The original Whisper PyTorch models provided by OpenAI
are converted to custom ggml format in order to be able to load them in C/C++.
Conversion is 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 download:
$ ./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.wavTo convert the files yourself, use the convert-pt-to-ggml.py script. Here is an example usage.
The original PyTorch files are assumed to have been downloaded into ~/.cache/whisper
Change ~/path/to/repo/whisper/ to the location for your copy of the Whisper source:
mkdir models/whisper-medium
python models/convert-pt-to-ggml.py ~/.cache/whisper/medium.pt ~/path/to/repo/whisper/ ./models/whisper-medium
mv ./models/whisper-medium/ggml-model.bin models/ggml-medium.bin
rmdir models/whisper-medium
A third option to obtain the model files is to download them from Hugging Face:
https://huggingface.co/ggerganov/whisper.cpp/tree/main
| Model | Disk | SHA |
|---|---|---|
| tiny | 75 MiB | bd577a113a864445d4c299885e0cb97d4ba92b5f |
| tiny.en | 75 MiB | c78c86eb1a8faa21b369bcd33207cc90d64ae9df |
| base | 142 MiB | 465707469ff3a37a2b9b8d8f89f2f99de7299dac |
| base.en | 142 MiB | 137c40403d78fd54d454da0f9bd998f78703390c |
| small | 466 MiB | 55356645c2b361a969dfd0ef2c5a50d530afd8d5 |
| small.en | 466 MiB | db8a495a91d927739e50b3fc1cc4c6b8f6c2d022 |
| medium | 1.5 GiB | fd9727b6e1217c2f614f9b698455c4ffd82463b4 |
| medium.en | 1.5 GiB | 8c30f0e44ce9560643ebd10bbe50cd20eafd3723 |
| large-v1 | 2.9 GiB | b1caaf735c4cc1429223d5a74f0f4d0b9b59a299 |
| large-v2 | 2.9 GiB | 0f4c8e34f21cf1a914c59d8b3ce882345ad349d6 |
| large-v3 | 2.9 GiB | ad82bf6a9043ceed055076d0fd39f5f186ff8062 |
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-medium
# convert the model to ggml
python3 ./whisper.cpp/models/convert-h5-to-ggml.py ./whisper-medium/ ./whisper .Initial support for https://huggingface.co/distil-whisper is available.
Currently, the chunk-based transcription strategy is not implemented, so there can be sub-optimal quality when using the distilled models with whisper.cpp.
# clone OpenAI whisper and whisper.cpp
git clone https://github.com/openai/whisper
git clone https://github.com/ggerganov/whisper.cpp
# get the models
cd whisper.cpp/models
git clone https://huggingface.co/distil-whisper/distil-medium.en
git clone https://huggingface.co/distil-whisper/distil-large-v2
# convert to ggml
python3 ./convert-h5-to-ggml.py ./distil-medium.en/ ../../whisper .
mv ggml-model.bin ggml-medium.en-distil.bin
python3 ./convert-h5-to-ggml.py ./distil-large-v2/ ../../whisper .
mv ggml-model.bin ggml-large-v2-distil.bin