urdu-tts-phonemes-finetuned

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8902

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: 1e-05
  • train_batch_size: 6
  • eval_batch_size: 2
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 2
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 48
  • total_eval_batch_size: 4
  • optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 70
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss
5.4748 1.5163 500 1.0756
4.4286 3.0304 1000 1.0000
4.2899 4.5467 1500 0.9708
4.1616 6.0607 2000 0.9536
4.0716 7.5771 2500 0.9420
4.0121 9.0911 3000 0.9242
3.9637 10.6074 3500 0.9217
3.9152 12.1215 4000 0.9091
3.8967 13.6378 4500 0.9034
3.8794 15.1519 5000 0.9066
3.8580 16.6682 5500 0.9018
3.8195 18.1822 6000 0.8976
3.8034 19.6986 6500 0.8946
3.7870 21.2126 7000 0.8929
3.7691 22.7289 7500 0.8952
3.7517 24.2430 8000 0.8890
3.7299 25.7593 8500 0.8941
3.7293 27.2733 9000 0.8908
3.7309 28.7897 9500 0.8911
3.7051 30.3037 10000 0.8860
3.6962 31.8200 10500 0.8879
3.6794 33.3341 11000 0.8842
3.6740 34.8504 11500 0.8866
3.6693 36.3645 12000 0.8834
3.6793 37.8808 12500 0.8885
3.6572 39.3948 13000 0.8844
3.6636 40.9112 13500 0.8826
3.6410 42.4252 14000 0.8840
3.6616 43.9415 14500 0.8921
3.6408 45.4556 15000 0.8882
3.6513 46.9719 15500 0.8869
3.6223 48.4860 16000 0.8887
3.6251 50.0 16500 0.8921
3.6284 51.5163 17000 0.8865
3.6264 53.0304 17500 0.8910
3.6112 54.5467 18000 0.8881
3.6109 56.0607 18500 0.8929
3.6175 57.5771 19000 0.8859
3.6266 59.0911 19500 0.8897
3.6035 60.6074 20000 0.8870
3.5990 62.1215 20500 0.8916
3.6005 63.6378 21000 0.8894
3.6143 65.1519 21500 0.8857
3.6044 66.6682 22000 0.8916
3.6021 68.1822 22500 0.8911
3.6110 69.6986 23000 0.8902

Framework versions

  • Transformers 5.0.0
  • Pytorch 2.10.0+cu128
  • Datasets 4.8.3
  • Tokenizers 0.22.2
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