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This model is a fine-tuned version of facebook/wav2vec2-base on the crowd-speech-africa, which was a crowd-sourced dataset collected using the afro-speech Space.

Training and evaluation data

The model was trained on a mixed audio data from 6 African languages - Igbo (ibo), Yoruba (yor), Rundi (run), Oshiwambo (kua), Shona (sna) and Oromo (gax).

  • Size of training set: 1977
  • Size of validation set: 396

Below is a distribution of the dataset (training and valdation)


Evaluation performance

It achieves the following results on the validation set:

  • F1: 0.5787048581502744
  • Accuracy: 0.6205357142857143

The confusion matrix below helps to give a better look at the model's performance across the digits. Through it, we can see the precision and recall of the model as well as other important insights.

confusion matrix

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 3e-05
  • train_batch_size: 64
  • eval_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • num_epochs: 150

Training results

Training Loss Epoch Validation Accuracy
2.0466 1 0.1130
0.0468 50 0.6116
0.0292 100 0.5305
0.0155 150 0.5319

Framework versions

  • Transformers 4.21.3
  • Pytorch 1.12.0
  • Datasets 1.14.0
  • Tokenizers 0.12.1
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Evaluation results