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urdu-audio-emotions

Audio Classification
Transformers
PyTorch
TensorBoard
wav2vec2
Generated from Trainer
Inference Endpoints
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urdu-audio-emotions / README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
base_model: facebook/wav2vec2-large-xlsr-53
model-index:
  - name: results
    results: []

results

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1638
  • Accuracy: 0.975

Model description

The model Urdu audio and classify in following categories

  • Angry
  • Happy
  • Neutral
  • Sad

Training and evaluation data

The dataset is available at https://www.kaggle.com/datasets/kingabzpro/urdu-emotion-dataset

Training procedure

Training code is available at https://www.kaggle.com/code/chtalhaanwar/urdu-emotions-hf

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.3838 1.0 10 1.3907 0.225
1.3732 2.0 20 1.3872 0.2125
1.3354 3.0 30 1.3116 0.6625
1.2689 4.0 40 1.1820 0.6375
1.1179 5.0 50 1.0075 0.7
0.9962 6.0 60 0.8707 0.7125
0.8842 7.0 70 0.7485 0.7625
0.786 8.0 80 0.6326 0.8
0.6757 9.0 90 0.5995 0.8
0.6104 10.0 100 0.4835 0.825
0.5821 11.0 110 0.3886 0.9
0.4721 12.0 120 0.3935 0.8625
0.3976 13.0 130 0.3020 0.925
0.4483 14.0 140 0.3171 0.9
0.2665 15.0 150 0.3016 0.9125
0.2119 16.0 160 0.2722 0.925
0.3376 17.0 170 0.3163 0.8875
0.1518 18.0 180 0.2681 0.9125
0.1559 19.0 190 0.3074 0.925
0.1031 20.0 200 0.3526 0.8875
0.1557 21.0 210 0.2254 0.9375
0.0846 22.0 220 0.2410 0.9375
0.0733 23.0 230 0.2369 0.925
0.0964 24.0 240 0.2273 0.9375
0.0574 25.0 250 0.2066 0.95
0.1113 26.0 260 0.2941 0.9125
0.1313 27.0 270 0.2715 0.925
0.0851 28.0 280 0.1725 0.9625
0.0402 29.0 290 0.2221 0.95
0.1075 30.0 300 0.2199 0.9625
0.0418 31.0 310 0.1699 0.95
0.1869 32.0 320 0.2287 0.9625
0.0637 33.0 330 0.3230 0.9125
0.0483 34.0 340 0.1602 0.975
0.0891 35.0 350 0.1615 0.975
0.0359 36.0 360 0.1571 0.975
0.1006 37.0 370 0.1809 0.9625
0.0417 38.0 380 0.1923 0.9625
0.0346 39.0 390 0.2035 0.9625
0.0417 40.0 400 0.1737 0.9625
0.0396 41.0 410 0.1833 0.9625
0.0202 42.0 420 0.1946 0.9625
0.0137 43.0 430 0.1785 0.9625
0.0214 44.0 440 0.1841 0.9625
0.0304 45.0 450 0.1690 0.9625
0.0199 46.0 460 0.1646 0.975
0.0122 47.0 470 0.1622 0.975
0.0324 48.0 480 0.1615 0.975
0.0269 49.0 490 0.1625 0.975
0.0245 50.0 500 0.1638 0.975

Framework versions

  • Transformers 4.18.0
  • Pytorch 1.11.0
  • Datasets 2.1.0
  • Tokenizers 0.12.1