Audio Data Preprocessing and Serialization for Speech Enhancement

import os import librosa import numpy as np from tqdm import tqdm

'clean_train_folder = 'D:/test_module/SEGAN/data/clean_trainset' 'noisy_train_folder = 'D:/test_module/SEGAN/data/noisy_trainset' 'clean_test_folder = 'data/test2/clean_testset' 'noisy_test_folder = 'data/test2/noisy_testset' 'serialized_train_folder = 'data/test2/serialized_train_data' 'serialized_test_folder = 'data/test2/serialized_test_data'

window_size = 2 ** 14 # about 1 second of samples sample_rate = 16000

def slice_signal(file, window_size, stride, sample_rate): ''' Helper function for slicing the audio file by window size and sample rate with [1-stride] percent overlap (default 50%). ''' wav, sr = librosa.load(file, sr=sample_rate) hop = int(window_size * stride) slices = [] for end_idx in range(window_size, len(wav), hop): start_idx = end_idx - window_size slice_sig = wav[start_idx:end_idx] slices.append(slice_sig) return slices

def process_and_serialize(data_type): ''' Serialize, down-sample the sliced signals and save on separate folder. ''' stride = 0.5

if data_type == 'train':
    clean_folder = clean_train_folder
    noisy_folder = noisy_train_folder
    serialized_folder = serialized_train_folder
else:
    clean_folder = clean_test_folder
    noisy_folder = noisy_test_folder
    serialized_folder = serialized_test_folder
if not os.path.exists(serialized_folder):
    os.makedirs(serialized_folder)

# walk through the path, slice the audio file, and save the serialized result
for root, dirs, files in os.walk(clean_folder):
    if len(files) == 0:
        continue
    for filename in tqdm(files, desc='Serialize and down-sample {} audios'.format(data_type)):
        clean_file = os.path.join(clean_folder, filename)
        noisy_file = os.path.join(noisy_folder, filename)
        # slice both clean signal and noisy signal
        clean_sliced = slice_signal(clean_file, window_size, stride, sample_rate)
        noisy_sliced = slice_signal(noisy_file, window_size, stride, sample_rate)
        # serialize - file format goes [original_file]_[slice_number].npy
        # ex) p293_154.wav_5.npy denotes 5th slice of p293_154.wav file
        for idx, slice_tuple in enumerate(zip(clean_sliced, noisy_sliced)):
            pair = np.array([slice_tuple[0], slice_tuple[1]])
            np.save(os.path.join(serialized_folder, '{}_{}'.format(filename, idx)), arr=pair)

def data_verify(data_type): ''' Verifies the length of each data after pre-process. ''' if data_type == 'train': serialized_folder = serialized_train_folder else: serialized_folder = serialized_test_folder

for root, dirs, files in os.walk(serialized_folder):
    for filename in tqdm(files, desc='Verify serialized {} audios'.format(data_type)):
        data_pair = np.load(os.path.join(root, filename))
        if data_pair.shape[1] != window_size:
            print('Snippet length not {} : {} instead'.format(window_size, data_pair.shape[1]))
            break

if name == 'main': process_and_serialize('train') data_verify('train') process_and_serialize('test') data_verify('test')