whisper-base / README.md
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metadata
library_name: transformers
language:
  - fa
license: apache-2.0
base_model: SadeghK/whisper-base
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_20_0
metrics:
  - wer
model-index:
  - name: whisper-base-fa - Sadegh Karimi
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 20.0
          type: mozilla-foundation/common_voice_20_0
          args: 'config: fa, split: train, test'
        metrics:
          - name: Wer
            type: wer
            value: 10.468362043705566

whisper-base-fa - Sadegh Karimi

This model is a fine-tuned version of SadeghK/whisper-base on the Common Voice 20.0 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0809
  • Wer: 10.4684

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: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Use adamw_torch 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: 500
  • training_steps: 40000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.1234 0.0493 1000 0.1698 21.8312
0.1303 0.0986 2000 0.1663 22.9153
0.1241 0.1479 3000 0.1623 20.8843
0.1223 0.1972 4000 0.1616 20.7470
0.1281 0.2465 5000 0.1522 19.3606
0.1111 0.2958 6000 0.1483 20.0901
0.1097 0.3451 7000 0.1452 19.0445
0.1439 0.3944 8000 0.1367 18.0251
0.1053 0.4437 9000 0.1347 17.5902
0.1248 0.4930 10000 0.1281 16.9486
0.1081 0.5423 11000 0.1252 15.9200
0.1062 0.5916 12000 0.1222 15.8167
0.1139 0.6409 13000 0.1181 15.6038
0.1011 0.6902 14000 0.1145 15.0918
0.098 0.7395 15000 0.1141 15.0194
0.1176 0.7888 16000 0.1091 14.1048
0.0933 0.8381 17000 0.1067 13.9028
0.0981 0.8874 18000 0.1042 13.6391
0.0909 0.9367 19000 0.1012 13.2119
0.0714 0.9860 20000 0.1001 13.1826
0.0491 1.0353 21000 0.0985 12.9251
0.059 1.0846 22000 0.0966 12.6799
0.0492 1.1339 23000 0.0959 12.4501
0.0625 1.1832 24000 0.0943 12.5241
0.0429 1.2325 25000 0.0946 12.4424
0.0403 1.2818 26000 0.0931 12.1370
0.0474 1.3311 27000 0.0921 11.7330
0.0484 1.3804 28000 0.0910 11.5710
0.0585 1.4297 29000 0.0896 11.7067
0.0431 1.4790 30000 0.0890 11.3875
0.045 1.5283 31000 0.0875 11.2842
0.0494 1.5776 32000 0.0862 11.5433
0.0448 1.6269 33000 0.0854 11.0282
0.0508 1.6762 34000 0.0849 11.0498
0.0432 1.7255 35000 0.0837 10.7583
0.0356 1.7748 36000 0.0826 10.8339
0.0353 1.8241 37000 0.0819 10.5300
0.043 1.8734 38000 0.0815 10.4838
0.0434 1.9227 39000 0.0812 10.5038
0.0382 1.9720 40000 0.0809 10.4684

Framework versions

  • Transformers 4.48.2
  • Pytorch 2.1.0+cu118
  • Datasets 3.2.0
  • Tokenizers 0.21.0