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wav2vec2-common_voice-tr-demo

This model is a fine-tuned version of ylacombe/w2v-bert-2.0 on the MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - TR dataset. It achieves the following results on the evaluation set:

  • Loss: nan
  • Wer: 1.0

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: 0.007448827845832091
  • train_batch_size: 20
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 40
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 5000
  • num_epochs: 15.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
No log 0.27 300 3.2930 1.0
5.6462 0.55 600 3.4159 1.0
5.6462 0.82 900 3.4422 1.0
3.3522 1.1 1200 3.3719 1.0
3.2605 1.37 1500 3.4026 1.0
3.2605 1.64 1800 3.4448 1.0
3.2766 1.92 2100 3.4736 0.9999
3.2766 2.19 2400 3.9828 1.0
3.2853 2.47 2700 3.5532 1.0
3.3389 2.74 3000 3.7819 1.0
3.3389 3.01 3300 3.2250 1.0
3.2186 3.29 3600 3.2373 1.0
3.2186 3.56 3900 3.2162 1.0
3.1916 3.84 4200 3.2368 1.0
3.2188 4.11 4500 3.2377 1.0
3.2188 4.38 4800 3.4207 1.0
5.3067 4.66 5100 nan 1.0
5.3067 4.93 5400 nan 1.0
0.0 5.21 5700 nan 1.0
0.0 5.48 6000 nan 1.0
0.0 5.75 6300 nan 1.0
0.0 6.03 6600 nan 1.0
0.0 6.3 6900 nan 1.0
0.0 6.58 7200 nan 1.0
0.0 6.85 7500 nan 1.0
0.0 7.12 7800 nan 1.0
0.0 7.4 8100 nan 1.0
0.0 7.67 8400 nan 1.0
0.0 7.95 8700 nan 1.0
0.0 8.22 9000 nan 1.0
0.0 8.49 9300 nan 1.0
0.0 8.77 9600 nan 1.0
0.0 9.04 9900 nan 1.0
0.0 9.32 10200 nan 1.0
0.0 9.59 10500 nan 1.0
0.0 9.86 10800 nan 1.0
0.0 10.14 11100 nan 1.0
0.0 10.41 11400 nan 1.0
0.0 10.68 11700 nan 1.0
0.0 10.96 12000 nan 1.0
0.0 11.23 12300 nan 1.0
0.0 11.51 12600 nan 1.0
0.0 11.78 12900 nan 1.0
0.0 12.05 13200 nan 1.0
0.0 12.33 13500 nan 1.0
0.0 12.6 13800 nan 1.0
0.0 12.88 14100 nan 1.0
0.0 13.15 14400 nan 1.0
0.0 13.42 14700 nan 1.0
0.0 13.7 15000 nan 1.0
0.0 13.97 15300 nan 1.0
0.0 14.25 15600 nan 1.0
0.0 14.52 15900 nan 1.0
0.0 14.79 16200 nan 1.0

Framework versions

  • Transformers 4.37.0.dev0
  • Pytorch 2.1.0+cu121
  • Datasets 2.14.5
  • Tokenizers 0.15.0
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Evaluation results