readme: ---
  language: it
  datasets:
  - common_voice
  metrics:
  - wer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: Wav2Vec2 Large 53 Italian by Gunjan Chhablani
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice it
        type: common_voice
        args: it
      metrics:
         - name: Test WER
           type: wer
           value: 11.49
  ---
  
  # Wav2Vec2-Large-XLSR-53-Italian
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Italian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. 
  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 transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  
  test_dataset = load_dataset("common_voice", "it", split="test[:2%]")
  
  processor = Wav2Vec2Processor.from_pretrained('gchhablani/wav2vec2-large-xlsr-it')
  model = Wav2Vec2ForCTC.from_pretrained('gchhablani/wav2vec2-large-xlsr-it') 
  
  resampler = torchaudio.transforms.Resample(48_000, 16_000)
  
  # Preprocessing the datasets.
  # We need to read the aduio 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("Prediction:", processor.batch_decode(predicted_ids))
  print("Reference:", test_dataset["sentence"][:2])
  ```
  
  
  ## Evaluation
  
  The model can be evaluated as follows on the Portuguese test data of Common Voice.
  
  
  ```python
  import torch
  import torchaudio
  from datasets import load_dataset, load_metric
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import re
  
  import unicodedata
  import jiwer
  
  def chunked_wer(targets, predictions, chunk_size=None):
      if chunk_size is None: return jiwer.wer(targets, predictions)
      start = 0
      end = chunk_size
      H, S, D, I = 0, 0, 0, 0
      while start < len(targets):
          chunk_metrics = jiwer.compute_measures(targets[start:end], predictions[start:end])
          H = H + chunk_metrics["hits"]
          S = S + chunk_metrics["substitutions"]
          D = D + chunk_metrics["deletions"]
          I = I + chunk_metrics["insertions"]
          start += chunk_size
          end += chunk_size
      return float(S + D + I) / float(H + S + D)
  
  allowed_characters = [
      " ",
      "'",
      'a',
      'b',
      'c',
      'd',
      'e',
      'f',
      'g',
      'h',
      'i',
      'j',
      'k',
      'l',
      'm',
      'n',
      'o',
      'p',
      'q',
      'r',
      's',
      't',
      'u',
      'v',
      'w',
      'x',
      'y',
      'z',
      'à',
      'á',
      'è',
      'é',
      'ì',
      'í',
      'ò',
      'ó',
      'ù',
      'ú',
      ]
  
  def remove_accents(input_str):
      if input_str in allowed_characters:
          return input_str
      if input_str == 'ø':
          return 'o'
      elif input_str=='ß' or input_str =='ß':
          return 'b'
      elif input_str=='ё':
          return 'e'
      elif input_str=='đ':
          return 'd'
      nfkd_form = unicodedata.normalize('NFKD', input_str)
      only_ascii = nfkd_form.encode('ASCII', 'ignore').decode()
      
      if only_ascii is None or only_ascii=='':
          return input_str
      else:
          return only_ascii
  def fix_accents(sentence):
      new_sentence=''
      for char in sentence:
          new_sentence+=remove_accents(char)
      return new_sentence
  
  test_dataset = load_dataset("common_voice", "it", split="test")
  wer = load_metric("wer")
  
  processor = Wav2Vec2Processor.from_pretrained('gchhablani/wav2vec2-large-xlsr-it')
  model = Wav2Vec2ForCTC.from_pretrained('gchhablani/wav2vec2-large-xlsr-it') 
  model.to("cuda")
  
  resampler = torchaudio.transforms.Resample(48_000, 16_000)
  
  chars_to_remove= [",", "?", ".", "!", "-", ";", ":", '""', "%", '"', "�",'ʿ','“','”','(','=','`','_','+','«','<','>','~','…','«','»','–','\[','\]','°','̇','´','ʾ','„','̇','̇','̇','¡'] # All extra characters
  
  chars_to_remove_regex = f'[{"".join(chars_to_remove)}]'
  
  # Preprocessing the datasets.
  # We need to read the aduio files as arrays
  def speech_file_to_array_fn(batch):
      batch["sentence"] = re.sub(chars_to_remove_regex, '', batch["sentence"]).lower().replace('‘',"'").replace('ʻ',"'").replace('ʼ',"'").replace('’',"'").replace('ʹ',"''").replace('̇','')
      batch["sentence"] = fix_accents(batch["sentence"])
      speech_array, sampling_rate = torchaudio.load(batch["path"])
      batch["speech"] = resampler(speech_array).squeeze().numpy()
      return batch
  
  test_dataset = test_d