readme: ---
  language: fr
  datasets:
  - common_voice
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: wav2vec2-large-xlsr-53-French by Ilyes Rebai
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice fr
        type: common_voice
        args: fr
      metrics:
         - name: Test WER
           type: wer
           value: 12.82
  ---
  ## Evaluation on Common Voice FR Test
  The script used for training and evaluation can be found here: https://github.com/irebai/wav2vec2
  
  
  ```python
  import torch
  import torchaudio
  from datasets import load_dataset, load_metric
  from transformers import (
      Wav2Vec2ForCTC,
      Wav2Vec2Processor,
  )
  import re
  
  model_name = "Ilyes/wav2vec2-large-xlsr-53-french"
  
  device = "cpu" # "cuda"
  
  model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device)
  processor = Wav2Vec2Processor.from_pretrained(model_name)
  
  ds = load_dataset("common_voice", "fr", split="test", cache_dir="./data/fr")
  
  chars_to_ignore_regex = '[\,\?\.\!\;\:\"\“\%\‘\”\�\‘\’\’\’\‘\…\·\!\ǃ\?\«\‹\»\›“\”\\ʿ\ʾ\„\∞\\|\.\,\;\:\*\—\–\─\―\_\/\:\ː\;\,\=\«\»\→]'
  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"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
      return batch
  resampler = torchaudio.transforms.Resample(48_000, 16_000)
      
  ds = ds.map(map_to_array)
  
  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"]
      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"]))
  ```
  
  ## Results
  
  WER=12.82%
  
  CER=4.40%
  