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--- |
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license: apache-2.0 |
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tags: |
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- afro-digits-speech |
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datasets: |
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- crowd-speech-africa |
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metrics: |
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- accuracy |
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model-index: |
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- name: afrospeech-wav2vec-run |
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results: |
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- task: |
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name: Audio Classification |
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type: audio-classification |
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dataset: |
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name: Afro Speech |
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type: chrisjay/crowd-speech-africa |
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args: no |
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metrics: |
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- name: Validation Accuracy |
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type: accuracy |
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value: 0.8 |
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--- |
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# afrospeech-wav2vec-run |
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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). |
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## Training and evaluation data |
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The model was trained on a mixed audio data from Rundi (`run`). |
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- Size of training set: 16 |
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- Size of validation set: 5 |
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Below is a distribution of the dataset (training and valdation) |
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## Evaluation performance |
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It achieves the following results on the [validation set](VALID_rundi_run_audio_data.csv): |
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- F1: 0.8 |
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- Accuracy: 0.8 |
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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. |
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## Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- num_epochs: 150 |
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## Training results |
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| Training Loss | Epoch | Validation Accuracy | |
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|:-------------:|:-----:|:--------:| |
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|0.00183 | 1 | 0.6 | |
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|0.0003991 | 50 | 0.8 | |
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| 0.0002174 | 100 | 0.6 | |
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|0.0043911 | 150 | 0.4 | |
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### Framework versions |
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- Transformers 4.21.3 |
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- Pytorch 1.12.0 |
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- Datasets 1.14.0 |
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- Tokenizers 0.12.1 |
