readme: ---
  language: ja
  datasets:
  - common_voice
  metrics:
  - wer
  - cer
  model-index:
  - name: wav2vec2-xls-r-300m finetuned on Japanese Hiragana with no word boundaries by Hyungshin Ryu of SLPlab
    results:
    - task:
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice Japanese
        type: common_voice
        args: ja
      metrics:
         - name: Test WER
           type: wer
           value: 90.66
         - name: Test CER
           type: cer
           value: 19.35
  ---
  # Wav2Vec2-XLS-R-300M-Japanese-Hiragana
  Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on Japanese Hiragana characters using the [Common Voice](https://huggingface.co/datasets/common_voice) and [JSUT](https://sites.google.com/site/shinnosuketakamichi/publication/jsut).
  The sentence outputs do not contain word boundaries. Audio inputs should be sampled at 16kHz.
  ## Usage
  The model can be used directly as follows:
  
  ```python
  !pip install mecab-python3
  !pip install unidic-lite
  !pip install pykakasi
  
  
  import torch
  import torchaudio
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  from datasets import load_dataset, load_metric
  import pykakasi
  import MeCab
  import re
  
  
  # load datasets, processor, and model
  test_dataset = load_dataset("common_voice", "ja", split="test")
  wer = load_metric("wer")
  cer = load_metric("cer")
  PTM = "slplab/wav2vec2-xls-r-300m-japanese-hiragana"
  print("PTM:", PTM)
  processor = Wav2Vec2Processor.from_pretrained(PTM)
  model = Wav2Vec2ForCTC.from_pretrained(PTM)
  device = "cuda"
  model.to(device)
  
  
  # preprocess datasets
  wakati = MeCab.Tagger("-Owakati")
  kakasi = pykakasi.kakasi()
  chars_to_ignore_regex = "[、,。]"
  
  def speech_file_to_array_fn_hiragana_nospace(batch):
      batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).strip()
      batch["sentence"] = ''.join([d['hira'] for d in kakasi.convert(batch["sentence"])])
      speech_array, sampling_rate = torchaudio.load(batch["path"])
      resampler = torchaudio.transforms.Resample(sampling_rate, 16000)
      batch["speech"] = resampler(speech_array).squeeze()
  
      return batch
  
  test_dataset = test_dataset.map(speech_file_to_array_fn_hiragana_nospace)
  
  
  #evaluate
  def evaluate(batch):
      inputs = processor(batch["speech"], sampling_rate=16000, return_tensors="pt", padding=True)
      with torch.no_grad():
          logits = model(inputs.input_values.to(device)).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)
  for i in range(10):
      print("="*20)
      print("Prd:", result[i]["pred_strings"])
      print("Ref:", result[i]["sentence"])
      
  print("WER: {:2f}%".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
  print("CER: {:2f}%".format(100 * cer.compute(predictions=result["pred_strings"], references=result["sentence"])))
  ```
  | Original Text  | Prediction |
  | ------------- | ------------- |
  | この料理は家庭で作れます。 | このりょうりはかていでつくれます |
  | 日本人は、決して、ユーモアと無縁な人種ではなかった。 | にっぽんじんはけしてゆうもあどむえんなじんしゅではなかった |
  | 木村さんに電話を貸してもらいました。 | きむらさんにでんわおかしてもらいました |
  
  ## Test Results
  **WER:** 90.66%,
  **CER:** 19.35%
  ## Training
  Trained on JSUT and train+valid set of Common Voice Japanese. Tested on test set of Common Voice Japanese.