readme: ---
  language: kr
  datasets:
  - aihub 자유대화 음성(노인남녀)
  tags:
  - automatic-speech-recognition
  license: apache-2.0
  ---
  
  
  # wav2vec2-xlsr-korean-senior
  
  Futher fine-tuned [fleek/wav2vec-large-xlsr-korean](https://huggingface.co/fleek/wav2vec-large-xlsr-korean) using the [AIhub 자유대화 음성(노인남녀)](https://aihub.or.kr/aidata/30704).
  
  - Total train data size: 808,642
  - Total vaild data size: 159,970
  
  When using this model, make sure that your speech input is sampled at 16kHz.
  
  The script used for training can be found here: https://github.com/hyyoka/wav2vec2-korean-senior
  
  
  ### Inference
  
  ``` py
  import torchaudio
  from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
  import re
  
  def clean_up(transcription):
      hangul = re.compile('[^ ㄱ-ㅣ가-힣]+')
      result = hangul.sub('', transcription)
      return result
  
  model_name "hyyoka/wav2vec2-xlsr-korean-senior"
  processor = Wav2Vec2Processor.from_pretrained(model_name)
  model = Wav2Vec2ForCTC.from_pretrained(model_name)
  speech_array, sampling_rate = torchaudio.load(wav_file)
  feat = processor(speech_array[0], 
                              sampling_rate=16000, 
                              padding=True,
                              max_length=800000, 
                              truncation=True,
                              return_attention_mask=True,
                              return_tensors="pt",
                              pad_token_id=49
                              )
  input = {'input_values': feat['input_values'],'attention_mask':feat['attention_mask']}
  
  outputs = model(**input, output_attentions=True)
  logits = outputs.logits
  predicted_ids = logits.argmax(axis=-1)
  transcription = processor.decode(predicted_ids[0])
  stt_result = clean_up(transcription)
  ```
  