sandeep-krishnamurthy commented on a change in pull request #13241: [MXNET-1210 ] Gluon Audio URL: https://github.com/apache/incubator-mxnet/pull/13241#discussion_r234324605
########## File path: example/gluon/urban_sounds/train.py ########## @@ -0,0 +1,166 @@ +# 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. +""" + The module to run training on the Urban sounds dataset +""" +import os +import time +import warnings +import mxnet as mx +from mxnet import gluon, nd, autograd +from mxnet.gluon.contrib.data.audio.datasets import AudioFolderDataset +from mxnet.gluon.contrib.data.audio.transforms import MFCC +import model +# Defining a function to evaluate accuracy +def evaluate_accuracy(data_iterator, net): + acc = mx.metric.Accuracy() + for _, (data, label) in enumerate(data_iterator): + output = net(data) + predictions = nd.argmax(output, axis=1) + predictions = predictions.reshape((-1, 1)) + acc.update(preds=predictions, labels=label) + return acc.get()[1] + + +def train(train_dir=None, train_csv=None, epochs=30, batch_size=32): + """ + The function responsible for running the training the model. + """ + try: + import librosa + except ImportError: + warnings.warn("The dependency librosa is not installed. Cannot continue") + return + if not train_dir or not os.path.exists(train_dir) or not train_csv: + warnings.warn("No train directory could be found ") + return + # Make a dataset from the local folder containing Audio data + print("\nMaking an Audio Dataset...\n") + tick = time.time() + aud_dataset = AudioFolderDataset(train_dir, has_csv=True, train_csv=train_csv, file_format='.wav', skip_rows=1) Review comment: 1. Why has_csv is required if we have train_csv? 2. Are we assuming train data is always csv? ---------------------------------------------------------------- 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: [email protected] With regards, Apache Git Services
