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ReVoiceAI-HuBERT-Thai-IPA

This model is a fine-tuned version of facebook/hubert-base-ls960 on the common_voice_17_0 (mapped to phonemic IPA) dataset. It achieves the following results on the evaluation set:

  • Loss: -1.0097
  • Wer: 0.2411
  • Cer: 0.0791

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: 0.0005
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 1000
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Wer Cer
-0.6917 1.0 936 -0.7912 0.4519 0.1585
-0.5931 2.0 1872 -0.8088 0.4398 0.1575
-0.5726 3.0 2808 -0.8250 0.4368 0.1507
-0.5981 4.0 3744 -0.8131 0.4270 0.1466
-0.5875 5.0 4680 -0.8349 0.4235 0.1435
-0.6103 6.0 5616 -0.8398 0.4353 0.1510
-0.628 7.0 6552 -0.8283 0.4069 0.1419
-0.6334 8.0 7488 -0.8420 0.3986 0.1353
-0.6452 9.0 8424 -0.8667 0.3889 0.1317
-0.6724 10.0 9360 -0.8883 0.3879 0.1341
-0.6735 11.0 10296 -0.8766 0.3996 0.1353
-0.6745 12.0 11232 -0.8851 0.3597 0.1231
-0.7151 13.0 12168 -0.9114 0.3450 0.1179
-0.7099 14.0 13104 -0.9199 0.3253 0.1107
-0.736 15.0 14040 -0.9214 0.3226 0.1083
-0.7644 16.0 14976 -0.9648 0.3106 0.1042
-0.7684 17.0 15912 -0.9583 0.3159 0.1060
-0.7815 18.0 16848 -0.9573 0.2921 0.0983
-0.8026 19.0 17784 -0.9406 0.3260 0.1073
-0.8239 20.0 18720 -0.9867 0.2845 0.0957
-0.8248 21.0 19656 -0.9921 0.2723 0.0903
-0.8463 22.0 20592 -1.0006 0.2632 0.0882
-0.8622 23.0 21528 -1.0012 0.2564 0.0849
-0.8485 24.0 22464 -1.0012 0.2558 0.0837
-0.856 25.0 23400 -1.0023 0.2570 0.0839
-0.8768 26.0 24336 -0.9986 0.2489 0.0820
-0.8987 27.0 25272 -1.0149 0.2416 0.0793
-0.8845 28.0 26208 -1.0041 0.2453 0.0802
-0.8812 29.0 27144 -1.0108 0.2409 0.0791
-0.8906 30.0 28080 -1.0097 0.2411 0.0791

Framework versions

  • Transformers 4.47.0
  • Pytorch 2.5.1
  • Datasets 3.6.0
  • Tokenizers 0.21.0
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Evaluation results