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import librosa
import random
import pickle
import os
import torch
from torch.utils.data import Dataset, DataLoader
from speechbrain.processing.signal_processing import reverberate
from torch.nn.utils.rnn import pad_sequence
def changed_index(ind, step = 0):
ind_bool = ind < ind.min() - 1
if step == -1 :
ind_bool[1:] = (ind+1)[:-1] == ind[1:]
else:
ind_bool[:-1] = (ind-step)[1:] == ind[:-1]
ind_bool = ~ind_bool
return ind_bool
def post_processing_VAD(vad_out, goal = 1, len_frame_ms = 20, sensitivity_ms = 200):
"""Post-processing of VAD models to change 0 label0 with 1 labels according to a sensitivity.
Arguments
---------
vad_out : float (Tensor)
Output of the VAD model.
goal : int (Tensor)
The goal of change.
len_frame_ms : float
Length of decision frame.
sensitivity_ms : float
Threshold to change labels that are less than it.
Returns
-------
vad_out : float (Tensor)
The pre-processed output.
"""
vad_out = torch.tensor(vad_out)
Th = max(int(sensitivity_ms // len_frame_ms), 1)
ind0,ind1 = torch.where(vad_out== goal)
if len(ind0) != 0:
ind1_max = vad_out.shape[-1] - 1
ind0_last_bool = changed_index(ind0.clone())
ind0_last = torch.where(ind0_last_bool)[0]
ind0_first = torch.zeros_like(ind0_last)
ind0_first[1:] = ind0_last[:-1] + 1
ind0_first[0] = 0
ind1_l1_bool = changed_index(ind1.clone(), step = 1)
ind1_l1_bool[ind0_last] = False
ind1_f1_bool = changed_index(ind1.clone(), step = -1)
ind1_f1_bool[ind0_first] = False
dif_bool = ind1[ind1_f1_bool] - ind1[ind1_l1_bool] > Th + 1
l1_bool_temp = ind1_l1_bool[ind1_l1_bool].clone()
l1_bool_temp[dif_bool] = False
ind1_l1_bool[ind1_l1_bool.clone()] = l1_bool_temp
f1_bool_temp = ind1_f1_bool[ind1_f1_bool].clone()
f1_bool_temp[dif_bool] = False
ind1_f1_bool[ind1_f1_bool.clone()] = f1_bool_temp
second_ind = ind1[ind1_l1_bool].clone()
for i in range(1,Th+1):
second_ind = torch.clip(ind1[ind1_l1_bool]+i,0,ind1_max)
desired_out = (second_ind < ind1[ind1_f1_bool])
temp_b = vad_out[ind0[ind1_l1_bool], second_ind].clone()
temp_b[desired_out] = goal
vad_out[ind0[ind1_l1_bool], second_ind] = temp_b.clone()
vad_out = vad_out.numpy()
return vad_out
def creat_data_pathes(base_path,
groups,
g_scale,
filenames = "train_files_path.txt"):
total_paths = []
i = 0
for group_name in groups:
with open( os.path.join(base_path,group_name,filenames ), 'rb') as fp:
pathes = pickle.load(fp)
pathes = pathes * g_scale[i]
for path in pathes:
total_paths.append(os.path.join(group_name,path))
i+=1
random.seed(12)
random.shuffle(total_paths)
random.shuffle(total_paths)
return total_paths
def pyannote_frame(len_inp,sinc_len):
f = (len_inp - 251 + sinc_len)//sinc_len
mp_0 = f // 3
conv1 = mp_0 - 4
mp_1 = conv1 // 3
conv2 = mp_1 - 4
mp_2 = conv2 // 3 # number of frames
return mp_2, len_inp// mp_2 # length of frame
def frame_target(target, num_frame, frame_shift):
len_ = num_frame * frame_shift
target = target[:,:len_]
target = target.reshape(target.shape[0],num_frame,frame_shift)
target = target.float().mean(-1,True)
target[target>0.5] = 1
target[target <= 0.5] = 0
return target
class VAD_DATASET(Dataset):
def __init__(self,
base_clean_path,
base_noise_path,
base_rever_path,
base_lbl,
clean_paths,
noise_paths,
reverb_paths,
sampling_rate = 16000,
max_length = 10 * 16000,
max_noise_n = 2, #max = 2
t_reverb = -1,
min_snr = -10,
is_post_process = False,
sens_ms = 100
):
self.base_clean_path = base_clean_path
self.base_noise_path = base_noise_path
self.base_rever_path = base_rever_path
self.base_lbl = base_lbl
self.clean_paths = clean_paths
self.noise_paths = noise_paths
self.reverb_paths = reverb_paths
self.is_post_process = is_post_process
self.sens_ms = sens_ms
self.len_clean = len(clean_paths)
self.len_noise = len(noise_paths)
self.len_reverb = len(reverb_paths)
self.sampling_rate = sampling_rate
self.max_length = max_length
self.max_noise_n = max_noise_n
self.t_reverb = t_reverb
self.len_snr = len(range(min_snr,31,2))
self.SNR_amount = range(min_snr,31,2)
print("Dataset is ready.")
def create_reverb(self, sig, reverb_filename):
reverb_ = torch.from_numpy(self.load_sample(reverb_filename))
reverb_sig = reverberate(sig.unsqueeze(dim = 0), reverb_, rescale_amp= 'peak')
return reverb_sig.squeeze()
def load_sample(self, path):
waveform, _ = librosa.load(path, sr=self.sampling_rate)
return waveform
def crop_noise(self, noise, len_x):
len_n = len(noise)
extra = len_n - len_x
if extra > 0:
first_ind = random.randint(0,extra - 1)
noise = noise[first_ind:first_ind+len_x]
return noise
def crop_audio(self, x):
len_x = len(x)
extra = len_x - self.max_length
if extra > 0:
first_ind = random.randint(0,extra - 1)
x = x[first_ind:first_ind+self.max_length]
len_x = self.max_length
return x, len_x
def creat_noisy_data(self, x_clean, noise, SNR):
sp_ener = torch.sum(x_clean**2)
noi_ener = torch.sum(noise**2)
a = (sp_ener/noi_ener)**0.5 * 10**(-SNR/20)
x_noisy = x_clean + a * noise
return x_noisy
def prepare_noise(self, path, len_x):
noise = self.load_sample(path)
len_n = len(noise)
if len_n < len_x:
repeat = len_x // len_n + 1
noise = [noise for _ in range(repeat)]
noise = np.concatenate(noise, axis=0)
noise = self.crop_noise(noise, len_x)
return noise
def creat_target(self, clean_flnm, len_x):
label_flnm = os.path.basename(clean_flnm).split("SPLIT")[0] + ".txt"
with open(os.path.join(self.base_lbl,label_flnm), 'rb') as handle:
framed_label = np.array(pickle.load( handle))
if self.is_post_process:
framed_label = framed_label[None,...]
framed_label = post_processing_VAD(framed_label,
goal = 1,
len_frame_ms = 20,
sensitivity_ms = self.sens_ms).squeeze()
label = np.repeat(framed_label, 320, axis=0)
if label.shape[-1] > len_x:
label = label[:len_x]
return label, framed_label
def __len__(self):
return len(self.clean_paths)
def __getitem__(self, index):
# load to tensors and normalization
x_clean = self.load_sample(os.path.join(self.base_clean_path,
self.clean_paths[index]))
x_clean, len_x = self.crop_audio(x_clean)
x_clean = x_clean * np.random.uniform(0.7,1,1)
noise = self.prepare_noise(os.path.join(self.base_noise_path,
self.noise_paths[random.sample(range(self.len_noise),
1)[0]]),
len_x)
x_clean = torch.from_numpy(x_clean)
noise = torch.from_numpy(noise)
is_reverb = torch.rand(1) < self.t_reverb
if is_reverb:
x_clean = self.create_reverb(x_clean,
os.path.join(self.base_rever_path,
self.reverb_paths[random.sample(range(self.len_reverb),
1)[0]]))
noise = self.create_reverb(noise,
os.path.join(self.base_rever_path,
self.reverb_paths[random.sample(range(self.len_reverb),
1)[0]]))
n_o_n = random.randint(1,self.max_noise_n)
if n_o_n == 2:
noise_2 = self.prepare_noise(os.path.join(self.base_noise_path,
self.noise_paths[random.sample(range(self.len_noise),
1)[0]]),
len_x)
noise_2 = torch.from_numpy(noise_2)
if is_reverb:
noise_2 = self.create_reverb(noise,
os.path.join(self.base_rever_path,
self.reverb_paths[random.sample(range(self.len_reverb),
1)[0]]))
noise = noise + noise_2
snr = self.SNR_amount[random.sample(range(self.len_snr),1)[0]]
x_noisy = self.creat_noisy_data(x_clean, noise, snr)
target, framed_target = self.creat_target(self.clean_paths[index], len_x)
target = torch.from_numpy(target)
framed_target = torch.from_numpy(framed_target)
return x_noisy, target, framed_target, is_reverb, n_o_n, snr
def collate_fn(batch):
inputs, targets, length_ratio = [], [], []
for noisy_input, target, framed_target, _, _, _ in batch:
inputs.append(noisy_input)
targets.append(target)
framed_target.append(framed_target)
length_ratio.append(len(noisy_input))
inputs = pad_sequence(inputs, batch_first=True, padding_value=0.0)
targets = pad_sequence(targets, batch_first=True, padding_value=0.0)
framed_target = pad_sequence(framed_target, batch_first=True, padding_value=0.0)
length_ratio = torch.tensor(length_ratio, dtype=torch.long) / inputs.shape[1]
return inputs, targets, framed_target, length_ratio
# for reading and preparing dataset
def audio_data_loader(base_clean_path,
base_noise_path,
base_rever_path,
clean_paths,
noise_paths,
reverb_paths,
sampling_rate,
max_length,
max_noise_n,
t_reverb,
min_snr,
batch_size,
num_workers,
pin_memory,
training
):
dataset = Enhancement_DATASET(base_clean_path,
base_noise_path,
base_rever_path,
clean_paths,
noise_paths,
reverb_paths,
sampling_rate,
max_length,
max_noise_n,
t_reverb,
min_snr
)
loader = DataLoader(dataset,
batch_size = batch_size,
shuffle = training,
drop_last = True,
collate_fn = collate_fn,
num_workers = num_workers,
pin_memory = pin_memory
)
return loader |