M9and2M/whisper_small_wolof_mix_hum_mach_data
Wolof ASR Model (Based on Whisper-Small) trained with mixed human and machine generated dataset
Model Overview
This repository hosts an Automatic Speech Recognition (ASR) model for the Wolof language, fine-tuned from OpenAI's Whisper-small model. This model aims to provide accurate transcription of Wolof audio data.
Model Details
- Model Base: Whisper-small
- Loss: 0.123
- WER: 0.16
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. In addition of this dataset, audios from YouTub videos are used to synthetize labeled data. This machine generated dataset is mixed with the training dataset and represents 19 % of the dataset used during the training.
- Training Dataset: 57 hours and 13 hours audio with machine generated transcripts
- 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: 1
- Gradient Accumulation Steps: 8
- Number of GPUs: 2
Optimizer : AdamW
- Learning Rate: 1e-7
Scheduler: OneCycleLR
- Max Learning Rate: 5e-5
Acknowledgements
This model was built using OpenAI's Whisper-small 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 mamadou.marone@ensea.fr
