readme: ---
  language: ary
  datasets:
  - mgb5 
  metrics:
  - wer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: XLSR Wav2Vec2 Moroccan Arabic dialect by Othmane Rifki
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: MGB5 from ELDA and https://arabicspeech.org/
        type: ELDA and https://arabicspeech.org/
        args: ary
      metrics:
         - name: Test WER
           type: wer
           value: 66.45
  ---
  
  # Wav2Vec2-Large-XLSR-53-Moroccan
  
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on [MGB5 Moroccan Arabic](http://www.islrn.org/resources/938-639-614-524-5/) kindly provided by [ELDA](http://www.elra.info/en/about/elda/) and [ArabicSpeech](https://arabicspeech.org/mgb5/).
  
  In order to have access to MGB5, please request it from ELDA.
  
  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 re
  import torch
  import librosa
  import torchaudio
  from datasets import load_dataset, load_metric
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import soundfile as sf
  
  
  dataset = load_dataset("ma_speech_corpus", split="test")
  
  processor = Wav2Vec2Processor.from_pretrained("othrif/wav2vec2-large-xlsr-moroccan")
  model = Wav2Vec2ForCTC.from_pretrained("othrif/wav2vec2-large-xlsr-moroccan")
  model.to("cuda")
  
  
  chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\'\\�]'
  
  def remove_special_characters(batch):
      batch["text"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).lower() + " "
      return batch
  
  
  dataset = dataset.map(remove_special_characters)
  dataset = dataset.select(range(10))
  
  def speech_file_to_array_fn(batch):
      start, stop = batch['segment'].split('_')
      speech_array, sampling_rate = torchaudio.load(batch["path"])
      speech_array, sampling_rate = sf.read(batch["path"], start=int(float(start) * sampling_rate),
                                            stop=int(float(stop) * sampling_rate))
      batch["speech"] = librosa.resample(speech_array, sampling_rate, 16_000)
      batch["sampling_rate"] = 16_000
      batch["target_text"] = batch["text"]
      return batch
  
  
  dataset = dataset.map(
      speech_file_to_array_fn
  )
  
  def predict(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["predicted"] = processor.batch_decode(pred_ids)
      return batch
  
  dataset = dataset.map(predict, batched=True, batch_size=32)
  
  for reference, predicted in zip(dataset["sentence"], dataset["predicted"]):
      print("reference:", reference)
      print("predicted:", predicted)
      print("--")
  ```
  
  Here's the output:
  ```
  reference: عشرين ألفريال الوحده وشي خمسميه دريال
  
  predicted: عشرين علف ريا لوحده وشي خمسميات ريال
  --
  reference: واحد جوج تلاتة ربعه خمسة ستة
  
  predicted: غيحك تويش تتبة نتاست
  --
  reference: هي هاديك غتجينا تقريبا ميه وسته وعشرين ألف ريال
  
  predicted: ياض كتجينا تقريبه ميه أو ستي و عشيناأفرين
  --
  reference: ###والصرف ليبقا نجيب بيه الصالون فلهوندا... أهاه نديروها علاش لا؟...
  
  predicted: أواصرف ليبقا نجيب يه اصالون فالهندا أه نديروها علاش لا
  --
  reference: ###صافي مشات... أنا أختي معندي مندير بهاد صداع الراس...
  
  predicted: صافي مشات أنا خصي معندي مندير بهاد داع راسك
    ف
  --
  reference: خلصو ليا غير لكريدي ديالي وديرو ليعجبكوم
  
  predicted: خلصو ليا غير لكريدي ديالي أوديرو لي عجبكوم
  --
  reference: أنا نتكلف يلاه لقى شي حاجه نشغل بيها راسي
  
  predicted: أنا نتكلف يالله لقا شي حاجه نشغل بيها راسي
  ```
  
  ## Evaluation
  
  The model can be evaluated as follows on the Arabic test data of Common Voice. 
  
  
  ```python
  import re
  import torch
  import librosa
  import torchaudio
  from datasets import load_dataset, load_metric
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import soundfile as sf
  
  eval_dataset = load_dataset("ma_speech_corpus", split="test")
  wer = load_metric("wer")
  
  processor = Wav2Vec2Processor.from_pretrained("othrif/wav2vec2-large-xlsr-moroccan")
  model = Wav2Vec2ForCTC.from_pretrained("othrif/wav2vec2-large-xlsr-moroccan")
  model.to("cuda")
  
  chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\'\\�]'
  
  def remove_special_characters(batch):
      batch["text"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).lower() + " "
      return batch
  
  
  eval_dataset = eval_dataset.map(remove_special_characters, remove_columns=["sentence"])
  #eval_dataset = eval_dataset.select(range(100))
  
  def speech_file_to_array_fn(batch):
      start, stop = batch['segment'].split('_')
      speech_array, sampling_rate = torchaudio.load(batch["path"])
      speech_array, sampling_rate = sf.read(batch["path"], start=int(float(start) * sampling_rate),
                                            stop=int(float(s