stu1130 commented on a change in pull request #13325: [MXNET-1210 ] Gluon Audio - Example URL: https://github.com/apache/incubator-mxnet/pull/13325#discussion_r236922372
########## File path: example/gluon/urban_sounds/datasets.py ########## @@ -0,0 +1,178 @@ +# 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 itertools import islice +import csv +from mxnet.gluon.data import Dataset +from mxnet import ndarray as nd +try: + import librosa +except ImportError as e: + raise ImportError("librosa dependency could not be resolved or \ + imported, could not load audio onto the numpy array. pip install librosa") + + + +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 file name and associated label + + Parameters + ---------- + root : str + Path to root directory. + transform : callable, default None + A function that takes data and label and transforms them + train_csv: str, default None + train_csv should be populated by the training csv filename + file_format: str, default '.wav' + The format of the audio files(.wav) + skip_header: boolean, default False + While reading from csv file, whether 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 `i`th label + items : list of tuples + List of all audio in (filename, label) pairs. + + """ + def __init__(self, root, train_csv=None, file_format='.wav', skip_header=False): + if not librosa: + warnings.warn("pip install librosa to continue.") + raise RuntimeError("Librosa not installed. Run pip install librosa and retry this step.") + self._root = os.path.expanduser(root) + self._exts = ['.wav'] + self._format = file_format + self._train_csv = train_csv + if file_format.lower() not in self._exts: + raise RuntimeError("format {} not supported currently.".format(file_format)) + skip_rows = 0 + if skip_header: + skip_rows = 1 + 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._train_csv: + # The audio files are organized in folder structure with + # directory name as label and audios in them + self._folder_structure(root) + else: + # train_csv contains mapping between filename and label + self._csv_labelled_dataset(root, skip_rows=skip_rows) + + #Generating the synset.txt file now + if not os.path.exists("./synset.txt"): + 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") + else: + warnings.warn("Synset file already exists in the current directory! Not generating synset.txt.") + + + def _folder_structure(self, root): + for folder in sorted(os.listdir(root)): + path = os.path.join(root, folder) + if not os.path.isdir(path): + warnings.warn('Ignoring {}, which is not a directory.'.format(path)) + 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 {} of type {}. Only support {}'\ + .format(filename, ext, ', '.join(self._exts))) + continue + self.items.append((file_name, label)) + + + def _csv_labelled_dataset(self, root, skip_rows=0): + with open(self._train_csv, "r") as traincsv: + for line in islice(csv.reader(traincsv), skip_rows, None): + filename = os.path.join(root, line[0]) + label = line[1].strip() + if label not in self.synsets: + self.synsets.append(label) + if self._format not in filename: + filename = filename+self._format + self.items.append((filename, nd.array([self.synsets.index(label)]).reshape((1,)))) + + + def __getitem__(self, idx): + """Retrieve the item (data, label) stored at idx in items""" + filename, label = self.items[idx] + # resampling_type is passed as kaiser_fast for a better performance + X1, _ = librosa.load(filename, res_type='kaiser_fast') + return nd.array(X1), label + + + def __len__(self): + """Retrieves the number of items in the dataset""" + return len(self.items) + + + def transform_first(self, fn, lazy=False): + """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. + lazy=False is passed to transform_first for dataset so that all tramsforms could be performed in + one shot and not during training. This is a performance consideration. + + Parameters + ---------- + fn : callable + A transformer function that takes the first element of a sample + as input and returns the transformed element. + lazy : bool, default True + If False, transforms all samples at once. Otherwise, + transforms each sample on demand. Note that if `fn` + is stochastic, you must set lazy to True or you will + get the same result on all epochs. + + Returns + ------- + Dataset + The transformed dataset. + + """ + return super(AudioFolderDataset, self).transform_first(fn, lazy=False) Review comment: ```suggestion return super(AudioFolderDataset, self).transform_first(fn, lazy=lazy) ``` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
