metadata
license: apache-2.0
base_model: jonatasgrosman/wav2vec2-large-xlsr-53-english
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: speech-emotion-recognition
results: []
speech-emotion-recognition
This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-english on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5578
- Accuracy: 0.8225
- Precision: 0.8278
- Recall: 0.8225
- F1: 0.8212
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: 5
- total_train_batch_size: 10
- 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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 1.3499 | 1.0 | 394 | 1.2619 | 0.7120 | 0.7251 | 0.7120 | 0.7116 |
| 0.6955 | 2.0 | 788 | 0.7781 | 0.7799 | 0.7919 | 0.7799 | 0.7793 |
| 0.8665 | 3.0 | 1182 | 0.5578 | 0.8225 | 0.8278 | 0.8225 | 0.8212 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1