readme: ---
  language:
  - ar
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - hf-asr-leaderboard
  - 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: 38.83
        name: Test WER With LM
      - type: cer
        value: 15.33
        name: Test CER With LM
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: ar
      metrics:
      - name: Test WER
        type: wer
        value: 89.8
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: ar
      metrics:
      - name: Test WER
        type: wer
        value: 87.46
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # wav2vec2-large-xlsr-300-arabic
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/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
  
  #### Evaluation Commands
  1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`
  
  ```bash
  python eval.py --model_id kingabzpro/wav2vec2-large-xlsr-300-arabic --dataset mozilla-foundation/common_voice_7_0 --config ur --split test
  ```
  
  
  ### Inference With LM
  
  ```python
  import torch
  from datasets import load_dataset
  from transformers import AutoModelForCTC, AutoProcessor
  import torchaudio.functional as F
  model_id = "kingabzpro/wav2vec2-large-xlsr-300-arabic"
  sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "ar", split="test", streaming=True, use_auth_token=True))
  sample = next(sample_iter)
  resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
  model = AutoModelForCTC.from_pretrained(model_id)
  processor = AutoProcessor.from_pretrained(model_id)
  input_values = processor(resampled_audio, return_tensors="pt").input_values
  with torch.no_grad():
      logits = model(input_values).logits
  transcription = processor.batch_decode(logits.numpy()).text
  ```
  
  ### 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
  