--- license: apache-2.0 tags: - afro-digits-speech datasets: - crowd-speech-africa metrics: - accuracy model-index: - name: afrospeech-wav2vec-yor results: - task: name: Audio Classification type: audio-classification dataset: name: Afro Speech type: chrisjay/crowd-speech-africa args: no metrics: - name: Validation Accuracy type: accuracy value: 0.83 --- # afrospeech-wav2vec-yor This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the [crowd-speech-africa](https://huggingface.co/datasets/chrisjay/crowd-speech-africa), which was a crowd-sourced dataset collected using the [afro-speech Space](https://huggingface.co/spaces/chrisjay/afro-speech). ## Training and evaluation data The model was trained on a mixed audio data from Yoruba (`yor`). - Size of training set: 22 - Size of validation set: 6 Below is a distribution of the dataset (training and valdation) ![digits-bar-plot-for-afrospeech](digits-bar-plot-for-afrospeech-wav2vec-yor.png) ## Evaluation performace It achieves the following results on the [validation set](VALID_yoruba_yor_audio_data.csv): - F1: 0.83 - Accuracy: 0.83 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](afrospeech-wav2vec-yor_confusion_matrix_VALID.png) ### 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 | |:-------------:|:-----:|:--------:| |0.596 | 1 | 0.5 | | 0.0220 | 50 | 0.5 | |0.00305 | 100 | 0.667 | |0.0993 | 150 | 0.667 | ### Framework versions - Transformers 4.21.3 - Pytorch 1.12.0 - Datasets 1.14.0 - Tokenizers 0.12.1