sandeep-krishnamurthy commented on a change in pull request #13325: [MXNET-1210 ] Gluon Audio - Example URL: https://github.com/apache/incubator-mxnet/pull/13325#discussion_r236478096
########## File path: example/gluon/urban_sounds/predict.py ########## @@ -0,0 +1,91 @@ +# 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. +""" Prediction module for Urban Sounds Classification +""" +import os +import warnings +import mxnet as mx +from mxnet import nd +from transforms import MFCC +from model import get_net + +def predict(prediction_dir='./Test'): + """The function is used to run predictions on the audio files in the directory `pred_directory`. + + Parameters + ---------- + net: + The model that has been trained. + prediction_dir: string, default ./Test + The directory that contains the audio files on which predictions are to be made + + """ + + try: + import librosa + except ImportError: + warnings.warn("Librosa is not installed! please run the following command pip install librosa.") + return + + if not os.path.exists(prediction_dir): + warnings.warn("The directory on which predictions are to be made is not found!") + return + + if len(os.listdir(prediction_dir)) == 0: + warnings.warn("The directory on which predictions are to be made is empty! Exiting...") + return + + # Loading synsets + if not os.path.exists('./synset.txt'): + warnings.warn("The synset or labels for the dataset do not exist. Please run the training script first.") + return + + with open("./synset.txt", "r") as f: + synset = [l.rstrip() for l in f] + net = get_net(len(synset)) + print("Trying to load the model with the saved parameters...") + if not os.path.exists("./net.params"): + warnings.warn("The model does not have any saved parameters... Cannot proceed! Train the model first") + return + + net.load_parameters("./net.params") + file_names = os.listdir(prediction_dir) + full_file_names = [os.path.join(prediction_dir, item) for item in file_names] + mfcc = MFCC() + print("\nStarting predictions for audio files in ", prediction_dir, " ....\n") + for filename in full_file_names: + # Argument kaiser_fast to res_type is faster than 'kaiser_best'. To reduce the load time, passing kaiser_fast. + X1, _ = librosa.load(filename, res_type='kaiser_fast') + transformed_test_data = mfcc(mx.nd.array(X1)) + output = net(transformed_test_data.reshape((1, -1))) + prediction = nd.argmax(output, axis=1) + print(filename, " -> ", synset[(int)(prediction.asscalar())]) + + +if __name__ == '__main__': + try: + import argparse + parser = argparse.ArgumentParser(description="Urban Sounds clsssification example - MXNet") Review comment: MXNet Gluon ---------------------------------------------------------------- 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
