readme: ---
  language: ja
  datasets:
  - common_voice
  metrics:
  - wer
  - cer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: XLSR Wav2Vec2 Japanese by Jonatas Grosman
    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: 81.80
         - name: Test CER
           type: cer
           value: 20.16
  ---
  
  # Fine-tuned XLSR-53 large model for speech recognition in Japanese
  
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Japanese using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice), [CSS10](https://github.com/Kyubyong/css10) and [JSUT](https://sites.google.com/site/shinnosuketakamichi/publication/jsut).
  When using this model, make sure that your speech input is sampled at 16kHz.
  
  This model has been fine-tuned thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :)
  
  The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
  
  ## Usage
  
  The model can be used directly (without a language model) as follows...
  
  Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library:
  
  ```python
  from huggingsound import SpeechRecognitionModel
  
  model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-japanese")
  audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
  
  transcriptions = model.transcribe(audio_paths)
  ```
  
  Writing your own inference script:
  
  ```python
  import torch
  import librosa
  from datasets import load_dataset
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  
  LANG_ID = "ja"
  MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-japanese"
  SAMPLES = 10
  
  test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
  
  processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
  model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
  
  # Preprocessing the datasets.
  # We need to read the audio files as arrays
  def speech_file_to_array_fn(batch):
      speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
      batch["speech"] = speech_array
      batch["sentence"] = batch["sentence"].upper()
      return batch
  
  test_dataset = test_dataset.map(speech_file_to_array_fn)
  inputs = processor(test_dataset["speech"], 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)
  predicted_sentences = processor.batch_decode(predicted_ids)
  
  for i, predicted_sentence in enumerate(predicted_sentences):
      print("-" * 100)
      print("Reference:", test_dataset[i]["sentence"])
      print("Prediction:", predicted_sentence)
  ```
  
  | Reference  | Prediction |
  | ------------- | ------------- |
  | 祖母は、おおむね機嫌よく、サイコロをころがしている。 | 人母は重にきね起くさいがしている |
  | 財布をなくしたので、交番へ行きます。 | 財布をなく手端ので勾番へ行きます |
  | 飲み屋のおやじ、旅館の主人、医者をはじめ、交際のある人にきいてまわったら、みんな、私より収入が多いはずなのに、税金は安い。 | ノ宮屋のお親じ旅館の主に医者をはじめ交際のアル人トに聞いて回ったらみんな私より収入が多いはなうに税金は安い |
  | 新しい靴をはいて出かけます。 | だらしい靴をはいて出かけます |
  | このためプラズマ中のイオンや電子の持つ平均運動エネルギーを温度で表現することがある | このためプラズマ中のイオンや電子の持つ平均運動エネルギーを温度で表弁することがある |
  | 松井さんはサッカーより野球のほうが上手です。 | 松井さんはサッカーより野球のほうが上手です |
  | 新しいお皿を使います。 | 新しいお皿を使います |
  | 結婚以来三年半ぶりの東京も、旧友とのお酒も、夜行列車も、駅で寝て、朝を待つのも久しぶりだ。 | 結婚ル二来三年半降りの東京も吸とのお酒も野越者も駅で寝て朝を待つの久しぶりた |
  | これまで、少年野球、ママさんバレーなど、地域スポーツを支え、市民に密着してきたのは、無数のボランティアだった。 | これまで少年野球<unk>三バレーなど地域スポーツを支え市民に満着してきたのは娘数のボランティアだった |
  | 靴を脱いで、スリッパをはきます。 | 靴を脱いでスイパーをはきます |
  
  ## Evaluation
  
  The model can be evaluated as follows on the Japanese test data of Common Voice.
  
  ```python
  import torch
  import re
  import librosa
  from datasets import load_dataset, load_metric
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  
  LANG_ID = "ja"
  MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-japanese"
  DEVICE = "cuda"
  
  CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
                     "؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
                     "{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。",
                     "、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽",
                     "『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "'", "ʻ", "ˆ"]
  
  test_dataset = load_dataset("common_voice", LANG_ID, split="test")
  
  wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py
  cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py
  
  chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"
  
  processor = Wav2Vec2Processor.fro