readme: ---
  language: ar
  datasets:
  - arabic_speech_corpus
  - mozilla-foundation/common_voice_6_1
  metrics:
  - wer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: muzamil47-wav2vec2-large-xlsr-53-arabic
    results:
    - task: 
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 6.1 (Arabic)
        type: mozilla-foundation/common_voice_6_1
        config: ar
      metrics:
         - name: Test WER
           type: wer
           value: 53.54
  ---
  
  # Wav2Vec2-Large-XLSR-53-Arabic
  
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Arabic using the [Common Voice](https://huggingface.co/datasets/common_voice). 
  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 librosa
  import torch
  from lang_trans.arabic import buckwalter
  
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  
  asr_model = "muzamil47/wav2vec2-large-xlsr-53-arabic-demo"
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  
  
  def load_file_to_data(file, srate=16_000):
      batch = {}
      speech, sampling_rate = librosa.load(file, sr=srate)
      batch["speech"] = speech
      batch["sampling_rate"] = sampling_rate
      return batch
  
  
  processor = Wav2Vec2Processor.from_pretrained(asr_model)
  model = Wav2Vec2ForCTC.from_pretrained(asr_model).to(device)
  
  
  def predict(data):
      features = processor(data["speech"], sampling_rate=data["sampling_rate"], return_tensors="pt", padding=True)
      input_values = features.input_values.to(device)
      try:
          attention_mask = features.attention_mask.to(device)
      except:
          attention_mask = None
      with torch.no_grad():
          predicted = torch.argmax(model(input_values, attention_mask=attention_mask).logits, dim=-1)
  
      data["predicted"] = processor.tokenizer.decode(predicted[0])
      print("predicted:", buckwalter.untrans(data["predicted"]))
      return data
  
  predict(load_file_to_data("common_voice_ar_19058307.mp3"))
  ```
  **Output Result**:
  ```shell
  predicted: هل يمكنني التحدث مع المسؤول هنا
  ```
  
  ## 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
  from lang_trans.arabic import buckwalter
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  
  asr_model = "muzamil47/wav2vec2-large-xlsr-53-arabic-demo"
  
  dataset = load_dataset("common_voice", "ar", split="test[:10]")
  
  resamplers = {  # all three sampling rates exist in test split
      48000: torchaudio.transforms.Resample(48000, 16000),
      44100: torchaudio.transforms.Resample(44100, 16000),
      32000: torchaudio.transforms.Resample(32000, 16000),
  }
  
  def prepare_example(example):
      speech, sampling_rate = torchaudio.load(example["path"])
      example["speech"] = resamplers[sampling_rate](speech).squeeze().numpy()
      return example
  
  dataset = dataset.map(prepare_example)
  processor = Wav2Vec2Processor.from_pretrained(asr_model)
  model = Wav2Vec2ForCTC.from_pretrained(asr_model).eval()
  
  def predict(batch):
      inputs = processor(batch["speech"], sampling_rate=16000, return_tensors="pt", padding=True)
      with torch.no_grad():
          predicted = torch.argmax(model(inputs.input_values).logits, dim=-1)
      predicted[predicted == -100] = processor.tokenizer.pad_token_id  # see fine-tuning script
      batch["predicted"] = processor.tokenizer.batch_decode(predicted)
      return batch
  
  dataset = dataset.map(predict, batched=True, batch_size=1, remove_columns=["speech"])
  
  for reference, predicted in zip(dataset["sentence"], dataset["predicted"]):
      print("reference:", reference)
      print("predicted:", buckwalter.untrans(predicted))
      print("--")
  
  ```
  **Output Results**:
  ```shell
  reference: ما أطول عودك!
  predicted: ما اطول عودك
  
  reference: ماتت عمتي منذ سنتين.
  predicted: ما تتعمتي منذو سنتين
  
  reference: الألمانية ليست لغة سهلة.
  predicted: الالمانية ليست لغة سهلة
  
  reference: طلبت منه أن يبعث الكتاب إلينا.
  predicted: طلبت منه ان يبعث الكتاب الينا
  
  reference: .السيد إيتو رجل متعلم
  predicted: السيد ايتو رجل متعلم
  
  reference: الحمد لله.
  predicted: الحمذ لللا
  
  reference: في الوقت نفسه بدأت الرماح والسهام تقع بين الغزاة
  predicted: في الوقت نفسه ابدات الرماح و السهام تقع بين الغزاء
  
  reference: لا أريد أن أكون ثقيلَ الظِّل ، أريد أن أكون رائعًا! !
  predicted: لا اريد ان اكون ثقيل الظل اريد ان اكون رائع
  
  reference: خذ مظلة معك في حال أمطرت.
  predicted: خذ مظلة معك في حال امطرت
  
  reference: .ركب توم السيارة
  predicted: ركب توم السيارة
  ```
  
  The model evaluation **(WER)** on the Arabic test data of Common Voice. 
  
  ```python
  import re
  
  import torch
  import torchaudio
  from datasets import load_dataset, load_metric
  from transformers import set_seed, Wav2Vec2ForCTC, Wav2Vec2Processor
  
  set_seed(42)
  
  test_dataset = load_dataset("common_voice", "ar", split="test"