PaddleX/paddlex/inference/common/batch_sampler/audio_batch_sampler.py

# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
#
# 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 pathlib import Path

from ....utils import logging
from ....utils.cache import CACHE_DIR
from ....utils.download import download
from .base_batch_sampler import BaseBatchSampler


class AudioBatchSampler(BaseBatchSampler):
    def __init__(self):
        """Initializes the BaseBatchSampler.

        Args:
            batch_size (int, optional): The size of each batch. Only support 1.
        """
        super().__init__()
        self.batch_size = 1

    def _download_from_url(self, in_path):
        """Download a file from a URL to a cache directory.

        Args:
            in_path (str): URL of the file to be downloaded.

        Returns:
            str: Path to the downloaded file.
        """
        file_name = Path(in_path).name
        save_path = Path(CACHE_DIR) / "predict_input" / file_name
        download(in_path, save_path, overwrite=True)
        return save_path.as_posix()

    def sample(self, inputs):
        """Generate list of input file path.

        Args:
            inputs (str): file path.

        Yields:
            list: list of file path.
        """
        if isinstance(inputs, str):
            if inputs.startswith("http"):
                inputs = self._download_from_url(inputs)
            yield [inputs]
        elif isinstance(inputs, list):
            yield inputs
        else:
            raise TypeError(
                f"Not supported input data type! Only `str` are supported, but got: {type(inputs)}."
            )

    @BaseBatchSampler.batch_size.setter
    def batch_size(self, batch_size):
        """Sets the batch size.

        Args:
            batch_size (int): The batch size to set.

        Raises:
            Warning: If the batch size is not equal 1.
        """
        # only support batch size 1
        if batch_size != 1:
            logging.warning(
                f"audio batch sampler only support batch size 1, but got {batch_size}."
            )
        else:
            self._batch_size = batch_size