Whisper Large v2
Whisper Large v2 is OpenAI's robust automatic speech recognition model, trained on 680,000 hours of multilingual and multitask supervised audio data. It supports transcription and translation across roughly 99 languages, delivering strong accuracy even on accented speech, background noise, and low-quality audio recordings.
Positioned as a reliable open-weight model for speech-to-text pipelines, Whisper Large v2 is widely used in production applications for podcast transcription, meeting notes, subtitle generation, and voice-driven data entry. Its large-model architecture provides noticeably better accuracy than the smaller Whisper variants on challenging audio conditions.
Key Features
Multilingual transcription across ~99 languages
Audio-to-English translation in a single inference pass
Robust handling of accented speech and noisy environments
Timestamp-level word alignment for subtitle workflows
Open-weight model suitable for on-premises deployment
Strong performance on long-form audio with automatic chunking
Ideal Use Cases
Automated podcast and video subtitle generation
Meeting transcription and minutes creation
Multilingual call-center audio processing
Voice command logging and voice-to-text data entry
Research transcription of interviews and field recordings
Example Prompts for Whisper Large v2
Technical Specifications
| Provider | OpenAI |
| Category | Audio |
| Modality | Audio -> Text |
| Parameters | 1.55B |
Frequently Asked Questions
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