DunnBC22's picture
update model card README.md
ef11a50
|
raw
history blame
3.97 kB
metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - audiofolder
metrics:
  - accuracy
model-index:
  - name: wav2vec2-base-Speech_Emotion_Recognition
    results: []

wav2vec2-base-Speech_Emotion_Recognition

This model is a fine-tuned version of facebook/wav2vec2-base on the audiofolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7264
  • Accuracy: 0.7539
  • Weighted f1: 0.7514
  • Micro f1: 0.7539
  • Macro f1: 0.7529
  • Weighted recall: 0.7539
  • Micro recall: 0.7539
  • Macro recall: 0.7577
  • Weighted precision: 0.7565
  • Micro precision: 0.7539
  • Macro precision: 0.7558

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: 3e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy Weighted f1 Micro f1 Macro f1 Weighted recall Micro recall Macro recall Weighted precision Micro precision Macro precision
1.5581 0.98 43 1.4046 0.4653 0.4080 0.4653 0.4174 0.4653 0.4653 0.4793 0.5008 0.4653 0.4974
1.5581 1.98 86 1.1566 0.5997 0.5836 0.5997 0.5871 0.5997 0.5997 0.6093 0.6248 0.5997 0.6209
1.5581 2.98 129 0.9733 0.6883 0.6845 0.6883 0.6860 0.6883 0.6883 0.6923 0.7012 0.6883 0.7009
1.5581 3.98 172 0.8313 0.7399 0.7392 0.7399 0.7409 0.7399 0.7399 0.7417 0.7415 0.7399 0.7432
1.5581 4.98 215 0.8708 0.7028 0.6963 0.7028 0.6970 0.7028 0.7028 0.7081 0.7148 0.7028 0.7114
1.5581 5.98 258 0.7969 0.7297 0.7267 0.7297 0.7277 0.7297 0.7297 0.7333 0.7393 0.7297 0.7382
1.5581 6.98 301 0.7349 0.7603 0.7613 0.7603 0.7631 0.7603 0.7603 0.7635 0.7699 0.7603 0.7702
1.5581 7.98 344 0.7714 0.7469 0.7444 0.7469 0.7456 0.7469 0.7469 0.7485 0.7554 0.7469 0.7563
1.5581 8.98 387 0.7183 0.7630 0.7615 0.7630 0.7631 0.7630 0.7630 0.7652 0.7626 0.7630 0.7637
1.5581 9.98 430 0.7264 0.7539 0.7514 0.7539 0.7529 0.7539 0.7539 0.7577 0.7565 0.7539 0.7558

Framework versions

  • Transformers 4.26.1
  • Pytorch 2.0.0+cu118
  • Datasets 2.11.0
  • Tokenizers 0.13.3