faster-whisper-timestamped-cs

faster-whisper-timestamped-cs

Table of Contents

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

• Model Description
• Intended Uses and Limitations
• How to Get Started with the Model
• Conversion Details
• Citation
• Additional information

</details>

Summary

The "langtech-veu/faster-whisper-timestamped-cs" is an acoustic model based on a faster-whisper version of langtech-veu/whisper-timestamped-cs suitable for Automatic Speech Recognition in Code-switching between Catalan and Spanish.

Model Description

The "langtech-veu/faster-whisper-timestamped-cs" is the result of converting the whisper-timestamped-cs 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 code-switching between Catalan and Spanish. The model intends to transcribe Catalan and Spanish 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 or Spanish using this model, you can follow this example:

from faster_whisper import WhisperModel

model_size = "langtech-veu/faster-whisper-timestamped-cs"

# 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.mp3", task="transcribe")
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 langtech-veu/whisper-timestamped-cs
   --output_dir faster-whisper-timestamped-cs
   --copy_files preprocessor_config.json 
   --quantization float16

Citation

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

@misc{BSC2025whispertimestampedcs,
      title={ASR models for Catalan and Spanish CS: whisper-timestamped-cs.}, 
      author={Takanori, Lucas; Solito, Sarah; Messaoudi, Abir; España i Bonet, Cristina},
      organization={Barcelona Supercomputing Center},
      url={https://huggingface.co/langtech-veu/faster-whisper-timestamped-cs},
      year={2025}
}

Additional Information

Author

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

Contact

For further information, please send an email to [email protected].

Copyright

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

License

Apache-2.0

Funding

This work has been promoted and financed by the Generalitat de Catalunya through the Aina project.

The training of the model was possible thanks to the computing time provided by Barcelona Supercomputing Center through MareNostrum 5.