chore: import upstream snapshot with attribution

python/paddle/audio/datasets/tess.py

@@ -0,0 +1,166 @@
+# Copyright (c) 2022 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 __future__ import annotations
+
+import os
+from typing import TYPE_CHECKING, Any, NamedTuple
+
+from paddle.dataset.common import DATA_HOME
+from paddle.utils import download
+
+from .dataset import AudioClassificationDataset
+
+if TYPE_CHECKING:
+    from .esc50 import _FeatTypeLiteral, _ModeLiteral
+
+__all__ = []
+
+
+class TESS(AudioClassificationDataset):
+    """
+    TESS is a set of 200 target words were spoken in the carrier phrase
+    "Say the word _____' by two actresses (aged 26 and 64 years) and
+    recordings were made of the set portraying each of seven emotions(anger,
+    disgust, fear, happiness, pleasant surprise, sadness, and neutral).
+    There are 2800 stimuli in total.
+
+    Reference:
+        Toronto emotional speech set (TESS) https://tspace.library.utoronto.ca/handle/1807/24487
+        https://doi.org/10.5683/SP2/E8H2MF
+
+    Args:
+       mode (str, optional): It identifies the dataset mode (train or dev). Defaults to train.
+       n_folds (int, optional): Split the dataset into n folds. 1 fold for dev dataset and n-1 for train dataset. Defaults to 5.
+       split (int, optional): It specify the fold of dev dataset. Defaults to 1.
+       feat_type (str, optional): It identifies the feature type that user wants to extract of an audio file. Defaults to raw.
+       archive(dict): it tells where to download the audio archive. Defaults to None.
+
+    Returns:
+        :ref:`api_paddle_io_Dataset`. An instance of TESS dataset.
+
+    Examples:
+
+        .. code-block:: pycon
+
+            >>> # doctest: +TIMEOUT(60)
+            >>> import paddle
+
+            >>> tess_dataset = paddle.audio.datasets.TESS(
+            ...     mode='dev',
+            ...     feat_type='raw',
+            ... )
+            >>> for idx in range(5):
+            ...     audio, label = tess_dataset[idx]
+            ...     # do something with audio, label
+            ...     print(audio.shape, label)
+            ...     # [audio_data_length] , label_id
+
+            >>> tess_dataset = paddle.audio.datasets.TESS(
+            ...     mode='dev',
+            ...     feat_type='mfcc',
+            ...     n_mfcc=40,
+            ... )
+            >>> for idx in range(5):
+            ...     audio, label = tess_dataset[idx]
+            ...     # do something with mfcc feature, label
+            ...     print(audio.shape, label)
+            ...     # [feature_dim, num_frames] , label_id
+    """
+
+    archive: dict[str, str] = {
+        'url': 'https://bj.bcebos.com/paddleaudio/datasets/TESS_Toronto_emotional_speech_set.zip',
+        'md5': '1465311b24d1de704c4c63e4ccc470c7',
+    }
+
+    label_list: list[str] = [
+        'angry',
+        'disgust',
+        'fear',
+        'happy',
+        'neutral',
+        'ps',  # pleasant surprise
+        'sad',
+    ]
+
+    audio_path: str = 'TESS_Toronto_emotional_speech_set'
+
+    class meta_info(NamedTuple):
+        speaker: str
+        word: str
+        emotion: str
+
+    def __init__(
+        self,
+        mode: _ModeLiteral = 'train',
+        n_folds: int = 5,
+        split: int = 1,
+        feat_type: _FeatTypeLiteral = 'raw',
+        archive: dict[str, str] | None = None,
+        **kwargs: Any,
+    ) -> None:
+        assert isinstance(n_folds, int) and (n_folds >= 1), (
+            f'the n_folds should be integer and n_folds >= 1, but got {n_folds}'
+        )
+        assert split in range(1, n_folds + 1), (
+            f'The selected split should be integer and should be 1 <= split <= {n_folds}, but got {split}'
+        )
+        if archive is not None:
+            self.archive = archive
+        files, labels = self._get_data(mode, n_folds, split)
+        super().__init__(
+            files=files, labels=labels, feat_type=feat_type, **kwargs
+        )
+
+    def _get_meta_info(self, files) -> list[meta_info]:
+        ret = []
+        for file in files:
+            basename_without_extend = os.path.basename(file)[:-4]
+            ret.append(self.meta_info(*basename_without_extend.split('_')))
+        return ret
+
+    def _get_data(
+        self, mode: str, n_folds: int, split: int
+    ) -> tuple[list[str], list[int]]:
+        if not os.path.isdir(os.path.join(DATA_HOME, self.audio_path)):
+            download.get_path_from_url(
+                self.archive['url'],
+                DATA_HOME,
+                self.archive['md5'],
+                decompress=True,
+            )
+
+        wav_files = []
+        for root, _, files in os.walk(os.path.join(DATA_HOME, self.audio_path)):
+            for file in files:
+                if file.endswith('.wav'):
+                    wav_files.append(os.path.join(root, file))
+
+        meta_info = self._get_meta_info(wav_files)
+
+        files = []
+        labels = []
+        for idx, sample in enumerate(meta_info):
+            _, _, emotion = sample
+            target = self.label_list.index(emotion)
+            fold = idx % n_folds + 1
+
+            if mode == 'train' and int(fold) != split:
+                files.append(wav_files[idx])
+                labels.append(target)
+
+            if mode != 'train' and int(fold) == split:
+                files.append(wav_files[idx])
+                labels.append(target)
+
+        return files, labels