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data_utils_SSL.py
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data_utils_SSL.py
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import os
import numpy as np
import torch
import torch.nn as nn
from torch import Tensor
import librosa
from torch.utils.data import Dataset
from RawBoost import ISD_additive_noise,LnL_convolutive_noise,SSI_additive_noise,normWav
from random import randrange
import random
___author__ = "Hemlata Tak"
__email__ = "[email protected]"
def genSpoof_list( dir_meta,is_train=False,is_eval=False):
d_meta = {}
file_list=[]
with open(dir_meta, 'r') as f:
l_meta = f.readlines()
if (is_train):
for line in l_meta:
_,key,_,_,label = line.strip().split()
file_list.append(key)
d_meta[key] = 1 if label == 'bonafide' else 0
return d_meta,file_list
elif(is_eval):
for line in l_meta:
key= line.strip()
file_list.append(key)
return file_list
else:
for line in l_meta:
_,key,_,_,label = line.strip().split()
file_list.append(key)
d_meta[key] = 1 if label == 'bonafide' else 0
return d_meta,file_list
def pad(x, max_len=64600):
x_len = x.shape[0]
if x_len >= max_len:
return x[:max_len]
# need to pad
num_repeats = int(max_len / x_len)+1
padded_x = np.tile(x, (1, num_repeats))[:, :max_len][0]
return padded_x
class Dataset_ASVspoof2019_train(Dataset):
def __init__(self,args,list_IDs, labels, base_dir,algo):
'''self.list_IDs : list of strings (each string: utt key),
self.labels : dictionary (key: utt key, value: label integer)'''
self.list_IDs = list_IDs
self.labels = labels
self.base_dir = base_dir
self.algo=algo
self.args=args
self.cut=64600 # take ~4 sec audio (64600 samples)
def __len__(self):
return len(self.list_IDs)
def __getitem__(self, index):
utt_id = self.list_IDs[index]
X,fs = librosa.load(self.base_dir+'flac/'+utt_id+'.flac', sr=16000)
Y=process_Rawboost_feature(X,fs,self.args,self.algo)
X_pad= pad(Y,self.cut)
x_inp= Tensor(X_pad)
target = self.labels[utt_id]
return x_inp, target
class Dataset_ASVspoof2021_eval(Dataset):
def __init__(self, list_IDs, base_dir):
'''self.list_IDs : list of strings (each string: utt key),
'''
self.list_IDs = list_IDs
self.base_dir = base_dir
self.cut=64600 # take ~4 sec audio (64600 samples)
def __len__(self):
return len(self.list_IDs)
def __getitem__(self, index):
utt_id = self.list_IDs[index]
X, fs = librosa.load(self.base_dir+'flac/'+utt_id+'.flac', sr=16000)
X_pad = pad(X,self.cut)
x_inp = Tensor(X_pad)
return x_inp,utt_id
#--------------RawBoost data augmentation algorithms---------------------------##
def process_Rawboost_feature(feature, sr,args,algo):
# Data process by Convolutive noise (1st algo)
if algo==1:
feature =LnL_convolutive_noise(feature,args.N_f,args.nBands,args.minF,args.maxF,args.minBW,args.maxBW,args.minCoeff,args.maxCoeff,args.minG,args.maxG,args.minBiasLinNonLin,args.maxBiasLinNonLin,sr)
# Data process by Impulsive noise (2nd algo)
elif algo==2:
feature=ISD_additive_noise(feature, args.P, args.g_sd)
# Data process by coloured additive noise (3rd algo)
elif algo==3:
feature=SSI_additive_noise(feature,args.SNRmin,args.SNRmax,args.nBands,args.minF,args.maxF,args.minBW,args.maxBW,args.minCoeff,args.maxCoeff,args.minG,args.maxG,sr)
# Data process by all 3 algo. together in series (1+2+3)
elif algo==4:
feature =LnL_convolutive_noise(feature,args.N_f,args.nBands,args.minF,args.maxF,args.minBW,args.maxBW,
args.minCoeff,args.maxCoeff,args.minG,args.maxG,args.minBiasLinNonLin,args.maxBiasLinNonLin,sr)
feature=ISD_additive_noise(feature, args.P, args.g_sd)
feature=SSI_additive_noise(feature,args.SNRmin,args.SNRmax,args.nBands,args.minF,
args.maxF,args.minBW,args.maxBW,args.minCoeff,args.maxCoeff,args.minG,args.maxG,sr)
# Data process by 1st two algo. together in series (1+2)
elif algo==5:
feature =LnL_convolutive_noise(feature,args.N_f,args.nBands,args.minF,args.maxF,args.minBW,args.maxBW,
args.minCoeff,args.maxCoeff,args.minG,args.maxG,args.minBiasLinNonLin,args.maxBiasLinNonLin,sr)
feature=ISD_additive_noise(feature, args.P, args.g_sd)
# Data process by 1st and 3rd algo. together in series (1+3)
elif algo==6:
feature =LnL_convolutive_noise(feature,args.N_f,args.nBands,args.minF,args.maxF,args.minBW,args.maxBW,
args.minCoeff,args.maxCoeff,args.minG,args.maxG,args.minBiasLinNonLin,args.maxBiasLinNonLin,sr)
feature=SSI_additive_noise(feature,args.SNRmin,args.SNRmax,args.nBands,args.minF,args.maxF,args.minBW,args.maxBW,args.minCoeff,args.maxCoeff,args.minG,args.maxG,sr)
# Data process by 2nd and 3rd algo. together in series (2+3)
elif algo==7:
feature=ISD_additive_noise(feature, args.P, args.g_sd)
feature=SSI_additive_noise(feature,args.SNRmin,args.SNRmax,args.nBands,args.minF,args.maxF,args.minBW,args.maxBW,args.minCoeff,args.maxCoeff,args.minG,args.maxG,sr)
# Data process by 1st two algo. together in Parallel (1||2)
elif algo==8:
feature1 =LnL_convolutive_noise(feature,args.N_f,args.nBands,args.minF,args.maxF,args.minBW,args.maxBW,
args.minCoeff,args.maxCoeff,args.minG,args.maxG,args.minBiasLinNonLin,args.maxBiasLinNonLin,sr)
feature2=ISD_additive_noise(feature, args.P, args.g_sd)
feature_para=feature1+feature2
feature=normWav(feature_para,0) #normalized resultant waveform
# original data without Rawboost processing
else:
feature=feature
return feature