---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: wav2vec2-lg-xlsr-en-speech-emotion-recognition-finetuned-gtzan
  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-lg-xlsr-en-speech-emotion-recognition-finetuned-gtzan

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 GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7145
- Accuracy: 0.88

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9771        | 1.0   | 225  | 1.7112          | 0.48     |
| 1.0169        | 2.0   | 450  | 1.1513          | 0.62     |
| 0.7104        | 3.0   | 675  | 0.8799          | 0.7      |
| 1.5425        | 4.0   | 900  | 0.7419          | 0.8      |
| 0.2908        | 5.0   | 1125 | 0.6713          | 0.8      |
| 0.8275        | 6.0   | 1350 | 0.6961          | 0.84     |
| 0.0298        | 7.0   | 1575 | 0.8689          | 0.82     |
| 0.0163        | 8.0   | 1800 | 0.7662          | 0.86     |
| 0.0162        | 9.0   | 2025 | 0.7143          | 0.88     |
| 0.2649        | 10.0  | 2250 | 0.7145          | 0.88     |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3