
# 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
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["audiofolder"], "metrics": ["accuracy", "f1", "recall", "precision"], "pipeline_tag": "audio-classification", "model-index": [{"name": "wav2vec2-base-is_vinyl_scratched_or_not", "results": []}]}
transformers pytorch wav2vec2 audio-classification generated_from_trainer en dataset:audiofolder license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #audio-classification #generated_from_trainer #en #dataset-audiofolder #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-base-is\_vinyl\_scratched\_or\_not
===========================================


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


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: URL


Intended uses & limitations
---------------------------


This model is intended to demonstrate my ability to solve a complex problem using technology.


Training and evaluation data
----------------------------


Dataset Source: URL


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



### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.12.1



### 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 ### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #wav2vec2 #audio-classification #generated_from_trainer #en #dataset-audiofolder #license-apache-2.0 #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #wav2vec2 #audio-classification #generated_from_trainer #en #dataset-audiofolder #license-apache-2.0 #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.12.1