whisper-large-v3
Whisper is a state-of-the-art model for automatic speech recognition (ASR) and speech translation, proposed in the paper Robust Speech Recognition via Large-Scale Weak Supervision by Alec Radford et al. from OpenAI. Trained on >5M hours of labeled data, Whisper demonstrates a strong ability to generalise to many datasets and domains in a zero-shot setting.
Params
1.5 B
Context
—
Downloads 30d
4.7 M
Likes
6,322
Download history
daily snapshots · 55 days
▲ 90 K in the last 30 days (1.9%)
5.1 M4.4 M
Aug 22Sep 1Sep 11Sep 20
6.0 M4.4 M
Jul 28Aug 15Sep 2Sep 20
Can you run it?
Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| model.safetensors | f16 | 9.3 GB | 10.9 GB | ✅ Runs comfortably |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Specifications
- Architecture
- WhisperForConditionalGeneration
- Parameters
- 1.5 B
- Tensor type
- F16
- Vocabulary
- 51,866
- Licence
- apache-2.0
- First seen on the Hub
- 2023-11-07
- Training datasets
- undisclosed
- Added to our catalog
- 2026-07-28
Family
Base model and the most-downloaded derivatives in the catalog.
Compare with any automatic-speech-recognition model