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--- |
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license: mit |
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datasets: |
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- M9and2M/Wolof_ASR_dataset |
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language: |
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- wo |
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metrics: |
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- wer |
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pipeline_tag: automatic-speech-recognition |
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tags: |
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- Wolof |
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- ASR |
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--- |
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# Wolof ASR Model (Based on Whisper-Small) trained with mixed human and machine generated dataset |
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## Model Overview |
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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. |
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## Model Details |
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- **Model Base**: Whisper-small |
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- **Loss**: 0.123 |
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- **WER**: 0.16 |
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## Dataset |
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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. |
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- **Training Dataset**: 57 hours and 13 hours audio with machine generated transcripts |
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- **Test Dataset**: 10 hours |
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For detailed information about the dataset, please refer to the [M9and2M/Wolof_ASR_dataset](https://huggingface.co/datasets/M9and2M/Wolof_ASR_dataset). |
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## Training |
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The training process was adapted from the code in the [Finetune Wa2vec 2.0 For Speech Recognition](https://github.com/khanld/ASR-Wa2vec-Finetune) 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. |
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The model was trained with the following configuration: |
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- **Seed**: 19 |
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- **Training Batch Size**: 1 |
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- **Gradient Accumulation Steps**: 8 |
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- **Number of GPUs**: 2 |
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### Optimizer : AdamW |
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- **Learning Rate**: 1e-7 |
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### Scheduler: OneCycleLR |
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- **Max Learning Rate**: 5e-5 |
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## Acknowledgements |
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This model was built using OpenAI's [Whisper-small](https://huggingface.co/openai/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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<!-- ## Citation [optional] |
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## More Information |
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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. |
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## Contact |
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For any inquiries or questions, please contact mamadou.marone@ensea.fr |
