đ wav2vec2-base-is_vinyl_scratched_or_not
This model is a fine - tuned version of facebook/wav2vec2-base on the audiofolder dataset, used for audio classification to predict whether a vinyl record is scratched.
đ Quick Start
This model is a fine - tuned version of 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
⨠Features
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
đ Documentation
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
đ§ Technical Details
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
đ License
This project is licensed under the Apache - 2.0 license.