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whisper-tiny-fi-lora

This model is a fine-tuned version of openai/whisper-tiny on the common_voice_11_0 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6958
  • Wer: 75.1203

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
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 5000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
3.7258 0.3690 100 3.6261 82.2153
2.1228 0.7380 200 1.9384 86.0270
0.951 1.1070 300 0.9630 82.1964
0.7932 1.4760 400 0.8430 82.5644
0.7635 1.8450 500 0.8000 85.1967
0.7242 2.2140 600 0.7735 79.3094
0.7099 2.5830 700 0.7573 82.7437
0.6686 2.9520 800 0.7504 80.5265
0.6476 3.3210 900 0.7415 78.7716
0.6494 3.6900 1000 0.7316 83.7626
0.6069 4.0590 1100 0.7307 78.5263
0.6463 4.4280 1200 0.7254 79.0358
0.5897 4.7970 1300 0.7210 78.9414
0.5816 5.1661 1400 0.7161 79.0924
0.5677 5.5351 1500 0.7174 76.4978
0.5584 5.9041 1600 0.7116 77.7715
0.5027 6.2731 1700 0.7081 76.0921
0.5214 6.6421 1800 0.7114 76.3657
0.5503 7.0111 1900 0.7113 76.3751
0.5057 7.3801 2000 0.7065 75.7713
0.5338 7.7491 2100 0.7052 76.4978
0.4457 8.1181 2200 0.7052 75.8562
0.5183 8.4871 2300 0.7017 76.7337
0.4988 8.8561 2400 0.7006 75.9600
0.4858 9.2251 2500 0.7001 75.6958
0.5024 9.5941 2600 0.7009 76.8752
0.5111 9.9631 2700 0.6998 75.6015
0.4985 10.3321 2800 0.6987 77.9791
0.4725 10.7011 2900 0.6975 77.4035
0.4497 11.0701 3000 0.6970 75.3090
0.4534 11.4391 3100 0.6972 75.4883
0.4839 11.8081 3200 0.6962 78.0262
0.4543 12.1771 3300 0.6970 75.7147
0.4586 12.5461 3400 0.6978 75.6581
0.4656 12.9151 3500 0.6997 76.3374
0.4177 13.2841 3600 0.6951 76.0449
0.4443 13.6531 3700 0.6965 75.3279
0.4698 14.0221 3800 0.6975 75.3562
0.4412 14.3911 3900 0.6957 75.2807
0.4027 14.7601 4000 0.6955 77.0356
0.4755 15.1292 4100 0.6963 75.4505
0.4487 15.4982 4200 0.6950 75.1014
0.4237 15.8672 4300 0.6967 75.2241
0.4222 16.2362 4400 0.6975 75.3090
0.408 16.6052 4500 0.6975 75.2618
0.4671 16.9742 4600 0.6947 75.0448
0.448 17.3432 4700 0.6954 75.2901
0.4253 17.7122 4800 0.6956 75.0826
0.44 18.0812 4900 0.6959 75.1392
0.4053 18.4502 5000 0.6958 75.1203

Framework versions

  • Transformers 4.42.3
  • Pytorch 2.1.2
  • Datasets 2.20.0
  • Tokenizers 0.19.1
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