readme: ---
  license: mit
  language:
  - ko
  metrics:
  - wer
  - cer
  tags:
  - transcribe
  - whisper
  ---
  
  # Fine-tune Whisper-small for Korean Speech Recognition sample data (PoC)
  
  Fine-tuning was performed using sample voices recorded from this csv data(https://github.com/hyeonsangjeon/job-transcribe/blob/main/meta_voice_data_3922.csv). 
  We do not publish sample voices, so if you want to fine-tune yourself from scratch, please record separately or use a public dataset.
  
  Fine tuning training based on the guide at https://huggingface.co/blog/fine-tune-whisper
  
  [Note] In the voice recording data used for training, the speaker spoke clearly and slowly as if reading a textbook.
  
  ## Training
  
  ### Base model
  
  OpenAI's `whisper-small` (https://huggingface.co/openai/whisper-small)
  
  ### Parameters
  We used heuristic parameters without separate hyperparameter tuning. The sampling rate is set to 16,000Hz.
  - learning_rate = 2e-5
  - epochs = 5
  - gradient_accumulation_steps = 4
  - per_device_train_batch_size = 4
  - fp16 = True
  - gradient_checkpointing = True
  - generation_max_length = 225
  
  ## Usage
  You need to install librosa package in order to convert wave to Mel Spectrogram. (`pip install librosa`)
  
  ### inference.py
  
  ```python
  import librosa
  import torch
  from transformers import WhisperProcessor, WhisperForConditionalGeneration
  
  # prepare your sample data (.wav)
  file = "nlp-voice-3922/data/0002d3428f0ddfa5a48eec5cc351daa8.wav"
  
  # Convert to Mel Spectrogram
  arr, sampling_rate = librosa.load(file, sr=16000)
  
  # Load whisper model and processor
  processor = WhisperProcessor.from_pretrained("openai/whisper-small")
  model = WhisperForConditionalGeneration.from_pretrained("daekeun-ml/whisper-small-ko-finetuned-single-speaker-3922samples")
  
  # Preprocessing
  input_features = processor(arr, return_tensors="pt", sampling_rate=sampling_rate).input_features 
  
  # Prediction
  forced_decoder_ids = processor.get_decoder_prompt_ids(language="ko", task="transcribe")
  predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids)
  transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
  
  print(transcription)
  ```