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whisper-large-pt

Automatic Speech Recognition
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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: 6.399303387769856

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: 0.4799
  • Wer: 6.3993

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-06
  • 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
0.077 3.53 1000 0.1616 5.4957
0.0155 7.05 2000 0.2549 6.1956
0.0045 10.58 3000 0.3122 5.9261
0.0017 14.11 4000 0.3317 6.0099
0.0018 17.64 5000 0.3604 6.0099
0.0009 21.16 6000 0.3779 6.1791
0.0012 24.69 7000 0.3470 6.0066
0.0013 28.22 8000 0.3838 6.1479
0.0007 31.75 9000 0.3839 6.0395
0.0003 35.27 10000 0.4090 6.2054
0.0003 38.8 11000 0.4053 6.2859
0.0002 42.33 12000 0.4235 6.3467
0.0002 45.86 13000 0.4326 6.3500
0.0001 49.38 14000 0.4415 6.3714
0.0001 52.91 15000 0.4506 6.3878
0.0001 56.44 16000 0.4586 6.4092
0.0001 59.96 17000 0.4663 6.3944
0.0001 63.49 18000 0.4730 6.3911
0.0001 67.02 19000 0.4778 6.3944
0.0001 70.55 20000 0.4799 6.3993

Framework versions

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