---
base_model: microsoft/wavlm-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wavlm-base_5
  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-base_5

This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4151
- Accuracy: 0.8974

## 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: 16
- eval_batch_size: 2
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3764        | 0.25  | 100  | 0.0277          | 0.9948   |
| 0.1211        | 0.5   | 200  | 0.0297          | 0.9981   |
| 0.2525        | 0.76  | 300  | 1.2840          | 0.9168   |
| 0.784         | 1.01  | 400  | 0.3443          | 0.8974   |
| 0.6053        | 1.26  | 500  | 0.3958          | 0.8974   |
| 0.6038        | 1.51  | 600  | 0.4848          | 0.8974   |
| 0.5996        | 1.76  | 700  | 0.3954          | 0.8974   |
| 0.5914        | 2.02  | 800  | 0.3970          | 0.8974   |
| 0.6077        | 2.27  | 900  | 0.4722          | 0.8974   |
| 0.5991        | 2.52  | 1000 | 0.4362          | 0.8974   |
| 0.5813        | 2.77  | 1100 | 0.3871          | 0.8974   |
| 0.5953        | 3.02  | 1200 | 0.4013          | 0.8974   |
| 0.5957        | 3.28  | 1300 | 0.4693          | 0.8974   |
| 0.5852        | 3.53  | 1400 | 0.3879          | 0.8974   |
| 0.6066        | 3.78  | 1500 | 0.4280          | 0.8974   |
| 0.6085        | 4.03  | 1600 | 0.4359          | 0.8974   |
| 0.5944        | 4.28  | 1700 | 0.4167          | 0.8974   |
| 0.5994        | 4.54  | 1800 | 0.4139          | 0.8974   |
| 0.5953        | 4.79  | 1900 | 0.4256          | 0.8974   |
| 0.5929        | 5.04  | 2000 | 0.4371          | 0.8974   |
| 0.6067        | 5.29  | 2100 | 0.4255          | 0.8974   |
| 0.5944        | 5.55  | 2200 | 0.4121          | 0.8974   |
| 0.5926        | 5.8   | 2300 | 0.4210          | 0.8974   |
| 0.594         | 6.05  | 2400 | 0.4057          | 0.8974   |
| 0.6042        | 6.3   | 2500 | 0.4252          | 0.8974   |
| 0.5971        | 6.55  | 2600 | 0.3958          | 0.8974   |
| 0.597         | 6.81  | 2700 | 0.4124          | 0.8974   |
| 0.5816        | 7.06  | 2800 | 0.4101          | 0.8974   |
| 0.5944        | 7.31  | 2900 | 0.4258          | 0.8974   |
| 0.6053        | 7.56  | 3000 | 0.4415          | 0.8974   |
| 0.5894        | 7.81  | 3100 | 0.4067          | 0.8974   |
| 0.5987        | 8.07  | 3200 | 0.4109          | 0.8974   |
| 0.5846        | 8.32  | 3300 | 0.4095          | 0.8974   |
| 0.5982        | 8.57  | 3400 | 0.4187          | 0.8974   |
| 0.5932        | 8.82  | 3500 | 0.4124          | 0.8974   |
| 0.6007        | 9.07  | 3600 | 0.4212          | 0.8974   |
| 0.6041        | 9.33  | 3700 | 0.4257          | 0.8974   |
| 0.5859        | 9.58  | 3800 | 0.4176          | 0.8974   |
| 0.5842        | 9.83  | 3900 | 0.4151          | 0.8974   |


### Framework versions

- Transformers 4.34.0.dev0
- Pytorch 2.0.0.post302
- Datasets 2.14.5
- Tokenizers 0.13.3