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PaddleSpeech/paddlespeech/s2t/transform/perturb.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.
# Modified from espnet(https://github.com/espnet/espnet)
import librosa
import numpy
import scipy
import soundfile
from paddlespeech.s2t.io.reader import SoundHDF5File
class SpeedPerturbation():
"""SpeedPerturbation
The speed perturbation in kaldi uses sox-speed instead of sox-tempo,
and sox-speed just to resample the input,
i.e pitch and tempo are changed both.
"Why use speed option instead of tempo -s in SoX for speed perturbation"
https://groups.google.com/forum/#!topic/kaldi-help/8OOG7eE4sZ8
Warning:
This function is very slow because of resampling.
I recommmend to apply speed-perturb outside the training using sox.
"""
def __init__(
self,
lower=0.9,
upper=1.1,
utt2ratio=None,
keep_length=True,
res_type="kaiser_best",
seed=None, ):
self.res_type = res_type
self.keep_length = keep_length
self.state = numpy.random.RandomState(seed)
if utt2ratio is not None:
self.utt2ratio = {}
# Use the scheduled ratio for each utterances
self.utt2ratio_file = utt2ratio
self.lower = None
self.upper = None
self.accept_uttid = True
with open(utt2ratio, "r") as f:
for line in f:
utt, ratio = line.rstrip().split(None, 1)
ratio = float(ratio)
self.utt2ratio[utt] = ratio
else:
self.utt2ratio = None
# The ratio is given on runtime randomly
self.lower = lower
self.upper = upper
def __repr__(self):
if self.utt2ratio is None:
return "{}(lower={}, upper={}, " "keep_length={}, res_type={})".format(
self.__class__.__name__,
self.lower,
self.upper,
self.keep_length,
self.res_type, )
else:
return "{}({}, res_type={})".format(
self.__class__.__name__, self.utt2ratio_file, self.res_type)
def __call__(self, x, uttid=None, train=True):
if not train:
return x
x = x.astype(numpy.float32)
if self.accept_uttid:
ratio = self.utt2ratio[uttid]
else:
ratio = self.state.uniform(self.lower, self.upper)
# Note1: resample requires the sampling-rate of input and output,
# but actually only the ratio is used.
y = librosa.resample(x, ratio, 1, res_type=self.res_type)
if self.keep_length:
diff = abs(len(x) - len(y))
if len(y) > len(x):
# Truncate noise
y = y[diff // 2:-((diff + 1) // 2)]
elif len(y) < len(x):
# Assume the time-axis is the first: (Time, Channel)
pad_width = [(diff // 2, (diff + 1) // 2)] + [
(0, 0) for _ in range(y.ndim - 1)
]
y = numpy.pad(
y, pad_width=pad_width, constant_values=0, mode="constant")
return y
class SpeedPerturbationSox():
"""SpeedPerturbationSox
The speed perturbation in kaldi uses sox-speed instead of sox-tempo,
and sox-speed just to resample the input,
i.e pitch and tempo are changed both.
To speed up or slow down the sound of a file,
use speed to modify the pitch and the duration of the file.
This raises the speed and reduces the time.
The default factor is 1.0 which makes no change to the audio.
2.0 doubles speed, thus time length is cut by a half and pitch is one interval higher.
"Why use speed option instead of tempo -s in SoX for speed perturbation"
https://groups.google.com/forum/#!topic/kaldi-help/8OOG7eE4sZ8
tempo option:
sox -t wav input.wav -t wav output.tempo0.9.wav tempo -s 0.9
speed option:
sox -t wav input.wav -t wav output.speed0.9.wav speed 0.9
If we use speed option like above, the pitch of audio also will be changed,
but the tempo option does not change the pitch.
"""
def __init__(
self,
lower=0.9,
upper=1.1,
utt2ratio=None,
keep_length=True,
sr=16000,
seed=None, ):
self.sr = sr
self.keep_length = keep_length
self.state = numpy.random.RandomState(seed)
try:
import soxbindings as sox
except:
try:
from paddlespeech.s2t.utils import dynamic_pip_install
package = "sox"
dynamic_pip_install.install(package)
package = "soxbindings"
dynamic_pip_install.install(package)
import soxbindings as sox
except:
raise RuntimeError("Can not install soxbindings on your system." )
if utt2ratio is not None:
self.utt2ratio = {}
# Use the scheduled ratio for each utterances
self.utt2ratio_file = utt2ratio
self.lower = None
self.upper = None
self.accept_uttid = True
with open(utt2ratio, "r") as f:
for line in f:
utt, ratio = line.rstrip().split(None, 1)
ratio = float(ratio)
self.utt2ratio[utt] = ratio
else:
self.utt2ratio = None
# The ratio is given on runtime randomly
self.lower = lower
self.upper = upper
def __repr__(self):
if self.utt2ratio is None:
return f"""{self.__class__.__name__}(
lower={self.lower},
upper={self.upper},
keep_length={self.keep_length},
sample_rate={self.sr})"""
else:
return f"""{self.__class__.__name__}(
utt2ratio={self.utt2ratio_file},
sample_rate={self.sr})"""
def __call__(self, x, uttid=None, train=True):
if not train:
return x
x = x.astype(numpy.float32)
if self.accept_uttid:
ratio = self.utt2ratio[uttid]
else:
ratio = self.state.uniform(self.lower, self.upper)
tfm = sox.Transformer()
tfm.set_globals(multithread=False)
tfm.speed(ratio)
y = tfm.build_array(input_array=x, sample_rate_in=self.sr)
if self.keep_length:
diff = abs(len(x) - len(y))
if len(y) > len(x):
# Truncate noise
y = y[diff // 2:-((diff + 1) // 2)]
elif len(y) < len(x):
# Assume the time-axis is the first: (Time, Channel)
pad_width = [(diff // 2, (diff + 1) // 2)] + [
(0, 0) for _ in range(y.ndim - 1)
]
y = numpy.pad(
y, pad_width=pad_width, constant_values=0, mode="constant")
if y.ndim == 2 and x.ndim == 1:
# (T, C) -> (T)
y = y.sequence(1)
return y
class BandpassPerturbation():
"""BandpassPerturbation
Randomly dropout along the frequency axis.
The original idea comes from the following:
"randomly-selected frequency band was cut off under the constraint of
leaving at least 1,000 Hz band within the range of less than 4,000Hz."
(The Hitachi/JHU CHiME-5 system: Advances in speech recognition for
everyday home environments using multiple microphone arrays;
http://spandh.dcs.shef.ac.uk/chime_workshop/papers/CHiME_2018_paper_kanda.pdf)
"""
def __init__(self, lower=0.0, upper=0.75, seed=None, axes=(-1, )):
self.lower = lower
self.upper = upper
self.state = numpy.random.RandomState(seed)
# x_stft: (Time, Channel, Freq)
self.axes = axes
def __repr__(self):
return "{}(lower={}, upper={})".format(self.__class__.__name__,
self.lower, self.upper)
def __call__(self, x_stft, uttid=None, train=True):
if not train:
return x_stft
if x_stft.ndim == 1:
raise RuntimeError("Input in time-freq domain: "
"(Time, Channel, Freq) or (Time, Freq)")
ratio = self.state.uniform(self.lower, self.upper)
axes = [i if i >= 0 else x_stft.ndim - i for i in self.axes]
shape = [s if i in axes else 1 for i, s in enumerate(x_stft.shape)]
mask = self.state.randn(*shape) > ratio
x_stft *= mask
return x_stft
class VolumePerturbation():
def __init__(self,
lower=-1.6,
upper=1.6,
utt2ratio=None,
dbunit=True,
seed=None):
self.dbunit = dbunit
self.utt2ratio_file = utt2ratio
self.lower = lower
self.upper = upper
self.state = numpy.random.RandomState(seed)
if utt2ratio is not None:
# Use the scheduled ratio for each utterances
self.utt2ratio = {}
self.lower = None
self.upper = None
self.accept_uttid = True
with open(utt2ratio, "r") as f:
for line in f:
utt, ratio = line.rstrip().split(None, 1)
ratio = float(ratio)
self.utt2ratio[utt] = ratio
else:
# The ratio is given on runtime randomly
self.utt2ratio = None
def __repr__(self):
if self.utt2ratio is None:
return "{}(lower={}, upper={}, dbunit={})".format(
self.__class__.__name__, self.lower, self.upper, self.dbunit)
else:
return '{}("{}", dbunit={})'.format(
self.__class__.__name__, self.utt2ratio_file, self.dbunit)
def __call__(self, x, uttid=None, train=True):
if not train:
return x
x = x.astype(numpy.float32)
if self.accept_uttid:
ratio = self.utt2ratio[uttid]
else:
ratio = self.state.uniform(self.lower, self.upper)
if self.dbunit:
ratio = 10**(ratio / 20)
return x * ratio
class NoiseInjection():
"""Add isotropic noise"""
def __init__(
self,
utt2noise=None,
lower=-20,
upper=-5,
utt2ratio=None,
filetype="list",
dbunit=True,
seed=None, ):
self.utt2noise_file = utt2noise
self.utt2ratio_file = utt2ratio
self.filetype = filetype
self.dbunit = dbunit
self.lower = lower
self.upper = upper
self.state = numpy.random.RandomState(seed)
if utt2ratio is not None:
# Use the scheduled ratio for each utterances
self.utt2ratio = {}
with open(utt2noise, "r") as f:
for line in f:
utt, snr = line.rstrip().split(None, 1)
snr = float(snr)
self.utt2ratio[utt] = snr
else:
# The ratio is given on runtime randomly
self.utt2ratio = None
if utt2noise is not None:
self.utt2noise = {}
if filetype == "list":
with open(utt2noise, "r") as f:
for line in f:
utt, filename = line.rstrip().split(None, 1)
signal, rate = soundfile.read(filename, dtype="int16")
# Load all files in memory
self.utt2noise[utt] = (signal, rate)
elif filetype == "sound.hdf5":
self.utt2noise = SoundHDF5File(utt2noise, "r")
else:
raise ValueError(filetype)
else:
self.utt2noise = None
if utt2noise is not None and utt2ratio is not None:
if set(self.utt2ratio) != set(self.utt2noise):
raise RuntimeError("The uttids mismatch between {} and {}".
format(utt2ratio, utt2noise))
def __repr__(self):
if self.utt2ratio is None:
return "{}(lower={}, upper={}, dbunit={})".format(
self.__class__.__name__, self.lower, self.upper, self.dbunit)
else:
return '{}("{}", dbunit={})'.format(
self.__class__.__name__, self.utt2ratio_file, self.dbunit)
def __call__(self, x, uttid=None, train=True):
if not train:
return x
x = x.astype(numpy.float32)
# 1. Get ratio of noise to signal in sound pressure level
if uttid is not None and self.utt2ratio is not None:
ratio = self.utt2ratio[uttid]
else:
ratio = self.state.uniform(self.lower, self.upper)
if self.dbunit:
ratio = 10**(ratio / 20)
scale = ratio * numpy.sqrt((x**2).mean())
# 2. Get noise
if self.utt2noise is not None:
# Get noise from the external source
if uttid is not None:
noise, rate = self.utt2noise[uttid]
else:
# Randomly select the noise source
noise = self.state.choice(list(self.utt2noise.values()))
# Normalize the level
noise /= numpy.sqrt((noise**2).mean())
# Adjust the noise length
diff = abs(len(x) - len(noise))
offset = self.state.randint(0, diff)
if len(noise) > len(x):
# Truncate noise
noise = noise[offset:-(diff - offset)]
else:
noise = numpy.pad(
noise, pad_width=[offset, diff - offset], mode="wrap")
else:
# Generate white noise
noise = self.state.normal(0, 1, x.shape)
# 3. Add noise to signal
return x + noise * scale
class RIRConvolve():
def __init__(self, utt2rir, filetype="list"):
self.utt2rir_file = utt2rir
self.filetype = filetype
self.utt2rir = {}
if filetype == "list":
with open(utt2rir, "r") as f:
for line in f:
utt, filename = line.rstrip().split(None, 1)
signal, rate = soundfile.read(filename, dtype="int16")
self.utt2rir[utt] = (signal, rate)
elif filetype == "sound.hdf5":
self.utt2rir = SoundHDF5File(utt2rir, "r")
else:
raise NotImplementedError(filetype)
def __repr__(self):
return '{}("{}")'.format(self.__class__.__name__, self.utt2rir_file)
def __call__(self, x, uttid=None, train=True):
if not train:
return x
x = x.astype(numpy.float32)
if x.ndim != 1:
# Must be single channel
raise RuntimeError(
"Input x must be one dimensional array, but got {}".format(
x.shape))
rir, rate = self.utt2rir[uttid]
if rir.ndim == 2:
# FIXME(kamo): Use chainer.convolution_1d?
# return [Time, Channel]
return numpy.stack(
[scipy.convolve(x, r, mode="same") for r in rir], axis=-1)
else:
return scipy.convolve(x, rir, mode="same")