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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: wav2vec2-base-is_vinyl_scratched_or_not
results: []
language:
- en
pipeline_tag: audio-classification
---
# wav2vec2-base-is_vinyl_scratched_or_not
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1039
- Accuracy: 0.9752
- F1: 0.9638
- Recall: 0.9576
- Precision: 0.9700
## Model description
This is a binary classifier that predicts whether or not the vinyl record played in the audio sample is scratched.
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
## Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
## Training and evaluation data
Dataset Source: https://www.kaggle.com/datasets/seandaly/detecting-scratch-noise-in-vinyl-playback
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.6671 | 0.98 | 21 | 0.6235 | 0.6560 | 0.0 | 0.0 | 0.0 |
| 0.4954 | 1.98 | 42 | 0.2824 | 0.9417 | 0.9095 | 0.8517 | 0.9757 |
| 0.2406 | 2.98 | 63 | 0.1755 | 0.9563 | 0.9336 | 0.8941 | 0.9769 |
| 0.169 | 3.98 | 84 | 0.1545 | 0.9592 | 0.9386 | 0.9068 | 0.9727 |
| 0.1287 | 4.98 | 105 | 0.1249 | 0.9606 | 0.9407 | 0.9068 | 0.9772 |
| 0.1102 | 5.98 | 126 | 0.1159 | 0.9723 | 0.9595 | 0.9534 | 0.9657 |
| 0.0923 | 6.98 | 147 | 0.1073 | 0.9665 | 0.9516 | 0.9576 | 0.9456 |
| 0.0877 | 7.98 | 168 | 0.1039 | 0.9752 | 0.9638 | 0.9576 | 0.9700 |
| 0.0807 | 8.98 | 189 | 0.1088 | 0.9679 | 0.9536 | 0.9576 | 0.9496 |
| 0.0744 | 9.98 | 210 | 0.1041 | 0.9752 | 0.9638 | 0.9576 | 0.9700 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.12.1 |