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M9and2M/whisper_small_wolof_mix_hum_mach_data

sourceHugging Facemitupdated 2y agoView on Hugging Face
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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