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using niger-mali feature extractor
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metadata
license: apache-2.0
base_model: jonatasgrosman/wav2vec2-large-xlsr-53-arabic
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: tamasheq-99-2.feature_ext
    results: []

tamasheq-99-2.feature_ext

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

  • Loss: 1.9535
  • Wer: 0.9815

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: 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: 200

Training results

Training Loss Epoch Step Validation Loss Wer
9.2797 15.79 300 2.8964 1.0
2.9763 31.58 600 2.7486 1.0
2.011 47.37 900 1.5549 0.9778
0.8448 63.16 1200 1.6495 0.9852
0.6122 78.95 1500 1.7794 0.9852
0.5039 94.74 1800 1.9535 0.9815

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.4
  • Tokenizers 0.13.3