Whisper Large V3 Turbo - French Quebecois (CTranslate2)
This is the CTranslate2 optimized version of the fine-tuned model ele-sage/whisper-large-v3-turbo-fr-quebecois.
It contains the model weights converted to Float16 for significantly faster inference and lower memory usage compared to the original Transformers model. This format is natively supported by WhisperX and Faster-Whisper.
For details regarding the training dataset (Assemblée Nationale, Common Voice), hyperparameters, and evaluation metrics, please refer to the original model card.
⚡ How to Use
Option 1: Using WhisperX (Recommended for Alignment)
Installation & Setup:
Follow the instructions from the official repository: WhisperX GitHub.
Usage: Command Line & Python
Simply use ele-sage/whisper-large-v3-turbo-fr-quebecois-ct2 as the model argument.
# Example CLI usage
whisperx path/to/audio.wav --model large-v2 ele-sage/whisper-large-v3-turbo-fr-quebecois-ct2 --language fr
Option 2: Using Faster-Whisper
Faster-Whisper is the standalone engine behind WhisperX.
Installation:
pip install faster-whisper
Python:
from faster_whisper import WhisperModel
model_id = "ele-sage/whisper-large-v3-turbo-fr-quebecois-ct2"
model = WhisperModel(model_id, device="cuda", compute_type="float16")
segments, info = model.transcribe("path/to/audio.wav", beam_size=5)
print("Detected language '%s' with probability %f" % (info.language, info.language_probability))
for segment in segments:
print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
Important consideration:
The model was fine-tuned specifically on French data, which might degrade performance on other languages.
Citation
If you use this model, please consider citing the original Whisper paper:
@misc{radford2022whisper,
doi = {10.48550/ARXIV.2212.04356},
url = {https://arxiv.org/abs/2212.04356},
author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
title = {Robust Speech Recognition via Large-Scale Weak Supervision},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}