---
library_name: transformers
base_model: microsoft/wavlm-base
tags:
- generated_from_trainer
model-index:
- name: wavlm-emotion
  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-emotion

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: 1.9485

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 1.0   | 96   | 1.9509          |
| 1.9473        | 2.0   | 192  | 1.9486          |
| 1.9469        | 3.0   | 288  | 1.9512          |
| 1.9469        | 4.0   | 384  | 1.9495          |
| 1.9471        | 5.0   | 480  | 1.9488          |
| 1.9468        | 6.0   | 576  | 1.9474          |
| 1.9478        | 7.0   | 672  | 1.9510          |
| 1.9487        | 8.0   | 768  | 1.9510          |
| 1.9487        | 9.0   | 864  | 1.9466          |
| 1.9471        | 10.0  | 960  | 1.9465          |
| 1.9471        | 11.0  | 1056 | 1.9505          |
| 1.9477        | 12.0  | 1152 | 1.9467          |
| 1.9479        | 13.0  | 1248 | 1.9465          |
| 1.948         | 14.0  | 1344 | 1.9439          |
| 1.9473        | 15.0  | 1440 | 1.9488          |
| 1.9488        | 16.0  | 1536 | 1.9432          |
| 1.9472        | 17.0  | 1632 | 1.9491          |
| 1.9465        | 18.0  | 1728 | 1.9461          |
| 1.9468        | 19.0  | 1824 | 1.9507          |
| 1.9481        | 20.0  | 1920 | 1.9480          |
| 1.9471        | 21.0  | 2016 | 1.9442          |
| 1.9477        | 22.0  | 2112 | 1.9466          |
| 1.9474        | 23.0  | 2208 | 1.9504          |
| 1.9468        | 24.0  | 2304 | 1.9496          |
| 1.9476        | 25.0  | 2400 | 1.9464          |
| 1.9476        | 26.0  | 2496 | 1.9466          |
| 1.9471        | 27.0  | 2592 | 1.9473          |
| 1.9467        | 28.0  | 2688 | 1.9485          |
| 1.9468        | 29.0  | 2784 | 1.9477          |
| 1.9471        | 30.0  | 2880 | 1.9484          |
| 1.9458        | 31.0  | 2976 | 1.9472          |
| 1.9476        | 32.0  | 3072 | 1.9475          |
| 1.9468        | 33.0  | 3168 | 1.9482          |
| 1.9468        | 34.0  | 3264 | 1.9495          |
| 1.9463        | 35.0  | 3360 | 1.9497          |
| 1.9474        | 36.0  | 3456 | 1.9490          |
| 1.9462        | 37.0  | 3552 | 1.9481          |
| 1.9458        | 38.0  | 3648 | 1.9490          |
| 1.9461        | 39.0  | 3744 | 1.9486          |
| 1.9446        | 40.0  | 3840 | 1.9488          |
| 1.946         | 41.0  | 3936 | 1.9490          |
| 1.9467        | 42.0  | 4032 | 1.9487          |
| 1.9466        | 43.0  | 4128 | 1.9485          |
| 1.9463        | 44.0  | 4224 | 1.9486          |
| 1.9459        | 45.0  | 4320 | 1.9486          |
| 1.9458        | 46.0  | 4416 | 1.9487          |
| 1.9464        | 47.0  | 4512 | 1.9485          |
| 1.946         | 48.0  | 4608 | 1.9485          |
| 1.9459        | 49.0  | 4704 | 1.9485          |
| 1.9459        | 50.0  | 4800 | 1.9485          |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0