I have a pickle file having my training data in format as given below:
'Ses01M_script03_1_F011': {'audio_data': array([ 97, 173, 139, ...,  74,  22,  56], dtype=int16),
  'emo_label': 0,
  'gen_label': 0,
  'transcript': 'mmm',
  'features': array([[-0.72736262,  0.43802561,  2.14846694, ..., -2.69805505,
          -2.6575799 , -2.62012389],
         [ 1.64373051,  1.45195623,  2.5100556 , ..., -3.49289518,
          -3.33669469, -3.24162892],
         [ 0.06487407,  2.2071517 ,  2.77620356, ..., -4.97157829,
          -5.32930986, -9.44662813],
         ...,
         [ 1.9662066 ,  2.45063788,  2.64235023, ..., -5.53699691,
          -5.63285812, -8.2494293 ],
         [ 2.46171323,  2.83857374,  2.91717236, ..., -5.23233626,
          -6.12257485, -6.33155109],
         [ 2.27569999,  2.81201242,  2.78668791, ..., -3.50327683,
          -3.70423857, -3.75825296]])},
 ...}

so basically its a dictionary. I want to load this information using dataloader , For that i want to create a speechdatagenrator class similar to given below:


import numpy as np
import torch
from utils import utils_wav

class SpeechDataGenerator():
    """Speech dataset."""

    def __init__(self, manifest, mode):
        """
        Read the textfile and get the paths
        """
        self.mode=mode
        self.audio_links = [line.rstrip('\n').split(' ')[0] for line in open(manifest)] # here manifest is the location where training data is saved.
        self.emo_labels = [int(line.rstrip('\n').split(' ')[1]) for line in open(manifest)]
        self.gen_labels = [int(line.rstrip('\n').split(' ')[2]) for line in open(manifest)]
        

    def __len__(self):
        return len(self.audio_links)

    def __getitem__(self, idx):
        audio_link =self.audio_links[idx]
        class_id = self.emo_labels[idx]
        #lang_label=lang_id[self.audio_links[idx].split('/')[-2]]
        audio_data = utils_wav.load_data_wav(audio_link,min_dur_sec=10)
        sample = {'raw_speech': torch.from_numpy(np.ascontiguousarray(audio_data)), 'labels': torch.from_numpy(np.ascontiguousarray(class_id))}
        return sample
        
    


Can any on please tell me how to modify this class to read this pickle file, inside which there is a dictionary to load features , emo_labels and gen_labels.
seeking guidance.
good day all.