---
base_model: microsoft/wavlm-base
tags:
- generated_from_trainer
task: audio-classification
model-index:
- name: wavlm_finetuned_emodb
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# wavlm_finetuned_emodb

This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9254
- Uar: 0.8148
- Acc: 0.8529

## Model description

This model predict given audio waveform to one of four common emotion categories: anger, happiness, sadness, and neutral
## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Uar    | Acc    |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 1.3857        | 0.1538 | 1    | 1.3786          | 0.25   | 0.1985 |
| 1.3322        | 0.3077 | 2    | 1.3549          | 0.2914 | 0.2426 |
| 1.3112        | 0.4615 | 3    | 1.3165          | 0.5375 | 0.6103 |
| 1.2981        | 0.6154 | 4    | 1.2905          | 0.5    | 0.6029 |
| 1.1317        | 0.7692 | 5    | 1.2923          | 0.4907 | 0.5956 |
| 1.2078        | 0.9231 | 6    | 1.2619          | 0.5556 | 0.6471 |
| 0.9237        | 1.0769 | 7    | 1.2254          | 0.5741 | 0.6618 |
| 0.8396        | 1.2308 | 8    | 1.2247          | 0.5556 | 0.6471 |
| 1.0354        | 1.3846 | 9    | 1.2076          | 0.5556 | 0.6471 |
| 0.9205        | 1.5385 | 10   | 1.1891          | 0.5833 | 0.6691 |
| 0.9071        | 1.6923 | 11   | 1.1704          | 0.6481 | 0.7206 |
| 0.8132        | 1.8462 | 12   | 1.1988          | 0.6939 | 0.5735 |
| 0.8994        | 2.0    | 13   | 1.1960          | 0.6574 | 0.5221 |
| 0.7924        | 2.1538 | 14   | 1.1579          | 0.6658 | 0.5662 |
| 0.7386        | 2.3077 | 15   | 1.1401          | 0.6944 | 0.7574 |
| 0.6324        | 2.4615 | 16   | 1.1202          | 0.6111 | 0.6912 |
| 0.7282        | 2.6154 | 17   | 1.1090          | 0.5833 | 0.6691 |
| 0.673         | 2.7692 | 18   | 1.0907          | 0.6111 | 0.6912 |
| 0.623         | 2.9231 | 19   | 1.0578          | 0.7872 | 0.8235 |
| 0.4954        | 3.0769 | 20   | 1.0357          | 0.8475 | 0.8676 |
| 0.5201        | 3.2308 | 21   | 1.0365          | 0.7778 | 0.8235 |
| 0.5608        | 3.3846 | 22   | 1.0346          | 0.75   | 0.8015 |
| 0.6334        | 3.5385 | 23   | 1.0047          | 0.7685 | 0.8162 |
| 0.3737        | 3.6923 | 24   | 0.9585          | 0.8658 | 0.8897 |
| 0.5369        | 3.8462 | 25   | 0.9527          | 0.9178 | 0.8824 |
| 0.3599        | 4.0    | 26   | 0.9682          | 0.8906 | 0.8382 |
| 0.7642        | 4.1538 | 27   | 0.9418          | 0.8951 | 0.8456 |
| 0.4882        | 4.3077 | 28   | 0.9095          | 0.9310 | 0.9265 |
| 0.5011        | 4.4615 | 29   | 0.9378          | 0.8426 | 0.875  |
| 0.3707        | 4.6154 | 30   | 0.9630          | 0.7963 | 0.8382 |
| 0.381         | 4.7692 | 31   | 0.9721          | 0.7870 | 0.8309 |
| 0.2307        | 4.9231 | 32   | 0.9522          | 0.7963 | 0.8382 |
| 0.2829        | 5.0769 | 33   | 0.9598          | 0.7870 | 0.8309 |
| 0.2581        | 5.2308 | 34   | 0.9458          | 0.8056 | 0.8456 |
| 0.4658        | 5.3846 | 35   | 0.9442          | 0.8148 | 0.8529 |
| 0.2133        | 5.5385 | 36   | 0.9524          | 0.7870 | 0.8309 |
| 0.1107        | 5.6923 | 37   | 0.9601          | 0.7870 | 0.8309 |
| 0.3599        | 5.8462 | 38   | 0.9605          | 0.7778 | 0.8235 |
| 0.3085        | 6.0    | 39   | 0.9522          | 0.7918 | 0.8309 |
| 0.2739        | 6.1538 | 40   | 0.9564          | 0.7870 | 0.8309 |
| 0.3279        | 6.3077 | 41   | 0.9582          | 0.7870 | 0.8309 |
| 0.1346        | 6.4615 | 42   | 0.9646          | 0.7685 | 0.8162 |
| 0.1429        | 6.6154 | 43   | 0.9695          | 0.7685 | 0.8162 |
| 0.1           | 6.7692 | 44   | 0.9692          | 0.7685 | 0.8162 |
| 0.1852        | 6.9231 | 45   | 0.9651          | 0.7685 | 0.8162 |
| 0.1028        | 7.0769 | 46   | 0.9378          | 0.8056 | 0.8456 |
| 0.2071        | 7.2308 | 47   | 0.9154          | 0.8195 | 0.8529 |
| 0.1752        | 7.3846 | 48   | 0.8882          | 0.8566 | 0.8824 |
| 0.0907        | 7.5385 | 49   | 0.8704          | 0.8843 | 0.9044 |
| 0.1263        | 7.6923 | 50   | 0.8719          | 0.8798 | 0.8971 |
| 0.068         | 7.8462 | 51   | 0.8738          | 0.8798 | 0.8971 |
| 0.0589        | 8.0    | 52   | 0.8881          | 0.8566 | 0.8824 |
| 0.1494        | 8.1538 | 53   | 0.9001          | 0.8473 | 0.875  |
| 0.1137        | 8.3077 | 54   | 0.9120          | 0.8288 | 0.8603 |
| 0.0522        | 8.4615 | 55   | 0.9212          | 0.8148 | 0.8529 |
| 0.0666        | 8.6154 | 56   | 0.9251          | 0.8148 | 0.8529 |
| 0.0867        | 8.7692 | 57   | 0.9270          | 0.8148 | 0.8529 |
| 0.0764        | 8.9231 | 58   | 0.9264          | 0.8148 | 0.8529 |
| 0.0526        | 9.0769 | 59   | 0.9259          | 0.8148 | 0.8529 |
| 0.2877        | 9.2308 | 60   | 0.9254          | 0.8148 | 0.8529 |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1