---
tags:
- audio-classification
- generated_from_trainer
metrics:
- accuracy
- precision
- f1
model-index:
- name: wavlm-large
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# wavlm-large

This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) on the galsenai/waxal_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5936
- Accuracy: 0.8950
- Precision: 0.9789
- F1: 0.9334

## 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 32.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|
| 4.7405        | 1.01  | 500   | 5.1525          | 0.0      | 0.0       | 0.0    |
| 4.4299        | 2.02  | 1000  | 5.8969          | 0.0      | 0.0       | 0.0    |
| 4.2868        | 3.04  | 1500  | 4.9304          | 0.0019   | 0.0031    | 0.0023 |
| 3.6242        | 4.05  | 2000  | 4.3396          | 0.0409   | 0.0224    | 0.0237 |
| 2.686         | 5.06  | 2500  | 3.9399          | 0.0549   | 0.0320    | 0.0308 |
| 1.9284        | 6.07  | 3000  | 3.7736          | 0.0500   | 0.0779    | 0.0442 |
| 1.3936        | 7.08  | 3500  | 3.5380          | 0.0947   | 0.1381    | 0.0916 |
| 1.0764        | 8.1   | 4000  | 3.3281          | 0.1584   | 0.3514    | 0.1839 |
| 0.872         | 9.11  | 4500  | 2.9592          | 0.2755   | 0.6027    | 0.3315 |
| 0.7026        | 10.12 | 5000  | 2.5049          | 0.3971   | 0.6971    | 0.4587 |
| 0.603         | 11.13 | 5500  | 2.1485          | 0.5479   | 0.8074    | 0.6129 |
| 0.5042        | 12.15 | 6000  | 1.6532          | 0.7014   | 0.8604    | 0.7544 |
| 0.4542        | 13.16 | 6500  | 1.4057          | 0.7435   | 0.8941    | 0.7990 |
| 0.388         | 14.17 | 7000  | 1.2338          | 0.7802   | 0.9219    | 0.8332 |
| 0.3515        | 15.18 | 7500  | 0.9898          | 0.8170   | 0.9433    | 0.8681 |
| 0.3195        | 16.19 | 8000  | 1.1404          | 0.8067   | 0.9523    | 0.8635 |
| 0.2882        | 17.21 | 8500  | 0.9811          | 0.8177   | 0.9540    | 0.8746 |
| 0.2695        | 18.22 | 9000  | 0.9483          | 0.8318   | 0.9616    | 0.8878 |
| 0.2535        | 19.23 | 9500  | 0.6694          | 0.8844   | 0.9692    | 0.9198 |
| 0.2437        | 20.24 | 10000 | 0.7546          | 0.8700   | 0.9656    | 0.9125 |
| 0.2376        | 21.25 | 10500 | 0.6698          | 0.8810   | 0.9695    | 0.9202 |
| 0.2214        | 22.27 | 11000 | 0.7156          | 0.8727   | 0.9726    | 0.9174 |
| 0.2148        | 23.28 | 11500 | 0.5982          | 0.8931   | 0.9711    | 0.9286 |
| 0.2087        | 24.29 | 12000 | 0.7109          | 0.8814   | 0.9757    | 0.9243 |
| 0.2039        | 25.3  | 12500 | 0.6577          | 0.8897   | 0.9799    | 0.9306 |
| 0.1997        | 26.32 | 13000 | 0.7307          | 0.8746   | 0.9774    | 0.9203 |
| 0.1896        | 27.33 | 13500 | 0.6143          | 0.8905   | 0.9748    | 0.9290 |
| 0.1869        | 28.34 | 14000 | 0.6380          | 0.8909   | 0.9739    | 0.9287 |
| 0.185         | 29.35 | 14500 | 0.6932          | 0.8871   | 0.9791    | 0.9289 |
| 0.1813        | 30.36 | 15000 | 0.5936          | 0.8950   | 0.9789    | 0.9334 |
| 0.1801        | 31.38 | 15500 | 0.6150          | 0.8947   | 0.9801    | 0.9334 |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2