Whisper (OpenAI)
Speech-To-Text

Introducing Whisper: Advanced Multilingual ASR System
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About Whisper (OpenAI)
Introducing Whisper: OpenAI's Whisper is an advanced neural net aimed at achieving human-level robustness and accuracy in English speech recognition. Trained on a massive dataset of 680,000 hours of multilingual and multitask supervised data, Whisper excels in handling accents, background noise, and technical language. Its versatility includes transcribing multiple languages and translating them into English, leveraging an encoder-decoder Transformer architecture. Comparison to Existing Approaches: Unlike traditional models that rely on smaller, paired audio-text datasets, Whisper's training on a vast and diverse dataset provides unmatched robustness. Although it may not lead in specific benchmarks like LibriSpeech, Whisper demonstrates 50% fewer errors in zero-shot performance across varied datasets. Its capacity for speech-to-text translation, particularly outperforming the state-of-the-art in CoVoST2 to English translation, sets it apart. Impact and Availability: Whisper is poised to revolutionize application development by enabling integration of high-accuracy voice interfaces. OpenAI has made its paper, model card, and code publicly available, fostering further exploration and innovation in the field.
Key Features
- High robustness to accents and background noise
- Supports multiple languages
- Translates languages into English
- Encoder-decoder Transformer architecture
- Processes 30-second audio chunks
- Predicts text captions with special tokens integration
- Improved zero-shot performance
- Open-source with detailed resources
- Enables voice interfaces for applications
- Outperforms on CoVoST2 for English translation