readme: ---
  language: ja
  datasets:
  - common_voice
  metrics:
  - wer
  - cer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  
  model-index:
  - name: Wav2Vec2 Japanese by NTQAI
    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: 81.3
         - name: Test CER
           type: cer
           value: 21.9
  ---
  # Wav2Vec2-Large-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), [JSUT](https://sites.google.com/site/shinnosuketakamichi/publication/jsut), [TEDxJP](https://github.com/laboroai/TEDxJP-10K) and some other data. This model is a model trained on public data. If you want to use trained model with more 600 hours of data and higher accuracy please contact nha.nguyen@ntq-solution.com.vn
  
  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 librosa
  from datasets import load_dataset
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  LANG_ID = "ja"
  MODEL_ID = "NTQAI/wav2vec2-large-japanese"
  SAMPLES = 3
  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。 | 祖母思い切れを最布ロぼがしている |
  | 財布をなくしたので、交番へ行きます。 | 財布をなく時間ので交番でへ行きます |
  | 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。 | ロみ屋のおやし旅館の主人に医をはめ交載のあの人に聞いて回ったらみんな私より収入が多い発ずなのに請金は安い |
  
  ## 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 = "NTQAI/wav2vec2-large-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.from_pretrained(MODEL_ID)
  model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
  model.to(DEVICE)
  # Preprocessing the datasets.
  # We need to read the audio files as arrays
  def speech_file_to_array_fn(batch):
      with warnings.catch_warnings():
          warnings.simplefilter("ignore")
          speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
      batch["speech"] = speech_array
      batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper()
      return batch
  test_dataset = test_dataset.map(speech_file_to_array_fn)
  # Preprocessing the datasets.
  # We need to read the audio 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(DEVICE), attention_mask=inputs.attention_mask.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)
  predictions =