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import random
from random import randrange
import librosa
import numpy as np
import torch
import torch.nn as nn
from RawBoost import (
ISD_additive_noise,
LnL_convolutive_noise,
SSI_additive_noise,
normWav,
)
from torch import Tensor
from torch.utils.data import Dataset
__author__ = "Hemlata Tak"
__email__ = "tak@eurecom.fr"
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().split(" ")
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, metafile, algo):
"""self.list_IDs : list of strings (each string: utt key),
self.labels: dictionary (key: utt key, value: label integer)"""
self.uttpath_labels = []
with open(metafile, "r") as f:
for line in f:
items = line.strip().split()
lb = 1 if items[-1] == "bonafide" else 0
self.uttpath_labels.append((items[0], lb))
self.algo = algo
self.args = args
self.cut = 64600 # take ~4 sec audio (64600 samples)
def __len__(self):
return len(self.uttpath_labels)
def __getitem__(self, index):
path, target = self.uttpath_labels[index]
X, fs = librosa.load(path, sr=16000)
Y = process_Rawboost_feature(X, fs, self.args, self.algo)
X_pad = pad(Y, self.cut)
x_inp = Tensor(X_pad)
return x_inp, target
class Dataset_ASVspoof2021_eval(Dataset):
def __init__(self, list_IDs):
"""self.list_IDs : list of strings (each string: utt key),"""
self.list_IDs = list_IDs
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(utt_id, 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
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