epochs over this mixture dataset. The large v3 model shows improved performance over a wide variety of languages, showing 10% to 20% reduction of errors compared to Whisper large v2 . For more details on the different checkpoints available, refer to the section Model details. Disclaimer : Content for this model card has partly been written by the 🤗 Hugging Face team, and partly copied and pasted from the…
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