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metadata
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
  - hf-asr-leaderboard
  - pashto
  - ps
datasets:
  - google/fleurs
metrics:
  - wer
model-index:
  - name: Whisper Small Pashto
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: google/fleurs ps_af
          type: google/fleurs
          args: 'config: ps_af, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 63.10532687651331

Whisper Small Pashto

This model is a fine-tuned version of openai/whisper-small on the google/fleurs ps_af dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1800
  • Wer: 63.1053

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-07
  • train_batch_size: 32
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 10
  • training_steps: 5200
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
2.0871 14.29 100 2.0102 230.2739
1.465 28.57 200 1.4969 137.2427
1.1617 42.86 300 1.2716 76.3242
1.0019 57.14 400 1.1645 71.3756
0.9052 71.43 500 1.1051 69.7866
0.8334 85.71 600 1.0691 68.2657
0.7838 100.0 700 1.0483 67.1686
0.7539 114.29 800 1.0363 66.4195
0.7377 128.57 900 1.0297 66.2001
0.7325 142.86 1000 1.0277 66.0033
0.6952 157.14 1100 1.0122 65.0575
0.6531 171.43 1200 1.0014 64.4219
0.6189 185.71 1300 0.9945 63.7939
0.5993 200.0 1400 0.9896 63.3550
0.5757 214.29 1500 0.9864 63.2264
0.5601 228.57 1600 0.9845 62.9162
0.5482 242.86 1700 0.9833 62.8178
0.5382 257.14 1800 0.9827 62.8405
0.5325 271.43 1900 0.9823 62.7648
0.5287 285.71 2000 0.9822 62.8178
0.3494 357.14 2500 1.0026 61.6147
0.2287 428.57 3000 1.0533 61.5163
0.1525 500.0 3500 1.1041 62.0536
0.1089 571.43 4000 1.1451 62.5076
0.0871 642.86 4500 1.1704 62.9313
0.0797 714.29 5000 1.1791 63.1659
0.0799 728.57 5100 1.1800 63.1053
0.0791 742.86 5200 1.1803 63.1129

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.13.0+cu117
  • Datasets 2.7.1.dev0
  • Tokenizers 0.13.2