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
  - hf-asr-leaderboard
  license: apache-2.0
  model-index:
  - name: elgeish-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
        split: test
        args:
          language: ar
      metrics:
         - name: Test WER
           type: wer
           value: 26.55
         - name: Validation WER
           type: wer
           value: 23.39
  ---
  
  # 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
  import torch
  import torchaudio
  from datasets import load_dataset
  from lang_trans.arabic import buckwalter
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  
  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("elgeish/wav2vec2-large-xlsr-53-arabic")
  model = Wav2Vec2ForCTC.from_pretrained("elgeish/wav2vec2-large-xlsr-53-arabic").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("--")
  ```
  
  Here's the output:
  
  ```
  reference: ألديك قلم ؟
  predicted: هلديك قالر
  --
  reference: ليست هناك مسافة على هذه الأرض أبعد من يوم أمس.
  predicted: ليست نالك مسافة على هذه الأرض أبعد من يوم أمس
  --
  reference: إنك تكبر المشكلة.
  predicted: إنك تكبر المشكلة
  --
  reference: يرغب أن يلتقي بك.
  predicted: يرغب أن يلتقي بك
  --
  reference: إنهم لا يعرفون لماذا حتى.
  predicted: إنهم لا يعرفون لماذا حتى
  --
  reference: سيسعدني مساعدتك أي وقت تحب.
  predicted: سيسئدني مساعد سكرأي وقت تحب
  --
  reference: أَحَبُّ نظريّة علمية إليّ هي أن حلقات زحل مكونة بالكامل من الأمتعة المفقودة.
  predicted: أحب ناضريةً علمية إلي  هي أنحل قتزح المكونا بالكامل من الأمت عن المفقودة
  --
  reference: سأشتري له قلماً.
  predicted: سأشتري له قلما
  --
  reference: أين المشكلة ؟
  predicted: أين المشكل
  --
  reference: وَلِلَّهِ يَسْجُدُ مَا فِي السَّمَاوَاتِ وَمَا فِي الْأَرْضِ مِنْ دَابَّةٍ وَالْمَلَائِكَةُ وَهُمْ لَا يَسْتَكْبِرُونَ
  predicted: ولله يسجد ما في السماوات وما في الأرض من دابة والملائكة وهم لا يستكبرون
  --
  ```
  
  ## Evaluation
  
  The model can be evaluated as follows on the Arabic test data of Common Voice:
  
  ```python
  import jiwer
  import torch
  import torchaudio
  from datasets import load_dataset
  from lang_trans.arabic import buckwalter
  from transformers import set_seed, Wav2Vec2ForCTC, Wav2Vec2Processor
  
  set_seed(42)
  test_split = load_dataset("common_voice", "ar", split="test")
  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
  
  test_split = test_split.map(prepare_example)
  processor = Wav2Vec2Processor.from_pretrained("elgeish/wav2vec2-large-xlsr-53-arabic")
  model = Wav2Vec2ForCTC.from_pretrained("elgeish/wav2vec2-large-xlsr-53-arabic").to("cuda").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.to("cuda")).logits, dim=-1)
      predicted[predicted == -100] = processor.t