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wav2vec2-classifier

Audio Classification
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wav2vec2
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wav2vec2-classifier / README.md
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metadata
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: wav2vec2-classifier
    results: []

wav2vec2-classifier

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

  • Loss: 0.6244
  • Accuracy: 0.8471
  • Precision: 0.8748
  • Recall: 0.8471
  • F1: 0.8488
  • Binary: 0.8939

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
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1 Binary
No log 0.17 50 4.1183 0.0437 0.0069 0.0437 0.0092 0.3226
No log 0.35 100 3.7518 0.0461 0.0070 0.0461 0.0098 0.3272
No log 0.52 150 3.5442 0.0825 0.0303 0.0825 0.0290 0.3527
No log 0.69 200 3.3675 0.1068 0.0360 0.1068 0.0473 0.3697
No log 0.86 250 3.2266 0.1214 0.0494 0.1214 0.0498 0.3784
3.7123 1.04 300 3.0319 0.2015 0.1672 0.2015 0.1388 0.4388
3.7123 1.21 350 2.8840 0.2694 0.2341 0.2694 0.2132 0.4871
3.7123 1.38 400 2.6910 0.3859 0.3375 0.3859 0.3187 0.5694
3.7123 1.55 450 2.5564 0.4223 0.4081 0.4223 0.3680 0.5934
3.7123 1.73 500 2.4118 0.4684 0.4887 0.4684 0.4192 0.6265
3.7123 1.9 550 2.2664 0.5170 0.4814 0.5170 0.4632 0.6612
2.7681 2.07 600 2.1127 0.6214 0.6252 0.6214 0.5918 0.7350
2.7681 2.24 650 1.9688 0.6165 0.6309 0.6165 0.5828 0.7308
2.7681 2.42 700 1.8190 0.6553 0.6452 0.6553 0.6192 0.7580
2.7681 2.59 750 1.7073 0.6626 0.6724 0.6626 0.6309 0.7631
2.7681 2.76 800 1.6781 0.6553 0.6746 0.6553 0.6282 0.7573
2.7681 2.93 850 1.5448 0.6942 0.7092 0.6942 0.6662 0.7852
2.0663 3.11 900 1.4279 0.75 0.7486 0.75 0.7261 0.8252
2.0663 3.28 950 1.3753 0.7403 0.7526 0.7403 0.7185 0.8184
2.0663 3.45 1000 1.2878 0.7718 0.7685 0.7718 0.7524 0.8396
2.0663 3.62 1050 1.2287 0.7670 0.7899 0.7670 0.7543 0.8354
2.0663 3.8 1100 1.1488 0.7791 0.8148 0.7791 0.7687 0.8454
2.0663 3.97 1150 1.1336 0.7718 0.8118 0.7718 0.7645 0.8396
1.5924 4.14 1200 1.0735 0.7840 0.8185 0.7840 0.7763 0.8488
1.5924 4.31 1250 1.0218 0.8058 0.8291 0.8058 0.7960 0.8641
1.5924 4.49 1300 0.9783 0.7937 0.8176 0.7937 0.7868 0.8556
1.5924 4.66 1350 0.9595 0.8083 0.8247 0.8083 0.8020 0.8667
1.5924 4.83 1400 0.9167 0.8155 0.8394 0.8155 0.8072 0.8718
1.2937 5.0 1450 0.8874 0.8083 0.8392 0.8083 0.8068 0.8658
1.2937 5.18 1500 0.8918 0.7913 0.8305 0.7913 0.7893 0.8541
1.2937 5.35 1550 0.8706 0.7937 0.8411 0.7937 0.7948 0.8558
1.2937 5.52 1600 0.8037 0.8350 0.8804 0.8350 0.8347 0.8845
1.2937 5.69 1650 0.8356 0.8010 0.8518 0.8010 0.8019 0.8626
1.2937 5.87 1700 0.8261 0.8131 0.8491 0.8131 0.8159 0.8694
1.0749 6.04 1750 0.7995 0.8131 0.8568 0.8131 0.8145 0.8721
1.0749 6.21 1800 0.7737 0.8301 0.8674 0.8301 0.8315 0.8830
1.0749 6.38 1850 0.7524 0.8228 0.8660 0.8228 0.8236 0.8755
1.0749 6.56 1900 0.7203 0.8301 0.8728 0.8301 0.8342 0.8830
1.0749 6.73 1950 0.7239 0.8398 0.8751 0.8398 0.8426 0.8881
1.0749 6.9 2000 0.6872 0.8422 0.8797 0.8422 0.8454 0.8896
0.916 7.08 2050 0.6973 0.8398 0.8791 0.8398 0.8431 0.8879
0.916 7.25 2100 0.6895 0.8350 0.8783 0.8350 0.8403 0.8845
0.916 7.42 2150 0.6613 0.8495 0.8832 0.8495 0.8510 0.8947
0.916 7.59 2200 0.6550 0.8325 0.8633 0.8325 0.8348 0.8847
0.916 7.77 2250 0.6565 0.8422 0.8746 0.8422 0.8434 0.8896
0.916 7.94 2300 0.6689 0.8350 0.8777 0.8350 0.8381 0.8864
0.7992 8.11 2350 0.6816 0.8252 0.8624 0.8252 0.8236 0.8786
0.7992 8.28 2400 0.6394 0.8447 0.8784 0.8447 0.8465 0.8932
0.7992 8.46 2450 0.6732 0.8252 0.8502 0.8252 0.8219 0.8786
0.7992 8.63 2500 0.6593 0.8544 0.8836 0.8544 0.8542 0.8990
0.7992 8.8 2550 0.6510 0.8374 0.8690 0.8374 0.8365 0.8871
0.7992 8.97 2600 0.6500 0.8398 0.8761 0.8398 0.8417 0.8888
0.7376 9.15 2650 0.6393 0.8374 0.8665 0.8374 0.8381 0.8871
0.7376 9.32 2700 0.6284 0.8422 0.8716 0.8422 0.8435 0.8905
0.7376 9.49 2750 0.6225 0.8471 0.8761 0.8471 0.8483 0.8939
0.7376 9.66 2800 0.6219 0.8519 0.8836 0.8519 0.8544 0.8973
0.7376 9.84 2850 0.6244 0.8471 0.8748 0.8471 0.8488 0.8939

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.3.0
  • Datasets 2.19.1
  • Tokenizers 0.15.1