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metadata
license: apache-2.0
base_model: openai/whisper-tiny
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - nadsoft/QASR-Speech-Resource
metrics:
  - wer
model-index:
  - name: hamsa-tiny-finetuned-qasr
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: nadsoft/QASR-Speech-Resource default
          type: nadsoft/QASR-Speech-Resource
        metrics:
          - name: Wer
            type: wer
            value: 25.45148200004746

hamsa-tiny-finetuned-qasr

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

  • Loss: 0.3310
  • Wer: 25.4515

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: 64
  • 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: 150000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.643 0.1 2500 0.6272 51.4156
0.5445 0.2 5000 0.5443 40.7508
0.4944 0.3 7500 0.5005 38.5676
0.4722 0.4 10000 0.4747 39.1490
0.4659 0.5 12500 0.4541 35.6867
0.4261 0.6 15000 0.4383 36.0877
0.4166 0.7 17500 0.4257 31.8968
0.4051 0.8 20000 0.4160 32.5898
0.4107 0.9 22500 0.4070 32.9291
0.3753 1.0 25000 0.3996 30.2095
0.3755 1.1 27500 0.3943 32.4497
0.3749 1.2 30000 0.3893 31.3320
0.3697 1.3 32500 0.3856 30.2024
0.3574 1.4 35000 0.3802 27.4662
0.3583 1.5 37500 0.3774 28.9257
0.3619 1.6 40000 0.3731 28.9447
0.3414 1.7 42500 0.3702 27.6751
0.3465 1.8 45000 0.3667 27.2716
0.3489 1.9 47500 0.3640 25.7695
0.3173 2.0 50000 0.3623 26.2773
0.3227 2.11 52500 0.3608 25.5844
0.3236 2.21 55000 0.3592 26.8564
0.324 2.31 57500 0.3565 27.4639
0.3315 2.41 60000 0.3555 26.7187
0.3238 2.51 62500 0.3531 26.3343
0.3406 2.61 65000 0.3513 26.4031
0.3214 2.71 67500 0.3496 25.1999
0.3197 2.81 70000 0.3481 25.4657
0.3232 2.91 72500 0.3463 24.6684
0.3136 3.01 75000 0.3456 25.8668
0.3082 3.11 77500 0.3445 26.3248
0.3058 3.21 80000 0.3439 25.3874
0.3217 3.31 82500 0.3434 25.1857
0.3158 3.41 85000 0.3417 24.5521
0.3021 3.51 87500 0.3414 25.6295
0.2912 3.61 90000 0.3405 24.7941
0.281 3.71 92500 0.3402 24.5426
0.3017 3.81 95000 0.3391 25.1809
0.2986 3.91 97500 0.3387 25.1145
0.2996 4.01 100000 0.3377 24.6185
0.2734 4.11 102500 0.3374 24.7229
0.3088 4.21 105000 0.3373 24.2578
0.2794 4.31 107500 0.3361 25.6532
0.2988 4.41 110000 0.3357 25.7813
0.3085 4.51 112500 0.3352 24.8345
0.2888 4.61 115000 0.3346 24.5687
0.2923 4.71 117500 0.3342 25.0006
0.2782 4.81 120000 0.3336 25.7766
0.2948 4.91 122500 0.3334 25.2355
0.2791 5.01 125000 0.3329 25.6057
0.2988 5.11 127500 0.3333 25.6129
0.2933 5.21 130000 0.3330 25.7291
0.2801 5.31 132500 0.3321 25.7529
0.2885 5.41 135000 0.3325 25.7861
0.2953 5.51 137500 0.3319 25.0742
0.2677 5.61 140000 0.3319 25.2379
0.2833 5.71 142500 0.3315 25.5749
0.2923 5.81 145000 0.3313 25.6627
0.2602 5.91 147500 0.3311 25.4467
0.2757 6.01 150000 0.3310 25.4515

Framework versions

  • Transformers 4.37.0.dev0
  • Pytorch 2.1.2+cu121
  • Datasets 2.16.2.dev0
  • Tokenizers 0.15.0