This model is the result of converting the BSC-LT/whisper-3cat-balearic into a lighter model using a Python module called faster-whisper.
The model can be used for Automatic Speech Recognition (ASR) in Catalan, especially in the Balearic accent. The model intends to transcribe Catalan audio files to plain text without punctuation.
To transcribe audio in Catalan using this model, you can follow this example:
python
1from faster_whisper import WhisperModel
23model_size ="BSC-LT/faster-whisper-3cat-balearic"45# Run on GPU with FP166model = WhisperModel(model_size, device="cuda", compute_type="float16")78# or run on GPU with INT89#model = WhisperModel(model_size, device="cuda", compute_type="int8_float16")10# or run on CPU with INT811# model = WhisperModel(model_size, device="cpu", compute_type="int8")1213segments, info = model.transcribe("audio_in_catalan.mp3", beam_size=5, task="transcribe",language="ca")1415print("Detected language '%s' with probability %f"%(info.language, info.language_probability))1617for segment in segments:18print("[%.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:
This work is funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the project ILENIA with reference 2022/TL22/00215337.
The conversion of the model was possible thanks to the computing time provided by Barcelona Supercomputing Center through MareNostrum 5.
We acknowledge EuroHPC Joint Undertaking for awarding us access to MareNostrum5 as BSC, Spain.