79 lines
3.7 KiB
Markdown
79 lines
3.7 KiB
Markdown
## Whisper model files in custom ggml format
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The [original Whisper PyTorch models provided by OpenAI](https://github.com/openai/whisper/blob/main/whisper/__init__.py#L17-L27)
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are converted to custom `ggml` format in order to be able to load them in C/C++.
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Conversion is performed using the [convert-pt-to-ggml.py](convert-pt-to-ggml.py) script.
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You can either obtain the original models and generate the `ggml` files yourself using the conversion script,
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or you can use the [download-ggml-model.sh](download-ggml-model.sh) script to download the already converted models.
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Currently, they are hosted on the following locations:
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- https://huggingface.co/ggerganov/whisper.cpp
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- https://ggml.ggerganov.com
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Sample download:
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```java
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$ ./download-ggml-model.sh base.en
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Downloading ggml model base.en ...
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models/ggml-base.en.bin 100%[=============================================>] 141.11M 5.41MB/s in 22s
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Done! Model 'base.en' saved in 'models/ggml-base.en.bin'
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You can now use it like this:
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$ ./main -m models/ggml-base.en.bin -f samples/jfk.wav
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```
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To convert the files yourself, use the convert-pt-to-ggml.py script. Here is an example usage.
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The original PyTorch files are assumed to have been downloaded into ~/.cache/whisper
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Change `~/path/to/repo/whisper/` to the location for your copy of the Whisper source:
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```
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mkdir models/whisper-medium
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python models/convert-pt-to-ggml.py ~/.cache/whisper/medium.pt ~/path/to/repo/whisper/ ./models/whisper-medium
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mv ./models/whisper-medium/ggml-model.bin models/ggml-medium.bin
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rmdir models/whisper-medium
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```
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A third option to obtain the model files is to download them from Hugging Face:
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https://huggingface.co/ggerganov/whisper.cpp/tree/main
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## Available models
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| Model | Disk | SHA |
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| --- | --- | --- |
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| tiny | 75 MiB | `bd577a113a864445d4c299885e0cb97d4ba92b5f` |
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| tiny.en | 75 MiB | `c78c86eb1a8faa21b369bcd33207cc90d64ae9df` |
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| base | 142 MiB | `465707469ff3a37a2b9b8d8f89f2f99de7299dac` |
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| base.en | 142 MiB | `137c40403d78fd54d454da0f9bd998f78703390c` |
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| small | 466 MiB | `55356645c2b361a969dfd0ef2c5a50d530afd8d5` |
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| small.en | 466 MiB | `db8a495a91d927739e50b3fc1cc4c6b8f6c2d022` |
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| medium | 1.5 GiB | `fd9727b6e1217c2f614f9b698455c4ffd82463b4` |
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| medium.en | 1.5 GiB | `8c30f0e44ce9560643ebd10bbe50cd20eafd3723` |
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| large-v1 | 2.9 GiB | `b1caaf735c4cc1429223d5a74f0f4d0b9b59a299` |
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| large-v2 | 2.9 GiB | `0f4c8e34f21cf1a914c59d8b3ce882345ad349d6` |
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| large-v3 | 2.9 GiB | `ad82bf6a9043ceed055076d0fd39f5f186ff8062` |
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## Model files for testing purposes
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The model files prefixed with `for-tests-` are empty (i.e. do not contain any weights) and are used by the CI for
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testing purposes. They are directly included in this repository for convenience and the Github Actions CI uses them to
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run various sanitizer tests.
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## Fine-tuned models
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There are community efforts for creating fine-tuned Whisper models using extra training data. For example, this
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[blog post](https://huggingface.co/blog/fine-tune-whisper) describes a method for fine-tuning using Hugging Face (HF)
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Transformer implementation of Whisper. The produced models are in slightly different format compared to the original
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OpenAI format. To read the HF models you can use the [convert-h5-to-ggml.py](convert-h5-to-ggml.py) script like this:
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```bash
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git clone https://github.com/openai/whisper
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git clone https://github.com/ggerganov/whisper.cpp
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# clone HF fine-tuned model (this is just an example)
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git clone https://huggingface.co/openai/whisper-medium
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# convert the model to ggml
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python3 ./whisper.cpp/models/convert-h5-to-ggml.py ./whisper-medium/ ./whisper .
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```
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