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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - common_voice_13_0
metrics:
  - wer
model-index:
  - name: b22-wav2vec2-large-xls-r-romansh-colab
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_13_0
          type: common_voice_13_0
          config: rm-vallader
          split: test
          args: rm-vallader
        metrics:
          - name: Wer
            type: wer
            value: 0.4990684676292501

b22-wav2vec2-large-xls-r-romansh-colab

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_13_0 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7362
  • Wer: 0.4991

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: 4
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
6.8045 0.76 100 2.9604 1.0
2.9578 1.52 200 3.0626 1.0
2.9565 2.29 300 3.0432 1.0
2.9533 3.05 400 2.9304 1.0
2.9263 3.81 500 2.9134 1.0
2.9174 4.58 600 2.9022 1.0
2.9282 5.34 700 2.8967 1.0
2.8973 6.11 800 2.8477 1.0
2.6047 6.87 900 2.0269 1.0
1.6468 7.63 1000 1.0780 0.9029
1.1006 8.4 1100 0.8305 0.8319
0.8708 9.16 1200 0.7704 0.8055
0.7708 9.92 1300 0.6815 0.7385
0.6608 10.68 1400 0.6738 0.7212
0.6014 11.45 1500 0.6535 0.6940
0.5419 12.21 1600 0.6608 0.6639
0.4961 12.97 1700 0.6568 0.6372
0.4462 13.74 1800 0.6557 0.6362
0.4169 14.5 1900 0.6487 0.5985
0.3951 15.27 2000 0.7126 0.6376
0.3643 16.03 2100 0.6539 0.5859
0.3243 16.79 2200 0.6803 0.5946
0.3243 17.56 2300 0.6619 0.5745
0.2869 18.32 2400 0.6826 0.5592
0.2895 19.08 2500 0.6980 0.5524
0.2612 19.84 2600 0.6599 0.5445
0.2492 20.61 2700 0.6533 0.5394
0.2485 21.37 2800 0.7103 0.5494
0.2352 22.14 2900 0.7339 0.5501
0.2136 22.9 3000 0.7154 0.5470
0.2079 23.66 3100 0.7360 0.5389
0.2011 24.43 3200 0.7481 0.5263
0.1925 25.19 3300 0.7409 0.5186
0.193 25.95 3400 0.7334 0.5091
0.1874 26.71 3500 0.7493 0.5075
0.1802 27.48 3600 0.7362 0.5102
0.1736 28.24 3700 0.7427 0.5033
0.1725 29.01 3800 0.7404 0.5033
0.1684 29.77 3900 0.7362 0.4991

Framework versions

  • Transformers 4.26.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.4
  • Tokenizers 0.13.3