readme: ---
  language: fr
  datasets:
  - common_voice
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning
  license: apache-2.0
  model-index:
  - name: wav2vec2-large-xlsr-53-French_punctuation by Ilyes Rebai
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice
        args: fr
      metrics:
         - name: Test WER and CER on text and puctuation prediction
           types: [wer, cer]
           values:  [19.47%, 6.66%]
         - name: Test WER and CER on text without punctuation
           types: [wer, cer]
           values:  [17.88%, 6.37%]
  
  ---
  ## Evaluation on Common Voice FR Test
  ```python
  import re
  import torch
  import torchaudio
  from datasets import load_dataset, load_metric
  from transformers import (
      Wav2Vec2ForCTC,
      Wav2Vec2Processor,
  )
  
  
  
  model_name = "Ilyes/wav2vec2-large-xlsr-53-french_punctuation"
  
  
  model = Wav2Vec2ForCTC.from_pretrained(model_name).to('cuda')
  processor = Wav2Vec2Processor.from_pretrained(model_name)
  
  
  ds = load_dataset("common_voice", "fr", split="test")
  
  
  chars_to_ignore_regex = '[\;\:\"\“\%\‘\”\�\‘\’\’\’\‘\…\·\ǃ\«\‹\»\›“\”\\ʿ\ʾ\„\∞\\|\;\:\*\—\–\─\―\_\/\:\ː\;\=\«\»\→]'
  def normalize_text(text):
      text = text.lower().strip()
      text = re.sub('œ', 'oe', text)
      text = re.sub('æ', 'ae', text)
      text = re.sub("’|´|′|ʼ|‘|ʻ|`", "'", text)
      text = re.sub("'+ ", " ", text)
      text = re.sub(" '+", " ", text)
      text = re.sub("'$", " ", text)
      text = re.sub("' ", " ", text)
      text = re.sub("−|‐", "-", text)
      text = re.sub(" -", "", text)
      text = re.sub("- ", "", text)
      text = re.sub(chars_to_ignore_regex, '', text)
      return text
  
  
  
  def map_to_array(batch):
      speech, _ = torchaudio.load(batch["path"])
      batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
      batch["sampling_rate"] = resampler.new_freq
      batch["sentence"] = normalize_text(batch["sentence"])
      return batch
  
  ds = ds.map(map_to_array)
  
  resampler = torchaudio.transforms.Resample(48_000, 16_000)
  def map_to_pred(batch):
      features = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt")
      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)
      batch["target"] = batch["sentence"]
      # remove duplicates
      batch["target"] = re.sub('\.+', '.', batch["target"])
      batch["target"] = re.sub('\?+', '?', batch["target"])
      batch["target"] = re.sub('!+', '!', batch["target"])
      batch["target"] = re.sub(',+', ',', batch["target"])
      return batch
  
  result = ds.map(map_to_pred, batched=True, batch_size=16, remove_columns=list(ds.features.keys()))
  wer = load_metric("wer")
  print(wer.compute(predictions=result["predicted"], references=result["target"]))
  ```
  ## Some results
  
  | Reference  | Prediction |
  | ------------- | ------------- |
  | il vécut à new york et y enseigna une grande partie de sa vie. | il a vécu à new york et y enseigna une grande partie de sa vie. |
  | au classement par nations, l'allemagne est la tenante du titre. | au classement der nation l'allemagne est la tenante du titre. |
  | voici un petit calcul pour fixer les idées. | voici un petit calcul pour fixer les idées. |
  | oh! tu dois être beau avec | oh! tu dois être beau avec. |
  | babochet vous le voulez? | baboche, vous le voulez? |
  | la commission est, par conséquent, défavorable à cet amendement. | la commission est, par conséquent, défavorable à cet amendement. |
  
  All the references and predictions of the test corpus are already available in this repository.
  
  ## Results
  
  text + punctuation 
  
  WER=21.47% CER=7.21%
  
  
  text (without punctuation)
  
  WER=19.71% CER=6.91%
  
  