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metadata
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
  - generated_from_trainer
datasets:
  - common_voice_15_0
metrics:
  - wer
model-index:
  - name: wav2vec2-large-xlsr-53-br
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_15_0
          type: common_voice_15_0
          config: br
          split: None
          args: br
        metrics:
          - name: Wer
            type: wer
            value: 54.71511888739345

wav2vec2-large-xlsr-53-br

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice_15_0 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7879
  • Wer: 54.7151
  • Cer: 19.2493

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: 6e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 300
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
6.3257 2.18 500 3.0700 100.0 99.0871
2.2071 4.36 1000 1.1541 80.0449 29.4230
1.0019 6.54 1500 0.8986 69.2059 24.3938
0.7796 8.71 2000 0.8015 63.3737 22.1296
0.6677 10.89 2500 0.8014 61.4984 21.4568
0.5937 13.07 3000 0.7623 58.9323 20.4929
0.5454 15.25 3500 0.7975 57.8466 20.2585
0.5075 17.43 4000 0.7831 56.7250 19.7879
0.4837 19.61 4500 0.7902 55.9623 19.5101
0.4529 21.79 5000 0.7851 54.9753 19.0924
0.4381 23.97 5500 0.7865 55.1727 19.3211
0.4208 26.14 6000 0.8168 55.1817 19.3967
0.4197 28.32 6500 0.7879 54.7151 19.2493

Framework versions

  • Transformers 4.39.1
  • Pytorch 2.0.1+cu117
  • Datasets 2.18.0
  • Tokenizers 0.15.2