chore: import upstream snapshot with attribution

python/paddle/audio/datasets/dataset.py

@@ -0,0 +1,98 @@
+# 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 paddle
+
+from ..features import MFCC, LogMelSpectrogram, MelSpectrogram, Spectrogram
+
+feat_funcs = {
+    'raw': None,
+    'melspectrogram': MelSpectrogram,
+    'mfcc': MFCC,
+    'logmelspectrogram': LogMelSpectrogram,
+    'spectrogram': Spectrogram,
+}
+
+
+class AudioClassificationDataset(paddle.io.Dataset):
+    """
+    Base class of audio classification dataset.
+    """
+
+    def __init__(
+        self,
+        files: list[str],
+        labels: list[int],
+        feat_type: str = 'raw',
+        sample_rate: int | None = None,
+        **kwargs,
+    ):
+        """
+        Args:
+            files (:obj:`List[str]`): A list of absolute path of audio files.
+            labels (:obj:`List[int]`): Labels of audio files.
+            feat_type (:obj:`str`, `optional`, defaults to `raw`):
+                It identifies the feature type that user wants to extract an audio file.
+        """
+        super().__init__()
+
+        if feat_type not in feat_funcs.keys():
+            raise RuntimeError(
+                f"Unknown feat_type: {feat_type}, it must be one in {list(feat_funcs.keys())}"
+            )
+
+        self.files = files
+        self.labels = labels
+
+        self.feat_type = feat_type
+        self.sample_rate = sample_rate
+        self.feat_config = (
+            kwargs  # Pass keyword arguments to customize feature config
+        )
+
+    def _get_data(self, input_file: str):
+        raise NotImplementedError
+
+    def _convert_to_record(self, idx):
+        file, label = self.files[idx], self.labels[idx]
+        waveform, sample_rate = paddle.audio.load(file)
+        self.sample_rate = sample_rate
+
+        feat_func = feat_funcs[self.feat_type]
+
+        record = {}
+        if len(waveform.shape) == 2:
+            waveform = waveform.squeeze(0)  # 1D input
+        waveform = paddle.to_tensor(waveform, dtype=paddle.float32)
+        if feat_func is not None:
+            waveform = waveform.unsqueeze(0)  # (batch_size, T)
+            if self.feat_type != 'spectrogram':
+                feature_extractor = feat_func(
+                    sr=self.sample_rate, **self.feat_config
+                )
+            else:
+                feature_extractor = feat_func(**self.feat_config)
+            record['feat'] = feature_extractor(waveform).squeeze(0)
+        else:
+            record['feat'] = waveform
+        record['label'] = label
+        return record
+
+    def __getitem__(self, idx):
+        record = self._convert_to_record(idx)
+        return record['feat'], record['label']
+
+    def __len__(self):
+        return len(self.files)