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license: apache-2.0
base_model: facebook/hubert-large-ll60k
tags:
- generated_from_trainer
model-index:
- name: hubert_large_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. -->
# hubert_large_emodb
This model is a fine-tuned version of [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9789
- Uar: 0.8800
- Acc: 0.8897
For the test Set:
- UAR: 0.805
- 0.845
FI scores:
labels: ['anger', 'happiness', 'sadness', 'neutral']
Result per class (F1 score): [0.84, 0.364, 1.0, 1.0]
## Model description
This model is to predict one of four emotion categories: 'anger', 'happiness', 'sadness', 'neutral'
## Intended uses & limitations
How to use:
```
from transformers import pipeline
pipe = pipeline("audio-classification", model="Bagus/hubert_large_emodb")
pipe('file.wav')
## 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 | 1.3865 | 0.25 | 0.1985 |
| No log | 0.31 | 2 | 1.3794 | 0.25 | 0.1985 |
| No log | 0.46 | 3 | 1.3745 | 0.25 | 0.1985 |
| No log | 0.62 | 4 | 1.3684 | 0.3227 | 0.3162 |
| No log | 0.77 | 5 | 1.3592 | 0.4722 | 0.5809 |
| No log | 0.92 | 6 | 1.3487 | 0.3981 | 0.5221 |
| 1.4402 | 1.08 | 7 | 1.3406 | 0.4444 | 0.5588 |
| 1.4402 | 1.23 | 8 | 1.3359 | 0.5278 | 0.625 |
| 1.4402 | 1.38 | 9 | 1.3305 | 0.5418 | 0.6324 |
| 1.4402 | 1.54 | 10 | 1.3228 | 0.5790 | 0.6544 |
| 1.4402 | 1.69 | 11 | 1.3078 | 0.6392 | 0.6985 |
| 1.4402 | 1.85 | 12 | 1.2832 | 0.6577 | 0.7132 |
| 1.4402 | 2.0 | 13 | 1.2445 | 0.6670 | 0.7206 |
| 1.0783 | 2.15 | 14 | 1.2087 | 0.6715 | 0.7279 |
| 1.0783 | 2.31 | 15 | 1.1857 | 0.6579 | 0.7059 |
| 1.0783 | 2.46 | 16 | 1.1746 | 0.6488 | 0.6912 |
| 1.0783 | 2.62 | 17 | 1.1666 | 0.6397 | 0.6765 |
| 1.0783 | 2.77 | 18 | 1.1393 | 0.6443 | 0.6838 |
| 1.0783 | 2.92 | 19 | 1.1079 | 0.6810 | 0.7279 |
| 0.9255 | 3.08 | 20 | 1.0908 | 0.7271 | 0.7721 |
| 0.9255 | 3.23 | 21 | 1.0786 | 0.7131 | 0.7647 |
| 0.9255 | 3.38 | 22 | 1.0697 | 0.6574 | 0.7279 |
| 0.9255 | 3.54 | 23 | 1.0711 | 0.6111 | 0.6912 |
| 0.9255 | 3.69 | 24 | 1.0651 | 0.6389 | 0.7132 |
| 0.9255 | 3.85 | 25 | 1.0596 | 0.6481 | 0.7206 |
| 0.9255 | 4.0 | 26 | 1.0566 | 0.6667 | 0.7353 |
| 0.6547 | 4.15 | 27 | 1.0562 | 0.6667 | 0.7353 |
| 0.6547 | 4.31 | 28 | 1.0553 | 0.7222 | 0.7794 |
| 0.6547 | 4.46 | 29 | 1.0549 | 0.7316 | 0.7794 |
| 0.6547 | 4.62 | 30 | 1.0546 | 0.7456 | 0.7868 |
| 0.6547 | 4.77 | 31 | 1.0516 | 0.7549 | 0.7941 |
| 0.6547 | 4.92 | 32 | 1.0428 | 0.7456 | 0.7868 |
| 0.7058 | 5.08 | 33 | 1.0312 | 0.7502 | 0.7941 |
| 0.7058 | 5.23 | 34 | 1.0235 | 0.7594 | 0.8015 |
| 0.7058 | 5.38 | 35 | 1.0143 | 0.7732 | 0.8162 |
| 0.7058 | 5.54 | 36 | 1.0079 | 0.7963 | 0.8382 |
| 0.7058 | 5.69 | 37 | 1.0049 | 0.7963 | 0.8382 |
| 0.7058 | 5.85 | 38 | 1.0051 | 0.7778 | 0.8235 |
| 0.7058 | 6.0 | 39 | 1.0066 | 0.7593 | 0.8088 |
| 0.4919 | 6.15 | 40 | 1.0119 | 0.7407 | 0.7941 |
| 0.4919 | 6.31 | 41 | 1.0172 | 0.7222 | 0.7794 |
| 0.4919 | 6.46 | 42 | 1.0191 | 0.7130 | 0.7721 |
| 0.4919 | 6.62 | 43 | 1.0175 | 0.7130 | 0.7721 |
| 0.4919 | 6.77 | 44 | 1.0144 | 0.7222 | 0.7794 |
| 0.4919 | 6.92 | 45 | 1.0094 | 0.7222 | 0.7794 |
| 0.5048 | 7.08 | 46 | 1.0050 | 0.7593 | 0.8088 |
| 0.5048 | 7.23 | 47 | 0.9984 | 0.7870 | 0.8309 |
| 0.5048 | 7.38 | 48 | 0.9948 | 0.7778 | 0.8235 |
| 0.5048 | 7.54 | 49 | 0.9917 | 0.7825 | 0.8235 |
| 0.5048 | 7.69 | 50 | 0.9884 | 0.8195 | 0.8529 |
| 0.5048 | 7.85 | 51 | 0.9846 | 0.8242 | 0.8529 |
| 0.5048 | 8.0 | 52 | 0.9827 | 0.8152 | 0.8382 |
| 0.4133 | 8.15 | 53 | 0.9816 | 0.8337 | 0.8529 |
| 0.4133 | 8.31 | 54 | 0.9812 | 0.8522 | 0.8676 |
| 0.4133 | 8.46 | 55 | 0.9810 | 0.8522 | 0.8676 |
| 0.4133 | 8.62 | 56 | 0.9810 | 0.8707 | 0.8824 |
| 0.4133 | 8.77 | 57 | 0.9806 | 0.8800 | 0.8897 |
| 0.4133 | 8.92 | 58 | 0.9796 | 0.8800 | 0.8897 |
| 0.4717 | 9.08 | 59 | 0.9793 | 0.8800 | 0.8897 |
| 0.4717 | 9.23 | 60 | 0.9789 | 0.8800 | 0.8897 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.13.3
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