readme: ---
  language: ja
  datasets:
  - common_voice
  metrics:
  - wer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: XLSR Wav2Vec2 Japanese Hiragana by Chien Vu
    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: 24.74
         - name: Test CER
           type: cer
           value: 10.99
  ---
  # 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 Japanese speech corpus of Saruwatari-lab, University of Tokyo [JSUT](https://sites.google.com/site/shinnosuketakamichi/publication/jsut).
  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
  !pip install mecab-python3
  !pip install unidic-lite
  !pip install pykakasi
  !python -m unidic download
  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 = '[\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\,\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\、\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\。\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\．\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\「\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\」\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\…\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\？\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\・]'
  kakasi = pykakasi.kakasi()
  kakasi.setMode("J","H")
  kakasi.setMode("K","H")
  kakasi.setMode("r","Hepburn")
  conv = kakasi.getConverter()
  # load data, processor and model
  test_dataset = load_dataset("common_voice", "ja", split="test[:2%]")
  processor = Wav2Vec2Processor.from_pretrained("vumichien/wav2vec2-large-xlsr-japanese-hỉragana")
  model = Wav2Vec2ForCTC.from_pretrained("vumichien/wav2vec2-large-xlsr-japanese-hỉragana")
  resampler = lambda sr, y: librosa.resample(y.numpy().squeeze(), sr, 16_000)
  # Preprocessing the datasets.
  def speech_file_to_array_fn(batch):
      batch["sentence"] = conv.do(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()
      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 mecab-python3
  !pip install unidic-lite
  !pip install pykakasi
  !python -m unidic download
  import torch
  import librosa
  import torchaudio
  from datasets import load_dataset, load_metric
  import MeCab
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import re
  #config
  wakati = MeCab.Tagger("-Owakati")
  chars_to_ignore_regex = '[\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\,\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\、\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\。\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\．\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\「\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\」\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\…\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\？\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\・]'
  kakasi = pykakasi.kakasi()
  kakasi.setMode("J","H")
  kakasi.setMode("K","H")
  kakasi.setMode("r","Hepburn")
  conv = kakasi.getConverter()
  # load data, processor and model
  test_dataset = load_dataset("common_voice", "ja", split="test")
  wer = load_metric("wer")
  cer = load_metric("cer")
  processor = Wav2Vec2Processor.from_pretrained("vumichien/wav2vec2-large-xlsr-japanese-hỉragana")
  model = Wav2Vec2ForCTC.from_pretrained("vumichien/wav2vec2-large-xlsr-japanese-hỉragana")
  model.to("cuda")
  resampler = lambda sr, y: librosa.resample(y.numpy().squeeze(), sr, 16_000)
  # Preprocessing the datasets.
  def speech_file_to_array_fn(batch):
      batch["sentence"] = conv.do(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()
      return batch
  test_dataset = test_dataset.map(speech_file_to_array_fn)
  # evaluate function
  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