--- 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
Click to expand - [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)
## 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 To see an updated and functional version of this code, please see our our [Notebook](https://colab.research.google.com/drive/1v_3m1aR9CwYXgPVBlhwDI9Hz4V5Dlh95?usp=sharing ). ### 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="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](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 . ### 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.