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352 lines
12 KiB
352 lines
12 KiB
# Copyright (c) 2021 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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# Modified from espnet(https://github.com/espnet/espnet)
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import librosa
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import numpy
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import scipy
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import soundfile
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from paddlespeech.s2t.io.reader import SoundHDF5File
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class SpeedPerturbation():
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"""SpeedPerturbation
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The speed perturbation in kaldi uses sox-speed instead of sox-tempo,
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and sox-speed just to resample the input,
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i.e pitch and tempo are changed both.
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"Why use speed option instead of tempo -s in SoX for speed perturbation"
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https://groups.google.com/forum/#!topic/kaldi-help/8OOG7eE4sZ8
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Warning:
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This function is very slow because of resampling.
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I recommmend to apply speed-perturb outside the training using sox.
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"""
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def __init__(
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self,
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lower=0.9,
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upper=1.1,
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utt2ratio=None,
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keep_length=True,
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res_type="kaiser_best",
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seed=None, ):
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self.res_type = res_type
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self.keep_length = keep_length
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self.state = numpy.random.RandomState(seed)
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if utt2ratio is not None:
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self.utt2ratio = {}
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# Use the scheduled ratio for each utterances
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self.utt2ratio_file = utt2ratio
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self.lower = None
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self.upper = None
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self.accept_uttid = True
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with open(utt2ratio, "r") as f:
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for line in f:
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utt, ratio = line.rstrip().split(None, 1)
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ratio = float(ratio)
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self.utt2ratio[utt] = ratio
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else:
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self.utt2ratio = None
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# The ratio is given on runtime randomly
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self.lower = lower
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self.upper = upper
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def __repr__(self):
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if self.utt2ratio is None:
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return "{}(lower={}, upper={}, " "keep_length={}, res_type={})".format(
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self.__class__.__name__,
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self.lower,
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self.upper,
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self.keep_length,
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self.res_type, )
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else:
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return "{}({}, res_type={})".format(
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self.__class__.__name__, self.utt2ratio_file, self.res_type)
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def __call__(self, x, uttid=None, train=True):
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if not train:
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return x
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x = x.astype(numpy.float32)
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if self.accept_uttid:
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ratio = self.utt2ratio[uttid]
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else:
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ratio = self.state.uniform(self.lower, self.upper)
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# Note1: resample requires the sampling-rate of input and output,
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# but actually only the ratio is used.
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y = librosa.resample(x, ratio, 1, res_type=self.res_type)
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if self.keep_length:
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diff = abs(len(x) - len(y))
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if len(y) > len(x):
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# Truncate noise
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y = y[diff // 2:-((diff + 1) // 2)]
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elif len(y) < len(x):
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# Assume the time-axis is the first: (Time, Channel)
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pad_width = [(diff // 2, (diff + 1) // 2)] + [
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(0, 0) for _ in range(y.ndim - 1)
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]
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y = numpy.pad(
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y, pad_width=pad_width, constant_values=0, mode="constant")
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return y
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class BandpassPerturbation():
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"""BandpassPerturbation
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Randomly dropout along the frequency axis.
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The original idea comes from the following:
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"randomly-selected frequency band was cut off under the constraint of
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leaving at least 1,000 Hz band within the range of less than 4,000Hz."
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(The Hitachi/JHU CHiME-5 system: Advances in speech recognition for
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everyday home environments using multiple microphone arrays;
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http://spandh.dcs.shef.ac.uk/chime_workshop/papers/CHiME_2018_paper_kanda.pdf)
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"""
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def __init__(self, lower=0.0, upper=0.75, seed=None, axes=(-1, )):
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self.lower = lower
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self.upper = upper
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self.state = numpy.random.RandomState(seed)
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# x_stft: (Time, Channel, Freq)
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self.axes = axes
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def __repr__(self):
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return "{}(lower={}, upper={})".format(self.__class__.__name__,
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self.lower, self.upper)
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def __call__(self, x_stft, uttid=None, train=True):
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if not train:
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return x_stft
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if x_stft.ndim == 1:
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raise RuntimeError("Input in time-freq domain: "
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"(Time, Channel, Freq) or (Time, Freq)")
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ratio = self.state.uniform(self.lower, self.upper)
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axes = [i if i >= 0 else x_stft.ndim - i for i in self.axes]
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shape = [s if i in axes else 1 for i, s in enumerate(x_stft.shape)]
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mask = self.state.randn(*shape) > ratio
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x_stft *= mask
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return x_stft
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class VolumePerturbation():
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def __init__(self,
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lower=-1.6,
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upper=1.6,
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utt2ratio=None,
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dbunit=True,
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seed=None):
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self.dbunit = dbunit
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self.utt2ratio_file = utt2ratio
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self.lower = lower
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self.upper = upper
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self.state = numpy.random.RandomState(seed)
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if utt2ratio is not None:
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# Use the scheduled ratio for each utterances
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self.utt2ratio = {}
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self.lower = None
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self.upper = None
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self.accept_uttid = True
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with open(utt2ratio, "r") as f:
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for line in f:
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utt, ratio = line.rstrip().split(None, 1)
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ratio = float(ratio)
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self.utt2ratio[utt] = ratio
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else:
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# The ratio is given on runtime randomly
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self.utt2ratio = None
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def __repr__(self):
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if self.utt2ratio is None:
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return "{}(lower={}, upper={}, dbunit={})".format(
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self.__class__.__name__, self.lower, self.upper, self.dbunit)
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else:
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return '{}("{}", dbunit={})'.format(
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self.__class__.__name__, self.utt2ratio_file, self.dbunit)
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def __call__(self, x, uttid=None, train=True):
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if not train:
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return x
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x = x.astype(numpy.float32)
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if self.accept_uttid:
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ratio = self.utt2ratio[uttid]
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else:
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ratio = self.state.uniform(self.lower, self.upper)
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if self.dbunit:
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ratio = 10**(ratio / 20)
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return x * ratio
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class NoiseInjection():
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"""Add isotropic noise"""
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def __init__(
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self,
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utt2noise=None,
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lower=-20,
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upper=-5,
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utt2ratio=None,
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filetype="list",
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dbunit=True,
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seed=None, ):
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self.utt2noise_file = utt2noise
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self.utt2ratio_file = utt2ratio
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self.filetype = filetype
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self.dbunit = dbunit
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self.lower = lower
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self.upper = upper
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self.state = numpy.random.RandomState(seed)
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if utt2ratio is not None:
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# Use the scheduled ratio for each utterances
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self.utt2ratio = {}
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with open(utt2noise, "r") as f:
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for line in f:
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utt, snr = line.rstrip().split(None, 1)
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snr = float(snr)
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self.utt2ratio[utt] = snr
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else:
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# The ratio is given on runtime randomly
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self.utt2ratio = None
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if utt2noise is not None:
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self.utt2noise = {}
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if filetype == "list":
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with open(utt2noise, "r") as f:
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for line in f:
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utt, filename = line.rstrip().split(None, 1)
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signal, rate = soundfile.read(filename, dtype="int16")
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# Load all files in memory
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self.utt2noise[utt] = (signal, rate)
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elif filetype == "sound.hdf5":
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self.utt2noise = SoundHDF5File(utt2noise, "r")
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else:
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raise ValueError(filetype)
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else:
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self.utt2noise = None
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if utt2noise is not None and utt2ratio is not None:
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if set(self.utt2ratio) != set(self.utt2noise):
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raise RuntimeError("The uttids mismatch between {} and {}".
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format(utt2ratio, utt2noise))
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def __repr__(self):
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if self.utt2ratio is None:
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return "{}(lower={}, upper={}, dbunit={})".format(
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self.__class__.__name__, self.lower, self.upper, self.dbunit)
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else:
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return '{}("{}", dbunit={})'.format(
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self.__class__.__name__, self.utt2ratio_file, self.dbunit)
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def __call__(self, x, uttid=None, train=True):
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if not train:
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return x
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x = x.astype(numpy.float32)
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# 1. Get ratio of noise to signal in sound pressure level
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if uttid is not None and self.utt2ratio is not None:
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ratio = self.utt2ratio[uttid]
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else:
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ratio = self.state.uniform(self.lower, self.upper)
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if self.dbunit:
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ratio = 10**(ratio / 20)
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scale = ratio * numpy.sqrt((x**2).mean())
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# 2. Get noise
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if self.utt2noise is not None:
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# Get noise from the external source
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if uttid is not None:
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noise, rate = self.utt2noise[uttid]
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else:
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# Randomly select the noise source
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noise = self.state.choice(list(self.utt2noise.values()))
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# Normalize the level
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noise /= numpy.sqrt((noise**2).mean())
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# Adjust the noise length
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diff = abs(len(x) - len(noise))
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offset = self.state.randint(0, diff)
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if len(noise) > len(x):
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# Truncate noise
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noise = noise[offset:-(diff - offset)]
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else:
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noise = numpy.pad(
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noise, pad_width=[offset, diff - offset], mode="wrap")
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else:
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# Generate white noise
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noise = self.state.normal(0, 1, x.shape)
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# 3. Add noise to signal
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return x + noise * scale
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class RIRConvolve():
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def __init__(self, utt2rir, filetype="list"):
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self.utt2rir_file = utt2rir
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self.filetype = filetype
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self.utt2rir = {}
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if filetype == "list":
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with open(utt2rir, "r") as f:
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for line in f:
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utt, filename = line.rstrip().split(None, 1)
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signal, rate = soundfile.read(filename, dtype="int16")
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self.utt2rir[utt] = (signal, rate)
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elif filetype == "sound.hdf5":
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self.utt2rir = SoundHDF5File(utt2rir, "r")
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else:
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raise NotImplementedError(filetype)
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def __repr__(self):
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return '{}("{}")'.format(self.__class__.__name__, self.utt2rir_file)
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def __call__(self, x, uttid=None, train=True):
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if not train:
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return x
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x = x.astype(numpy.float32)
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if x.ndim != 1:
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# Must be single channel
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raise RuntimeError(
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"Input x must be one dimensional array, but got {}".format(
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x.shape))
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rir, rate = self.utt2rir[uttid]
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if rir.ndim == 2:
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# FIXME(kamo): Use chainer.convolution_1d?
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# return [Time, Channel]
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return numpy.stack(
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[scipy.convolve(x, r, mode="same") for r in rir], axis=-1)
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else:
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return scipy.convolve(x, rir, mode="same")
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