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metadata
language:
  - pt
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 Portuguese
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_13_0 pt
          type: mozilla-foundation/common_voice_13_0
          config: pt
          split: test
          args: pt
        metrics:
          - name: Wer
            type: wer
            value: 7.419577432392469

Whisper Large Portuguese

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

  • Loss: 0.4317
  • Wer: 7.4196

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
0.0299 3.53 1000 0.1977 6.2416
0.0071 7.05 2000 0.2617 6.7805
0.0055 10.58 3000 0.2787 7.3670
0.0024 14.11 4000 0.3019 7.2717
0.0034 17.64 5000 0.3056 7.4902
0.0016 21.16 6000 0.3180 7.5691
0.0021 24.69 7000 0.3234 7.7432
0.0028 28.22 8000 0.3293 7.9848
0.0003 31.75 9000 0.3540 7.8944
0.0003 35.27 10000 0.3439 7.8862
0.0005 38.8 11000 0.3673 8.1704
0.0008 42.33 12000 0.3510 7.9371
0.0001 45.86 13000 0.3695 7.8895
0.0 49.38 14000 0.3879 7.6151
0.0 52.91 15000 0.3993 7.5461
0.0 56.44 16000 0.4087 7.5116
0.0 59.96 17000 0.4170 7.4672
0.0 63.49 18000 0.4241 7.4344
0.0 67.02 19000 0.4294 7.4179
0.0 70.55 20000 0.4317 7.4196

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.4
  • Tokenizers 0.15.1