text: import json
  import os
  import math
  import librosa
  
  DATASET_PATH = "19-and-16-output-final_dataset"
  JSON_PATH = "19-and-16-output-final_dataset.json"
  SAMPLE_RATE = 22050
  TRACK_DURATION = 2 # measured in seconds
  SAMPLES_PER_TRACK = SAMPLE_RATE * TRACK_DURATION
  
  
  def save_mfcc(dataset_path, json_path, num_mfcc=13, n_fft=2048, hop_length=512, num_segments=5):
      """Extracts MFCCs from music dataset and saves them into a json file along witgh genre labels.
  
          :param dataset_path (str): Path to dataset
          :param json_path (str): Path to json file used to save MFCCs
          :param num_mfcc (int): Number of coefficients to extract
          :param n_fft (int): Interval we consider to apply FFT. Measured in # of samples
          :param hop_length (int): Sliding window for FFT. Measured in # of samples
          :param: num_segments (int): Number of segments we want to divide sample tracks into
          :return:
          """
  
      # dictionary to store mapping, labels, and MFCCs
      data = {
          "mapping": [],
          "labels": [],
          "mfcc": []
      }
  
      samples_per_segment = int(SAMPLES_PER_TRACK / num_segments)
      num_mfcc_vectors_per_segment = math.ceil(samples_per_segment / hop_length)
  
      # loop through all genre sub-folder
      for i, (dirpath, dirnames, filenames) in enumerate(os.walk(dataset_path)):
  
          # ensure we're processing a genre sub-folder level
          if dirpath is not dataset_path:
  
              # save genre label (i.e., sub-folder name) in the mapping
              semantic_label = dirpath.split("/")[-1]
              data["mapping"].append(semantic_label)
              print("\nProcessing: {}".format(semantic_label))
  
              # process all audio files in genre sub-dir
              for f in filenames:
  
  		# load audio file
                  file_path = os.path.join(dirpath, f)
                  signal, sample_rate = librosa.load(file_path, sr=SAMPLE_RATE)
  
                  # process all segments of audio file
                  for d in range(num_segments):
  
                      # calculate start and finish sample for current segment
                      start = samples_per_segment * d
                      finish = start + samples_per_segment
  
                      # extract mfcc
                      mfcc = librosa.feature.mfcc(signal[start:finish], sample_rate, n_mfcc=num_mfcc, n_fft=n_fft, hop_length=hop_length)
                      mfcc = mfcc.T
  
                      # store only mfcc feature with expected number of vectors
                      if len(mfcc) == num_mfcc_vectors_per_segment:
                          data["mfcc"].append(mfcc.tolist())
                          data["labels"].append(i-1)
                          print("{}, segment:{}".format(file_path, d+1))
  
      # save MFCCs to json file
      with open(json_path, "w") as fp:
          json.dump(data, fp, indent=4)
          
          
  if __name__ == "__main__":
      save_mfcc(DATASET_PATH, JSON_PATH, num_segments=10)
  