marone_wolof_wav2vec2-xls-r-300m

Wolof ASR Model (Based on Whisper-Small)

Model Overview

This repository hosts an Automatic Speech Recognition (ASR) model for the Wolof language, fine-tuned from Wav2Vce2.0 model. This model aims to provide accurate transcription of Wolof audio data.

Model Details

• Model Base: wav2vec2-xls-r-300m
• Loss: 0.1604
• WER: 0.24

Dataset

The dataset used for training and evaluating this model is a collection from various sources, ensuring a rich and diverse set of Wolof audio samples. The collection is available in my Hugging Face account is used by keeping only the audios with duration shorter than 6 second.

• Training Dataset: 57 hours
• Test Dataset: 10 hours

For detailed information about the dataset, please refer to the M9and2M/Wolof_ASR_dataset.

Training

The training process was adapted from the code in the Finetune Wa2vec 2.0 For Speech Recognition written to fine-tune Wav2Vec2.0 for speech recognition. Special thanks to the author, Duy Khanh, Le for providing a robust and flexible training framework.

The model was trained with the following configuration:

• Seed: 19
• Training Batch Size: 4
• Gradient Accumulation Steps: 8
• Number of GPUs: 2

Optimizer : AdamW

• Learning Rate: 1e-6

Scheduler: OneCycleLR

• Max Learning Rate: 5e-5

Acknowledgements

This model was built using Facebook's Wav2Vec2.0 architecture and fine-tuned with a dataset collected from various sources. Special thanks to the creators and contributors of the dataset.

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More Information

This model has been developed in the context of my Master Thesis at ETSIT-UPM, Madrid under the supervision of Prof. Luis A. Hernández Gómez.

Contact

For any inquiries or questions, please contact [email protected]