Transcribe with Whisper Large V3
4.2%
WER
99
Languages
8.0x
Speed
MIT
License
About Whisper Large V3
Whisper Large V3 is OpenAI's flagship open-source speech recognition model. With 1.55 billion parameters, it offers exceptional accuracy across 99 languages. It uses a transformer encoder-decoder architecture trained on 680,000 hours of multilingual audio data.
Model Info
- ProviderOpenAI
- Architecture-
- LicenseMIT
- UpdatedMar 2026
Frequently Asked Questions
Whisper Large V3 is a speech-to-text model by OpenAI. STT.ai hosts Whisper Large V3 on our GPU infrastructure so you can use it without provisioning your own hardware — upload audio or video and pick Whisper Large V3 from the model picker.
On standard benchmarks, Whisper Large V3 achieves around 4.2% Word Error Rate. Real-world accuracy depends on audio quality, accent, and language; for noisy or accented recordings, expect a few percentage points higher WER.
Whisper Large V3 runs on STT.ai's free tier — every visitor gets 600 minutes/month at no cost. Paid plans add longer per-file limits, private transcripts, and priority queueing.
Whisper Large V3 is released under MIT, a permissive open-source license. You can self-host Whisper Large V3 on your own hardware or use our hosted version — both are commercially usable.
Whisper Large V3 supports 99 languages. Auto-detection picks the right language for most audio; you can also specify it manually for a small accuracy lift.
Whisper Large V3 processes audio at about 8.0x real-time on our GPUs. A 1-hour audio file finishes in under 7 minutes; longer files queue and notify by email when done.
Whisper Large V3 has 1.55B parameters. Larger models tend to be more accurate but slower; STT.ai hosts Whisper Large V3 on GPU so the parameter count doesn't affect your client-side performance.
Whisper Large V3 accepts every format STT.ai supports — MP3, WAV, M4A, FLAC, OGG, MP4, MKV, MOV, WebM, AVI, and others. Output as TXT, SRT, VTT, DOCX, JSON, or PDF.
Yes. Speaker diarization runs alongside Whisper Large V3 for every transcription — each speaker is labeled and you can rename them in the editor afterwards.
Yes. Whisper Large V3 runs in our managed environment — audio is processed and deleted by default and never used for training without explicit opt-in. Pro plans add client-side encryption for transcripts at rest.
Use the compare-stt tool to run Whisper Large V3 against any other supported model on the same audio — you'll see WER, segment count, speaker labels, and confidence scores side-by-side. The Whisper Large V3 vs Whisper Large V3 comparison is the most commonly run.
Yes. Specify "whisper-large-v3" as the model parameter on the /v1/transcribe endpoint. Python and Node.js SDKs include Whisper Large V3 examples. Free API tier includes 100 minutes/month.
Yes. Because Whisper Large V3 is MIT-licensed, you can self-host it. STT.ai's open-source page lists the project repo and weights. Most production teams use our hosted version to skip GPU procurement, model swaps, and ops.