vandanavk commented on a change in pull request #13241: [MXNET-1210 ] Gluon Audio URL: https://github.com/apache/incubator-mxnet/pull/13241#discussion_r234355891
########## File path: python/mxnet/gluon/contrib/data/audio/datasets.py ########## @@ -0,0 +1,171 @@ +# 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 +from ....data import Dataset +from ..... import ndarray as nd +try: + import librosa +except ImportError as e: + warnings.warn("gluon/contrib/data/audio/datasets.py : librosa dependency could not be resolved or \ + imported, could not load audio onto the numpy array.") + + +class AudioFolderDataset(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) + skip_rows: int, default 0 + While reading from csv file, how many rows to skip at the start of the file to avoid reading in header + + 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, has_csv=False, train_csv=None, file_format='.wav', skip_rows=0): + self._root = os.path.expanduser(root) + self._exts = ['.wav'] + self._format = file_format + self._has_csv = has_csv + self._train_csv = train_csv + self._list_audio_files(self._root, skip_rows=skip_rows) + + + def _list_audio_files(self, root, skip_rows=0): + """ + Populates synsets - a map of index to label for the data items. + Populates the data in the dataset, making tuples of (data, label) + """ + self.synsets = [] + self.items = [] + if not self._has_csv: + 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: + data_tmp = [] + label_tmp = [] + skipped_rows = 0 + with open(self._train_csv, "r") as traincsv: + for line in traincsv: + skipped_rows = skipped_rows + 1 + if skipped_rows <= skip_rows: + continue + filename = os.path.join(root, line.split(",")[0]) + label = line.split(",")[1].strip() + if label not in self.synsets: + self.synsets.append(label) + data_tmp.append(os.path.join(self._root, line.split(",")[0])) + label_tmp.append(self.synsets.index(label)) + + #Generating the synset.txt file now + with open("./synset.txt", "w") as synsets_file: + for item in self.synsets: + synsets_file.write(item+os.linesep) + print("Synsets is generated as synset.txt") + + self._label = nd.array(label_tmp) + for i, _ in enumerate(data_tmp): + if self._format not in data_tmp[i]: + self.items.append((data_tmp[i]+self._format, self._label[i])) + + def __getitem__(self, idx): + """ + Retrieve the item (data, label) stored at idx in items + """ + filename = self.items[idx][0] + label = self.items[idx][1] + + if librosa is not None: + X1, _ = librosa.load(filename, res_type='kaiser_fast') + return nd.array(X1), label + + else: + warnings.warn(" Dependency librosa is not installed! \ + Cannot load the audio(wav) file into the numpy.ndarray.") + return self.items[idx][0], self.items[idx][1] + + def __len__(self): + """ + Retrieves the number of items in the dataset + """ + return len(self.items) + + + def transform_first(self, fn, lazy=True): + """Returns a new dataset with the first element of each sample + transformed by the transformer function `fn`. + + This is useful, for example, when you only want to transform data + while keeping label as is. + + Parameters + ---------- + fn : callable + A transformer function that takes the first elemtn of a sample Review comment: nit: typo ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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