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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,
# 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.
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), ] )

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