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Add V3 Support (#578)

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* Add V3 Support

* update conversion example

---------

Co-authored-by: oscaarjs <oscar.johansson@conversy.se>
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Oscaarjs
2023-11-24 23:16:12 +01:00
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@@ -104,7 +104,7 @@ pip install --force-reinstall "faster-whisper @ https://github.com/guillaumekln/
```python
from faster_whisper import WhisperModel
model_size = "large-v2"
model_size = "large-v3"
# Run on GPU with FP16
model = WhisperModel(model_size, device="cuda", compute_type="float16")
@@ -185,17 +185,17 @@ Here is a non exhaustive list of open-source projects using faster-whisper. Feel
## Model conversion
When loading a model from its size such as `WhisperModel("large-v2")`, the correspondig CTranslate2 model is automatically downloaded from the [Hugging Face Hub](https://huggingface.co/guillaumekln).
When loading a model from its size such as `WhisperModel("large-v3")`, the correspondig CTranslate2 model is automatically downloaded from the [Hugging Face Hub](https://huggingface.co/Systran).
We also provide a script to convert any Whisper models compatible with the Transformers library. They could be the original OpenAI models or user fine-tuned models.
For example the command below converts the [original "large-v2" Whisper model](https://huggingface.co/openai/whisper-large-v2) and saves the weights in FP16:
For example the command below converts the [original "large-v3" Whisper model](https://huggingface.co/openai/whisper-large-v3) and saves the weights in FP16:
```bash
pip install transformers[torch]>=4.23
ct2-transformers-converter --model openai/whisper-large-v2 --output_dir whisper-large-v2-ct2 \
--copy_files tokenizer.json --quantization float16
ct2-transformers-converter --model openai/whisper-large-v3 --output_dir whisper-large-v3-ct2
--copy_files tokenizer.json preprocessor_config.json --quantization float16
```
* The option `--model` accepts a model name on the Hub or a path to a model directory.
@@ -207,12 +207,12 @@ Models can also be converted from the code. See the [conversion API](https://ope
1. Directly load the model from a local directory:
```python
model = faster_whisper.WhisperModel("whisper-large-v2-ct2")
model = faster_whisper.WhisperModel("whisper-large-v3-ct2")
```
2. [Upload your model to the Hugging Face Hub](https://huggingface.co/docs/transformers/model_sharing#upload-with-the-web-interface) and load it from its name:
```python
model = faster_whisper.WhisperModel("username/whisper-large-v2-ct2")
model = faster_whisper.WhisperModel("username/whisper-large-v3-ct2")
```
## Comparing performance against other implementations