piyazon / ASR-cv-corpus-ug-14

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Model's Last Updated: September 18 2025
automatic-speech-recognition

Introduction of ASR-cv-corpus-ug-14

Model Details of ASR-cv-corpus-ug-14

ASR-cv-corpus-ug-14

This model is a fine-tuned version of piyazon/ASR-cv-corpus-ug-13 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0034
  • Wer: 0.0033
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: 3e-05
  • train_batch_size: 32
  • eval_batch_size: 8
  • seed: 42
  • 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: 400
  • num_epochs: 10
  • mixed_precision_training: Native AMP
Training results
Training Loss Epoch Step Validation Loss Wer
0.0033 0.2560 500 0.0061 0.0088
0.0065 0.5120 1000 0.0050 0.0078
0.0056 0.7680 1500 0.0040 0.0062
0.0051 1.0241 2000 0.0056 0.0077
0.0041 1.2801 2500 0.0054 0.0075
0.0035 1.5361 3000 0.0043 0.0065
0.0041 1.7921 3500 0.0051 0.0076
0.0042 2.0481 4000 0.0040 0.0054
0.003 2.3041 4500 0.0040 0.0063
0.0027 2.5602 5000 0.0047 0.0069
0.0037 2.8162 5500 0.0050 0.0073
0.0031 3.0722 6000 0.0038 0.0058
0.0018 3.3282 6500 0.0036 0.0054
0.0022 3.5842 7000 0.0039 0.0053
0.0019 3.8402 7500 0.0043 0.0055
0.0017 4.0963 8000 0.0042 0.0052
0.0021 4.3523 8500 0.0042 0.0056
0.002 4.6083 9000 0.0053 0.0068
0.0027 4.8643 9500 0.0052 0.0071
0.0015 5.1203 10000 0.0038 0.0047
0.001 5.3763 10500 0.0036 0.0042
0.0013 5.6324 11000 0.0035 0.0041
0.001 5.8884 11500 0.0038 0.0051
0.001 6.1444 12000 0.0041 0.0046
0.0011 6.4004 12500 0.0036 0.0043
0.0006 6.6564 13000 0.0036 0.0045
0.0008 6.9124 13500 0.0036 0.0046
0.0005 7.1685 14000 0.0035 0.0037
0.0004 7.4245 14500 0.0035 0.0040
0.0005 7.6805 15000 0.0033 0.0040
0.0006 7.9365 15500 0.0031 0.0036
0.0003 8.1925 16000 0.0033 0.0038
0.0004 8.4485 16500 0.0032 0.0038
0.0003 8.7046 17000 0.0031 0.0036
0.0002 8.9606 17500 0.0030 0.0037
0.0001 9.2166 18000 0.0032 0.0035
0.0001 9.4726 18500 0.0034 0.0034
0.0001 9.7286 19000 0.0034 0.0033
0.0001 9.9846 19500 0.0034 0.0033
Framework versions
  • Transformers 4.56.1
  • Pytorch 2.8.0+cu128
  • Datasets 4.1.0
  • Tokenizers 0.22.0

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