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Whisper Small Telugu - Naga Budigam

This model is a fine-tuned version of openai/whisper-small on the Chai_Bisket_Stories_16-08-2021_14-17 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2875
  • Wer: 38.1492

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: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 15000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.2064 0.66 500 0.2053 60.1707
0.1399 1.33 1000 0.1535 49.3269
0.1093 1.99 1500 0.1365 44.5516
0.0771 2.66 2000 0.1316 42.1136
0.0508 3.32 2500 0.1395 41.1384
0.0498 3.99 3000 0.1386 40.5395
0.0302 4.65 3500 0.1529 40.9529
0.0157 5.32 4000 0.1719 40.6667
0.0183 5.98 4500 0.1723 40.3646
0.0083 6.65 5000 0.1911 40.4335
0.0061 7.31 5500 0.2109 40.4176
0.0055 7.98 6000 0.2075 39.7021
0.0039 8.64 6500 0.2186 40.2639
0.0026 9.31 7000 0.2254 39.1032
0.0035 9.97 7500 0.2289 39.2834
0.0016 10.64 8000 0.2332 39.1456
0.0016 11.3 8500 0.2395 39.4371
0.0016 11.97 9000 0.2447 39.2410
0.0009 12.63 9500 0.2548 38.7799
0.0008 13.3 10000 0.2551 38.7481
0.0008 13.96 10500 0.2621 38.8276
0.0007 14.63 11000 0.2633 38.6686
0.0003 15.29 11500 0.2711 38.4566
0.0005 15.96 12000 0.2772 38.7852
0.0001 16.62 12500 0.2771 38.2658
0.0001 17.29 13000 0.2808 38.2393
0.0001 17.95 13500 0.2815 38.1810
0.0 18.62 14000 0.2854 38.2022
0.0 19.28 14500 0.2872 38.1333
0.0 19.95 15000 0.2875 38.1492

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.13.0
  • Datasets 2.7.1
  • Tokenizers 0.13.2
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Evaluation results

Model card error

This model's model-index metadata is invalid: Schema validation error. "model-index[0].results[0].dataset.type" with value "IndicSUPERB train and validation splits" fails to match the required pattern: /^(?:[\w-]+\/)?[\w-.]+$/