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# -*- coding: utf-8 -*-
import numpy as np
import paddle
from paddle import nn
import paddle.nn.functional as F
import paddleaudio
import paddleaudio.functional as audio_F
import random
## 1. RandomTimeStrech
class TimeStrech(nn.Layer):
def __init__(self, scale):
super(TimeStrech, self).__init__()
self.scale = scale
def forward(self, x):
mel_size = x.shape[-1]
x = F.interpolate(x, scale_factor=(1, self.scale), align_corners=False,
mode='bilinear').squeeze()
if x.shape[-1] < mel_size:
noise_length = (mel_size - x.shape[-1])
random_pos = random.randint(0, x.shape[-1]) - noise_length
if random_pos < 0:
random_pos = 0
noise = x[..., random_pos:random_pos + noise_length]
x = paddle.concat([x, noise], axis=-1)
else:
x = x[..., :mel_size]
return x.unsqueeze(1)
## 2. PitchShift
class PitchShift(nn.Layer):
def __init__(self, shift):
super(PitchShift, self).__init__()
self.shift = shift
def forward(self, x):
if len(x.shape) == 2:
x = x.unsqueeze(0)
x = x.squeeze()
mel_size = x.shape[1]
shift_scale = (mel_size + self.shift) / mel_size
x = F.interpolate(x.unsqueeze(1), scale_factor=(shift_scale, 1.), align_corners=False,
mode='bilinear').squeeze(1)
x = x[:, :mel_size]
if x.shape[1] < mel_size:
pad_size = mel_size - x.shape[1]
x = paddle.cat([x, paddle.zeros(x.shape[0], pad_size, x.shape[2])], axis=1)
x = x.squeeze()
return x.unsqueeze(1)
## 3. ShiftBias
class ShiftBias(nn.Layer):
def __init__(self, bias):
super(ShiftBias, self).__init__()
self.bias = bias
def forward(self, x):
return x + self.bias
## 4. Scaling
class SpectScaling(nn.Layer):
def __init__(self, scale):
super(SpectScaling, self).__init__()
self.scale = scale
def forward(self, x):
return x * self.scale
## 5. Time Flip
class TimeFlip(nn.Layer):
def __init__(self, length):
super(TimeFlip, self).__init__()
self.length = round(length)
def forward(self, x):
if self.length > 1:
start = np.random.randint(0, x.shape[-1] - self.length)
x_ret = x.clone()
x_ret[..., start:start + self.length] = paddle.flip(x[..., start:start + self.length], axis=[-1])
x = x_ret
return x
class PhaseShuffle2D(nn.Layer):
def __init__(self, n=2):
super(PhaseShuffle2D, self).__init__()
self.n = n
self.random = random.Random(1)
def forward(self, x, move=None):
# x.size = (B, C, M, L)
if move is None:
move = self.random.randint(-self.n, self.n)
if move == 0:
return x
else:
left = x[:, :, :, :move]
right = x[:, :, :, move:]
shuffled = paddle.concat([right, left], axis=3)
return shuffled
def build_transforms():
transforms = [
lambda M: TimeStrech(1+ (np.random.random()-0.5)*M*0.2),
lambda M: SpectScaling(1 + (np.random.random()-1)*M*0.1),
lambda M: PhaseShuffle2D(192),
]
N, M = len(transforms), np.random.random()
composed = nn.Sequential(
*[trans(M) for trans in np.random.choice(transforms, N)]
)
return composed
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