Audio · openai
whisper-large-v3-turbo
The faster Whisper large-v3 turbo checkpoint from OpenAI. Same job as large-v3: transcribe audio on a machine you control.
Freshness uses the repository open date. This record is 761 days old, opened 2024-10-01. Popularity uses 5,742,443 downloads and 3,493 likes listed by Hugging Face. Checked 2026-11-01. Neither number grades answers. Formulas.
Hugging Face reports 5.1 GB of storage for the whole repository. That figure can include several packs, so the fit table does not use it as the size of one file.
Will it fit?
Save a machine on the hardware page and this block fills in. The table already has the sizes.
| Pack | Size | Saved machine |
|---|---|---|
| Largest published checkpoint | 1.6 GB weights + 0.5 GB allowance. File: model.safetensors. | Choose a machine |
| FP16 estimate | 1.6 GB weights + 0.5 GB allowance. | Choose a machine |
Estimate rows use the parameter count. A measured row names a file in the repository. The allowance is fixed: it stands in for context or activations, and it is not a measurement from a run.
How to run it
Speech models in this directory are small beside a chat model. The runtime is the one named in the publisher repository. Whisper checkpoints are commonly run in faster-whisper or whisper.cpp.
Publisher repository on Hugging Face
Others in Audio
Kokoro-82M · hexgrad
whisper-large-v3 · openai
This directory is assembled from public model records. Freshness is age. Popularity is downloads and likes. A fit figure is an estimate from a parameter count or from a published file size, not a run on our hardware. How the numbers are made. AI disclosure.