README.md

metadata

language:
  - ar
license: apache-2.0
tags:
  - automatic-speech-recognition
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_7_0
metrics:
  - wer
  - cer
model-index:
  - name: wav2vec2-xls-r-300m-arabic
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          type: mozilla-foundation/common_voice_7_0
          name: Common Voice ar
          args: ar
        metrics:
          - type: wer
            value: 43.55
            name: Test WER
          - type: cer
            value: 16.66
            name: Test CER

wav2vec2-large-xlsr-300-arabic

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

• Loss: 0.4514
• Wer: 0.4256
• Cer: 0.1528

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0003
• train_batch_size: 64
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1000
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
5.4375	1.8	500	3.3330	1.0	1.0
2.2187	3.6	1000	0.7790	0.6501	0.2338
0.9471	5.4	1500	0.5353	0.5015	0.1822
0.7416	7.19	2000	0.4889	0.4490	0.1640
0.6358	8.99	2500	0.4514	0.4256	0.1528

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.2+cu102
• Datasets 1.18.2.dev0
• Tokenizers 0.11.0