readme: ---
  language: ar
  datasets:
  - https://arabicspeech.org/
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  license: apache-2.0
  model-index:
  - name: XLSR Wav2Vec2 Egyptian by Zaid Alyafeai and Othmane Rifki
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: arabicspeech.org MGB-3
        type: arabicspeech.org MGB-3
        args: ar  
      metrics:
         - name: Test WER
           type: wer
           value: 55.2
  ---
  # Test Wav2Vec2 with egyptian arabic
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Egyptian using the [arabicspeech.org MGB-3](https://arabicspeech.org/mgb3-asr/)
  When using this model, make sure that your speech input is sampled at 16kHz.
  ## Usage
  The model can be used directly (without a language model) as follows:
  ```python
  import torch
  import torchaudio
  from datasets import load_dataset
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  dataset = load_dataset("arabic_speech_corpus", split="test")
  processor = Wav2Vec2Processor.from_pretrained("othrif/wav2vec_test")
  model = Wav2Vec2ForCTC.from_pretrained("othrif/wav2vec_test")
  resampler = torchaudio.transforms.Resample(48_000, 16_000)
  # Preprocessing the datasets.
  # We need to read the aduio files as arrays
  def speech_file_to_array_fn(batch):
  \\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
  \\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
  \\treturn batch
  test_dataset = test_dataset.map(speech_file_to_array_fn)
  inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
  with torch.no_grad():
  \\tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
  predicted_ids = torch.argmax(logits, dim=-1)
  print("Prediction:", processor.batch_decode(predicted_ids))
  print("Reference:", test_dataset["sentence"][:2])
  ```