Whisper AI Introduction

Whisper uses a simple end-to-end approach implemented as an encoder-decoder Transformer architecture. The input audio is split into 30-second chunks and converted into a log-Mel spectrogram. This is passed through an encoder, while a decoder predicts the corresponding text caption. The model is trained to handle multiple tasks by inserting special tokens that direct it to perform language identification, add timestamps, transcribe speech, or translate to English. Whisper's training on a large, diverse dataset allows it to be more robust to variations in accents, background noise, and technical language compared to models trained on smaller, more specific datasets.