use scikit-learn instead sklearn

paddlespeech/audio/datasets/dataset.py

@@ -0,0 +1,100 @@
+# Copyright (c) 2021 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 typing import List
+
+import numpy as np
+import paddle
+
+from ..backends.soundfile_backend import soundfile_load as load_audio
+from ..compliance.kaldi import fbank as kaldi_fbank
+from ..compliance.kaldi import mfcc as kaldi_mfcc
+from ..compliance.librosa import melspectrogram
+from ..compliance.librosa import mfcc
+
+feat_funcs = {
+    'raw': None,
+    'melspectrogram': melspectrogram,
+    'mfcc': mfcc,
+    'kaldi_fbank': kaldi_fbank,
+    'kaldi_mfcc': kaldi_mfcc,
+}
+
+
+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,
+                 **kwargs):
+        """
+        Ags:
+            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 of an audio file.
+        """
+        super(AudioClassificationDataset, self).__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]
+
+        if self.sample_rate is None:
+            waveform, sample_rate = load_audio(file)
+        else:
+            waveform, sample_rate = load_audio(file, sr=self.sample_rate)
+
+        feat_func = feat_funcs[self.feat_type]
+
+        record = {}
+        if self.feat_type in ['kaldi_fbank', 'kaldi_mfcc']:
+            waveform = paddle.to_tensor(waveform).unsqueeze(0)  # (C, T)
+            record['feat'] = feat_func(
+                waveform=waveform, sr=self.sample_rate, **self.feat_config)
+        else:
+            record['feat'] = feat_func(
+                waveform, sample_rate,
+                **self.feat_config) if feat_func else waveform
+        record['label'] = label
+        return record
+
+    def __getitem__(self, idx):
+        record = self._convert_to_record(idx)
+        if self.feat_type in ['kaldi_fbank', 'kaldi_mfcc']:
+            return self.keys[idx], record['feat'], record['label']
+        else:
+            return np.array(record['feat']).transpose(), np.array(
+                record['label'], dtype=np.int64)
+
+    def __len__(self):
+        return len(self.files)

setup.py

@@ -121,7 +121,7 @@ base = [
     "webrtcvad",
     "yacs>=0.1.8",
     "zhon",
-    "sklearn",
+    "scikit-learn",
 ]
 
 server = ["pattern_singleton", "websockets"]