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144 lines
4.0 KiB
144 lines
4.0 KiB
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import random
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import numpy as np
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import paddle
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import paddle.nn.functional as F
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from paddle import nn
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## 1. RandomTimeStrech
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class TimeStrech(nn.Layer):
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def __init__(self, scale):
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super().__init__()
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self.scale = scale
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def forward(self, x: paddle.Tensor):
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mel_size = x.shape[-1]
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x = F.interpolate(
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x,
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scale_factor=(1, self.scale),
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align_corners=False,
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mode='bilinear').squeeze()
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if x.shape[-1] < mel_size:
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noise_length = (mel_size - x.shape[-1])
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random_pos = random.randint(0, x.shape[-1]) - noise_length
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if random_pos < 0:
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random_pos = 0
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noise = x[..., random_pos:random_pos + noise_length]
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x = paddle.concat([x, noise], axis=-1)
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else:
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x = x[..., :mel_size]
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return x.unsqueeze(1)
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## 2. PitchShift
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class PitchShift(nn.Layer):
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def __init__(self, shift):
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super().__init__()
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self.shift = shift
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def forward(self, x: paddle.Tensor):
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if len(x.shape) == 2:
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x = x.unsqueeze(0)
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x = x.squeeze()
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mel_size = x.shape[1]
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shift_scale = (mel_size + self.shift) / mel_size
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x = F.interpolate(
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x.unsqueeze(1),
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scale_factor=(shift_scale, 1.),
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align_corners=False,
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mode='bilinear').squeeze(1)
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x = x[:, :mel_size]
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if x.shape[1] < mel_size:
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pad_size = mel_size - x.shape[1]
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x = paddle.cat(
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[x, paddle.zeros(x.shape[0], pad_size, x.shape[2])], axis=1)
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x = x.squeeze()
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return x.unsqueeze(1)
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## 3. ShiftBias
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class ShiftBias(nn.Layer):
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def __init__(self, bias):
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super().__init__()
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self.bias = bias
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def forward(self, x: paddle.Tensor):
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return x + self.bias
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## 4. Scaling
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class SpectScaling(nn.Layer):
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def __init__(self, scale):
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super().__init__()
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self.scale = scale
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def forward(self, x: paddle.Tensor):
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return x * self.scale
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## 5. Time Flip
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class TimeFlip(nn.Layer):
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def __init__(self, length):
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super().__init__()
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self.length = round(length)
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def forward(self, x: paddle.Tensor):
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if self.length > 1:
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start = np.random.randint(0, x.shape[-1] - self.length)
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x_ret = x.clone()
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x_ret[..., start:start + self.length] = paddle.flip(
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x[..., start:start + self.length], axis=[-1])
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x = x_ret
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return x
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class PhaseShuffle2D(nn.Layer):
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def __init__(self, n: int=2):
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super().__init__()
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self.n = n
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self.random = random.Random(1)
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def forward(self, x: paddle.Tensor, move=None):
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# x.size = (B, C, M, L)
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if move is None:
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move = self.random.randint(-self.n, self.n)
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if move == 0:
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return x
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else:
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left = x[:, :, :, :move]
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right = x[:, :, :, move:]
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shuffled = paddle.concat([right, left], axis=3)
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return shuffled
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def build_transforms():
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transforms = [
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lambda M: TimeStrech(1 + (np.random.random() - 0.5) * M * 0.2),
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lambda M: SpectScaling(1 + (np.random.random() - 1) * M * 0.1),
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lambda M: PhaseShuffle2D(192),
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]
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N, M = len(transforms), np.random.random()
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composed = nn.Sequential(
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* [trans(M) for trans in np.random.choice(transforms, N)])
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return composed
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