Whisper

Whisper is an automatic speech recognition (ASR) system that transcribes and translates spoken language with high accuracy across multiple languages. This open-source tool, developed by OpenAI, is designed to handle diverse accents, background noise, and technical language, making it suitable for a wide range of real-world applications.

Key features:
- Multilingual Support: Transcribes speech in multiple languages and translates from those languages into English.
- Robustness to Accents and Noise: Trained to handle diverse accents and background noise for improved accuracy in real-world scenarios.
- Technical Language Support: Recognizes technical terms and jargon, enhancing performance in specialized domains.
- Zero-Shot Performance: Demonstrates strong performance across various datasets without specific fine-tuning.
- Ease of Use: Open-source models and inference code allow for simple integration into applications.

How it works:
1. Audio input is split into 30-second chunks.
2. Audio chunks are converted into log-Mel spectrograms.
3. Spectrograms are passed through an encoder.
4. A decoder predicts the corresponding text caption, including special tokens for language identification, phrase-level timestamps, multilingual speech transcription, and to-English speech translation.

Use of AI:
Whisper utilizes a Transformer-based architecture, a type of generative artificial intelligence. This architecture enables the model to learn complex patterns in speech data and generate accurate transcriptions.

AI foundation model:
Whisper is built on a custom Transformer-based architecture, which is a type of large language model (LLM). This architecture allows the model to process sequential data like speech effectively.

Target users:
- Developers working on speech recognition applications
- Researchers in natural language processing

How to access:
Whisper is available as open-source models and inference code, allowing developers to integrate it into their applications.