
# 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).

Whisper `large-v3` has the same architecture as the previous large models except the following minor differences:

1. The input uses 128 Mel frequency bins instead of 80
2. A new language token for Cantonese
{"language": ["zh", "aa", "af"], "license": "apache-2.0", "library_name": "diffusers", "tags": ["medical", "code", "suibian"], "metrics": ["accuracy"], "pipeline_tag": "text-to-image"}
diffusers medical code suibian text-to-image zh aa af arxiv:2212.04356 license:apache-2.0 region:us