readme: ---
  language: ja
  datasets:
  - csj
  tags:
  - audio
  - automatic-speech-recognition
  license: cc-by-nc-4.0
  ---
  
  ### Usage
  
  ```python
  #!pip install transformers==4.17.0
  #!pip install https://github.com/kpu/kenlm/archive/master.zip
  #!pip install pyctcdecode==0.4.0
  from transformers.file_utils import cached_path, hf_bucket_url
  from importlib.machinery import SourceFileLoader
  from transformers import Wav2Vec2ProcessorWithLM
  from IPython.lib.display import Audio
  import torchaudio
  import torch
  
  # Load model & processor
  model_name = "nguyenvulebinh/wav2vec2-base-ja"
  model = SourceFileLoader("model", cached_path(hf_bucket_url(model_name,filename="model_handling.py"))).load_module().Wav2Vec2ForCTC.from_pretrained(model_name)
  processor = Wav2Vec2ProcessorWithLM.from_pretrained(model_name)
  
  # Load an example audio (16k)
  audio, sample_rate = torchaudio.load(cached_path(hf_bucket_url(model_name, filename="sample.wav")))
  input_data = processor.feature_extractor(audio[0], sampling_rate=16000, return_tensors='pt')
  
  # Infer
  output = model(**input_data)
  
  # Output transcript without LM
  print(processor.tokenizer.decode(output.logits.argmax(dim=-1)[0].detach().cpu().numpy()))
  
  # Output transcript with LM
  print(processor.decode(output.logits.cpu().detach().numpy()[0], beam_width=100).text)
  ```
  
  ### Model Parameters License
  
  The ASR model parameters are made available for non-commercial use only, under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You can find details at: https://creativecommons.org/licenses/by-nc/4.0/legalcode
  
  
  ### Contact 
  
  nguyenvulebinh@gmail.com
  
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