---
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