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_r234060945
########## File path: example/gluon/urban_sounds/urban_sounds.py ########## @@ -0,0 +1,181 @@ +# 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. + +""" + Urban Sounds Dataset: + + To be able to run this example: + + 1. Download the dataset(train.zip, test.zip) required for this example from the location: + **https://drive.google.com/drive/folders/0By0bAi7hOBAFUHVXd1JCN3MwTEU** + 2. Extract both the zip archives into the **current directory** - + after unzipping you would get 2 new folders namely,\ + **Train** and **Test** and two csv files - **train_csv.csv**, **test_csv.csv** + 3. Apache MXNet is installed on the machine. For instructions, go to the link: + **https://mxnet.incubator.apache.org/install/ ** + 4. Librosa is installed. To install, follow the instructions here: + **https://librosa.github.io/librosa/install.html** + +""" +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 Loader, MFCC +try: + import argparse +except ImportError as er: + warnings.warn("Argument parsing module could not be imported and hence \ + no arguments passed to the script can actually be parsed.") + + +# Defining a neural network with number of labels +def get_net(num_labels=10): + net = gluon.nn.Sequential() + with net.name_scope(): + net.add(gluon.nn.Dense(256, activation="relu")) # 1st layer (256 nodes) + net.add(gluon.nn.Dense(256, activation="relu")) # 2nd hidden layer + net.add(gluon.nn.Dense(num_labels)) + net.collect_params().initialize(mx.init.Normal(1.)) + return net + + +# 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. + """ + 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', has_csv=True, train_csv='./train.csv', file_format='.wav', skip_rows=1) + tock = time.time() + + print("Loading the dataset took ", (tock-tick), " seconds.") + print("\n=======================================\n") + + print("The synset for the dataset are: ", aud_dataset.synsets) + print("Seeing one item from the dataset here...\n") + print(aud_dataset.__getitem__(0)) + print("Number of output classes = ", len(aud_dataset.synsets)) + # Get the model to train + net = get_net(len(aud_dataset.synsets)) + print("\nNeural Network = \n") + print(net) + print("\nModel - Neural Network Generated!\n") + print("=======================================\n") + + #Define the loss - Softmax CE Loss + softmax_loss = gluon.loss.SoftmaxCELoss(from_logits=False, sparse_label=True) + print("Loss function initialized!\n") + print("=======================================\n") + + #Define the trainer with the optimizer + trainer = gluon.Trainer(net.collect_params(), 'adadelta') + print("Optimizer - Trainer function initialized!\n") + print("=======================================\n") + + + print("Loading the dataset to the Gluon's OOTB Dataloader...") + + #Getting the data loader out of the AudioDataset and passing the transform + aud_transform = gluon.data.vision.transforms.Compose([Loader(), MFCC()]) + tick = time.time() + + audio_train_loader = gluon.data.DataLoader(aud_dataset.transform_first(aud_transform), batch_size=32, shuffle=True) + tock = time.time() + print("Time taken to load data and apply transform here is ", (tock-tick), " seconds.") + print("=======================================\n") + + + print("Starting the training....\n") + # Training loop + tick = time.time() + batch_size = batch_size + num_examples = len(aud_dataset) + + for e in range(epochs): + cumulative_loss = 0 + for _, (data, label) in enumerate(audio_train_loader): + with autograd.record(): + output = net(data) + loss = softmax_loss(output, label) + loss.backward() + trainer.step(batch_size) + cumulative_loss += mx.nd.sum(loss).asscalar() + + if e%5 == 0: + train_accuracy = evaluate_accuracy(audio_train_loader, net) + print("Epoch %s. Loss: %s Train accuracy : %s " % (e, cumulative_loss/num_examples, train_accuracy)) + print("\n------------------------------\n") + + train_accuracy = evaluate_accuracy(audio_train_loader, net) + tock = time.time() + print("\nFinal training accuracy: ", train_accuracy) + + print("Training the sound classification for ", epochs, " epochs, MLP model took ", (tock-tick), " seconds") + print("====================== END ======================\n") + + +if __name__ == '__main__': + + parser = argparse.ArgumentParser(description="Urban Sounds clsssification example - MXNet") + parser.add_argument('--train', '-t', help="Enter the folder path that contains your audio files", type=str) + parser.add_argument('--csv', '-c', help="Enter the filename of the csv that contains filename\ + to label mapping", type=str) + parser.add_argument('--epochs', '-e', help="Enter the number of epochs \ + you would want to run the training for.", type=int) + parser.add_argument('--batch_size', '-b', help="Enter the batch_size of data", type=int) + args = parser.parse_args() + + if args: + if args.train: + train_dir = args.train + else: + train_dir = './Train' + + if args.csv: + train_csv = args.csv + else: + train_csv = './train.csv' + + if args.epochs: + epochs = args.epochs + else: + epochs = 35 Review comment: Yes. 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