metadata

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: wav2vec2-large-xlsr-53-english-finetuned-babycry-v3
    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

wav2vec2-large-xlsr-53-english-finetuned-babycry-v3

This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-english on the audiofolder dataset. It achieves the following results on the evaluation set:

Loss: 0.7337
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: 0.001
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
0.949	0.5435	25	0.7351	{'accuracy': 0.8152173913043478}	0.7322	0.6646	0.8152
0.7488	1.0870	50	0.7795	{'accuracy': 0.8152173913043478}	0.7322	0.6646	0.8152
0.6911	1.6304	75	0.7066	{'accuracy': 0.8152173913043478}	0.7322	0.6646	0.8152
0.8113	2.1739	100	0.8012	{'accuracy': 0.8152173913043478}	0.7322	0.6646	0.8152
0.634	2.7174	125	0.7801	{'accuracy': 0.8152173913043478}	0.7322	0.6646	0.8152
0.6503	3.2609	150	0.7712	{'accuracy': 0.8152173913043478}	0.7322	0.6646	0.8152
0.7523	3.8043	175	0.7078	{'accuracy': 0.8152173913043478}	0.7322	0.6646	0.8152
0.5493	4.3478	200	0.7484	{'accuracy': 0.8152173913043478}	0.7322	0.6646	0.8152
0.7253	4.8913	225	0.7341	{'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