---
license: apache-2.0
language:
- ca
datasets:
- projecte-aina/3catparla_asr
tags:
- audio
- automatic-speech-recognition
- catalan
- faster-whisper
- whisper-large-v3
- catalonia
- barcelona-supercomputing-center
- projecte-aina
- 3catparla
---
# faster-whisper-large-v3-ca-3catparla

## Table of Contents
<details>
<summary>Click to expand</summary>

- [Model Description](#model-description)
- [Intended Uses and Limitations](#intended-uses-and-limitations)
- [How to Get Started with the Model](#how-to-get-started-with-the-model)
- [Conversion Details](#conversion-details)
- [Citation](#citation)
- [Additional information](#additional-information)

</details>

## Summary

The "faster-whisper-large-v3-ca-3catparla" is an acoustic model based on a [faster-whisper](https://github.com/guillaumekln/faster-whisper/tree/master) version of [projecte-aina/whisper-large-v3-ca-3catparla](https://huggingface.co/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](https://huggingface.co/projecte-aina/whisper-large-v3-ca-3catparla) into a lighter model using a python module called [faster-whisper](https://github.com/guillaumekln/faster-whisper/tree/master).

The specific dataset used to create the [projecte-aina/whisper-large-v3-ca-3catparla](https://huggingface.co/projecte-aina/whisper-large-v3-ca-3catparla) model is called ["3CatParla"](https://huggingface.co/datasets/projecte-aina/3catparla_asr).

## 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

### Installation

In order to use this model, you may install [faster-whisper](https://github.com/guillaumekln/faster-whisper/tree/master) 

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

```python
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="translate",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](https://huggingface.co/openai/whisper-large-v3) model using [faster-whisper](https://github.com/guillaumekln/faster-whisper/tree/master). The procedure to create the model is as follows:

```bash
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:
```bibtex
@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](https://huggingface.co/BSC-LT) of the [Barcelona Supercomputing Center](https://www.bsc.es/) by [Carlos Daniel Hernández Mena](https://huggingface.co/carlosdanielhernandezmena).

### Contact
For further information, please send an email to <langtech@bsc.es>.

### Copyright
Copyright(c) 2024 by Language Technologies Unit, Barcelona Supercomputing Center.

### License

[Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)

### Funding
This work has been promoted and financed by the Generalitat de Catalunya through the [Aina project](https://projecteaina.cat/).

The conversion of the model was possible thanks to the compute time provided by [Barcelona Supercomputing Center](https://www.bsc.es/) through MareNostrum 5.