faster-whisper-bsc-large-v3-cat
Table of Contents
- Model Description
- Intended Uses and Limitations
- How to Get Started with the Model
- Conversion Details
- Citation
- Additional information
Summary
The "faster-whisper-bsc-large-v3-cat" is an acoustic model based on a faster-whisper version of whisper-bsc-large-v3-cat suitable for Automatic Speech Recognition in Catalan.
Model Description
The "faster-whisper-bsc-large-v3-cat" is the result of converting the whisper-bsc-large-v3-cat into a lighter model using a Python module called faster-whisper.
Intended Uses and Limitations
This model can be used for Automatic Speech Recognition (ASR) in Catalan. The model intends to transcribe Catalan audio files to plain text without punctuation.
How to Get Started with the Model
To see an updated and functional version of this code, please visit our Notebook.
Installation
To use this model, you may install faster-whisper
Create a virtual environment:
python -m venv /path/to/venv
Activate the environment:
source /path/to/venv/bin/activate
Install the modules:
pip install faster-whisper
For Inference
To transcribe audio in Catalan using this model, you can follow this example:
from faster_whisper import WhisperModel
model_size = "BSC-LT/faster-whisper-bsc-large-v3-cat"
# Run on GPU with FP16
model = WhisperModel(model_size, device="cuda", compute_type="float16")
# or run on GPU with INT8
#model = WhisperModel(model_size, device="cuda", compute_type="int8_float16")
# or run on CPU with INT8
# model = WhisperModel(model_size, device="cpu", compute_type="int8")
segments, info = model.transcribe("audio_in_catalan.mp3", beam_size=5, task="transcribe",language="ca")
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))
Conversion Details
Conversion procedure
This model is not a direct result of training. It is a conversion of a Whisper model using faster-whisper. The procedure to create the model is as follows:
ct2-transformers-converter --model BSC-LT/whisper-bsc-large-v3-cat
--output_dir faster-whisper-bsc-large-v3-cat
--copy_files preprocessor_config.json
--quantization float16
Citation
If this model contributes to your research, please cite the work:
@misc{takanori2025whisperbsclarge3cat,
title={Acoustic Model in Catalan: whisper-bsc-large-v3-cat.},
author={Sanchez Shiromizu, Lucas Takanori; Hernandez Mena, Carlos Daniel; Messaoudi, Abir; España i Bonet, Cristina; Cortada Garcia, Marti},
organization={Barcelona Supercomputing Center},
url={https://huggingface.co/langtech-veu/faster-whisper-bsc-large-v3-cat},
year={2025}
}
Additional Information
Author
The conversion process was performed during May (2025) in the Language Technologies Laboratory of the Barcelona Supercomputing Center by Abir Messaoudi.
Contact
For further information, please send an email to langtech@bsc.es.
Copyright
Copyright(c) 2025 by Language Technologies Laboratory, Barcelona Supercomputing Center.
License
Funding
This work is funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the project ILENIA with reference 2022/TL22/00215337.
The conversion of the model was possible thanks to the computing time provided by Barcelona Supercomputing Center through MareNostrum 5.