readme: ---
  language: fa
  datasets:
  - shemo
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  widget:
  - label: ShEMO sample 250
    src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-shemo/resolve/main/sample250.flac
  - label: ShEMO sample 52
    src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-shemo/resolve/main/sample52.flac
  model-index:
  - name: XLSR Wav2Vec2 Persian (Farsi) ShEMO by Mehrdad Farahani
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: ShEMO fa
        type: shemo
        args: fa  
      metrics:
         - name: Test WER
           type: wer
           value: 30.00
           
  ---
  
  # Wav2Vec2-Large-XLSR-53-Persian ShEMO
  
  Fine-tuned [Wav2Vec2-Large-XLSR-53-Persian V2](https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-v2) in Persian (Farsi) using [ShEMO](https://www.kaggle.com/mansourehk/shemo-persian-speech-emotion-detection-database). 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:
  
  **Requirements**
  ```bash
  # requirement packages
  !pip install git+https://github.com/huggingface/datasets.git
  !pip install git+https://github.com/huggingface/transformers.git
  !pip install torchaudio
  !pip install librosa
  !pip install jiwer
  !pip install hazm
  !pip install num2fawords
  ```
  
  
  **Prediction**
  ```python
  import librosa
  import torch
  import torchaudio
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  from datasets import load_dataset
  
  from num2fawords import words, ordinal_words
  import numpy as np
  import hazm
  import re
  import string
  
  import IPython.display as ipd
  
  _normalizer = hazm.Normalizer()
  
  chars_to_ignore = [
      ",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�",
      "#", "!", "؟", "?", "«", "»", "،", "(", ")", "؛", "'ٔ", "٬",'ٔ', ",", "?", 
      ".", "!", "-", ";", ":",'"',"“", "%", "‘", "”", "�", "–", "…", "_", "”", '“', '„',
      'ā', 'š',
      # "ء", 
  ]
  
  # In case of farsi
  chars_to_ignore = chars_to_ignore + list(string.ascii_lowercase + string.digits)
  
  chars_to_mapping = {
      'ك': 'ک', 'دِ': 'د', 'بِ': 'ب', 'زِ': 'ز', 'ذِ': 'ذ', 'شِ': 'ش', 'سِ': 'س', 'ى': 'ی',
      'ي': 'ی', 'أ': 'ا', 'ؤ': 'و', "ے": "ی", "ۀ": "ه", "ﭘ": "پ", "ﮐ": "ک", "ﯽ": "ی",
      "ﺎ": "ا", "ﺑ": "ب", "ﺘ": "ت", "ﺧ": "خ", "ﺩ": "د", "ﺱ": "س", "ﻀ": "ض", "ﻌ": "ع",
      "ﻟ": "ل", "ﻡ": "م", "ﻢ": "م", "ﻪ": "ه", "ﻮ": "و", 'ﺍ': "ا", 'ة': "ه",
      'ﯾ': "ی", 'ﯿ': "ی", 'ﺒ': "ب", 'ﺖ': "ت", 'ﺪ': "د", 'ﺮ': "ر", 'ﺴ': "س", 'ﺷ': "ش",
      'ﺸ': "ش", 'ﻋ': "ع", 'ﻤ': "م", 'ﻥ': "ن", 'ﻧ': "ن", 'ﻭ': "و", 'ﺭ': "ر", "ﮔ": "گ",
          
      # "ها": "  ها", "ئ": "ی",
          
      "a": " ای ", "b": " بی ", "c": " سی ", "d": " دی ", "e": " ایی ", "f": " اف ",
      "g": " جی ", "h": " اچ ", "i": " آی ", "j": " جی ", "k": " کی ", "l": " ال ",
      "m": " ام ", "n": " ان ", "o": " او ", "p": " پی ", "q": " کیو ", "r": " آر ",
      "s": " اس ", "t": " تی ", "u": " یو ", "v": " وی ", "w": " دبلیو ", "x": " اکس ",
      "y": " وای ", "z": " زد ",
      "\u200c": " ", "\u200d": " ", "\u200e": " ", "\u200f": " ", "\ufeff": " ",
  }
  
  def multiple_replace(text, chars_to_mapping):
      pattern = "|".join(map(re.escape, chars_to_mapping.keys()))
      return re.sub(pattern, lambda m: chars_to_mapping[m.group()], str(text))
  
  def remove_special_characters(text, chars_to_ignore_regex):
      text = re.sub(chars_to_ignore_regex, '', text).lower() + " "
      return text
  
  def normalizer(batch, chars_to_ignore, chars_to_mapping):
      chars_to_ignore_regex = f"""[{"".join(chars_to_ignore)}]"""
      text = batch["sentence"].lower().strip()
      
      text = _normalizer.normalize(text)
      text = multiple_replace(text, chars_to_mapping)
      text = remove_special_characters(text, chars_to_ignore_regex)
      text = re.sub(" +", " ", text)
      _text = []
      for word in text.split():
          try:
              word = int(word)
              _text.append(words(word))
          except:
              _text.append(word)
              
      text = " ".join(_text) + " "
      
      text = text.strip() + " "
  
      batch["sentence"] = text
      return batch
  
  
  def speech_file_to_array_fn(batch):
      speech_array, sampling_rate = torchaudio.load(batch["path"])
      speech_array = speech_array.squeeze().numpy()
      speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000)
  
      batch["speech"] = speech_array
      return batch
  
  
  def predict(batch):
      features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
  
      input_values = features.input_values.to(device)
      attention_mask = features.attention_mask.to(device)
  
      with torch.no_grad():
          logits = model(input_values, attention_mask=attention_mask).logits 
          
      pred_ids = torch.argmax(logits, dim=-1)
  
      batch["predicted"] = processor.batch_decode(pred_ids)[0]
      return batch
  
  
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  processo