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superb-wav2vec2

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metadata
library_name: transformers
base_model: vasista22/ccc-wav2vec2-base-SUPERB
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: superb-wav2vec2
    results: []

superb-wav2vec2

This model is a fine-tuned version of vasista22/ccc-wav2vec2-base-SUPERB on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0003
  • Wer: 0.0233

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: 0.0004
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 132
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
3.0211 0.4082 50 2.0267 0.9861
1.9496 0.8163 100 1.7685 0.9849
1.7178 1.2245 150 1.4738 0.8240
1.3801 1.6327 200 1.1281 0.8227
1.189 2.0408 250 0.8568 0.5723
0.9318 2.4490 300 0.6622 0.5615
0.7042 2.8571 350 0.3612 0.3023
0.5805 3.2653 400 0.4220 0.4606
0.4229 3.6735 450 0.1465 0.1417
0.3913 4.0816 500 0.1350 0.1688
0.2645 4.4898 550 0.1030 0.1421
0.2809 4.8980 600 0.0867 0.0977
0.2344 5.3061 650 0.0901 0.1367
0.1703 5.7143 700 0.0659 0.1246
0.1718 6.1224 750 0.0432 0.0545
0.1442 6.5306 800 0.0636 0.0824
0.1494 6.9388 850 0.0431 0.0448
0.1492 7.3469 900 0.0328 0.0478
0.1185 7.7551 950 0.0376 0.0621
0.107 8.1633 1000 0.0249 0.0241
0.1159 8.5714 1050 0.0350 0.0396
0.1015 8.9796 1100 0.0232 0.0334
0.1203 9.3878 1150 0.0341 0.0780
0.0835 9.7959 1200 0.0178 0.0458
0.1239 10.2041 1250 0.0231 0.0543
0.0859 10.6122 1300 0.0163 0.0289
0.0732 11.0204 1350 0.0309 0.0494
0.063 11.4286 1400 0.0168 0.0963
0.0693 11.8367 1450 0.0268 0.0619
0.0649 12.2449 1500 0.0328 0.0687
0.063 12.6531 1550 0.0173 0.0438
0.0574 13.0612 1600 0.0118 0.0506
0.0438 13.4694 1650 0.0101 0.0510
0.0556 13.8776 1700 0.0064 0.0291
0.0536 14.2857 1750 0.0098 0.0225
0.047 14.6939 1800 0.0157 0.0251
0.0588 15.1020 1850 0.0097 0.0291
0.0397 15.5102 1900 0.0113 0.0541
0.0375 15.9184 1950 0.0173 0.0531
0.0411 16.3265 2000 0.0079 0.0394
0.0382 16.7347 2050 0.0056 0.0340
0.0448 17.1429 2100 0.0064 0.0287
0.0359 17.5510 2150 0.0053 0.0261
0.032 17.9592 2200 0.0091 0.0400
0.0295 18.3673 2250 0.0018 0.0275
0.03 18.7755 2300 0.0034 0.0259
0.0246 19.1837 2350 0.0280 0.0368
0.0465 19.5918 2400 0.0099 0.0297
0.0264 20.0 2450 0.0063 0.0111
0.025 20.4082 2500 0.0015 0.0370
0.04 20.8163 2550 0.0020 0.0344
0.0203 21.2245 2600 0.0055 0.0356
0.0241 21.6327 2650 0.0024 0.0299
0.0465 22.0408 2700 0.0022 0.0392
0.0283 22.4490 2750 0.0026 0.0149
0.0134 22.8571 2800 0.0015 0.0177
0.0177 23.2653 2850 0.0041 0.0177
0.0288 23.6735 2900 0.0011 0.0147
0.0216 24.0816 2950 0.0034 0.0287
0.0147 24.4898 3000 0.0046 0.0155
0.0118 24.8980 3050 0.0021 0.0235
0.0113 25.3061 3100 0.0012 0.0261
0.0135 25.7143 3150 0.0006 0.0261
0.0118 26.1224 3200 0.0008 0.0287
0.0083 26.5306 3250 0.0004 0.0257
0.0148 26.9388 3300 0.0006 0.0261
0.0081 27.3469 3350 0.0005 0.0263
0.0192 27.7551 3400 0.0004 0.0237
0.0096 28.1633 3450 0.0004 0.0231
0.0083 28.5714 3500 0.0003 0.0215
0.0056 28.9796 3550 0.0004 0.0233
0.0082 29.3878 3600 0.0003 0.0233
0.0102 29.7959 3650 0.0003 0.0233

Framework versions

  • Transformers 4.45.0.dev0
  • Pytorch 2.4.0
  • Datasets 2.21.0
  • Tokenizers 0.19.1