whisper-base-en / README.md
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metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-base
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: whisper-base-en
    results: []

whisper-base-en

This model is a fine-tuned version of openai/whisper-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1334
  • Wer: 6.9935

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-06
  • train_batch_size: 8
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • 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
  • training_steps: 3000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.5784 0.4 100 0.3881 20.8915
0.2412 0.8 200 0.2176 12.1310
0.1962 1.2 300 0.1909 10.6681
0.182 1.6 400 0.1782 9.7530
0.1683 2.0 500 0.1697 8.9785
0.1418 2.4 600 0.1639 8.9699
0.1605 2.8 700 0.1590 8.4593
0.13 3.2 800 0.1550 7.9774
0.1353 3.6 900 0.1518 7.7623
0.13 4.0 1000 0.1491 7.4897
0.1288 4.4 1100 0.1467 7.4897
0.12 4.8 1200 0.1448 7.4180
0.1161 5.2 1300 0.1428 7.3807
0.113 5.6 1400 0.1414 7.5356
0.1022 6.0 1500 0.1399 6.9505
0.1029 6.4 1600 0.1390 6.9361
0.0981 6.8 1700 0.1379 6.8070
0.1051 7.2 1800 0.1369 6.8357
0.0927 7.6 1900 0.1362 6.8988
0.0973 8.0 2000 0.1354 6.8042
0.0898 8.4 2100 0.1348 6.7497
0.0929 8.8 2200 0.1342 6.7870
0.0937 9.2 2300 0.1338 7.0623
0.0901 9.6 2400 0.1334 6.9935

Framework versions

  • Transformers 4.46.2
  • Pytorch 2.5.1+cu121
  • Datasets 3.1.0
  • Tokenizers 0.20.3