---
library_name: transformers
license: apache-2.0
base_model: ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: wav2vec2-lg-xlsr-en-speech-emotion-recognition-finetuned-babycry-v2
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value:
        accuracy: 0.8043478260869565
    - name: F1
      type: f1
      value: 0.7171293871136721
    - name: Precision
      type: precision
      value: 0.6469754253308129
    - name: Recall
      type: recall
      value: 0.8043478260869565
---

<!-- 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-babycry-v2

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 audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8522
- Accuracy: {'accuracy': 0.8043478260869565}
- F1: 0.7171
- Precision: 0.6470
- Recall: 0.8043

## 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: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy                         | F1     | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------:|:------:|:---------:|:------:|
| 0.6078        | 0.4854 | 25   | 0.8682          | {'accuracy': 0.8043478260869565} | 0.7171 | 0.6470    | 0.8043 |
| 0.7269        | 0.9709 | 50   | 0.8559          | {'accuracy': 0.8043478260869565} | 0.7171 | 0.6470    | 0.8043 |
| 0.6815        | 1.4563 | 75   | 0.8204          | {'accuracy': 0.8043478260869565} | 0.7171 | 0.6470    | 0.8043 |
| 0.6144        | 1.9417 | 100  | 0.8417          | {'accuracy': 0.8043478260869565} | 0.7171 | 0.6470    | 0.8043 |
| 0.6246        | 2.4272 | 125  | 0.8454          | {'accuracy': 0.8043478260869565} | 0.7171 | 0.6470    | 0.8043 |
| 0.5687        | 2.9126 | 150  | 0.8527          | {'accuracy': 0.8043478260869565} | 0.7171 | 0.6470    | 0.8043 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1