Hugging Face's logo

xezpeleta
/
whisper-medium-eu

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
Basque
whisper
whisper-event
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Metrics Community
1
whisper-medium-eu / README.md
xezpeleta's picture
xezpeleta
End of training
9ddd378 verified
preview code
|
raw
history blame contribute delete
2.93 kB
metadata
library_name: transformers
language:
  - eu
license: apache-2.0
base_model: openai/whisper-medium
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_17_0
metrics:
  - wer
model-index:
  - name: Whisper Medium Basque
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_17_0 eu
          type: mozilla-foundation/common_voice_17_0
          config: eu
          split: test
          args: eu
        metrics:
          - name: Wer
            type: wer
            value: 8.8020814247499

Whisper Medium Basque

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

  • Loss: 0.1787
  • Wer: 8.8021

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: 6.25e-06
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Use OptimizerNames.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: 8000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.3171 0.0625 500 0.3369 25.5304
0.1852 0.125 1000 0.2409 17.3110
0.2353 0.1875 1500 0.2050 14.2228
0.1569 1.037 2000 0.1815 12.2861
0.125 1.0995 2500 0.1692 11.1144
0.12 1.162 3000 0.1600 10.6975
0.069 2.0115 3500 0.1540 9.7649
0.0606 2.074 4000 0.1550 9.8199
0.0434 2.1365 4500 0.1580 9.4571
0.0455 2.199 5000 0.1533 9.1410
0.0216 3.0485 5500 0.1620 9.0842
0.017 3.111 6000 0.1704 9.0980
0.0174 3.1735 6500 0.1681 9.0723
0.0098 4.023 7000 0.1725 8.8625
0.0076 4.0855 7500 0.1765 8.8351
0.007 4.148 8000 0.1787 8.8021

Framework versions

  • Transformers 4.46.0.dev0
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.2.dev0
  • Tokenizers 0.20.0