The original Whisper PyTorch models provided by OpenAI
have been converted to custom ggml format in order to be able to load them in C/C++. The conversion has been performed
using the convert-pt-to-ggml.py script. You can either obtain the original models and generate
the ggml files yourself using the conversion script, or you can use the download-ggml-model.sh
script to download the already converted models. Currently, they are hosted on the following locations:
Sample usage:
$ ./download-ggml-model.sh base.en
Downloading ggml model base.en ...
models/ggml-base.en.bin 100%[=============================================>] 141.11M 5.41MB/s in 22s
Done! Model 'base.en' saved in 'models/ggml-base.en.bin'
You can now use it like this:
$ ./main -m models/ggml-base.en.bin -f samples/jfk.wavA third option to obtain the model files is to download them from Hugging Face:
https://huggingface.co/datasets/ggerganov/whisper.cpp/tree/main
| Model | Disk | Mem | SHA |
|---|---|---|---|
| tiny | 75 MB | ~390 MB | bd577a113a864445d4c299885e0cb97d4ba92b5f |
| tiny.en | 75 MB | ~390 MB | c78c86eb1a8faa21b369bcd33207cc90d64ae9df |
| base | 142 MB | ~500 MB | 465707469ff3a37a2b9b8d8f89f2f99de7299dac |
| base.en | 142 MB | ~500 MB | 137c40403d78fd54d454da0f9bd998f78703390c |
| small | 466 MB | ~1.0 GB | 55356645c2b361a969dfd0ef2c5a50d530afd8d5 |
| small.en | 466 MB | ~1.0 GB | db8a495a91d927739e50b3fc1cc4c6b8f6c2d022 |
| medium | 1.5 GB | ~2.6 GB | fd9727b6e1217c2f614f9b698455c4ffd82463b4 |
| medium.en | 1.5 GB | ~2.6 GB | 8c30f0e44ce9560643ebd10bbe50cd20eafd3723 |
| large-v1 | 2.9 GB | ~4.7 GB | b1caaf735c4cc1429223d5a74f0f4d0b9b59a299 |
| large | 2.9 GB | ~4.7 GB | 0f4c8e34f21cf1a914c59d8b3ce882345ad349d6 |
The model files prefixed with for-tests- are empty (i.e. do not contain any weights) and are used by the CI for
testing purposes. They are directly included in this repository for convenience and the Github Actions CI uses them to
run various sanitizer tests.
There are community efforts for creating fine-tuned Whisper models using extra training data. For example, this blog post describes a method for fine-tuning using Hugging Face (HF) Transformer implementation of Whisper. The produced models are in slightly different format compared to the original OpenAI format. To read the HF models you can use the convert-h5-to-ggml.py script like this:
git clone https://github.com/openai/whisper
git clone https://github.com/ggerganov/whisper.cpp
# clone HF fine-tuned model (this is just an example)
git clone https://huggingface.co/openai/whisper-base.en
# convert the model to ggml
python3 ./whisper.cpp/models/convert-h5-to-ggml.py ./whisper-medium/ ./whisper .