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metadata
license: apache-2.0
base_model: jonatasgrosman/wav2vec2-large-xlsr-53-english
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: wav2vec2-large-xlsr-53-english-finetuned-ravdess-v5
    results: []

wav2vec2-large-xlsr-53-english-finetuned-ravdess-v5

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

  • Loss: 0.8443
  • Accuracy: 0.7257

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: 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: 30

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 9 2.0697 0.1424
2.0767 2.0 18 2.0585 0.2292
2.0642 3.0 27 2.0382 0.2118
2.0463 4.0 36 1.9870 0.2361
1.9984 5.0 45 1.8878 0.3160
1.8817 6.0 54 1.7381 0.3785
1.743 7.0 63 1.6483 0.4062
1.6047 8.0 72 1.5459 0.4340
1.4919 9.0 81 1.4229 0.4653
1.4067 10.0 90 1.3539 0.4479
1.4067 11.0 99 1.2584 0.5243
1.3039 12.0 108 1.2465 0.5243
1.2376 13.0 117 1.1980 0.5451
1.1504 14.0 126 1.1339 0.625
1.0479 15.0 135 1.1273 0.6007
0.9986 16.0 144 1.0976 0.6215
0.9289 17.0 153 1.0150 0.6528
0.9288 18.0 162 0.9629 0.6667
0.8092 19.0 171 0.9882 0.6528
0.7641 20.0 180 0.9357 0.6806
0.7641 21.0 189 0.9578 0.6840
0.7073 22.0 198 0.8655 0.6806
0.7277 23.0 207 1.0007 0.6632
0.6614 24.0 216 0.8399 0.7222
0.6571 25.0 225 0.8995 0.6875
0.6304 26.0 234 0.8523 0.7118
0.6298 27.0 243 0.8918 0.7049
0.5929 28.0 252 0.8510 0.7222
0.5915 29.0 261 0.8443 0.7257
0.5807 30.0 270 0.8536 0.7257

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.4
  • Tokenizers 0.13.3