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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - common_voice_13_0
metrics:
  - wer
model-index:
  - name: b32-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: 0.4585468095016302

b32-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: 0.4636
  • Wer: 0.4585

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: 3e-05
  • 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: 500
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
8.8775 3.05 400 3.2335 1.0
3.0144 6.11 800 2.9346 1.0
2.919 9.16 1200 2.8833 0.9988
1.8698 12.21 1600 0.8435 0.6490
0.6704 15.27 2000 0.5729 0.5249
0.448 18.32 2400 0.4981 0.4823
0.3501 21.37 2800 0.4763 0.4662
0.2999 24.43 3200 0.4610 0.4567
0.2773 27.48 3600 0.4636 0.4585

Framework versions

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