OpenAI Whisper

OpenAI Whisper is a speech recognition system that converts spoken language into written text. The model processes audio inputs through an encoder-decoder transformer architecture, splitting audio into 30-second segments and generating text using a language model. Trained on 680,000 hours of multilingual data from diverse sources, it supports transcription in 99 languages and translation to English. Its training dataset includes varied accents, acoustic conditions, and domain-specific terminology, enabling accurate results in noisy environments. Whisper achieves a 92% accuracy rate, with performance adjustable across five model sizes to balance speed and resource requirements.

Open AI Whisper Features

• Multilingual transcription: Recognizes speech in 99 languages, including low-resource dialects, using 117,000 hours of non-English training data.
• Speech-to-English translation: Converts non-English audio into English text without intermediate transcription steps.
• Transformer-based processing: Uses encoder layers to convert audio spectrograms into mathematical representations, decoded into text with timestamp predictions.
• Noise resistance: Maintains accuracy in environments with background noise or overlapping speakers through exposure to diverse acoustic conditions during training.
• Scalable model variants: Offers five size options (e.g., Tiny, Base, Small, Medium, Large) to optimize transcription speed and hardware efficiency.