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---
license: apache-2.0
tags:
- Speech-Emotion-Recognition
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Wav2vec2-xlsr-Shemo-Ravdess-4EMO
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. -->
# Wav2vec2-xlsr-Shemo-Ravdess-4EMO
This model is a fine-tuned version of [makhataei/Wav2vec2-xlsr-Shemo-Ravdess-4EMO](https://huggingface.co/makhataei/Wav2vec2-xlsr-Shemo-Ravdess-4EMO) on the minoosh/shEMO dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9089
- Accuracy: 0.6712
## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 35
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9106 | 1.0 | 250 | 1.0288 | 0.6259 |
| 0.8205 | 2.0 | 500 | 0.9426 | 0.6576 |
| 0.7767 | 3.0 | 750 | 0.9707 | 0.6553 |
| 0.803 | 4.0 | 1000 | 0.9698 | 0.6644 |
| 0.7489 | 5.0 | 1250 | 0.9583 | 0.6463 |
| 0.7734 | 6.0 | 1500 | 0.9138 | 0.6757 |
| 0.7603 | 7.0 | 1750 | 0.8905 | 0.6712 |
| 0.7741 | 8.0 | 2000 | 0.9169 | 0.6599 |
| 0.7569 | 9.0 | 2250 | 0.9369 | 0.6417 |
| 0.7854 | 10.0 | 2500 | 0.9256 | 0.6599 |
| 0.7572 | 11.0 | 2750 | 0.9320 | 0.6621 |
| 0.7537 | 12.0 | 3000 | 0.8960 | 0.6825 |
| 0.745 | 13.0 | 3250 | 0.9495 | 0.6599 |
| 0.7598 | 14.0 | 3500 | 0.9196 | 0.6667 |
| 0.7536 | 15.0 | 3750 | 0.9464 | 0.6599 |
| 0.7428 | 16.0 | 4000 | 0.9407 | 0.6485 |
| 0.757 | 17.0 | 4250 | 0.9251 | 0.6689 |
| 0.7694 | 18.0 | 4500 | 0.9246 | 0.6576 |
| 0.7501 | 19.0 | 4750 | 0.9283 | 0.6621 |
| 0.7464 | 20.0 | 5000 | 0.9333 | 0.6531 |
| 0.7569 | 21.0 | 5250 | 0.9062 | 0.6667 |
| 0.745 | 22.0 | 5500 | 0.9569 | 0.6485 |
| 0.7404 | 23.0 | 5750 | 0.9062 | 0.6667 |
| 0.7384 | 24.0 | 6000 | 0.8948 | 0.6780 |
| 0.7524 | 25.0 | 6250 | 0.9296 | 0.6599 |
| 0.7574 | 26.0 | 6500 | 0.8925 | 0.6825 |
| 0.7876 | 27.0 | 6750 | 0.9061 | 0.6712 |
| 0.7692 | 28.0 | 7000 | 0.9319 | 0.6508 |
| 0.7352 | 29.0 | 7250 | 0.9145 | 0.6644 |
| 0.7496 | 30.0 | 7500 | 0.9068 | 0.6735 |
| 0.7406 | 31.0 | 7750 | 0.9024 | 0.6735 |
| 0.7334 | 32.0 | 8000 | 0.9231 | 0.6576 |
| 0.761 | 33.0 | 8250 | 0.9073 | 0.6712 |
| 0.7476 | 34.0 | 8500 | 0.9097 | 0.6667 |
| 0.7868 | 35.0 | 8750 | 0.9089 | 0.6712 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
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