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wav2vec2-large-xls-r-300m-amharic-ASR-final

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metadata
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
  - generated_from_trainer
datasets:
  - common_voice_16_1
metrics:
  - wer
model-index:
  - name: wav2vec2-large-xls-r-300m-amharic-ASR-final
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_16_1
          type: common_voice_16_1
          config: am
          split: test
          args: am
        metrics:
          - name: Wer
            type: wer
            value: 0.9092728485657104

Visualize in Weights & Biases

wav2vec2-large-xls-r-300m-amharic-ASR-final

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_16_1 dataset. It achieves the following results on the evaluation set:

  • Loss: 1.8430
  • Wer: 0.9093
  • Cer: 0.3582

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: 100
  • num_epochs: 80
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
12.9379 5.0 100 4.1617 1.0 0.9997
4.0909 10.0 200 4.0177 1.0 0.9531
3.9876 15.0 300 3.9620 1.0 0.9404
3.9269 20.0 400 3.9533 0.9987 0.9172
3.095 25.0 500 2.1131 1.0173 0.5117
0.9792 30.0 600 1.7629 0.9373 0.4255
0.4635 35.0 700 1.8393 0.9560 0.4060
0.3034 40.0 800 1.8041 0.9099 0.3959
0.2285 45.0 900 1.8211 0.9173 0.3769
0.189 50.0 1000 1.8982 0.9306 0.3854
0.1692 55.0 1100 1.8831 0.9393 0.3795
0.1511 60.0 1200 1.8967 0.9346 0.3739
0.1326 65.0 1300 1.9037 0.9420 0.3823
0.1185 70.0 1400 1.8853 0.9146 0.3653
0.1102 75.0 1500 1.8515 0.9079 0.3598
0.1059 80.0 1600 1.8430 0.9093 0.3582

Framework versions

  • Transformers 4.42.0
  • Pytorch 2.3.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1