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wav2vec2-common_voice-tr-demo-dist

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Turkish
wav2vec2
common_voice
Generated from Trainer
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wav2vec2-common_voice-tr-demo-dist / README.md
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metadata
language:
  - tr
license: apache-2.0
tags:
  - automatic-speech-recognition
  - common_voice
  - generated_from_trainer
datasets:
  - common_voice
model-index:
  - name: wav2vec2-common_voice-tr-demo-dist
    results: []

wav2vec2-common_voice-tr-demo-dist

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3934
  • Wer: 0.3305

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
3.5459 0.23 100 3.6773 1.0
3.2247 0.46 200 3.1491 0.9999
2.3457 0.69 300 2.4236 1.0041
0.9149 0.92 400 0.9471 0.7684
0.6622 1.15 500 0.7518 0.6863
0.7205 1.38 600 0.6387 0.6402
0.6978 1.61 700 0.5611 0.5739
0.5317 1.84 800 0.5061 0.5418
0.5222 2.07 900 0.4839 0.5344
0.4467 2.3 1000 0.5060 0.5339
0.3196 2.53 1100 0.4619 0.5213
0.276 2.76 1200 0.4595 0.5020
0.3569 2.99 1300 0.4339 0.4901
0.2236 3.22 1400 0.4602 0.4887
0.293 3.45 1500 0.4376 0.4639
0.1677 3.68 1600 0.4371 0.4605
0.1838 3.91 1700 0.4116 0.4589
0.1225 4.14 1800 0.4144 0.4495
0.2301 4.37 1900 0.4250 0.4567
0.1931 4.6 2000 0.4081 0.4470
0.1427 4.83 2100 0.4295 0.4482
0.361 5.06 2200 0.4374 0.4445
0.3272 5.29 2300 0.4088 0.4258
0.3686 5.52 2400 0.4087 0.4258
0.3087 5.75 2500 0.4100 0.4371
0.4637 5.98 2600 0.4038 0.4219
0.1485 6.21 2700 0.4361 0.4197
0.1341 6.44 2800 0.4217 0.4132
0.1185 6.67 2900 0.4244 0.4097
0.1588 6.9 3000 0.4212 0.4181
0.0697 7.13 3100 0.3981 0.4073
0.0491 7.36 3200 0.3992 0.4010
0.088 7.59 3300 0.4206 0.4022
0.0731 7.82 3400 0.3998 0.3841
0.2767 8.05 3500 0.4195 0.3829
0.1725 8.28 3600 0.4167 0.3946
0.1242 8.51 3700 0.4177 0.3821
0.1133 8.74 3800 0.3993 0.3802
0.1952 8.97 3900 0.4132 0.3904
0.1399 9.2 4000 0.4010 0.3795
0.047 9.43 4100 0.4128 0.3703
0.049 9.66 4200 0.4319 0.3670
0.0994 9.89 4300 0.4118 0.3631
0.1209 10.11 4400 0.4296 0.3722
0.0484 10.34 4500 0.4130 0.3615
0.2065 10.57 4600 0.3958 0.3668
0.133 10.8 4700 0.4102 0.3679
0.0622 11.03 4800 0.4137 0.3585
0.0999 11.26 4900 0.4042 0.3583
0.0346 11.49 5000 0.4183 0.3573
0.072 11.72 5100 0.4060 0.3530
0.0365 11.95 5200 0.3968 0.3483
0.0615 12.18 5300 0.3958 0.3485
0.1067 12.41 5400 0.3987 0.3453
0.0253 12.64 5500 0.4182 0.3405
0.0636 12.87 5600 0.4199 0.3458
0.0506 13.1 5700 0.4056 0.3412
0.0944 13.33 5800 0.4061 0.3381
0.1187 13.56 5900 0.4113 0.3381
0.0237 13.79 6000 0.3973 0.3343
0.0166 14.02 6100 0.4001 0.3357
0.1189 14.25 6200 0.3931 0.3315
0.0375 14.48 6300 0.3944 0.3329
0.0537 14.71 6400 0.3953 0.3308
0.045 14.94 6500 0.3933 0.3303

Framework versions

  • Transformers 4.18.0
  • Pytorch 1.9.1+cu102
  • Datasets 1.13.3
  • Tokenizers 0.11.6