Whisper v3 is OpenAI's third-generation automatic speech recognition model, trained on a large multilingual dataset spanning 99 languages. It substantially improves transcription accuracy over Whisper v2, particularly for non-English languages, accented speech, and noisy audio conditions. OpenAI released Whisper as an open-weight model, and v3 is widely deployed both via the OpenAI API and self-hosted.
The model handles transcription and translation tasks, converting spoken audio directly to text in the source language or translating to English. It performs well across diverse acoustic environments, making it suitable for podcast transcription, meeting notes, multilingual customer support, and accessibility tooling.
Key Features
Speech recognition across 99 languages with strong multilingual accuracy
Audio-to-English translation in addition to same-language transcription
Improved robustness on accented speech and background noise vs. v2
Handles diverse audio formats and varying recording quality
Available as open-weight model for self-hosted and on-premise deployments
Supports long-form audio via chunked transcription pipelines
Ideal Use Cases
Podcast and video subtitle generation across multiple languages
Meeting and interview transcription for note-taking workflows
Multilingual customer support call transcription and analysis
Accessibility tooling for real-time or recorded speech-to-text
Example Prompts for Whisper v3
Technical Specifications
| Provider | OpenAI |
| Category | Audio |
| Modality | Audio -> Text |
Frequently Asked Questions
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