---
license: apache-2.0
language:
- es
- ca
base_model:
- openai/whisper-large-v3
pipeline_tag: automatic-speech-recognition
tags:
- bsc
- projecte-aina
- barcelona-supercomputing-center
- automatic-speech-recognition
- whisper-large-v3
- code-switching
- spanish
- catalan
---

# whisper-timestamped-cs

## 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)
- [Training Details](#training-details)
- [Citation](#citation)
- [Additional Information](#additional-information)

</details>

## Summary

The "whisper-timestamped-cs" is an acoustic model based on ["openai/whisper-large-v3"](https://huggingface.co/openai/whisper-large-v3) suitable for Automatic Speech Recognition in code-switching conditions between Spanish and Catalan.

## Model Description

The "whisper-timestamped-cs" is an acoustic model suitable for Automatic Speech Recognition in code-switching conditions between Spanish and Catalan. It is the result of finetuning the model ["openai/whisper-large-v3"](https://huggingface.co/openai/whisper-large-v3) with 2 hours of synthetic code-switching data in Spanish/Catalan generated by the [Projecte AINA](https://projecteaina.cat/) from Barcelona, Spain.

## Intended Uses and Limitations

This model can be used for Automatic Speech Recognition (ASR) in code-switching conditions between Spanish and Catalan. The model is intended to transcribe audio files to plain text.

### Installation

To use this model, you may install [whisper-timestamped](https://github.com/linto-ai/whisper-timestamped):

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 git+https://github.com/linto-ai/whisper-timestamped
```

### For Inference
To transcribe audio in code-switching using this model, you can follow this example:

```python
import whisper_timestamped as whisper

model = whisper.load_model("langtech-veu/whisper-timestamped-cs", device="cpu")
result = whisper.transcribe(model, "/path/to/the/audio.wav")

import json
print(json.dumps(result, indent = 2, ensure_ascii = False))
```

## Training Details

### Training data

The specific dataset used to create the model is a corpus called CAESAR-tiny, which has not been released at the moment.

## Citation
If this model contributes to your research, please cite the work:
```bibtex
@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/whisper-timestamped-cs},
      year={2025}
}
```

## Additional Information

### Author

The fine-tuning process was performed during 2025 in the [Language Technologies Laboratory](https://huggingface.co/BSC-LT) of the [Barcelona Supercomputing Center](https://www.bsc.es/).

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

### Copyright
Copyright(c) 2025 by Language Technologies Laboratory, 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 training of the model was possible thanks to the computing time provided by [Barcelona Supercomputing Center](https://www.bsc.es/) through MareNostrum 5.