def from_files(
        cls,
        train_files: Optional[Sequence[str]] = None,
        train_targets: Optional[Sequence[Any]] = None,
        val_files: Optional[Sequence[str]] = None,
        val_targets: Optional[Sequence[Any]] = None,
        test_files: Optional[Sequence[str]] = None,
        test_targets: Optional[Sequence[Any]] = None,
        predict_files: Optional[Sequence[str]] = None,
        sampling_rate: int = 16000,
        n_fft: int = 400,
        input_cls: Type[Input] = AudioClassificationFilesInput,
        transform: INPUT_TRANSFORM_TYPE = AudioClassificationInputTransform,
        transform_kwargs: Optional[Dict] = None,
        target_formatter: Optional[TargetFormatter] = None,
        **data_module_kwargs: Any,
    ) -> "AudioClassificationData":
        """Load the :class:`~flash.audio.classification.data.AudioClassificationData` from lists of files and
        corresponding lists of targets.

        The supported file extensions for precomputed spectrograms are: ``.jpg``, ``.jpeg``, ``.png``, ``.ppm``,
        ``.bmp``, ``.pgm``, ``.tif``, ``.tiff``, ``.webp``, and ``.npy``.
        The supported file extensions for raw audio (where spectrograms will be computed automatically) are: ``.aiff``,
        ``.au``, ``.avr``, ``.caf``, ``.flac``, ``.mat``, ``.mat4``, ``.mat5``, ``.mpc2k``, ``.ogg``, ``.paf``,
        ``.pvf``, ``.rf64``, ``.ircam``, ``.voc``, ``.w64``, ``.wav``, ``.nist``, and ``.wavex``.
        The targets can be in any of our
        :ref:`supported classification target formats <formatting_classification_targets>`.
        To learn how to customize the transforms applied for each stage, read our
        :ref:`customizing transforms guide <customizing_transforms>`.

        Args:
            train_files: The list of spectrogram image files to use when training.
            train_targets: The list of targets to use when training.
            val_files: The list of spectrogram image files to use when validating.
            val_targets: The list of targets to use when validating.
            test_files: The list of spectrogram image files to use when testing.
            test_targets: The list of targets to use when testing.
            predict_files: The list of spectrogram image files to use when predicting.
            sampling_rate: Sampling rate to use when loading raw audio files.
            n_fft: The size of the FFT to use when creating spectrograms from raw audio.
            target_formatter: Optionally provide a :class:`~flash.core.data.utilities.classification.TargetFormatter` to
                control how targets are handled. See :ref:`formatting_classification_targets` for more details.
            input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data.
            transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use.
            transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms.
            data_module_kwargs: Additional keyword arguments to provide to the
                :class:`~flash.core.data.data_module.DataModule` constructor.

        Returns:
            The constructed :class:`~flash.audio.classification.data.AudioClassificationData`.

        Examples
        ________

        .. testsetup::

            >>> from PIL import Image
            >>> rand_image = Image.fromarray(np.random.randint(0, 255, (64, 64, 3), dtype="uint8"))
            >>> _ = [rand_image.save(f"spectrogram_{i}.png") for i in range(1, 4)]
            >>> _ = [rand_image.save(f"predict_spectrogram_{i}.png") for i in range(1, 4)]

        .. doctest::

            >>> from flash import Trainer
            >>> from flash.audio import AudioClassificationData
            >>> from flash.image import ImageClassifier
            >>> datamodule = AudioClassificationData.from_files(
            ...     train_files=["spectrogram_1.png", "spectrogram_2.png", "spectrogram_3.png"],
            ...     train_targets=["meow", "bark", "meow"],
            ...     predict_files=[
            ...         "predict_spectrogram_1.png",
            ...         "predict_spectrogram_2.png",
            ...         "predict_spectrogram_3.png",
            ...     ],
            ...     transform_kwargs=dict(spectrogram_size=(128, 128)),
            ...     batch_size=2,
            ... )
            >>> datamodule.num_classes
            2
            >>> datamodule.labels
            ['bark', 'meow']
            >>> model = ImageClassifier(backbone="resnet18", num_classes=datamodule.num_classes)
            >>> trainer = Trainer(fast_dev_run=True)
            >>> trainer.fit(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
            Training...
            >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
            Predicting...

        .. testcleanup::

            >>> import os
            >>> _ = [os.remove(f"spectrogram_{i}.png") for i in range(1, 4)]
            >>> _ = [os.remove(f"predict_spectrogram_{i}.png") for i in range(1, 4)]

        """

        ds_kw = {
            "sampling_rate": sampling_rate,
            "n_fft": n_fft,
            "target_formatter": target_formatter,
        }

        train_input = input_cls(RunningStage.TRAINING, train_files, train_targets, **ds_kw)
        ds_kw["target_formatter"] = getattr(train_input, "target_formatter", None)

        return cls(
            train_input,
            input_cls(RunningStage.VALIDATING, val_files, val_targets, **ds_kw),
            input_cls(RunningStage.TESTING, test_files, test_targets, **ds_kw),
            input_cls(RunningStage.PREDICTING, predict_files, **ds_kw),
            transform=transform,
            transform_kwargs=transform_kwargs,
            **data_module_kwargs,
        )
Load the :class:`~flash.audio.classification.data.AudioClassificationData` from lists of files and
        corresponding lists of targets.

        The supported file extensions for precomputed spectrograms are: ``.jpg``, ``.jpeg``, ``.png``, ``.ppm``,
        ``.bmp``, ``.pgm``, ``.tif``, ``.tiff``, ``.webp``, and ``.npy``.
        The supported file extensions for raw audio (where spectrograms will be computed automatically) are: ``.aiff``,
        ``.au``, ``.avr``, ``.caf``, ``.flac``, ``.mat``, ``.mat4``, ``.mat5``, ``.mpc2k``, ``.ogg``, ``.paf``,
        ``.pvf``, ``.rf64``, ``.ircam``, ``.voc``, ``.w64``, ``.wav``, ``.nist``, and ``.wavex``.
        The targets can be in any of our
        :ref:`supported classification target formats <formatting_classification_targets>`.
        To learn how to customize the transforms applied for each stage, read our
        :ref:`customizing transforms guide <customizing_transforms>`.

        Args:
            train_files: The list of spectrogram image files to use when training.
            train_targets: The list of targets to use when training.
            val_files: The list of spectrogram image files to use when validating.
            val_targets: The list of targets to use when validating.
            test_files: The list of spectrogram image files to use when testing.
            test_targets: The list of targets to use when testing.
            predict_files: The list of spectrogram image files to use when predicting.
            sampling_rate: Sampling rate to use when loading raw audio files.
            n_fft: The size of the FFT to use when creating spectrograms from raw audio.
            target_formatter: Optionally provide a :class:`~flash.core.data.utilities.classification.TargetFormatter` to
                control how targets are handled. See :ref:`formatting_classification_targets` for more details.
            input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data.
            transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use.
            transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms.
            data_module_kwargs: Additional keyword arguments to provide to the
                :class:`~flash.core.data.data_module.DataModule` constructor.

        Returns:
            The constructed :class:`~flash.audio.classification.data.AudioClassificationData`.

        Examples
        ________

        .. testsetup::

            >>> from PIL import Image
            >>> rand_image = Image.fromarray(np.random.randint(0, 255, (64, 64, 3), dtype="uint8"))
            >>> _ = [rand_image.save(f"spectrogram_{i}.png") for i in range(1, 4)]
            >>> _ = [rand_image.save(f"predict_spectrogram_{i}.png") for i in range(1, 4)]

        .. doctest::

            >>> from flash import Trainer
            >>> from flash.audio import AudioClassificationData
            >>> from flash.image import ImageClassifier
            >>> datamodule = AudioClassificationData.from_files(
            ...     train_files=["spectrogram_1.png", "spectrogram_2.png", "spectrogram_3.png"],
            ...     train_targets=["meow", "bark", "meow"],
            ...     predict_files=[
            ...         "predict_spectrogram_1.png",
            ...         "predict_spectrogram_2.png",
            ...         "predict_spectrogram_3.png",
            ...     ],
            ...     transform_kwargs=dict(spectrogram_size=(128, 128)),
            ...     batch_size=2,
            ... )
            >>> datamodule.num_classes
            2
            >>> datamodule.labels
            ['bark', 'meow']
            >>> model = ImageClassifier(backbone="resnet18", num_classes=datamodule.num_classes)
            >>> trainer = Trainer(fast_dev_run=True)
            >>> trainer.fit(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
            Training...
            >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
            Predicting...

        .. testcleanup::

            >>> import os
            >>> _ = [os.remove(f"spectrogram_{i}.png") for i in range(1, 4)]
            >>> _ = [os.remove(f"predict_spectrogram_{i}.png") for i in range(1, 4)]

        
src/flash/audio/classification/data.py