README.md

---
license: apache-2.0
tags:
- afro-digits-speech
datasets:
- crowd-speech-africa
metrics:
- accuracy
model-index:
- name: afrospeech-wav2vec-gax
  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: 1.0
---


# afrospeech-wav2vec-gax

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 Oromo (`gax`).

- Size of training set: 32
- Size of validation set: 8

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

![digits-bar-plot-for-afrospeech](digits-bar-plot-for-afrospeech-wav2vec-gax.png)

## Evaluation performance
It achieves the following results on the [validation set](VALID_oromo_gax_audio_data.csv):
- F1: 1.0
- Accuracy: 1.0

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-gax_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 |
|:-------------:|:-----:|:--------:|
| 0.0699       | 1    | 1.0  |
| 0.0021        | 50   | 0.875  |
| 0.0026       | 100   | 0.875  |
| 0.0017       | 150   | 0.875  |



## Framework versions

- Transformers 4.21.3
- Pytorch 1.12.0
- Datasets 1.14.0
- Tokenizers 0.12.1