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PaddleSpeech/paddlespeech/vector/io/augment.py

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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this is modified from SpeechBrain
# https://github.com/speechbrain/speechbrain/blob/085be635c07f16d42cd1295045bc46c407f1e15b/speechbrain/lobes/augment.py
import math
import os
from typing import List
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddleaudio import load as load_audio
from paddleaudio.datasets.rirs_noises import OpenRIRNoise
from paddlespeech.s2t.utils.log import Log
from paddlespeech.vector.io.signal_processing import compute_amplitude
from paddlespeech.vector.io.signal_processing import convolve1d
from paddlespeech.vector.io.signal_processing import dB_to_amplitude
from paddlespeech.vector.io.signal_processing import notch_filter
from paddlespeech.vector.io.signal_processing import reverberate
logger = Log(__name__).getlog()
# TODO: Complete type-hint and doc string.
class DropFreq(nn.Layer):
def __init__(
self,
drop_freq_low=1e-14,
drop_freq_high=1,
drop_count_low=1,
drop_count_high=2,
drop_width=0.05,
drop_prob=1, ):
super(DropFreq, self).__init__()
self.drop_freq_low = drop_freq_low
self.drop_freq_high = drop_freq_high
self.drop_count_low = drop_count_low
self.drop_count_high = drop_count_high
self.drop_width = drop_width
self.drop_prob = drop_prob
def forward(self, waveforms):
# Don't drop (return early) 1-`drop_prob` portion of the batches
dropped_waveform = waveforms.clone()
if paddle.rand([1]) > self.drop_prob:
return dropped_waveform
# Add channels dimension
if len(waveforms.shape) == 2:
dropped_waveform = dropped_waveform.unsqueeze(-1)
# Pick number of frequencies to drop
drop_count = paddle.randint(
low=self.drop_count_low, high=self.drop_count_high + 1, shape=[1])
# Pick a frequency to drop
drop_range = self.drop_freq_high - self.drop_freq_low
drop_frequency = (
paddle.rand([drop_count]) * drop_range + self.drop_freq_low)
# Filter parameters
filter_length = 101
pad = filter_length // 2
# Start with delta function
drop_filter = paddle.zeros([1, filter_length, 1])
drop_filter[0, pad, 0] = 1
# Subtract each frequency
for frequency in drop_frequency:
notch_kernel = notch_filter(frequency, filter_length,
self.drop_width)
drop_filter = convolve1d(drop_filter, notch_kernel, pad)
# Apply filter
dropped_waveform = convolve1d(dropped_waveform, drop_filter, pad)
# Remove channels dimension if added
return dropped_waveform.squeeze(-1)
class DropChunk(nn.Layer):
def __init__(
self,
drop_length_low=100,
drop_length_high=1000,
drop_count_low=1,
drop_count_high=10,
drop_start=0,
drop_end=None,
drop_prob=1,
noise_factor=0.0, ):
super(DropChunk, self).__init__()
self.drop_length_low = drop_length_low
self.drop_length_high = drop_length_high
self.drop_count_low = drop_count_low
self.drop_count_high = drop_count_high
self.drop_start = drop_start
self.drop_end = drop_end
self.drop_prob = drop_prob
self.noise_factor = noise_factor
# Validate low < high
if drop_length_low > drop_length_high:
raise ValueError("Low limit must not be more than high limit")
if drop_count_low > drop_count_high:
raise ValueError("Low limit must not be more than high limit")
# Make sure the length doesn't exceed end - start
if drop_end is not None and drop_end >= 0:
if drop_start > drop_end:
raise ValueError("Low limit must not be more than high limit")
drop_range = drop_end - drop_start
self.drop_length_low = min(drop_length_low, drop_range)
self.drop_length_high = min(drop_length_high, drop_range)
def forward(self, waveforms, lengths):
# Reading input list
lengths = (lengths * waveforms.shape[1]).astype('int64')
batch_size = waveforms.shape[0]
dropped_waveform = waveforms.clone()
# Don't drop (return early) 1-`drop_prob` portion of the batches
if paddle.rand([1]) > self.drop_prob:
return dropped_waveform
# Store original amplitude for computing white noise amplitude
clean_amplitude = compute_amplitude(waveforms, lengths.unsqueeze(1))
# Pick a number of times to drop
drop_times = paddle.randint(
low=self.drop_count_low,
high=self.drop_count_high + 1,
shape=[batch_size], )
# Iterate batch to set mask
for i in range(batch_size):
if drop_times[i] == 0:
continue
# Pick lengths
length = paddle.randint(
low=self.drop_length_low,
high=self.drop_length_high + 1,
shape=[drop_times[i]], )
# Compute range of starting locations
start_min = self.drop_start
if start_min < 0:
start_min += lengths[i]
start_max = self.drop_end
if start_max is None:
start_max = lengths[i]
if start_max < 0:
start_max += lengths[i]
start_max = max(0, start_max - length.max())
# Pick starting locations
start = paddle.randint(
low=start_min,
high=start_max + 1,
shape=[drop_times[i]], )
end = start + length
# Update waveform
if not self.noise_factor:
for j in range(drop_times[i]):
if start[j] < end[j]:
dropped_waveform[i, start[j]:end[j]] = 0.0
else:
# Uniform distribution of -2 to +2 * avg amplitude should
# preserve the average for normalization
noise_max = 2 * clean_amplitude[i] * self.noise_factor
for j in range(drop_times[i]):
# zero-center the noise distribution
noise_vec = paddle.rand([length[j]], dtype='float32')
noise_vec = 2 * noise_max * noise_vec - noise_max
dropped_waveform[i, int(start[j]):int(end[j])] = noise_vec
return dropped_waveform
class Resample(nn.Layer):
def __init__(
self,
orig_freq=16000,
new_freq=16000,
lowpass_filter_width=6, ):
super(Resample, self).__init__()
self.orig_freq = orig_freq
self.new_freq = new_freq
self.lowpass_filter_width = lowpass_filter_width
# Compute rate for striding
self._compute_strides()
assert self.orig_freq % self.conv_stride == 0
assert self.new_freq % self.conv_transpose_stride == 0
def _compute_strides(self):
# Compute new unit based on ratio of in/out frequencies
base_freq = math.gcd(self.orig_freq, self.new_freq)
input_samples_in_unit = self.orig_freq // base_freq
self.output_samples = self.new_freq // base_freq
# Store the appropriate stride based on the new units
self.conv_stride = input_samples_in_unit
self.conv_transpose_stride = self.output_samples
def forward(self, waveforms):
if not hasattr(self, "first_indices"):
self._indices_and_weights(waveforms)
# Don't do anything if the frequencies are the same
if self.orig_freq == self.new_freq:
return waveforms
unsqueezed = False
if len(waveforms.shape) == 2:
waveforms = waveforms.unsqueeze(1)
unsqueezed = True
elif len(waveforms.shape) == 3:
waveforms = waveforms.transpose([0, 2, 1])
else:
raise ValueError("Input must be 2 or 3 dimensions")
# Do resampling
resampled_waveform = self._perform_resample(waveforms)
if unsqueezed:
resampled_waveform = resampled_waveform.squeeze(1)
else:
resampled_waveform = resampled_waveform.transpose([0, 2, 1])
return resampled_waveform
def _perform_resample(self, waveforms):
# Compute output size and initialize
batch_size, num_channels, wave_len = waveforms.shape
window_size = self.weights.shape[1]
tot_output_samp = self._output_samples(wave_len)
resampled_waveform = paddle.zeros((batch_size, num_channels,
tot_output_samp))
# eye size: (num_channels, num_channels, 1)
eye = paddle.eye(num_channels).unsqueeze(2)
# Iterate over the phases in the polyphase filter
for i in range(self.first_indices.shape[0]):
wave_to_conv = waveforms
first_index = int(self.first_indices[i].item())
if first_index >= 0:
# trim the signal as the filter will not be applied
# before the first_index
wave_to_conv = wave_to_conv[:, :, first_index:]
# pad the right of the signal to allow partial convolutions
# meaning compute values for partial windows (e.g. end of the
# window is outside the signal length)
max_index = (tot_output_samp - 1) // self.output_samples
end_index = max_index * self.conv_stride + window_size
current_wave_len = wave_len - first_index
right_padding = max(0, end_index + 1 - current_wave_len)
left_padding = max(0, -first_index)
wave_to_conv = paddle.nn.functional.pad(
wave_to_conv, [left_padding, right_padding], data_format='NCL')
conv_wave = paddle.nn.functional.conv1d(
x=wave_to_conv,
# weight=self.weights[i].repeat(num_channels, 1, 1),
weight=self.weights[i].expand((num_channels, 1, -1)),
stride=self.conv_stride,
groups=num_channels, )
# we want conv_wave[:, i] to be at
# output[:, i + n*conv_transpose_stride]
dilated_conv_wave = paddle.nn.functional.conv1d_transpose(
conv_wave, eye, stride=self.conv_transpose_stride)
# pad dilated_conv_wave so it reaches the output length if needed.
left_padding = i
previous_padding = left_padding + dilated_conv_wave.shape[-1]
right_padding = max(0, tot_output_samp - previous_padding)
dilated_conv_wave = paddle.nn.functional.pad(
dilated_conv_wave, [left_padding, right_padding],
data_format='NCL')
dilated_conv_wave = dilated_conv_wave[:, :, :tot_output_samp]
resampled_waveform += dilated_conv_wave
return resampled_waveform
def _output_samples(self, input_num_samp):
samp_in = int(self.orig_freq)
samp_out = int(self.new_freq)
tick_freq = abs(samp_in * samp_out) // math.gcd(samp_in, samp_out)
ticks_per_input_period = tick_freq // samp_in
# work out the number of ticks in the time interval
# [ 0, input_num_samp/samp_in ).
interval_length = input_num_samp * ticks_per_input_period
if interval_length <= 0:
return 0
ticks_per_output_period = tick_freq // samp_out
# Get the last output-sample in the closed interval,
# i.e. replacing [ ) with [ ]. Note: integer division rounds down.
# See http://en.wikipedia.org/wiki/Interval_(mathematics) for an
# explanation of the notation.
last_output_samp = interval_length // ticks_per_output_period
# We need the last output-sample in the open interval, so if it
# takes us to the end of the interval exactly, subtract one.
if last_output_samp * ticks_per_output_period == interval_length:
last_output_samp -= 1
# First output-sample index is zero, so the number of output samples
# is the last output-sample plus one.
num_output_samp = last_output_samp + 1
return num_output_samp
def _indices_and_weights(self, waveforms):
# Lowpass filter frequency depends on smaller of two frequencies
min_freq = min(self.orig_freq, self.new_freq)
lowpass_cutoff = 0.99 * 0.5 * min_freq
assert lowpass_cutoff * 2 <= min_freq
window_width = self.lowpass_filter_width / (2.0 * lowpass_cutoff)
assert lowpass_cutoff < min(self.orig_freq, self.new_freq) / 2
output_t = paddle.arange(start=0.0, end=self.output_samples)
output_t /= self.new_freq
min_t = output_t - window_width
max_t = output_t + window_width
min_input_index = paddle.ceil(min_t * self.orig_freq)
max_input_index = paddle.floor(max_t * self.orig_freq)
num_indices = max_input_index - min_input_index + 1
max_weight_width = num_indices.max()
j = paddle.arange(max_weight_width, dtype='float32')
input_index = min_input_index.unsqueeze(1) + j.unsqueeze(0)
delta_t = (input_index / self.orig_freq) - output_t.unsqueeze(1)
weights = paddle.zeros_like(delta_t)
inside_window_indices = delta_t.abs().less_than(
paddle.to_tensor(window_width))
# raised-cosine (Hanning) window with width `window_width`
weights[inside_window_indices] = 0.5 * (1 + paddle.cos(
2 * math.pi * lowpass_cutoff / self.lowpass_filter_width *
delta_t.masked_select(inside_window_indices)))
t_eq_zero_indices = delta_t.equal(paddle.zeros_like(delta_t))
t_not_eq_zero_indices = delta_t.not_equal(paddle.zeros_like(delta_t))
# sinc filter function
weights = paddle.where(
t_not_eq_zero_indices,
weights * paddle.sin(2 * math.pi * lowpass_cutoff * delta_t) /
(math.pi * delta_t), weights)
# limit of the function at t = 0
weights = paddle.where(t_eq_zero_indices, weights * 2 * lowpass_cutoff,
weights)
# size (output_samples, max_weight_width)
weights /= self.orig_freq
self.first_indices = min_input_index
self.weights = weights
class SpeedPerturb(nn.Layer):
def __init__(
self,
orig_freq,
speeds=[90, 100, 110],
perturb_prob=1.0, ):
super(SpeedPerturb, self).__init__()
self.orig_freq = orig_freq
self.speeds = speeds
self.perturb_prob = perturb_prob
# Initialize index of perturbation
self.samp_index = 0
# Initialize resamplers
self.resamplers = []
for speed in self.speeds:
config = {
"orig_freq": self.orig_freq,
"new_freq": self.orig_freq * speed // 100,
}
self.resamplers.append(Resample(**config))
def forward(self, waveform):
# Don't perturb (return early) 1-`perturb_prob` portion of the batches
if paddle.rand([1]) > self.perturb_prob:
return waveform.clone()
# Perform a random perturbation
self.samp_index = paddle.randint(len(self.speeds), shape=[1]).item()
perturbed_waveform = self.resamplers[self.samp_index](waveform)
return perturbed_waveform
class AddNoise(nn.Layer):
def __init__(
self,
noise_dataset=None, # None for white noise
num_workers=0,
snr_low=0,
snr_high=0,
mix_prob=1.0,
start_index=None,
normalize=False, ):
super(AddNoise, self).__init__()
self.num_workers = num_workers
self.snr_low = snr_low
self.snr_high = snr_high
self.mix_prob = mix_prob
self.start_index = start_index
self.normalize = normalize
self.noise_dataset = noise_dataset
self.noise_dataloader = None
def forward(self, waveforms, lengths=None):
if lengths is None:
lengths = paddle.ones([len(waveforms)])
# Copy clean waveform to initialize noisy waveform
noisy_waveform = waveforms.clone()
lengths = (lengths * waveforms.shape[1]).astype('int64').unsqueeze(1)
# Don't add noise (return early) 1-`mix_prob` portion of the batches
if paddle.rand([1]) > self.mix_prob:
return noisy_waveform
# Compute the average amplitude of the clean waveforms
clean_amplitude = compute_amplitude(waveforms, lengths)
# Pick an SNR and use it to compute the mixture amplitude factors
SNR = paddle.rand((len(waveforms), 1))
SNR = SNR * (self.snr_high - self.snr_low) + self.snr_low
noise_amplitude_factor = 1 / (dB_to_amplitude(SNR) + 1)
new_noise_amplitude = noise_amplitude_factor * clean_amplitude
# Scale clean signal appropriately
noisy_waveform *= 1 - noise_amplitude_factor
# Loop through clean samples and create mixture
if self.noise_dataset is None:
white_noise = paddle.normal(shape=waveforms.shape)
noisy_waveform += new_noise_amplitude * white_noise
else:
tensor_length = waveforms.shape[1]
noise_waveform, noise_length = self._load_noise(
lengths,
tensor_length, )
# Rescale and add
noise_amplitude = compute_amplitude(noise_waveform, noise_length)
noise_waveform *= new_noise_amplitude / (noise_amplitude + 1e-14)
noisy_waveform += noise_waveform
# Normalizing to prevent clipping
if self.normalize:
abs_max, _ = paddle.max(
paddle.abs(noisy_waveform), axis=1, keepdim=True)
noisy_waveform = noisy_waveform / abs_max.clip(min=1.0)
return noisy_waveform
def _load_noise(self, lengths, max_length):
"""
Load a batch of noises
args
lengths(Paddle.Tensor): Num samples of waveforms with shape (N, 1).
max_length(int): Width of a batch.
"""
lengths = lengths.squeeze(1)
batch_size = len(lengths)
# Load a noise batch
if self.noise_dataloader is None:
def noise_collate_fn(batch):
def pad(x, target_length, mode='constant', **kwargs):
x = np.asarray(x)
w = target_length - x.shape[0]
assert w >= 0, f'Target length {target_length} is less than origin length {x.shape[0]}'
return np.pad(x, [0, w], mode=mode, **kwargs)
ids = [item['id'] for item in batch]
lengths = np.asarray([item['feat'].shape[0] for item in batch])
waveforms = list(
map(lambda x: pad(x, max(max_length, lengths.max().item())),
[item['feat'] for item in batch]))
waveforms = np.stack(waveforms)
return {'ids': ids, 'feats': waveforms, 'lengths': lengths}
# Create noise data loader.
self.noise_dataloader = paddle.io.DataLoader(
self.noise_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=self.num_workers,
collate_fn=noise_collate_fn,
return_list=True, )
self.noise_data = iter(self.noise_dataloader)
noise_batch, noise_len = self._load_noise_batch_of_size(batch_size)
# Select a random starting location in the waveform
start_index = self.start_index
if self.start_index is None:
start_index = 0
max_chop = (noise_len - lengths).min().clip(min=1)
start_index = paddle.randint(high=max_chop, shape=[1])
# Truncate noise_batch to max_length
noise_batch = noise_batch[:, start_index:start_index + max_length]
noise_len = (noise_len - start_index).clip(max=max_length).unsqueeze(1)
return noise_batch, noise_len
def _load_noise_batch_of_size(self, batch_size):
"""Concatenate noise batches, then chop to correct size"""
noise_batch, noise_lens = self._load_noise_batch()
# Expand
while len(noise_batch) < batch_size:
noise_batch = paddle.concat((noise_batch, noise_batch))
noise_lens = paddle.concat((noise_lens, noise_lens))
# Contract
if len(noise_batch) > batch_size:
noise_batch = noise_batch[:batch_size]
noise_lens = noise_lens[:batch_size]
return noise_batch, noise_lens
def _load_noise_batch(self):
"""Load a batch of noises, restarting iteration if necessary."""
try:
batch = next(self.noise_data)
except StopIteration:
self.noise_data = iter(self.noise_dataloader)
batch = next(self.noise_data)
noises, lens = batch['feats'], batch['lengths']
return noises, lens
class AddReverb(nn.Layer):
def __init__(
self,
rir_dataset,
reverb_prob=1.0,
rir_scale_factor=1.0,
num_workers=0, ):
super(AddReverb, self).__init__()
self.rir_dataset = rir_dataset
self.reverb_prob = reverb_prob
self.rir_scale_factor = rir_scale_factor
# Create rir data loader.
def rir_collate_fn(batch):
def pad(x, target_length, mode='constant', **kwargs):
x = np.asarray(x)
w = target_length - x.shape[0]
assert w >= 0, f'Target length {target_length} is less than origin length {x.shape[0]}'
return np.pad(x, [0, w], mode=mode, **kwargs)
ids = [item['id'] for item in batch]
lengths = np.asarray([item['feat'].shape[0] for item in batch])
waveforms = list(
map(lambda x: pad(x, lengths.max().item()),
[item['feat'] for item in batch]))
waveforms = np.stack(waveforms)
return {'ids': ids, 'feats': waveforms, 'lengths': lengths}
self.rir_dataloader = paddle.io.DataLoader(
self.rir_dataset,
collate_fn=rir_collate_fn,
num_workers=num_workers,
shuffle=True,
return_list=True, )
self.rir_data = iter(self.rir_dataloader)
def forward(self, waveforms, lengths=None):
"""
Arguments
---------
waveforms : tensor
Shape should be `[batch, time]` or `[batch, time, channels]`.
lengths : tensor
Shape should be a single dimension, `[batch]`.
Returns
-------
Tensor of shape `[batch, time]` or `[batch, time, channels]`.
"""
if lengths is None:
lengths = paddle.ones([len(waveforms)])
# Don't add reverb (return early) 1-`reverb_prob` portion of the time
if paddle.rand([1]) > self.reverb_prob:
return waveforms.clone()
# Add channels dimension if necessary
channel_added = False
if len(waveforms.shape) == 2:
waveforms = waveforms.unsqueeze(-1)
channel_added = True
# Load and prepare RIR
rir_waveform = self._load_rir()
# Compress or dilate RIR
if self.rir_scale_factor != 1:
rir_waveform = F.interpolate(
rir_waveform.transpose([0, 2, 1]),
scale_factor=self.rir_scale_factor,
mode="linear",
align_corners=False,
data_format='NCW', )
# (N, C, L) -> (N, L, C)
rir_waveform = rir_waveform.transpose([0, 2, 1])
rev_waveform = reverberate(
waveforms,
rir_waveform,
self.rir_dataset.sample_rate,
rescale_amp="avg")
# Remove channels dimension if added
if channel_added:
return rev_waveform.squeeze(-1)
return rev_waveform
def _load_rir(self):
try:
batch = next(self.rir_data)
except StopIteration:
self.rir_data = iter(self.rir_dataloader)
batch = next(self.rir_data)
rir_waveform = batch['feats']
# Make sure RIR has correct channels
if len(rir_waveform.shape) == 2:
rir_waveform = rir_waveform.unsqueeze(-1)
return rir_waveform
class AddBabble(nn.Layer):
def __init__(
self,
speaker_count=3,
snr_low=0,
snr_high=0,
mix_prob=1, ):
super(AddBabble, self).__init__()
self.speaker_count = speaker_count
self.snr_low = snr_low
self.snr_high = snr_high
self.mix_prob = mix_prob
def forward(self, waveforms, lengths=None):
if lengths is None:
lengths = paddle.ones([len(waveforms)])
babbled_waveform = waveforms.clone()
lengths = (lengths * waveforms.shape[1]).unsqueeze(1)
batch_size = len(waveforms)
# Don't mix (return early) 1-`mix_prob` portion of the batches
if paddle.rand([1]) > self.mix_prob:
return babbled_waveform
# Pick an SNR and use it to compute the mixture amplitude factors
clean_amplitude = compute_amplitude(waveforms, lengths)
SNR = paddle.rand((batch_size, 1))
SNR = SNR * (self.snr_high - self.snr_low) + self.snr_low
noise_amplitude_factor = 1 / (dB_to_amplitude(SNR) + 1)
new_noise_amplitude = noise_amplitude_factor * clean_amplitude
# Scale clean signal appropriately
babbled_waveform *= 1 - noise_amplitude_factor
# For each speaker in the mixture, roll and add
babble_waveform = waveforms.roll((1, ), axis=0)
babble_len = lengths.roll((1, ), axis=0)
for i in range(1, self.speaker_count):
babble_waveform += waveforms.roll((1 + i, ), axis=0)
babble_len = paddle.concat(
[babble_len, babble_len.roll((1, ), axis=0)], axis=-1).max(
axis=-1, keepdim=True)
# Rescale and add to mixture
babble_amplitude = compute_amplitude(babble_waveform, babble_len)
babble_waveform *= new_noise_amplitude / (babble_amplitude + 1e-14)
babbled_waveform += babble_waveform
return babbled_waveform
class TimeDomainSpecAugment(nn.Layer):
def __init__(
self,
perturb_prob=1.0,
drop_freq_prob=1.0,
drop_chunk_prob=1.0,
speeds=[95, 100, 105],
sample_rate=16000,
drop_freq_count_low=0,
drop_freq_count_high=3,
drop_chunk_count_low=0,
drop_chunk_count_high=5,
drop_chunk_length_low=1000,
drop_chunk_length_high=2000,
drop_chunk_noise_factor=0, ):
super(TimeDomainSpecAugment, self).__init__()
self.speed_perturb = SpeedPerturb(
perturb_prob=perturb_prob,
orig_freq=sample_rate,
speeds=speeds, )
self.drop_freq = DropFreq(
drop_prob=drop_freq_prob,
drop_count_low=drop_freq_count_low,
drop_count_high=drop_freq_count_high, )
self.drop_chunk = DropChunk(
drop_prob=drop_chunk_prob,
drop_count_low=drop_chunk_count_low,
drop_count_high=drop_chunk_count_high,
drop_length_low=drop_chunk_length_low,
drop_length_high=drop_chunk_length_high,
noise_factor=drop_chunk_noise_factor, )
def forward(self, waveforms, lengths=None):
if lengths is None:
lengths = paddle.ones([len(waveforms)])
with paddle.no_grad():
# Augmentation
waveforms = self.speed_perturb(waveforms)
waveforms = self.drop_freq(waveforms)
waveforms = self.drop_chunk(waveforms, lengths)
return waveforms
class EnvCorrupt(nn.Layer):
def __init__(
self,
reverb_prob=1.0,
babble_prob=1.0,
noise_prob=1.0,
rir_dataset=None,
noise_dataset=None,
num_workers=0,
babble_speaker_count=0,
babble_snr_low=0,
babble_snr_high=0,
noise_snr_low=0,
noise_snr_high=0,
rir_scale_factor=1.0, ):
super(EnvCorrupt, self).__init__()
# Initialize corrupters
if rir_dataset is not None and reverb_prob > 0.0:
self.add_reverb = AddReverb(
rir_dataset=rir_dataset,
num_workers=num_workers,
reverb_prob=reverb_prob,
rir_scale_factor=rir_scale_factor, )
if babble_speaker_count > 0 and babble_prob > 0.0:
self.add_babble = AddBabble(
speaker_count=babble_speaker_count,
snr_low=babble_snr_low,
snr_high=babble_snr_high,
mix_prob=babble_prob, )
if noise_dataset is not None and noise_prob > 0.0:
self.add_noise = AddNoise(
noise_dataset=noise_dataset,
num_workers=num_workers,
snr_low=noise_snr_low,
snr_high=noise_snr_high,
mix_prob=noise_prob, )
def forward(self, waveforms, lengths=None):
if lengths is None:
lengths = paddle.ones([len(waveforms)])
# Augmentation
with paddle.no_grad():
if hasattr(self, "add_reverb"):
try:
waveforms = self.add_reverb(waveforms, lengths)
except Exception:
pass
if hasattr(self, "add_babble"):
waveforms = self.add_babble(waveforms, lengths)
if hasattr(self, "add_noise"):
waveforms = self.add_noise(waveforms, lengths)
return waveforms
def build_augment_pipeline(target_dir=None) -> List[paddle.nn.Layer]:
"""build augment pipeline
Note: this pipeline cannot be used in the paddle.DataLoader
Returns:
List[paddle.nn.Layer]: all augment process
"""
logger.info("start to build the augment pipeline")
noise_dataset = OpenRIRNoise('noise', target_dir=target_dir)
rir_dataset = OpenRIRNoise('rir', target_dir=target_dir)
wavedrop = TimeDomainSpecAugment(
sample_rate=16000,
speeds=[100], )
speed_perturb = TimeDomainSpecAugment(
sample_rate=16000,
speeds=[95, 100, 105], )
add_noise = EnvCorrupt(
noise_dataset=noise_dataset,
reverb_prob=0.0,
noise_prob=1.0,
noise_snr_low=0,
noise_snr_high=15,
rir_scale_factor=1.0, )
add_rev = EnvCorrupt(
rir_dataset=rir_dataset,
reverb_prob=1.0,
noise_prob=0.0,
rir_scale_factor=1.0, )
add_rev_noise = EnvCorrupt(
noise_dataset=noise_dataset,
rir_dataset=rir_dataset,
reverb_prob=1.0,
noise_prob=1.0,
noise_snr_low=0,
noise_snr_high=15,
rir_scale_factor=1.0, )
return [wavedrop, speed_perturb, add_noise, add_rev, add_rev_noise]
def waveform_augment(waveforms: paddle.Tensor,
augment_pipeline: List[paddle.nn.Layer]) -> paddle.Tensor:
"""process the augment pipeline and return all the waveforms
Args:
waveforms (paddle.Tensor): original batch waveform
augment_pipeline (List[paddle.nn.Layer]): agument pipeline process
Returns:
paddle.Tensor: all the audio waveform including the original waveform and augmented waveform
"""
# stage 0: store the original waveforms
waveforms_aug_list = [waveforms]
# augment the original batch waveform
for aug in augment_pipeline:
# stage 1: augment the data
waveforms_aug = aug(waveforms) # (N, L)
if waveforms_aug.shape[1] >= waveforms.shape[1]:
# Trunc
waveforms_aug = waveforms_aug[:, :waveforms.shape[1]]
else:
# Pad
lengths_to_pad = waveforms.shape[1] - waveforms_aug.shape[1]
waveforms_aug = F.pad(
waveforms_aug.unsqueeze(-1), [0, lengths_to_pad],
data_format='NLC').squeeze(-1)
# stage 2: append the augmented waveform into the list
waveforms_aug_list.append(waveforms_aug)
# get the all the waveforms
return paddle.concat(waveforms_aug_list, axis=0)
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