readme: ---
  language: ko
  datasets:
  - kresnik/zeroth_korean
  tags:
  - speech
  - audio
  - automatic-speech-recognition
  license: apache-2.0
  
  model-index:
  - name: 'Wav2Vec2 XLSR Korean'
    results: 
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Zeroth Korean
          type: kresnik/zeroth_korean
          args: clean
        metrics:
          - name: Test WER
            type: wer
            value: 4.74
          - name: Test CER
            type: cer
            value: 1.78
  
  ---
  
  
  ## Evaluation on Zeroth-Korean ASR corpus
  
  [Google colab notebook(Korean)](https://colab.research.google.com/github/indra622/tutorials/blob/master/wav2vec2_korean_tutorial.ipynb)
  
  ```
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  from datasets import load_dataset
  import soundfile as sf
  import torch
  from jiwer import wer
  
  processor = Wav2Vec2Processor.from_pretrained("kresnik/wav2vec2-large-xlsr-korean")
  
  model = Wav2Vec2ForCTC.from_pretrained("kresnik/wav2vec2-large-xlsr-korean").to('cuda')
  
  ds = load_dataset("kresnik/zeroth_korean", "clean")
  
  test_ds = ds['test']
  
  def map_to_array(batch):
      speech, _ = sf.read(batch["file"])
      batch["speech"] = speech
      return batch
  
  test_ds = test_ds.map(map_to_array)
  
  def map_to_pred(batch):
      inputs = processor(batch["speech"], sampling_rate=16000, return_tensors="pt", padding="longest")
      input_values = inputs.input_values.to("cuda")
      
      with torch.no_grad():
          logits = model(input_values).logits
  
      predicted_ids = torch.argmax(logits, dim=-1)
      transcription = processor.batch_decode(predicted_ids)
      batch["transcription"] = transcription
      return batch
  
  result = test_ds.map(map_to_pred, batched=True, batch_size=16, remove_columns=["speech"])
  
  print("WER:", wer(result["text"], result["transcription"]))
  
  ```
  
  ### Expected WER: 4.74%
  ### Expected CER: 1.78%