[GitHub] gaurav-gireesh commented on a change in pull request #13241: [MXNET-1210 ][WIP] Gluon Audio

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gaurav-gireesh commented on a change in pull request #13241: [MXNET-1210 ][WIP] Gluon Audio
URL: https://github.com/apache/incubator-mxnet/pull/13241#discussion_r233171700
 
 

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 File path: python/mxnet/gluon/contrib/data/audio/datasets.py
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+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+
+# coding: utf-8
+# pylint: disable=
+""" Audio Dataset container."""
+__all__ = ['AudioFolderDataset']
+
+import os
+import warnings
+import sklearn
+from mxnet import gluon, nd
+
+
+class AudioFolderDataset(gluon.data.dataset.Dataset):
+    """A dataset for loading Audio files stored in a folder structure like::
+
+        root/children_playing/0.wav
+        root/siren/23.wav
+        root/drilling/26.wav
+        root/dog_barking/42.wav
+            OR
+        Files(wav) and a csv file that has filename and associated label
+
+    Parameters
+    ----------
+    root : str
+        Path to root directory.
+
+    transform : callable, default None
+        A function that takes data and label and transforms them
+
+    has_csv: default True
+        If True, it means that a csv file has filename and its corresponding label
+        If False, we have folder like structure
+
+    train_csv: str, default None
+        If has_csv is True, train_csv should be populated by the training csv filename
+
+    file_format: str, default '.wav'
+        The format of the audio files(.wav, .mp3)
+
+    Attributes
+    ----------
+    synsets : list
+        List of class names. `synsets[i]` is the name for the integer label `i`
+    items : list of tuples
+        List of all audio in (filename, label) pairs.
+    """
+    def __init__(self, root, transform=None, has_csv=False, train_csv=None, file_format='.wav'):
+        self._root = os.path.expanduser(root)
+        self._transform = transform
+        self._exts = ['.wav']
+        self._format = file_format
+        self._has_csv = has_csv
+        self._train_csv = train_csv
+        self._list_audio_files(self._root)
+
+
+    def _list_audio_files(self, root):
+        """
+            Populates synsets - a map of index to label for the data items.
+            Populates the data in the dataset, making tuples of (data, label)
+        """
+        if not self._has_csv:
+            self.synsets = []
+            self.items = []
+
+            for folder in sorted(os.listdir(root)):
+                path = os.path.join(root, folder)
+                if not os.path.isdir(path):
+                    warnings.warn('Ignoring %s, which is not a directory.'%path, stacklevel=3)
+                    continue
+                label = len(self.synsets)
+                self.synsets.append(folder)
+                for filename in sorted(os.listdir(path)):
+                    file_name = os.path.join(path, filename)
+                    ext = os.path.splitext(file_name)[1]
+                    if ext.lower() not in self._exts:
+                        warnings.warn('Ignoring %s of type %s. Only support %s'%(filename, ext, ', '.join(self._exts)))
+                        continue
+                    self.items.append((file_name, label))
+        else:
+            self.synsets = []
+            self.items = []
+            data_tmp = []
+            label_tmp = []
+            with open(self._train_csv, "r") as traincsv:
+                for line in traincsv:
+                    filename = os.path.join(root, line.split(",")[0])
+                    label = line.split(",")[1].strip()
+                    data_tmp.append(os.path.join(self._root, line.split(",")[0]))
+                    label_tmp.append(line.split(",")[1].strip())
+            data_tmp = data_tmp[1:]
+            label_tmp = label_tmp[1:]
+            le = sklearn.preprocessing.LabelEncoder()
 
 Review comment:
   Yes, I am working to find a workaround here. We needed label encoding here because we read from a csv file that contains filename(location) and the corresponding label. It is different from the use case in ImageFolderDataset that requires each class(or label) to be a folder( with images inside them), and hence Folder can be encountered once and put in the synsets(no repetition there). However, in the csv file, a label can occur multiple times. Looking into this for a better way to get around this.
   Thanks!
   

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