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data_preprocess.py
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data_preprocess.py
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import os
import librosa
import numpy as np
import matplotlib.pyplot as plt
from configuration import get_config
from utils import keyword_spot
config = get_config() # get arguments from parser
# downloaded dataset path
audio_path= r'C:\Users\LG\Documents\DataSets\Vctk\data' # utterance dataset
clean_path = r'C:\Users\LG\Documents\Deep_learning\speaker_vertification\clean_testset_wav' # clean dataset
noisy_path = r'C:\Users\LG\Documents\Deep_learning\speaker_vertification\noisy_testset_wav' # noisy dataset
def extract_noise():
""" Extract noise and save the spectrogram (as numpy array in config.noise_path)
Need: paired clean and noisy data set
"""
print("start noise extraction!")
os.makedirs(config.noise_path, exist_ok=True) # make folder to save noise file
total = len(os.listdir(clean_path)) # total length of audio files
batch_frames = config.N * config.M * config.tdsv_frame # TD-SV frame number of each batch
stacked_noise = []
stacked_len = 0
k = 0
for i, path in enumerate(os.listdir(clean_path)):
clean, sr = librosa.core.load(os.path.join(clean_path, path), sr=8000) # load clean audio
noisy, _ = librosa.core.load(os.path.join(noisy_path, path), sr=sr) # load noisy audio
noise = clean - noisy # get noise audio by subtract clean voice from the noisy audio
S = librosa.core.stft(y=noise, n_fft=config.nfft,
win_length=int(config.window * sr), hop_length=int(config.hop * sr)) # perform STFT
stacked_noise.append(S)
stacked_len += S.shape[1]
if i % 100 == 0:
print("%d processing..." % i)
if stacked_len < batch_frames: # if noise frames is short than batch frames, then continue to stack the noise
continue
stacked_noise = np.concatenate(stacked_noise, axis=1)[:,:batch_frames] # concat noise and slice
np.save(os.path.join(config.noise_path, "noise_%d.npy" % k), stacked_noise) # save spectrogram as numpy file
print(" %dth file saved" % k, stacked_noise.shape)
stacked_noise = [] # reset list
stacked_len = 0
k += 1
print("noise extraction is end! %d noise files" % k)
def save_spectrogram_tdsv():
""" Select text specific utterance and perform STFT with the audio file.
Audio spectrogram files are divided as train set and test set and saved as numpy file.
Need : utterance data set (VTCK)
"""
print("start text dependent utterance selection")
os.makedirs(config.train_path, exist_ok=True) # make folder to save train file
os.makedirs(config.test_path, exist_ok=True) # make folder to save test file
utterances_spec = []
for folder in os.listdir(audio_path):
utter_path= os.path.join(audio_path, folder, os.listdir(os.path.join(audio_path, folder))[0])
if os.path.splitext(os.path.basename(utter_path))[0][-3:] != '001': # if the text utterance doesn't exist pass
print(os.path.basename(utter_path)[:4], "001 file doesn't exist")
continue
utter, sr = librosa.core.load(utter_path, config.sr) # load the utterance audio
utter_trim, index = librosa.effects.trim(utter, top_db=14) # trim the beginning and end blank
if utter_trim.shape[0]/sr <= config.hop*(config.tdsv_frame+2): # if trimmed file is too short, then pass
print(os.path.basename(utter_path), "voice trim fail")
continue
S = librosa.core.stft(y=utter_trim, n_fft=config.nfft,
win_length=int(config.window * sr), hop_length=int(config.hop * sr)) # perform STFT
S = keyword_spot(S) # keyword spot (for now, just slice last 80 frames which contains "Call Stella")
utterances_spec.append(S) # make spectrograms list
utterances_spec = np.array(utterances_spec) # list to numpy array
np.random.shuffle(utterances_spec) # shuffle spectrogram (by person)
total_num = utterances_spec.shape[0]
train_num = (total_num//10)*9 # split total data 90% train and 10% test
print("selection is end")
print("total utterances number : %d"%total_num, ", shape : ", utterances_spec.shape)
print("train : %d, test : %d"%(train_num, total_num- train_num))
np.save(os.path.join(config.train_path, "train.npy"), utterances_spec[:train_num]) # save spectrogram as numpy file
np.save(os.path.join(config.test_path, "test.npy"), utterances_spec[train_num:])
def save_spectrogram_tisv():
""" Full preprocess of text independent utterance. The log-mel-spectrogram is saved as numpy file.
Each partial utterance is splitted by voice detection using DB
and the first and the last 180 frames from each partial utterance are saved.
Need : utterance data set (VTCK)
"""
print("start text independent utterance feature extraction")
os.makedirs(config.train_path, exist_ok=True) # make folder to save train file
os.makedirs(config.test_path, exist_ok=True) # make folder to save test file
utter_min_len = (config.tisv_frame * config.hop + config.window) * config.sr # lower bound of utterance length
total_speaker_num = len(os.listdir(audio_path))
train_speaker_num= (total_speaker_num//10)*9 # split total data 90% train and 10% test
print("total speaker number : %d"%total_speaker_num)
print("train : %d, test : %d"%(train_speaker_num, total_speaker_num-train_speaker_num))
for i, folder in enumerate(os.listdir(audio_path)):
speaker_path = os.path.join(audio_path, folder) # path of each speaker
print("%dth speaker processing..."%i)
utterances_spec = []
k=0
for utter_name in os.listdir(speaker_path):
utter_path = os.path.join(speaker_path, utter_name) # path of each utterance
utter, sr = librosa.core.load(utter_path, config.sr) # load utterance audio
intervals = librosa.effects.split(utter, top_db=20) # voice activity detection
for interval in intervals:
if (interval[1]-interval[0]) > utter_min_len: # If partial utterance is sufficient long,
utter_part = utter[interval[0]:interval[1]] # save first and last 180 frames of spectrogram.
S = librosa.core.stft(y=utter_part, n_fft=config.nfft,
win_length=int(config.window * sr), hop_length=int(config.hop * sr))
S = np.abs(S) ** 2
mel_basis = librosa.filters.mel(sr=config.sr, n_fft=config.nfft, n_mels=40)
S = np.log10(np.dot(mel_basis, S) + 1e-6) # log mel spectrogram of utterances
utterances_spec.append(S[:, :config.tisv_frame]) # first 180 frames of partial utterance
utterances_spec.append(S[:, -config.tisv_frame:]) # last 180 frames of partial utterance
utterances_spec = np.array(utterances_spec)
print(utterances_spec.shape)
if i<train_speaker_num: # save spectrogram as numpy file
np.save(os.path.join(config.train_path, "speaker%d.npy"%i), utterances_spec)
else:
np.save(os.path.join(config.test_path, "speaker%d.npy"%(i-train_speaker_num)), utterances_spec)
if __name__ == "__main__":
extract_noise()
if config.tdsv:
save_spectrogram_tdsv()
else:
save_spectrogram_tisv()