faster-whisper-large-v3-ca-3catparla

faster-whisper-large-v3-ca-3catparla

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 "faster-whisper-large-v3-ca-3catparla" is an acoustic model based on a faster-whisper version of projecte-aina/whisper-large-v3-ca-3catparla suitable for Automatic Speech Recognition in Catalan.

Model Description

The "faster-whisper-large-v3-ca-3catparla" is the result of converting the projecte-aina/whisper-large-v3-ca-3catparla into a lighter model using a python module called faster-whisper.

The specific dataset used to create the projecte-aina/whisper-large-v3-ca-3catparla model is called "3CatParla" (soon to be published).

Intended Uses and Limitations

This model can used for Automatic Speech Recognition (ASR) in Catalan. The model is intended to transcribe audio files in Catalan to plain text without punctuation.

How to Get Started with the Model

To see an updated and functional version of this code, please see our our Notebook.

Installation

In order 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

In order to transcribe audio in Catalan using this model, you can follow this example:

from faster_whisper import WhisperModel

model_size = "projecte-aina/faster-whisper-large-v3-ca-3catparla"

# 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 projecte-aina/whisper-large-v3-ca-3catparla 
   --output_dir faster-whisper-large-v3-ca-3catparla 
   --copy_files preprocessor_config.json 
   --quantization float16

Citation

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

@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}
}

<!-- @misc{mena2024fastwhis3catparla, title={Acoustic Model in Catalan: faster-whisper-large-v3-ca-3catparla.}, author={Hernandez Mena, Carlos Daniel; Armentano-Oller, Carme; Solito, Sarah; Külebi, Baybars}, organization={Barcelona Supercomputing Center}, url={https://huggingface.co/projecte-aina/faster-whisper-large-v3-ca-3catparla}, year={2024}, } -->

Additional Information

Author

The conversion process was perform during July (2024) in the Language Technologies Unit of the Barcelona Supercomputing Center by Carlos Daniel Hernández Mena.

Contact

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

Copyright

Copyright(c) 2024 by Language Technologies Unit, 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 conversion of the model was possible thanks to the compute time provided by Barcelona Supercomputing Center through MareNostrum 5.