mirror of
https://github.com/storytold/storyteller-ml.git
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Introduce code from Rust monorepo. This is a mess
This commit is contained in:
Brandon Thomas
@@ -0,0 +1,643 @@
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# Copyright (c) 2020, NVIDIA CORPORATION. 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 math
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from abc import ABC, abstractmethod
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from dataclasses import dataclass
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from typing import Any, Optional
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import torch
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from packaging import version
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from nemo.collections.asr.parts.numba.spec_augment import SpecAugmentNumba, spec_augment_launch_heuristics
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from nemo.collections.asr.parts.preprocessing.features import FilterbankFeatures
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from nemo.collections.asr.parts.submodules.spectr_augment import SpecAugment, SpecCutout
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from nemo.core.classes import NeuralModule, typecheck
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from nemo.core.neural_types import (
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AudioSignal,
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LengthsType,
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MelSpectrogramType,
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MFCCSpectrogramType,
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NeuralType,
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SpectrogramType,
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)
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from nemo.core.utils import numba_utils
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from nemo.core.utils.numba_utils import __NUMBA_MINIMUM_VERSION__
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from nemo.utils import logging
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try:
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import torchaudio
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import torchaudio.functional
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import torchaudio.transforms
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TORCHAUDIO_VERSION = version.parse(torchaudio.__version__)
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TORCHAUDIO_VERSION_MIN = version.parse('0.5')
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HAVE_TORCHAUDIO = True
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except ModuleNotFoundError:
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HAVE_TORCHAUDIO = False
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__all__ = [
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'AudioToMelSpectrogramPreprocessor',
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'AudioToMFCCPreprocessor',
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'SpectrogramAugmentation',
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'CropOrPadSpectrogramAugmentation',
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]
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class AudioPreprocessor(NeuralModule, ABC):
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"""
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An interface for Neural Modules that performs audio pre-processing,
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transforming the wav files to features.
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"""
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def __init__(self, win_length, hop_length):
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super().__init__()
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self.win_length = win_length
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self.hop_length = hop_length
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self.torch_windows = {
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'hann': torch.hann_window,
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'hamming': torch.hamming_window,
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'blackman': torch.blackman_window,
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'bartlett': torch.bartlett_window,
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'ones': torch.ones,
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None: torch.ones,
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}
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@typecheck()
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@torch.no_grad()
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def forward(self, input_signal, length):
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processed_signal, processed_length = self.get_features(input_signal, length)
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return processed_signal, processed_length
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@abstractmethod
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def get_features(self, input_signal, length):
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# Called by forward(). Subclasses should implement this.
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pass
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class AudioToMelSpectrogramPreprocessor(AudioPreprocessor):
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"""Featurizer module that converts wavs to mel spectrograms.
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We don't use torchaudio's implementation here because the original
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implementation is not the same, so for the sake of backwards-compatibility
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this will use the old FilterbankFeatures for now.
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Args:
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sample_rate (int): Sample rate of the input audio data.
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Defaults to 16000
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window_size (float): Size of window for fft in seconds
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Defaults to 0.02
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window_stride (float): Stride of window for fft in seconds
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Defaults to 0.01
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n_window_size (int): Size of window for fft in samples
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Defaults to None. Use one of window_size or n_window_size.
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n_window_stride (int): Stride of window for fft in samples
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Defaults to None. Use one of window_stride or n_window_stride.
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window (str): Windowing function for fft. can be one of ['hann',
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'hamming', 'blackman', 'bartlett']
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Defaults to "hann"
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normalize (str): Can be one of ['per_feature', 'all_features']; all
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other options disable feature normalization. 'all_features'
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normalizes the entire spectrogram to be mean 0 with std 1.
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'pre_features' normalizes per channel / freq instead.
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Defaults to "per_feature"
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n_fft (int): Length of FT window. If None, it uses the smallest power
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of 2 that is larger than n_window_size.
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Defaults to None
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preemph (float): Amount of pre emphasis to add to audio. Can be
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disabled by passing None.
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Defaults to 0.97
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features (int): Number of mel spectrogram freq bins to output.
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Defaults to 64
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lowfreq (int): Lower bound on mel basis in Hz.
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Defaults to 0
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highfreq (int): Lower bound on mel basis in Hz.
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Defaults to None
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log (bool): Log features.
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Defaults to True
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log_zero_guard_type(str): Need to avoid taking the log of zero. There
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are two options: "add" or "clamp".
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Defaults to "add".
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log_zero_guard_value(float, or str): Add or clamp requires the number
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to add with or clamp to. log_zero_guard_value can either be a float
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or "tiny" or "eps". torch.finfo is used if "tiny" or "eps" is
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passed.
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Defaults to 2**-24.
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dither (float): Amount of white-noise dithering.
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Defaults to 1e-5
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pad_to (int): Ensures that the output size of the time dimension is
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a multiple of pad_to.
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Defaults to 16
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frame_splicing (int): Defaults to 1
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exact_pad (bool): If True, sets stft center to False and adds padding, such that num_frames = audio_length
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// hop_length. Defaults to False.
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stft_exact_pad (bool): If True, uses pytorch_stft and convolutions with
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padding such that num_frames = num_samples / hop_length. If False,
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stft_conv will be used to determine how stft will be performed.
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Defaults to False. TODO:This feature is deprecated and will be removed in 1.1.0
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stft_conv (bool): If True, uses pytorch_stft and convolutions. If
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False, uses torch.stft. TODO:This feature is deprecated and will be removed in 1.1.0
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Defaults to False
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pad_value (float): The value that shorter mels are padded with.
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Defaults to 0
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mag_power (float): The power that the linear spectrogram is raised to
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prior to multiplication with mel basis.
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Defaults to 2 for a power spec
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"""
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def save_to(self, save_path: str):
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pass
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@classmethod
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def restore_from(cls, restore_path: str):
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pass
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@property
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def input_types(self):
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"""Returns definitions of module input ports.
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"""
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return {
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"input_signal": NeuralType(('B', 'T'), AudioSignal(freq=self._sample_rate)),
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"length": NeuralType(tuple('B'), LengthsType()),
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}
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@property
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def output_types(self):
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"""Returns definitions of module output ports.
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processed_signal:
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0: AxisType(BatchTag)
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1: AxisType(MelSpectrogramSignalTag)
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2: AxisType(ProcessedTimeTag)
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processed_length:
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0: AxisType(BatchTag)
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"""
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return {
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"processed_signal": NeuralType(('B', 'D', 'T'), MelSpectrogramType()),
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"processed_length": NeuralType(tuple('B'), LengthsType()),
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}
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def __init__(
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self,
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sample_rate=16000,
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window_size=0.02,
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window_stride=0.01,
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n_window_size=None,
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n_window_stride=None,
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window="hann",
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normalize="per_feature",
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n_fft=None,
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preemph=0.97,
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features=64,
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lowfreq=0,
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highfreq=None,
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log=True,
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log_zero_guard_type="add",
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log_zero_guard_value=2 ** -24,
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dither=1e-5,
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pad_to=16,
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frame_splicing=1,
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exact_pad=False,
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stft_exact_pad=False,
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stft_conv=False,
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pad_value=0,
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mag_power=2.0,
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):
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super().__init__(n_window_size, n_window_stride)
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self._sample_rate = sample_rate
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if window_size and n_window_size:
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raise ValueError(f"{self} received both window_size and " f"n_window_size. Only one should be specified.")
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if window_stride and n_window_stride:
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raise ValueError(
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f"{self} received both window_stride and " f"n_window_stride. Only one should be specified."
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)
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if window_size:
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n_window_size = int(window_size * self._sample_rate)
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if window_stride:
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n_window_stride = int(window_stride * self._sample_rate)
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self.featurizer = FilterbankFeatures(
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sample_rate=self._sample_rate,
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n_window_size=n_window_size,
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n_window_stride=n_window_stride,
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window=window,
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normalize=normalize,
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n_fft=n_fft,
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preemph=preemph,
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nfilt=features,
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lowfreq=lowfreq,
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highfreq=highfreq,
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log=log,
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log_zero_guard_type=log_zero_guard_type,
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log_zero_guard_value=log_zero_guard_value,
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dither=dither,
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pad_to=pad_to,
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frame_splicing=frame_splicing,
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exact_pad=exact_pad,
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stft_exact_pad=stft_exact_pad,
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stft_conv=stft_conv,
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pad_value=pad_value,
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mag_power=mag_power,
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)
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def get_features(self, input_signal, length):
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return self.featurizer(input_signal, length)
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@property
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def filter_banks(self):
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return self.featurizer.filter_banks
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class AudioToMFCCPreprocessor(AudioPreprocessor):
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"""Preprocessor that converts wavs to MFCCs.
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Uses torchaudio.transforms.MFCC.
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Args:
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sample_rate: The sample rate of the audio.
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Defaults to 16000.
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window_size: Size of window for fft in seconds. Used to calculate the
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win_length arg for mel spectrogram.
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Defaults to 0.02
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window_stride: Stride of window for fft in seconds. Used to caculate
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the hop_length arg for mel spect.
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Defaults to 0.01
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n_window_size: Size of window for fft in samples
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Defaults to None. Use one of window_size or n_window_size.
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n_window_stride: Stride of window for fft in samples
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Defaults to None. Use one of window_stride or n_window_stride.
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window: Windowing function for fft. can be one of ['hann',
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'hamming', 'blackman', 'bartlett', 'none', 'null'].
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Defaults to 'hann'
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n_fft: Length of FT window. If None, it uses the smallest power of 2
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that is larger than n_window_size.
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Defaults to None
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lowfreq (int): Lower bound on mel basis in Hz.
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Defaults to 0
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highfreq (int): Lower bound on mel basis in Hz.
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Defaults to None
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n_mels: Number of mel filterbanks.
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Defaults to 64
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n_mfcc: Number of coefficients to retain
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Defaults to 64
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dct_type: Type of discrete cosine transform to use
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norm: Type of norm to use
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log: Whether to use log-mel spectrograms instead of db-scaled.
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Defaults to True.
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"""
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@property
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def input_types(self):
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"""Returns definitions of module input ports.
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"""
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return {
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"input_signal": NeuralType(('B', 'T'), AudioSignal(freq=self._sample_rate)),
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"length": NeuralType(tuple('B'), LengthsType()),
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}
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@property
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def output_types(self):
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"""Returns definitions of module output ports.
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"""
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return {
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"processed_signal": NeuralType(('B', 'D', 'T'), MFCCSpectrogramType()),
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"processed_length": NeuralType(tuple('B'), LengthsType()),
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}
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def save_to(self, save_path: str):
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pass
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@classmethod
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def restore_from(cls, restore_path: str):
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pass
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def __init__(
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self,
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sample_rate=16000,
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window_size=0.02,
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window_stride=0.01,
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n_window_size=None,
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n_window_stride=None,
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window='hann',
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n_fft=None,
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lowfreq=0.0,
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highfreq=None,
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n_mels=64,
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n_mfcc=64,
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dct_type=2,
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norm='ortho',
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log=True,
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):
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self._sample_rate = sample_rate
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if not HAVE_TORCHAUDIO:
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logging.error('Could not import torchaudio. Some features might not work.')
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raise ModuleNotFoundError(
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"torchaudio is not installed but is necessary for "
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"AudioToMFCCPreprocessor. We recommend you try "
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"building it from source for the PyTorch version you have."
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)
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if window_size and n_window_size:
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raise ValueError(f"{self} received both window_size and " f"n_window_size. Only one should be specified.")
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if window_stride and n_window_stride:
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raise ValueError(
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f"{self} received both window_stride and " f"n_window_stride. Only one should be specified."
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)
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# Get win_length (n_window_size) and hop_length (n_window_stride)
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if window_size:
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n_window_size = int(window_size * self._sample_rate)
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if window_stride:
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n_window_stride = int(window_stride * self._sample_rate)
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super().__init__(n_window_size, n_window_stride)
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mel_kwargs = {}
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mel_kwargs['f_min'] = lowfreq
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mel_kwargs['f_max'] = highfreq
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mel_kwargs['n_mels'] = n_mels
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mel_kwargs['n_fft'] = n_fft or 2 ** math.ceil(math.log2(n_window_size))
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mel_kwargs['win_length'] = n_window_size
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mel_kwargs['hop_length'] = n_window_stride
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# Set window_fn. None defaults to torch.ones.
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window_fn = self.torch_windows.get(window, None)
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if window_fn is None:
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raise ValueError(
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f"Window argument for AudioProcessor is invalid: {window}."
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f"For no window function, use 'ones' or None."
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)
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mel_kwargs['window_fn'] = window_fn
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# Use torchaudio's implementation of MFCCs as featurizer
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self.featurizer = torchaudio.transforms.MFCC(
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sample_rate=self._sample_rate,
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n_mfcc=n_mfcc,
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dct_type=dct_type,
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norm=norm,
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log_mels=log,
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melkwargs=mel_kwargs,
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)
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def get_features(self, input_signal, length):
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features = self.featurizer(input_signal)
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seq_len = torch.ceil(length.to(torch.float32) / self.hop_length).to(dtype=torch.long)
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return features, seq_len
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class SpectrogramAugmentation(NeuralModule):
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"""
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Performs time and freq cuts in one of two ways.
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SpecAugment zeroes out vertical and horizontal sections as described in
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SpecAugment (https://arxiv.org/abs/1904.08779). Arguments for use with
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SpecAugment are `freq_masks`, `time_masks`, `freq_width`, and `time_width`.
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SpecCutout zeroes out rectangulars as described in Cutout
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(https://arxiv.org/abs/1708.04552). Arguments for use with Cutout are
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`rect_masks`, `rect_freq`, and `rect_time`.
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Args:
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freq_masks (int): how many frequency segments should be cut.
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Defaults to 0.
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time_masks (int): how many time segments should be cut
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Defaults to 0.
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freq_width (int): maximum number of frequencies to be cut in one
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segment.
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Defaults to 10.
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time_width (int): maximum number of time steps to be cut in one
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segment
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Defaults to 10.
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rect_masks (int): how many rectangular masks should be cut
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Defaults to 0.
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rect_freq (int): maximum size of cut rectangles along the frequency
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dimension
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Defaults to 5.
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rect_time (int): maximum size of cut rectangles along the time
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dimension
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Defaults to 25.
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"""
|
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||||
@property
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def input_types(self):
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"""Returns definitions of module input types
|
||||
"""
|
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return {
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"input_spec": NeuralType(('B', 'D', 'T'), SpectrogramType()),
|
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"length": NeuralType(tuple('B'), LengthsType(), optional=True),
|
||||
}
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@property
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def output_types(self):
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"""Returns definitions of module output types
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||||
"""
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return {"augmented_spec": NeuralType(('B', 'D', 'T'), SpectrogramType())}
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||||
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||||
def __init__(
|
||||
self,
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freq_masks=0,
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time_masks=0,
|
||||
freq_width=10,
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||||
time_width=10,
|
||||
rect_masks=0,
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||||
rect_time=5,
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rect_freq=20,
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||||
rng=None,
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||||
mask_value=0.0,
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||||
use_numba_spec_augment: bool = True,
|
||||
):
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||||
super().__init__()
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||||
|
||||
if rect_masks > 0:
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||||
self.spec_cutout = SpecCutout(rect_masks=rect_masks, rect_time=rect_time, rect_freq=rect_freq, rng=rng,)
|
||||
# self.spec_cutout.to(self._device)
|
||||
else:
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||||
self.spec_cutout = lambda input_spec: input_spec
|
||||
|
||||
if freq_masks + time_masks > 0:
|
||||
self.spec_augment = SpecAugment(
|
||||
freq_masks=freq_masks,
|
||||
time_masks=time_masks,
|
||||
freq_width=freq_width,
|
||||
time_width=time_width,
|
||||
rng=rng,
|
||||
mask_value=mask_value,
|
||||
)
|
||||
else:
|
||||
self.spec_augment = lambda input_spec: input_spec
|
||||
|
||||
# Check if numba is supported, and use a Numba kernel if it is
|
||||
if use_numba_spec_augment and numba_utils.numba_cuda_is_supported(__NUMBA_MINIMUM_VERSION__):
|
||||
self.spec_augment_numba = SpecAugmentNumba(
|
||||
freq_masks=freq_masks,
|
||||
time_masks=time_masks,
|
||||
freq_width=freq_width,
|
||||
time_width=time_width,
|
||||
rng=rng,
|
||||
mask_value=mask_value,
|
||||
)
|
||||
else:
|
||||
self.spec_augment_numba = None
|
||||
|
||||
@typecheck()
|
||||
def forward(self, input_spec, length=None):
|
||||
augmented_spec = self.spec_cutout(input_spec=input_spec)
|
||||
|
||||
# To run the Numba kernel, correct numba version is required as well as
|
||||
# tensor must be on GPU and length must be provided
|
||||
if self.spec_augment_numba is not None and spec_augment_launch_heuristics(augmented_spec, length):
|
||||
augmented_spec = self.spec_augment_numba(input_spec=augmented_spec, length=length)
|
||||
else:
|
||||
augmented_spec = self.spec_augment(input_spec=augmented_spec)
|
||||
return augmented_spec
|
||||
|
||||
|
||||
class CropOrPadSpectrogramAugmentation(NeuralModule):
|
||||
"""
|
||||
Pad or Crop the incoming Spectrogram to a certain shape.
|
||||
Args:
|
||||
audio_length (int): the final number of timesteps that is required.
|
||||
The signal will be either padded or cropped temporally to this
|
||||
size.
|
||||
"""
|
||||
|
||||
def __init__(self, audio_length):
|
||||
super(CropOrPadSpectrogramAugmentation, self).__init__()
|
||||
self.audio_length = audio_length
|
||||
|
||||
@typecheck()
|
||||
@torch.no_grad()
|
||||
def forward(self, input_signal, length):
|
||||
image = input_signal
|
||||
num_images = image.shape[0]
|
||||
|
||||
audio_length = self.audio_length
|
||||
image_len = image.shape[-1]
|
||||
|
||||
# Crop long signal
|
||||
if image_len > audio_length: # randomly slice
|
||||
cutout_images = []
|
||||
offset = torch.randint(low=0, high=image_len - audio_length + 1, size=[num_images])
|
||||
|
||||
for idx, offset in enumerate(offset):
|
||||
cutout_images.append(image[idx : idx + 1, :, offset : offset + audio_length])
|
||||
|
||||
image = torch.cat(cutout_images, dim=0)
|
||||
del cutout_images
|
||||
|
||||
else: # symmetrically pad short signal with zeros
|
||||
pad_left = (audio_length - image_len) // 2
|
||||
pad_right = (audio_length - image_len) // 2
|
||||
|
||||
if (audio_length - image_len) % 2 == 1:
|
||||
pad_right += 1
|
||||
|
||||
image = torch.nn.functional.pad(image, [pad_left, pad_right], mode="constant", value=0)
|
||||
|
||||
# Replace dynamic length sequences with static number of timesteps
|
||||
length = (length * 0) + audio_length
|
||||
|
||||
return image, length
|
||||
|
||||
@property
|
||||
def input_types(self):
|
||||
"""Returns definitions of module output ports.
|
||||
"""
|
||||
return {
|
||||
"input_signal": NeuralType(('B', 'D', 'T'), SpectrogramType()),
|
||||
"length": NeuralType(tuple('B'), LengthsType()),
|
||||
}
|
||||
|
||||
@property
|
||||
def output_types(self):
|
||||
"""Returns definitions of module output ports.
|
||||
"""
|
||||
return {
|
||||
"processed_signal": NeuralType(('B', 'D', 'T'), SpectrogramType()),
|
||||
"processed_length": NeuralType(tuple('B'), LengthsType()),
|
||||
}
|
||||
|
||||
def save_to(self, save_path: str):
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def restore_from(cls, restore_path: str):
|
||||
pass
|
||||
|
||||
|
||||
@dataclass
|
||||
class AudioToMelSpectrogramPreprocessorConfig:
|
||||
_target_: str = "nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor"
|
||||
sample_rate: int = 16000
|
||||
window_size: float = 0.02
|
||||
window_stride: float = 0.01
|
||||
n_window_size: Optional[int] = None
|
||||
n_window_stride: Optional[int] = None
|
||||
window: str = "hann"
|
||||
normalize: str = "per_feature"
|
||||
n_fft: Optional[int] = None
|
||||
preemph: float = 0.97
|
||||
features: int = 64
|
||||
lowfreq: int = 0
|
||||
highfreq: Optional[int] = None
|
||||
log: bool = True
|
||||
log_zero_guard_type: str = "add"
|
||||
log_zero_guard_value: float = 2 ** -24
|
||||
dither: float = 1e-5
|
||||
pad_to: int = 16
|
||||
frame_splicing: int = 1
|
||||
exact_pad: bool = False
|
||||
stft_exact_pad: bool = False
|
||||
stft_conv: bool = False
|
||||
pad_value: int = 0
|
||||
mag_power: float = 2.0
|
||||
|
||||
|
||||
@dataclass
|
||||
class AudioToMFCCPreprocessorConfig:
|
||||
_target_: str = 'nemo.collections.asr.modules.AudioToMFCCPreprocessor'
|
||||
sample_rate: int = 16000
|
||||
window_size: float = 0.02
|
||||
window_stride: float = 0.01
|
||||
n_window_size: Optional[int] = None
|
||||
n_window_stride: Optional[int] = None
|
||||
window: str = 'hann'
|
||||
n_fft: Optional[int] = None
|
||||
lowfreq: Optional[float] = 0.0
|
||||
highfreq: Optional[float] = None
|
||||
n_mels: int = 64
|
||||
n_mfcc: int = 64
|
||||
dct_type: int = 2
|
||||
norm: str = 'ortho'
|
||||
log: bool = True
|
||||
|
||||
|
||||
@dataclass
|
||||
class SpectrogramAugmentationConfig:
|
||||
_target_: str = "nemo.collections.asr.modules.SpectrogramAugmentation"
|
||||
freq_masks: int = 0
|
||||
time_masks: int = 0
|
||||
freq_width: int = 0
|
||||
time_width: Optional[Any] = 0
|
||||
rect_masks: int = 0
|
||||
rect_time: int = 0
|
||||
rect_freq: int = 0
|
||||
mask_value: float = 0
|
||||
rng: Optional[Any] = None # random.Random() type
|
||||
use_numba_spec_augment: bool = True
|
||||
|
||||
|
||||
@dataclass
|
||||
class CropOrPadSpectrogramAugmentationConfig:
|
||||
audio_length: int
|
||||
_target_: str = "nemo.collections.asr.modules.CropOrPadSpectrogramAugmentation"
|
||||
Reference in New Issue
Block a user