# wavlm-large_finetuned_RAVDESS

This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS).
It achieves the following results on the evaluation set:
- Loss: 0.3534
- Accuracy: 0.9028

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 9    | 2.0485          | 0.2326   |
| 2.0712        | 2.0   | 18   | 1.8028          | 0.2917   |
| 1.9355        | 3.0   | 27   | 1.7300          | 0.3229   |
| 1.7116        | 4.0   | 36   | 1.3749          | 0.4722   |
| 1.4907        | 5.0   | 45   | 1.0586          | 0.6493   |
| 1.1558        | 6.0   | 54   | 0.8834          | 0.6771   |
| 0.8621        | 7.0   | 63   | 0.9206          | 0.6944   |
| 0.6437        | 8.0   | 72   | 0.5895          | 0.8194   |
| 0.4634        | 9.0   | 81   | 0.7389          | 0.7743   |
| 0.3974        | 10.0  | 90   | 0.4569          | 0.8542   |
| 0.3974        | 11.0  | 99   | 0.5140          | 0.8438   |
| 0.3105        | 12.0  | 108  | 0.4273          | 0.8611   |
| 0.2094        | 13.0  | 117  | 0.3608          | 0.8993   |
| 0.1401        | 14.0  | 126  | 0.5715          | 0.8194   |
| 0.1249        | 15.0  | 135  | 0.3715          | 0.8854   |
| 0.0953        | 16.0  | 144  | 0.4112          | 0.8785   |
| 0.0955        | 17.0  | 153  | 0.3692          | 0.9062   |
| 0.0807        | 18.0  | 162  | 0.4395          | 0.8646   |
| 0.1077        | 19.0  | 171  | 0.3413          | 0.9201   |
| 0.0578        | 20.0  | 180  | 0.3534          | 0.9028   |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.5.0+cu118
- Datasets 3.0.1
- Tokenizers 0.20.1