CoolFace
Modelpublic

BSC-LT/faster-whisper-3cat-cv21-valencian

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face
0likes94downloads
Model Card

faster-whisper-3cat-cv21-valencian

Table of Contents

<details> <summary>Click to expand</summary>

</details>

Model Description

The "BSC-LT/faster-whisper-3cat-cv21-valencian" is an acoustic model based on a faster-whisper version of BSC-LT/whisper-3cat-cv21-valencian

Intended Uses and Limitations

This model is the result of converting the BSC-LT/whisper-3cat-cv21-valencian into a lighter model using a Python module called faster-whisper. The model can be used for Automatic Speech Recognition (ASR) in Catalan, especially in the Valencian accent. 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:

bash
python -m venv /path/to/venv

Activate the environment:

bash
source /path/to/venv/bin/activate

Install the modules:

bash
pip install faster-whisper

For Inference

To transcribe audio in Catalan using this model, you can follow this example:

python
from faster_whisper import WhisperModel

model_size = "BSC-LT/faster-whisper-3cat-cv21-valencian"

# 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:

bash
ct2-transformers-converter --model BSC-LT/whisper-3cat-cv21-valencian
   --output_dir faster-whisper-3cat-cv21-valencian
   --copy_files preprocessor_config.json 
   --quantization float16

Citation

If this model contributes to your research, please cite the work: <!--

bibtex
@inproceedings{hernandez20243catparla,
  title={3CatParla: A New Open-Source Corpus of Broadcast TV in Catalan for Automatic Speech Recognition},
  author={Hern{\'a}ndez Mena, Carlos Daniel and Armentano Oller, Carme and Solito, Sarah and K{\"u}lebi, Baybars},
  booktitle={Proc. IberSPEECH 2024},
  pages={176--180},
  year={2024}
}

-->

bibtext
@misc{BSC2025-fasterwhisper3catcv21valencian,
      title={Recognition models for adaptation to Catalan variants}, 
      author={Hernandez Mena, Carlos Daniel; Messaoudi, Abir; Armentaro Carme; España i Bonet, Cristina;},
      organization={Barcelona Supercomputing Center},
      url={https://huggingface.co/BSC-LT/faster-whisper-3cat-cv21-valencian},
      year={2025}
}

Additional Information

Author

The conversion process was performed during June (2025) in the Language Technologies Laboratory of the Barcelona Supercomputing Center.

Contact

For further information, please email <langtech@bsc.es>.

Copyright

Copyright(c) 2025 by Language Technologies Laboratory, Barcelona Supercomputing Center.

License

Apache-2.0

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.

We acknowledge EuroHPC Joint Undertaking for awarding us access to MareNostrum5 as BSC, Spain.