---
library_name: transformers
license: apache-2.0
base_model: jonatasgrosman/wav2vec2-large-xlsr-53-english
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: baby-cry-classification-finetuned-babycry-v4
  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: 0.8152173913043478
    - name: F1
      type: f1
      value: 0.7322311897943244
    - name: Precision
      type: precision
      value: 0.6645793950850661
    - name: Recall
      type: recall
      value: 0.8152173913043478
---

<!-- 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. -->

# baby-cry-classification-finetuned-babycry-v4

This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7255
- Accuracy: {'accuracy': 0.8152173913043478}
- F1: 0.7322
- Precision: 0.6646
- Recall: 0.8152

## 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: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy                         | F1     | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------:|:------:|:---------:|:------:|
| 0.6244        | 0.5435 | 25   | 0.7271          | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646    | 0.8152 |
| 0.6901        | 1.0870 | 50   | 0.7196          | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646    | 0.8152 |
| 0.5873        | 1.6304 | 75   | 0.7426          | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646    | 0.8152 |
| 0.8029        | 2.1739 | 100  | 0.7124          | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646    | 0.8152 |
| 0.5661        | 2.7174 | 125  | 0.7259          | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646    | 0.8152 |
| 0.6121        | 3.2609 | 150  | 0.7431          | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646    | 0.8152 |
| 0.7571        | 3.8043 | 175  | 0.7316          | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646    | 0.8152 |
| 0.5284        | 4.3478 | 200  | 0.7277          | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646    | 0.8152 |
| 0.7182        | 4.8913 | 225  | 0.7255          | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646    | 0.8152 |


### Framework versions

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