Hugging Face's logo

Bagus
/
whisper-base-common_voice_17_0-id

Automatic Speech Recognition
Transformers
Safetensors
whisper
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
whisper-base-common_voice_17_0-id / README.md
Bagus's picture
Bagus
End of training
e75ff83 verified
preview code
|
raw
history blame contribute delete
3.12 kB
metadata
license: apache-2.0
base_model: openai/whisper-base
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_17_0
metrics:
  - wer
model-index:
  - name: whisper-base-common_voice_17_0-id
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_17_0 id
          type: mozilla-foundation/common_voice_17_0
          config: id
          split: None
          args: id
        metrics:
          - name: Wer
            type: wer
            value: 0.1183813634043343

whisper-base-common_voice_17_0-id

This model is a fine-tuned version of openai/whisper-base on the mozilla-foundation/common_voice_17_0 id dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1441
  • Wer: 0.1184

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
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 20000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.3523 0.4229 1000 0.3129 0.2365
0.3002 0.8458 2000 0.2391 0.1964
0.1718 1.2688 3000 0.2049 0.1659
0.1537 1.6917 4000 0.1817 0.1516
0.0807 2.1146 5000 0.1643 0.1499
0.089 2.5375 6000 0.1562 0.1348
0.0883 2.9605 7000 0.1452 0.1268
0.0368 3.3834 8000 0.1446 0.1324
0.0463 3.8063 9000 0.1401 0.1286
0.0278 4.2292 10000 0.1436 0.1181
0.0157 4.6521 11000 0.1406 0.1125
0.0201 5.0751 12000 0.1392 0.1144
0.0121 5.4980 13000 0.1405 0.1129
0.0074 5.9209 14000 0.1385 0.1195
0.0064 6.3438 15000 0.1410 0.1115
0.0066 6.7668 16000 0.1415 0.1184
0.0029 7.1897 17000 0.1426 0.1190
0.0024 7.6126 18000 0.1429 0.1178
0.0021 8.0355 19000 0.1434 0.1180
0.0018 8.4584 20000 0.1441 0.1184

Framework versions

  • Transformers 4.42.0.dev0
  • Pytorch 2.1.0
  • Datasets 2.19.1
  • Tokenizers 0.19.1