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wavlm-common_voice-ur

This model is a fine-tuned version of microsoft/wavlm-large on the MOZILLA-FOUNDATION/COMMON_VOICE_11_0 - UR dataset. It achieves the following results on the evaluation set:

  • Loss: inf
  • Wer: 0.3781

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: 0.0003
  • train_batch_size: 4
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 2
  • total_train_batch_size: 8
  • total_eval_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 15.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
4.9073 0.11 100 inf 1.0
3.3187 0.22 200 inf 1.0
2.9683 0.32 300 inf 0.9991
2.454 0.43 400 inf 0.9915
1.1169 0.54 500 inf 0.7906
1.5943 0.65 600 inf 0.7260
0.9991 0.75 700 inf 0.7305
1.0608 0.86 800 inf 0.6655
1.4739 0.97 900 inf 0.6120
0.8682 1.08 1000 inf 0.6087
0.8025 1.18 1100 inf 0.5991
0.8468 1.29 1200 inf 0.5605
0.5896 1.4 1300 inf 0.5550
0.6304 1.51 1400 inf 0.5441
0.6533 1.61 1500 inf 0.5297
0.7636 1.72 1600 inf 0.5210
0.5155 1.83 1700 inf 0.5331
0.6266 1.94 1800 inf 0.5182
0.4286 2.05 1900 inf 0.4956
0.527 2.15 2000 inf 0.4935
0.4919 2.26 2100 inf 0.4933
0.3977 2.37 2200 inf 0.5015
0.5349 2.48 2300 inf 0.4942
0.5066 2.58 2400 inf 0.4684
0.6734 2.69 2500 inf 0.4870
0.5411 2.8 2600 inf 0.4919
0.3451 2.91 2700 inf 0.4607
0.3913 3.01 2800 inf 0.4558
0.3046 3.12 2900 inf 0.4685
0.2954 3.23 3000 inf 0.4638
0.5469 3.34 3100 inf 0.4495
0.2334 3.44 3200 inf 0.4547
0.3119 3.55 3300 inf 0.4619
0.6393 3.66 3400 inf 0.4541
0.4133 3.77 3500 inf 0.4456
0.4946 3.88 3600 inf 0.4369
0.3484 3.98 3700 inf 0.4335
0.3996 4.09 3800 inf 0.4717
0.2754 4.2 3900 inf 0.4414
0.3141 4.31 4000 inf 0.4390
0.2231 4.41 4100 inf 0.4353
0.2673 4.52 4200 inf 0.4410
0.2911 4.63 4300 inf 0.4337
0.3643 4.74 4400 inf 0.4362
0.2706 4.84 4500 inf 0.4359
0.2464 4.95 4600 inf 0.4249
0.1453 5.06 4700 inf 0.4293
0.2619 5.17 4800 inf 0.4201
0.1888 5.27 4900 inf 0.4222
0.2571 5.38 5000 inf 0.4333
0.1653 5.49 5100 inf 0.4192
0.2102 5.6 5200 inf 0.4232
0.1456 5.71 5300 inf 0.4198
0.3314 5.81 5400 inf 0.4169
0.1703 5.92 5500 inf 0.4118
0.1546 6.03 5600 inf 0.4147
0.2065 6.14 5700 inf 0.4291
0.1792 6.24 5800 inf 0.4175
0.2433 6.35 5900 inf 0.4157
0.352 6.46 6000 inf 0.4083
0.2406 6.57 6100 inf 0.4341
0.2397 6.67 6200 inf 0.4185
0.2145 6.78 6300 inf 0.4147
0.1733 6.89 6400 inf 0.4150
0.1867 7.0 6500 inf 0.4154
0.612 7.1 6600 inf 0.4159
0.1413 7.21 6700 inf 0.4162
0.2074 7.32 6800 inf 0.4146
0.1362 7.43 6900 inf 0.4087
0.2971 7.53 7000 inf 0.4061
0.1443 7.64 7100 inf 0.4132
0.3066 7.75 7200 inf 0.4059
0.2163 7.86 7300 inf 0.4026
0.1251 7.97 7400 inf 0.4022
0.154 8.07 7500 inf 0.3980
0.1809 8.18 7600 inf 0.4030
0.0985 8.29 7700 inf 0.3992
0.1672 8.4 7800 inf 0.4049
0.1508 8.5 7900 inf 0.3985
0.1893 8.61 8000 inf 0.3999
0.1045 8.72 8100 inf 0.4014
0.2569 8.83 8200 inf 0.3976
0.2654 8.93 8300 inf 0.4021
0.0641 9.04 8400 inf 0.3964
0.1145 9.15 8500 inf 0.3995
0.1808 9.26 8600 inf 0.3960
0.0766 9.36 8700 inf 0.3938
0.1537 9.47 8800 inf 0.3909
0.2864 9.58 8900 inf 0.4028
0.1372 9.69 9000 inf 0.3970
0.06 9.8 9100 inf 0.3911
0.0831 9.9 9200 inf 0.3954
0.1469 10.01 9300 inf 0.3952
0.0683 10.12 9400 inf 0.3899
0.0694 10.23 9500 inf 0.3918
0.0919 10.33 9600 inf 0.3895
0.1842 10.44 9700 inf 0.3945
0.0581 10.55 9800 inf 0.3979
0.1397 10.66 9900 inf 0.3911
0.0657 10.76 10000 inf 0.3886
0.1316 10.87 10100 inf 0.3877
0.1434 10.98 10200 inf 0.3858
0.05 11.09 10300 inf 0.3842
0.0565 11.19 10400 inf 0.3873
0.1696 11.3 10500 inf 0.3873
0.0819 11.41 10600 inf 0.3901
0.0631 11.52 10700 inf 0.3927
0.1276 11.63 10800 inf 0.3868
0.1002 11.73 10900 inf 0.3848
0.081 11.84 11000 inf 0.3873
0.1745 11.95 11100 inf 0.3895
0.097 12.06 11200 inf 0.4021
0.0875 12.16 11300 inf 0.3876
0.027 12.27 11400 inf 0.3873
0.0859 12.38 11500 inf 0.3863
0.1192 12.49 11600 inf 0.3799
0.1055 12.59 11700 inf 0.3795
0.0603 12.7 11800 inf 0.3785
0.111 12.81 11900 inf 0.3783
0.0313 12.92 12000 inf 0.3800
0.0241 13.02 12100 inf 0.3796
0.1072 13.13 12200 inf 0.3803
0.1758 13.24 12300 inf 0.3809
0.1334 13.35 12400 inf 0.3794
0.1372 13.46 12500 inf 0.3798
0.1919 13.56 12600 inf 0.3791
0.1753 13.67 12700 inf 0.3781
0.294 13.78 12800 inf 0.3788
0.3132 13.89 12900 inf 0.3786
0.0486 13.99 13000 inf 0.3778
0.1199 14.1 13100 inf 0.3777
0.0381 14.21 13200 inf 0.3808
0.0875 14.32 13300 inf 0.3795
0.0122 14.42 13400 inf 0.3797
0.1417 14.53 13500 inf 0.3780
0.1754 14.64 13600 inf 0.3788
0.0426 14.75 13700 inf 0.3780
0.0309 14.85 13800 inf 0.3787
0.1447 14.96 13900 inf 0.3796

Framework versions

  • Transformers 4.27.0.dev0
  • Pytorch 1.13.1
  • Datasets 2.8.0
  • Tokenizers 0.13.2
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Evaluation results