Update README.md

README.md

@@ -40,21 +40,26 @@ You need to install librosa package in order to convert wave to Mel Spectrogram.
 
 ```python
 import librosa
+import torch
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+
+# prepare your sample data (.wav)
 file = "nlp-voice-3922/data/0002d3428f0ddfa5a48eec5cc351daa8.wav"
-arr, sampling_rate = librosa.load(file, sr=16000)
 
-from transformers import WhisperProcessor, WhisperForConditionalGeneration
-from datasets import load_dataset
-import torch
+# Convert to Mel Spectrogram
+arr, sampling_rate = librosa.load(file, sr=16000)
 
-# load model and processor
+# 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)
+predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids)
+transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
 
 print(transcription)
 ```