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---
license: apache-2.0
base_model: facebook/wav2vec2-large-robust-ft-swbd-300h
tags:
- generated_from_trainer
model-index:
- name: wav2vec2_swbd_emodb
  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. -->

# finetuned

This model is a fine-tuned version of [facebook/wav2vec2-large-robust-ft-swbd-300h](https://huggingface.co/facebook/wav2vec2-large-robust-ft-swbd-300h) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0281
- Uar: 0.7318
- Acc: 0.7721

For the test set:
- UAR: 0.74
- ACC: 0.794


## Model description

This model is to predict four emotion categories given and audio file. Labels are anger', 'happiness', 'sadness', 'neutral'. This wav2vec2-based model is known cannot detect 'happiness'. 

## 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Uar    | Acc    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| No log        | 0.15  | 1    | 1.3899          | 0.25   | 0.1985 |
| No log        | 0.31  | 2    | 1.3850          | 0.25   | 0.1985 |
| No log        | 0.46  | 3    | 1.3815          | 0.25   | 0.1985 |
| No log        | 0.62  | 4    | 1.3772          | 0.25   | 0.1985 |
| No log        | 0.77  | 5    | 1.3714          | 0.25   | 0.4044 |
| No log        | 0.92  | 6    | 1.3656          | 0.25   | 0.4044 |
| 1.4878        | 1.08  | 7    | 1.3610          | 0.25   | 0.4044 |
| 1.4878        | 1.23  | 8    | 1.3583          | 0.25   | 0.4044 |
| 1.4878        | 1.38  | 9    | 1.3549          | 0.25   | 0.4044 |
| 1.4878        | 1.54  | 10   | 1.3518          | 0.25   | 0.4044 |
| 1.4878        | 1.69  | 11   | 1.3491          | 0.25   | 0.4044 |
| 1.4878        | 1.85  | 12   | 1.3458          | 0.25   | 0.4044 |
| 1.4878        | 2.0   | 13   | 1.3425          | 0.25   | 0.4044 |
| 1.2316        | 2.15  | 14   | 1.3401          | 0.25   | 0.4044 |
| 1.2316        | 2.31  | 15   | 1.3380          | 0.25   | 0.4044 |
| 1.2316        | 2.46  | 16   | 1.3354          | 0.25   | 0.4044 |
| 1.2316        | 2.62  | 17   | 1.3326          | 0.25   | 0.4044 |
| 1.2316        | 2.77  | 18   | 1.3292          | 0.2778 | 0.4265 |
| 1.2316        | 2.92  | 19   | 1.3250          | 0.2963 | 0.4412 |
| 1.3835        | 3.08  | 20   | 1.3212          | 0.3519 | 0.4853 |
| 1.3835        | 3.23  | 21   | 1.3158          | 0.4029 | 0.5221 |
| 1.3835        | 3.38  | 22   | 1.3096          | 0.5047 | 0.6029 |
| 1.3835        | 3.54  | 23   | 1.3019          | 0.5695 | 0.6544 |
| 1.3835        | 3.69  | 24   | 1.2944          | 0.6485 | 0.7059 |
| 1.3835        | 3.85  | 25   | 1.2856          | 0.6534 | 0.6985 |
| 1.3835        | 4.0   | 26   | 1.2773          | 0.6768 | 0.7059 |
| 1.1038        | 4.15  | 27   | 1.2688          | 0.6540 | 0.6691 |
| 1.1038        | 4.31  | 28   | 1.2554          | 0.6404 | 0.6471 |
| 1.1038        | 4.46  | 29   | 1.2404          | 0.6359 | 0.6397 |
| 1.1038        | 4.62  | 30   | 1.2222          | 0.6586 | 0.6765 |
| 1.1038        | 4.77  | 31   | 1.2057          | 0.6631 | 0.6838 |
| 1.1038        | 4.92  | 32   | 1.1874          | 0.6769 | 0.6985 |
| 1.075         | 5.08  | 33   | 1.1624          | 0.6953 | 0.7206 |
| 1.075         | 5.23  | 34   | 1.1427          | 0.7182 | 0.75   |
| 1.075         | 5.38  | 35   | 1.1270          | 0.7182 | 0.75   |
| 1.075         | 5.54  | 36   | 1.1085          | 0.7227 | 0.7574 |
| 1.075         | 5.69  | 37   | 1.0982          | 0.7227 | 0.7574 |
| 1.075         | 5.85  | 38   | 1.0943          | 0.7227 | 0.7574 |
| 1.075         | 6.0   | 39   | 1.0930          | 0.7136 | 0.7426 |
| 0.7211        | 6.15  | 40   | 1.0903          | 0.7091 | 0.7353 |
| 0.7211        | 6.31  | 41   | 1.0858          | 0.7091 | 0.7353 |
| 0.7211        | 6.46  | 42   | 1.0816          | 0.7045 | 0.7279 |
| 0.7211        | 6.62  | 43   | 1.0734          | 0.7091 | 0.7353 |
| 0.7211        | 6.77  | 44   | 1.0617          | 0.7136 | 0.7426 |
| 0.7211        | 6.92  | 45   | 1.0536          | 0.7136 | 0.7426 |
| 0.6595        | 7.08  | 46   | 1.0450          | 0.7318 | 0.7721 |
| 0.6595        | 7.23  | 47   | 1.0370          | 0.7364 | 0.7794 |
| 0.6595        | 7.38  | 48   | 1.0323          | 0.7364 | 0.7794 |
| 0.6595        | 7.54  | 49   | 1.0301          | 0.7364 | 0.7794 |
| 0.6595        | 7.69  | 50   | 1.0307          | 0.7364 | 0.7794 |
| 0.6595        | 7.85  | 51   | 1.0302          | 0.7318 | 0.7721 |
| 0.6595        | 8.0   | 52   | 1.0307          | 0.7318 | 0.7721 |
| 0.5067        | 8.15  | 53   | 1.0317          | 0.7318 | 0.7721 |
| 0.5067        | 8.31  | 54   | 1.0324          | 0.7318 | 0.7721 |
| 0.5067        | 8.46  | 55   | 1.0324          | 0.7318 | 0.7721 |
| 0.5067        | 8.62  | 56   | 1.0326          | 0.7273 | 0.7647 |
| 0.5067        | 8.77  | 57   | 1.0315          | 0.7318 | 0.7721 |
| 0.5067        | 8.92  | 58   | 1.0297          | 0.7318 | 0.7721 |
| 0.5617        | 9.08  | 59   | 1.0287          | 0.7318 | 0.7721 |
| 0.5617        | 9.23  | 60   | 1.0281          | 0.7318 | 0.7721 |


### Framework versions

- Transformers 4.32.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.13.3