README.md

metadata

tags:
  - generated_from_trainer
base_model: batoula187/wav2vec2-large-xls-r-300m-arabic-colab
datasets:
  - common_voice_17_0
metrics:
  - wer
model-index:
  - name: wav2vec2-large-xls-r-300m-arabic-colab
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: common_voice_17_0
          type: common_voice_17_0
          config: ar
          split: test[:10%]
          args: ar
        metrics:
          - type: wer
            value: 0.627304825421734
            name: Wer

wav2vec2-large-xls-r-300m-arabic-colab

This model is a fine-tuned version of batoula187/wav2vec2-large-xls-r-300m-arabic-colab on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:

• Loss: 1.5330
• Wer: 0.6273

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: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 3
• total_train_batch_size: 24
• 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
0.0457	1.6901	200	1.5030	0.6377
0.0408	3.3803	400	1.4683	0.6503
0.0693	5.0704	600	1.6023	0.6897
0.0766	6.7606	800	1.3947	0.6709
0.0653	8.4507	1000	1.5052	0.6858
0.0542	10.1408	1200	1.6550	0.6999
0.0535	11.8310	1400	1.4820	0.6591
0.0645	13.5211	1600	1.5134	0.6732
0.0583	15.2113	1800	1.4606	0.6561
0.0551	16.9014	2000	1.4476	0.6534
0.0462	18.5915	2200	1.5556	0.6557
0.0447	20.2817	2400	1.5289	0.6503
0.0395	21.9718	2600	1.5145	0.6434
0.0327	23.6620	2800	1.5916	0.6475
0.0317	25.3521	3000	1.5830	0.6526
0.0276	27.0423	3200	1.5935	0.6432
0.026	28.7324	3400	1.5330	0.6273

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.0+cu121
• Datasets 2.19.2
• Tokenizers 0.19.1