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wav2vec2_xls_r_300m_FLEURS_Shona_1hr_v1

Automatic Speech Recognition
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wav2vec2
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metadata
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
  - generated_from_trainer
datasets:
  - fleurs
metrics:
  - wer
model-index:
  - name: wav2vec2_xls_r_300m_FLEURS_Shona_1hr_v1
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: fleurs
          type: fleurs
          config: sn_zw
          split: validation
          args: sn_zw
        metrics:
          - name: Wer
            type: wer
            value: 1

Visualize in Weights & Biases

wav2vec2_xls_r_300m_FLEURS_Shona_1hr_v1

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

  • Loss: 12.4333
  • Wer: 1.0
  • Cer: 0.9978

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: 0.0001
  • 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: cosine
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
No log 1.0 8 14.4606 2.7557 0.9555
No log 2.0 16 14.3682 1.4924 0.9242
No log 3.0 24 14.1641 1.0992 0.9862
No log 4.0 32 13.8827 1.0229 0.9902
No log 5.0 40 13.4780 1.0076 0.9925
No log 6.0 48 12.8015 1.0 0.9976
No log 7.0 56 11.7499 1.0 1.0
No log 8.0 64 10.0606 1.0 1.0
No log 9.0 72 8.2175 1.0 1.0
No log 10.0 80 6.8124 1.0 1.0
No log 11.0 88 5.9146 1.0 1.0
No log 12.0 96 5.2321 1.0 1.0
10.8148 13.0 104 4.7869 1.0 1.0
10.8148 14.0 112 4.4701 1.0 1.0
10.8148 15.0 120 4.2285 1.0 1.0
10.8148 16.0 128 4.0551 1.0 1.0

Framework versions

  • Transformers 4.42.3
  • Pytorch 2.1.0+cu118
  • Datasets 2.20.0
  • Tokenizers 0.19.1