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whisper-large-eu

Automatic Speech Recognition
Transformers
PyTorch
Basque
whisper
whisper-event
Generated from Trainer
Eval Results
Inference Endpoints
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whisper-large-eu / README.md
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metadata
language:
  - eu
license: apache-2.0
base_model: openai/whisper-large
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_13_0
metrics:
  - wer
model-index:
  - name: Whisper Large Basque
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_13_0 eu
          type: mozilla-foundation/common_voice_13_0
          config: eu
          split: test
          args: eu
        metrics:
          - name: Wer
            type: wer
            value: 12.234193365466401

Whisper Large Basque

This model is a fine-tuned version of openai/whisper-large on the mozilla-foundation/common_voice_13_0 eu dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4369
  • Wer: 12.2342

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: 32
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 20000

Training results

Training Loss Epoch Step Validation Loss Wer
0.0196 4.01 1000 0.2825 15.4725
0.0039 9.01 2000 0.3072 14.2270
0.0031 14.01 3000 0.3170 13.7652
0.0023 19.0 4000 0.3310 13.6640
0.0014 24.0 5000 0.3384 13.5749
0.0034 29.0 6000 0.3425 13.7450
0.0011 33.01 7000 0.3476 13.0990
0.001 38.01 8000 0.3432 13.0990
0.0004 43.01 9000 0.3524 12.8033
0.0017 48.01 10000 0.3620 13.3946
0.0003 53.0 11000 0.3564 12.6190
0.0001 58.0 12000 0.3675 12.6352
0.0 63.0 13000 0.3878 12.4286
0.0 67.01 14000 0.3996 12.3577
0.0 72.01 15000 0.4088 12.3456
0.0 77.01 16000 0.4167 12.3091
0.0 82.01 17000 0.4241 12.3112
0.0 87.0 18000 0.4302 12.3193
0.0 92.0 19000 0.4351 12.2565
0.0 97.0 20000 0.4369 12.2342

Framework versions

  • Transformers 4.33.0.dev0
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.4
  • Tokenizers 0.13.3