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---
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
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