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whisper-large-gl-train

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Galician
whisper
whisper-event
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metadata
language:
  - gl
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 Galician
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_13_0 gl
          type: mozilla-foundation/common_voice_13_0
          config: gl
          split: validation
          args: gl
        metrics:
          - name: Wer
            type: wer
            value: 6.500536091031715

Whisper Large Galician

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

  • Loss: 0.3219
  • Wer: 6.5005

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
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.0476 5.83 1000 0.1829 6.3535
0.008 11.66 2000 0.2224 6.2705
0.0043 17.49 3000 0.2360 6.3397
0.0029 23.32 4000 0.2544 6.5386
0.0036 29.15 5000 0.2552 6.6977
0.0026 34.99 6000 0.2737 6.8568
0.0009 40.82 7000 0.2734 6.6320
0.0009 46.65 8000 0.2769 6.8187
0.0006 52.48 9000 0.2832 6.6164
0.0013 58.31 10000 0.2883 7.0176
0.0005 64.14 11000 0.2972 6.8983
0.0006 69.97 12000 0.2964 6.6735
0.0003 75.8 13000 0.3042 6.7392
0.0002 81.63 14000 0.3084 6.7426
0.0001 87.46 15000 0.3145 6.6631
0.0002 93.29 16000 0.3091 6.6666
0.0001 99.13 17000 0.3170 6.8758
0.0002 104.96 18000 0.3223 6.6337
0.0 110.79 19000 0.3219 6.4971
0.0001 116.62 20000 0.3219 6.5005

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.2.0+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.1