Whisper AI Introduction
Whisper is an open-source automatic speech recognition system from OpenAI that approaches human-level accuracy and robustness for transcribing and translating speech in multiple languages.
View MoreWhat is Whisper AI
Whisper is an artificial intelligence model developed by OpenAI for automatic speech recognition (ASR). Released in September 2022, Whisper was trained on 680,000 hours of multilingual and multitask supervised data collected from the web. It can transcribe speech in multiple languages, translate speech to English, and identify the language being spoken. OpenAI has open-sourced both the model and inference code to enable further research and development of speech processing applications.
How does Whisper AI work?
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.
Benefits of Whisper AI
Whisper offers several key benefits for speech recognition tasks. Its robustness allows it to handle a wide variety of audio inputs with different accents, background noise, and technical language. The model's multilingual capabilities enable it to transcribe and translate speech in multiple languages without needing separate models. As an open-source project, developers can use Whisper as a foundation to build upon and create more specialized or powerful models. Additionally, Whisper's strong zero-shot performance across diverse datasets makes it versatile for many applications without requiring fine-tuning.
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