Whisper — Robust Speech Recognition via Large-Scale Weak Supervision

Whisper is a versatile speech recognition model that has been extensively trained on a wide range of audio samples. It's a multitasking model that can handle multiple tasks, including multilingual speech recognition, speech translation, and language identification.

Note: Generative AI services are used as assistants in this blog post!!

Introduction

Pre-trained audio encoders, while able to learn high-quality representations of speech, have limitations due to their unsupervised nature. They lack an equally high-quality decoder, requiring a complex fine-tuning stage to be useful for tasks like speech recognition. Fine-tuning can improve performance but may also lead to brittle and spurious patterns that do not generalize well to different datasets. A model that performs exceptionally on a specific dataset may still make basic errors on another due to these dataset-specific quirks.

Unsupervised pre-training has enhanced the quality of audio encoders significantly, but the lack of an equivalent pre-trained decoder and the need for dataset-specific fine-tuning are key weaknesses. Ideally, a speech recognition system should function reliably across various environments without needing supervised fine-tuning for every deployment.