readme: ---
  language: it
  datasets:
  - common_voice
  tags:
  - speech
  - audio
  - automatic-speech-recognition
  license: apache-2.0
  ---
  
  ## Evaluation on Common Voice IT Test
  ```python
  import torchaudio
  from datasets import load_dataset, load_metric
  from transformers import (
      Wav2Vec2ForCTC,
      Wav2Vec2Processor,
  )
  import torch
  import re
  import sys
  
  model_name = "facebook/wav2vec2-large-xlsr-53-italian"
  device = "cuda"
  
  chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"]'  # noqa: W605
  
  model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device)
  processor = Wav2Vec2Processor.from_pretrained(model_name)
  
  ds = load_dataset("common_voice", "it", split="test", data_dir="./cv-corpus-6.1-2020-12-11")
  
  resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000)
  
  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
      
  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"]))
  ```
  **Result**: 22.1 %