readme: ---
  language: ar
  datasets:
  - common_voice
  - arabic_speech_corpus
  metrics:
  - wer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: Mohammed XLSR Wav2Vec2 Large 53
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice ar
        type: common_voice
        args: ar
      metrics:
         - name: Test WER
           type: wer
           value: 36.69
         - name: Validation WER
           type: wer
           value: 36.69
  ---
  # Wav2Vec2-Large-XLSR-53-Arabic
  
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
  on Arabic using the `train` splits of [Common Voice](https://huggingface.co/datasets/common_voice)
  and [Arabic Speech Corpus](https://huggingface.co/datasets/arabic_speech_corpus).
  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
  %%capture
  !pip install datasets
  !pip install transformers==4.4.0
  !pip install torchaudio
  !pip install jiwer
  !pip install tnkeeh
  
  import torch
  import torchaudio
  from datasets import load_dataset
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  
  test_dataset = load_dataset("common_voice", "ar", split="test[:2%]")
  
  
  processor = Wav2Vec2Processor.from_pretrained("mohammed/ar")
  model = Wav2Vec2ForCTC.from_pretrained("mohammed/ar")
  
  resampler = torchaudio.transforms.Resample(48_000, 16_000)
  
  # Preprocessing the datasets.
  # We need to read the audio files as arrays
  def speech_file_to_array_fn(batch):
      speech_array, sampling_rate = torchaudio.load(batch["path"])
      batch["speech"] = resampler(speech_array).squeeze().numpy()
      return 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():
      logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
  
  predicted_ids = torch.argmax(logits, dim=-1)
  
  print("The predicted sentence is: ", processor.batch_decode(predicted_ids))
  print("The original sentence is:", test_dataset["sentence"][:2])
  ```
  
  The output is:
  
  ```
  The predicted sentence is : ['ألديك قلم', 'ليست نارك مكسافة على هذه الأرض أبعد من يوم أمس']
  The original sentence is: ['ألديك قلم ؟', 'ليست هناك مسافة على هذه الأرض أبعد من يوم أمس.']
  ```
  
  ## Evaluation
  
  The model can be evaluated as follows on the Arabic test data of Common Voice:
  
  ```python
  
  import torch
  import torchaudio
  from datasets import load_dataset, load_metric
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import re
  # creating a dictionary with all diacritics
  dict = {
  'ِ': '',
  'ُ': '', 
  'ٓ': '', 
  'ٰ': '', 
  'ْ': '', 
  'ٌ': '', 
  'ٍ': '', 
  'ً': '', 
  'ّ': '', 
  'َ': '',
  '~': '',
  ',': '',
  'ـ': '',
  '—': '',
  '.': '',
  '!': '',
  '-': '',
  ';': '',
  ':': '',
  '\'': '',
  '"': '',
  '☭': '',
  '«': '',
  '»': '',
  '؛': '',
  'ـ': '',
  '_': '',
  '،': '',
  '“': '',
  '%': '',
  '‘': '',
  '”': '',
  '�': '',
  '_': '',
  ',': '',
  '?': '',
  '#': '',
  '‘': '',
  '.': '',
  '؛': '',
  'get': '',
  '؟': '',
  '  ': ' ',
  '\'ۖ ': '',
  '\'': '',
   '\'ۚ' : '',
   ' \'': '',
   '31': '', 
   '24': '',
   '39': ''
  } 
  
  # replacing multiple diacritics using dictionary (stackoverflow is amazing)
  def remove_special_characters(batch):
    # Create a regular expression  from the dictionary keys
    regex = re.compile("(%s)" % "|".join(map(re.escape, dict.keys())))
    # For each match, look-up corresponding value in dictionary
    batch["sentence"] = regex.sub(lambda mo: dict[mo.string[mo.start():mo.end()]], batch["sentence"])
    return batch 
    
  
  test_dataset = load_dataset("common_voice", "ar", split="test") 
  wer = load_metric("wer")
  
  processor = Wav2Vec2Processor.from_pretrained("mohammed/ar") 
  model = Wav2Vec2ForCTC.from_pretrained("mohammed/ar")
  model.to("cuda")
  
  
  resampler = torchaudio.transforms.Resample(48_000, 16_000)
  
  # Preprocessing the datasets.
  # We need to read the audio files as arrays
  def speech_file_to_array_fn(batch):
      speech_array, sampling_rate = torchaudio.load(batch["path"])
      batch["speech"] = resampler(speech_array).squeeze().numpy()
      return batch
  
  test_dataset = test_dataset.map(speech_file_to_array_fn)
  test_dataset = test_dataset.map(remove_special_characters)
  # Preprocessing the datasets.
  # We need to read the audio files as arrays
  def evaluate(batch):
      inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
  
      with torch.no_grad():
          logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
  
      pred_ids = torch.argmax(logits, dim=-1)
      batch["pred_strings"] = processor.batch_decode(pred_ids)
      return batch
  
  result = test_dataset.map(evaluate, batched=True, batch_size=8)
  
  print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
  ```
  
  **Test Result**: 36