metadata

license: apache-2.0
base_model: jonatasgrosman/wav2vec2-large-xlsr-53-english
tags:
  - generated_from_trainer
datasets:
  - narad/ravdess
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: wav2vec2-large-xlsr-53-english-finetuned-ravdess
    results:
      - task:
          name: Audio Classification
          type: audio-classification
        dataset:
          name: RAVDESS
          type: narad/ravdess
          config: all
          split: train
          args: all
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8298611111111112
          - name: Precision
            type: precision
            value: 0.8453025128787324
          - name: Recall
            type: recall
            value: 0.8298611111111112
          - name: F1
            type: f1
            value: 0.8329568451751053

wav2vec2-large-xlsr-53-english-finetuned-ravdess

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

Loss: 0.5624
Accuracy: 0.8299
Precision: 0.8453
Recall: 0.8299
F1: 0.8330

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: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 6
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.9765	1.0	288	1.9102	0.3090	0.3203	0.3090	0.1941
1.4803	2.0	576	1.4590	0.5660	0.5493	0.5660	0.4811
1.1625	3.0	864	1.2308	0.6215	0.6299	0.6215	0.5936
0.8354	4.0	1152	0.7821	0.7222	0.7555	0.7222	0.6869
0.2066	5.0	1440	0.7910	0.7708	0.8373	0.7708	0.7881
0.6335	6.0	1728	0.5624	0.8299	0.8453	0.8299	0.8330

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.1