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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- audiofolder |
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metrics: |
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- accuracy |
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- f1 |
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- recall |
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- precision |
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model-index: |
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- name: wav2vec2-base-is_vinyl_scratched_or_not |
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results: [] |
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language: |
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- en |
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pipeline_tag: audio-classification |
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--- |
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# wav2vec2-base-is_vinyl_scratched_or_not |
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1039 |
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- Accuracy: 0.9752 |
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- F1: 0.9638 |
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- Recall: 0.9576 |
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- Precision: 0.9700 |
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## Model description |
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This is a binary classifier that predicts whether or not the vinyl record played in the audio sample is scratched. |
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For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Audio-Projects/Classification/Vinyl%20Scratched%20or%20Not.ipynb |
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## Intended uses & limitations |
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This model is intended to demonstrate my ability to solve a complex problem using technology. |
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## Training and evaluation data |
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Dataset Source: https://www.kaggle.com/datasets/seandaly/detecting-scratch-noise-in-vinyl-playback |
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## Training procedure |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| |
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| 0.6671 | 0.98 | 21 | 0.6235 | 0.6560 | 0.0 | 0.0 | 0.0 | |
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| 0.4954 | 1.98 | 42 | 0.2824 | 0.9417 | 0.9095 | 0.8517 | 0.9757 | |
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| 0.2406 | 2.98 | 63 | 0.1755 | 0.9563 | 0.9336 | 0.8941 | 0.9769 | |
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| 0.169 | 3.98 | 84 | 0.1545 | 0.9592 | 0.9386 | 0.9068 | 0.9727 | |
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| 0.1287 | 4.98 | 105 | 0.1249 | 0.9606 | 0.9407 | 0.9068 | 0.9772 | |
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| 0.1102 | 5.98 | 126 | 0.1159 | 0.9723 | 0.9595 | 0.9534 | 0.9657 | |
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| 0.0923 | 6.98 | 147 | 0.1073 | 0.9665 | 0.9516 | 0.9576 | 0.9456 | |
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| 0.0877 | 7.98 | 168 | 0.1039 | 0.9752 | 0.9638 | 0.9576 | 0.9700 | |
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| 0.0807 | 8.98 | 189 | 0.1088 | 0.9679 | 0.9536 | 0.9576 | 0.9496 | |
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| 0.0744 | 9.98 | 210 | 0.1041 | 0.9752 | 0.9638 | 0.9576 | 0.9700 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.12.1 |
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- Datasets 2.8.0 |
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- Tokenizers 0.12.1 |
