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OMG_Emotion_Audio_MelSpectrum.py
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# -*- coding: utf-8 -*-
"""Experiments with the OMG-Emotion Dataset using the Audio Channel
More Information: Barros, P., Weber, C., & Wermter, S. (2016, July). Learning auditory neural representations for emotion recognition. In Neural Networks (IJCNN), 2016 International Joint Conference on (pp. 921-928). IEEE.
Parameters:
baseDirectory (String): Base directory where the experiment will be saved.
videosDirectory (String): Folder where the .wav files from the dataset are saved
dataFileTrain (String): File with the list of training samples
dataFileValidation (String): File with the list of validation samples
experimentName (String): Name of the experiment.
logManager (LogManager):
Author: Pablo Barros
Created on: 02.05.2018
Last Update: 16.06.2018
"""
import matplotlib
matplotlib.use('Agg')
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
from keras import backend as K
def set_keras_backend(backend):
if K.backend() != backend:
os.environ['KERAS_BACKEND'] = backend
reload(K)
assert K.backend() == backend
def runModel():
from KEF.Controllers import ExperimentManager
from KEF.DataLoaders import DataLoader_OMG_Emotion_Audio
from KEF.Implementations import Audio_CNN_OMG_Emotion
dataDirectory = "/data/OMG_Audio_PreTrainedRAVDESS/" # Where the experiment will be saved
videosDirectory = "/data/datasets/OMG-Emotion/audio_extraced_all/"
dataFileTrain = "/data/datasets/OMG-Emotion/omg_TrainVideos.csv"
dataFileValidation = "/data/datasets/OMG-Emotion/omg_ValidationVideos.csv"
""" Initianize all the parameters and modules necessary
image size: 64,64
"""
experimentManager = ExperimentManager.ExperimentManager(dataDirectory,"RAVDESS_PreTrained", verbose=True)
audioSeconds = 0.3
preProcessingProperties = [audioSeconds]
""" Loading the training and testing data
"""
dataLoader = DataLoader_OMG_Emotion_Audio.DataLoader_OMG_Audio(experimentManager.logManager, preProcessingProperties)
#
#
dataLoader.loadValidationData(videosDirectory, dataFileValidation)
dataLoader.loadTrainData(videosDirectory, dataFileTrain)
cnnModel = Audio_CNN_OMG_Emotion.Audio_CNN_OMG(experimentManager, "CNN", experimentManager.plotManager)
#
cnnModel.buildModel(dataLoader.dataTrain.dataX.shape[1:])
##
cnnModel.train(dataLoader.dataTrain, dataLoader.dataValidation, False)
##
cnnModel.save(experimentManager.modelDirectory)
##
print "Public Test Evaluation"
cnnModel.evaluate(dataLoader.dataValidation)
print "Private Test Evaluation"
cnnModel.evaluate(dataLoader.dataTest)
set_keras_backend("tensorflow")
print K.backend
if K.backend == "tensorflow":
import tensorflow as tf
sess = tf.Session()
K.set_session(sess)
with tf.device('/gpu:1'):
runModel()
else:
runModel()