Source code for flash.audio.classification.input_transform
# Copyright The PyTorch Lightning team.
#
# 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,
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from dataclasses import dataclass
from typing import Callable, Optional, Tuple
import torch
from flash.core.data.io.input import DataKeys
from flash.core.data.io.input_transform import InputTransform
from flash.core.data.transforms import ApplyToKeys
from flash.core.utilities.imports import _TORCHAUDIO_AVAILABLE, _TORCHVISION_AVAILABLE, requires
if _TORCHVISION_AVAILABLE:
from torchvision import transforms as T
if _TORCHAUDIO_AVAILABLE:
from torchaudio import transforms as TAudio
[docs]@dataclass
class AudioClassificationInputTransform(InputTransform):
spectrogram_size: Tuple[int, int] = (128, 128)
time_mask_param: Optional[int] = None
freq_mask_param: Optional[int] = None
def train_per_sample_transform(self) -> Callable:
transforms = []
if self.time_mask_param is not None:
transforms.append(TAudio.TimeMasking(time_mask_param=self.time_mask_param))
if self.freq_mask_param is not None:
transforms.append(TAudio.FrequencyMasking(freq_mask_param=self.freq_mask_param))
transforms += [T.ToTensor(), T.Resize(self.spectrogram_size)]
return T.Compose(
[
ApplyToKeys(DataKeys.INPUT, T.Compose(transforms)),
ApplyToKeys(DataKeys.TARGET, torch.as_tensor),
]
)
@requires("audio")
def per_sample_transform(self) -> Callable:
return T.Compose(
[
ApplyToKeys(DataKeys.INPUT, T.Compose([T.ToTensor(), T.Resize(self.spectrogram_size)])),
ApplyToKeys(DataKeys.TARGET, torch.as_tensor),
]
)