We don’t often focus on audio AI systems, but today we’re diving into automatic speech recognition (ASR) using OpenAI’s groundbreaking Whisper model. ASR systems are designed to automatically convert spoken language into text, typically requiring fine-tuning for specific tasks. Whisper, however, breaks that mold with its open-source, multilingual capabilities, allowing it to handle transcription and translation tasks without additional tuning. OpenAI, known for keeping most of their advanced models proprietary, made an exception by releasing Whisper as open-source in 2022. Now, in 2024, its latest V3 Turbo version has made waves with speeds eight times faster than its predecessor, large-v3, and the ability to run on cloud servers or even locally, all while maintaining comparable accuracy. Let’s explore what makes Whisper so efficient for handling multiple speech recognition tasks.

In today’s episode, we will cover:

Limitations of existing speech recognition models

Recent advances in speech recognition come from unsupervised pre-training techniques, which learn from raw audio without labels. However, these models still require fine-tuning to handle specific tasks, which can be complex. Additionally, there is a risk that models learn patterns specific to the training data and can still make mistakes when faced with new data. 

Despite improvements in encoders, the need for fine-tuning and the limitations of weak decoders continue to affect model performance. In an ideal world, speech recognition should perform well across various settings without constant adjustments. Some supervised training approaches across multiple datasets have shown more consistent results, but the amount of high-quality supervised data is still small.

What if there was an approach that allows models to perform well across languages and speech recognition tasks without specific fine-tuning?

Here comes Whisper model

In Whisper, OpenAI uses large-scale weak supervision to train the system for speech recognition. It is designed not only for speech recognition but also for translation tasks, transcribing speech into text in its original language or translating it into English, voice activity detection, and language identification.

There are 9 modifications of Whisper model of different sizes and capabilities:

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