readme: ---
  language: ja
  datasets:
    - common_voice
    - jsut
  metrics:
    - wer
    - cer
  tags:
    - audio
    - automatic-speech-recognition
    - speech
    - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
    - name: Japanese XLSR Wav2Vec2 Large 53
      results:
        - task:
            name: Speech Recognition
            type: automatic-speech-recognition
          dataset:
            name: Common Voice ja
            type: common_voice
            args: ja
          metrics:
            - name: Test WER
              type: wer
              value: 51.72
            - name: Test CER
              type: cer
              value: 24.89
  ---
  
  # Wav2Vec2-Large-XLSR-53-Japanese
  
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Japanese using the [Common Voice](https://huggingface.co/datasets/common_voice), and JSUT dataset{s}.
  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", "ja", split="test[:2%]")
  
  processor = Wav2Vec2Processor.from_pretrained("qqhann/w2v_hf_jsut_xlsr53")
  model = Wav2Vec2ForCTC.from_pretrained("qqhann/w2v_hf_jsut_xlsr53")
  
  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 Japanese test data of Common Voice.
  
  ```python
  !pip install torchaudio
  !pip install datasets transformers
  !pip install jiwer
  !pip install mecab-python3
  !pip install unidic-lite
  !python -m unidic download
  !pip install jaconv
  
  import torch
  import torchaudio
  from datasets import load_dataset, load_metric
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import re
  import MeCab
  from jaconv import kata2hira
  from typing import List
  
  # Japanese preprocessing
  tagger = MeCab.Tagger("-Owakati")
  chars_to_ignore_regex = '[\。\、\「\」\,\?\.\!\-\;\:\"\“\%\‘\”\�]'
  
  def text2kata(text):
      node = tagger.parseToNode(text)
      word_class = []
      while node:
          word = node.surface
          wclass = node.feature.split(',')
          if wclass[0] != u'BOS/EOS':
              if len(wclass) <= 6:
                  word_class.append((word))
              elif wclass[6] == None:
                  word_class.append((word))
              else:
                  word_class.append((wclass[6]))
          node = node.next
      return ' '.join(word_class)
  
  def hiragana(text):
      return kata2hira(text2kata(text))
  
  test_dataset = load_dataset("common_voice", "ja", split="test")
  wer = load_metric("wer")
  resampler = torchaudio.transforms.Resample(48_000, 16_000) # JSUT is already 16kHz
  # resampler = torchaudio.transforms.Resample(16_000, 16_000) # JSUT is already 16kHz
  
  processor = Wav2Vec2Processor.from_pretrained("qqhann/w2v_hf_jsut_xlsr53")
  model = Wav2Vec2ForCTC.from_pretrained("qqhann/w2v_hf_jsut_xlsr53")
  model.to("cuda")
  
  
  # Preprocessing the datasets.
  # We need to read the aduio files as arrays
  def speech_file_to_array_fn(batch):
      batch["sentence"] = hiragana(batch["sentence"]).strip()
      batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
      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)
  
  # Preprocessing the datasets.
  # We need to read the aduio files as arrays
  def evaluate(batch):
      inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
  
      with torch.no_grad():
          logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
  
      pred_ids = torch.argmax(logits, dim=-1)
      batch["pred_strings"] = processor.batch_decode(pred_ids)
      return batch
  
  result = test_dataset.map(evaluate, batched=True, batch_size=8)
  
  def cer_compute(predictions: List[str], references: List[str]):
      p = [" ".join(list(" " + pred.replace(" ", ""))).strip() for pred in predictions]
      r = [" ".join(list(" " + ref.replace(" ", ""))).strip() for ref in references]
      return wer.compute(predictions=p, references=r)
  
  print("WER: {:2f}".format(100 * wer.compute(predi