code: 
  # https://huggingface.co/vumichien/wav2vec2-large-xlsr-japanese
  
  import torch
  import torchaudio
  import librosa
  from datasets import load_dataset
  import MeCab
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import re
  
  # config
  wakati = MeCab.Tagger("-Owakati")
  chars_to_ignore_regex = '[\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\,\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\、\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\。\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\．\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\「\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\」\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\…\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\？\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\・]'
  
  # load data, processor and model
  test_dataset = load_dataset("common_voice", "ja", split="test[:2%]")
  processor = Wav2Vec2Processor.from_pretrained("vumichien/wav2vec2-large-xlsr-japanese")
  model = Wav2Vec2ForCTC.from_pretrained("vumichien/wav2vec2-large-xlsr-japanese")
  resampler = lambda sr, y: librosa.resample(y.numpy().squeeze(), sr, 16_000)
  
  # Preprocessing the datasets.
  def speech_file_to_array_fn(batch):
      batch["sentence"] = wakati.parse(batch["sentence"]).strip()
      batch["sentence"] = re.sub(chars_to_ignore_regex,'', batch["sentence"]).strip()
      speech_array, sampling_rate = torchaudio.load(batch["path"])
      batch["speech"] = resampler(sampling_rate, speech_array).squeeze()
      print(batch["sentence"])
      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])
  
apis: torch.no_grad torch.argmax