tachiwin_tutunaku / README.md
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metadata
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
  - generated_from_trainer
datasets:
  - audiofolder
metrics:
  - wer
model-index:
  - name: tachiwin_totonac
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: audiofolder
          type: audiofolder
          config: ljcamargo--totonac_alpha_1
          split: test
          args: ljcamargo--totonac_alpha_1
        metrics:
          - name: Wer
            type: wer
            value: 0.6465189873417722

tachiwin_totonac

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

  • Loss: 1.7535
  • Wer: 0.6465

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: 0.0003
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • 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: 500
  • num_epochs: 90

Training results

Training Loss Epoch Step Validation Loss Wer
5.1063 5.19 200 2.9834 1.0
2.9016 10.39 400 2.4405 0.9959
1.7606 15.58 600 1.1942 0.8532
1.0549 20.78 800 1.1132 0.7788
0.7553 25.97 1000 1.1224 0.6899
0.6639 31.51 1200 1.2641 0.7082
0.5344 36.7 1400 1.3247 0.6835
0.4527 41.9 1600 1.3915 0.7022
0.3839 47.09 1800 1.4051 0.6791
0.3065 52.29 2000 1.3899 0.6706
0.2714 57.48 2200 1.5455 0.6573
0.2437 62.68 2400 1.6798 0.6601
0.2103 67.87 2600 1.7406 0.6674
0.1899 73.06 2800 1.7625 0.6522
0.1841 78.26 3000 1.7443 0.6535
0.1544 83.45 3200 1.7405 0.6465
0.1461 88.65 3400 1.7535 0.6465

Framework versions

  • Transformers 4.31.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.13.1
  • Tokenizers 0.13.3