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import torch
import numpy as np
def add_noise_and_scale(front, noise, snr_l=0, snr_h=0, scale_lower=1.0, scale_upper=1.0):
"""
:param front: front-head audio, like vocal [samples,channel], will be normlized so any scale will be fine
:param noise: noise, [samples,channel], any scale
:param snr_l: Optional
:param snr_h: Optional
:param scale_lower: Optional
:param scale_upper: Optional
:return: scaled front and noise (noisy = front + noise), all_mel_e2e outputs are noramlized within [-1 , 1]
"""
snr = None
noise, front = normalize_energy_torch(noise), normalize_energy_torch(front) # set noise and vocal to equal range [-1,1]
# print("normalize:",torch.max(noise),torch.max(front))
if snr_l is not None and snr_h is not None:
front, noise, snr = _random_noise(front, noise, snr_l=snr_l, snr_h=snr_h) # remix them with a specific snr
noisy, noise, front = unify_energy_torch(noise + front, noise, front) # normalize noisy, noise and vocal energy into [-1,1]
# print("unify:", torch.max(noise), torch.max(front), torch.max(noisy))
scale = _random_scale(scale_lower, scale_upper) # random scale these three signal
# print("Scale",scale)
noisy, noise, front = noisy * scale, noise * scale, front * scale # apply scale
# print("after scale", torch.max(noisy), torch.max(noise), torch.max(front), snr, scale)
front, noise = _to_numpy(front), _to_numpy(noise) # [num_samples]
mixed_wav = front + noise
return front, noise, mixed_wav, snr, scale
def _random_scale(lower=0.3, upper=0.9):
return float(uniform_torch(lower, upper))
def _random_noise(clean, noise, snr_l=None, snr_h=None):
snr = uniform_torch(snr_l,snr_h)
clean_weight = 10 ** (float(snr) / 20)
return clean, noise/clean_weight, snr
def _to_numpy(wav):
return np.transpose(wav, (1, 0))[0].numpy() # [num_samples]
def normalize_energy(audio, alpha = 1):
'''
:param audio: 1d waveform, [batchsize, *],
:param alpha: the value of output range from: [-alpha,alpha]
:return: 1d waveform which value range from: [-alpha,alpha]
'''
val_max = activelev(audio)
return (audio / val_max) * alpha
def normalize_energy_torch(audio, alpha = 1):
'''
If the signal is almost empty(determined by threshold), if will only be divided by 2**15
:param audio: 1d waveform, 2**15
:param alpha: the value of output range from: [-alpha,alpha]
:return: 1d waveform which value range from: [-alpha,alpha]
'''
val_max = activelev_torch([audio])
return (audio / val_max) * alpha
def unify_energy(*args):
max_amp = activelev(args)
mix_scale = 1.0/max_amp
return [x * mix_scale for x in args]
def unify_energy_torch(*args):
max_amp = activelev_torch(args)
mix_scale = 1.0/max_amp
return [x * mix_scale for x in args]
def activelev(*args):
'''
need to update like matlab
'''
return np.max(np.abs([*args]))
def activelev_torch(*args):
'''
need to update like matlab
'''
res = []
args = args[0]
for each in args:
res.append(torch.max(torch.abs(each)))
return max(res)
def uniform_torch(lower, upper):
if(abs(lower-upper)<1e-5):
return upper
return (upper-lower)*torch.rand(1)+lower
if __name__ == "__main__":
wav1 = torch.randn(1, 32000)
wav2 = torch.randn(1, 32000)
target, noise, snr, scale = add_noise_and_scale(wav1, wav2)
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