---
license: apache-2.0
tags:
- Speech Emotion Recognition
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Wav2vec2-xlsr-Shemo
  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

This model is a fine-tuned version of [ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition](https://huggingface.co/ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition) on the minoosh/shEMO dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9168
- Accuracy: 0.7267

## 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: 0.003
- 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1825        | 1.0   | 150  | 1.1383          | 0.6267   |
| 1.3392        | 2.0   | 300  | 1.4398          | 0.5533   |
| 1.2058        | 3.0   | 450  | 1.1194          | 0.6300   |
| 1.0984        | 4.0   | 600  | 1.2049          | 0.6200   |
| 1.0033        | 5.0   | 750  | 1.0080          | 0.6500   |
| 0.9694        | 6.0   | 900  | 0.9878          | 0.6367   |
| 0.8506        | 7.0   | 1050 | 0.8965          | 0.7033   |
| 0.8068        | 8.0   | 1200 | 0.9359          | 0.6833   |
| 0.7674        | 9.0   | 1350 | 1.1235          | 0.6333   |
| 0.7817        | 10.0  | 1500 | 0.8682          | 0.6900   |
| 0.7172        | 11.0  | 1650 | 0.8289          | 0.7067   |
| 0.6989        | 12.0  | 1800 | 0.9318          | 0.7000   |
| 0.6127        | 13.0  | 1950 | 0.8712          | 0.6967   |
| 0.6311        | 14.0  | 2100 | 0.8965          | 0.7133   |
| 0.5901        | 15.0  | 2250 | 0.9008          | 0.7267   |
| 0.5667        | 16.0  | 2400 | 1.0093          | 0.7200   |
| 0.5652        | 17.0  | 2550 | 0.9032          | 0.7300   |
| 0.565         | 18.0  | 2700 | 0.9317          | 0.7267   |
| 0.5705        | 19.0  | 2850 | 1.0134          | 0.7133   |
| 0.4984        | 20.0  | 3000 | 0.9432          | 0.7367   |
| 0.5207        | 21.0  | 3150 | 0.9368          | 0.6933   |
| 0.5005        | 22.0  | 3300 | 0.9746          | 0.7033   |
| 0.5055        | 23.0  | 3450 | 1.0437          | 0.7133   |
| 0.4867        | 24.0  | 3600 | 1.0052          | 0.7067   |
| 0.5315        | 25.0  | 3750 | 0.9689          | 0.7200   |
| 0.4755        | 26.0  | 3900 | 0.8962          | 0.7367   |
| 0.5083        | 27.0  | 4050 | 0.9319          | 0.7300   |
| 0.4661        | 28.0  | 4200 | 0.9301          | 0.7233   |
| 0.4536        | 29.0  | 4350 | 0.9370          | 0.7267   |
| 0.4693        | 30.0  | 4500 | 0.9168          | 0.7267   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3