Main Purpose
The main purpose of Whisper, as described in the OpenAI Research blog, is to introduce a new automatic speech recognition (ASR) system. Whisper is trained on a large and diverse dataset and aims to improve the robustness and accuracy of speech recognition systems.
Key Features
- Large and Diverse Training Dataset: Whisper is trained on a large and diverse dataset, which includes both English and non-English audio. This diverse training data helps improve the system's performance and robustness.
- Zero-Shot Performance: Whisper demonstrates strong zero-shot performance across various datasets, making 50% fewer errors compared to other models. It excels in tasks such as speech-to-text translation and outperforms the supervised state-of-the-art model on CoVoST2 to English translation.
Use Case
- Speech Recognition: Whisper can be used in applications that require accurate and robust speech recognition, such as transcription services, voice assistants, and voice-controlled systems.
