def train(train_path):
data_folders = [
os.path.join(train_path, args.classcode + str(i))
for i in range(class_count)
]
aT.extract_features_and_train(data_folders, 1.0, 1.0, aT.shortTermWindow,
aT.shortTermStep, args.algorithm, model_name,
False)
def train_fold(fold_no):
data_folders = [
dataset_path + "/train" + str(fold_no) + "/" + args.classcode + str(i)
for i in range(class_count)
]
aT.extract_features_and_train(data_folders, 1.0, 1.0, aT.shortTermWindow,
aT.shortTermStep, args.algorithm, model_name,
False)
def trainClassifierWrapper(method, beat_feats, directories, model_name):
if len(directories) < 2:
raise Exception("At least 2 directories are needed")
aT.extract_features_and_train(directories,
1,
1,
aT.shortTermWindow,
aT.shortTermStep,
method.lower(),
model_name,
compute_beat=beat_feats)
def train_segment_classifier_and_create_model(music_parent_directory,
model_name):
# List all the subdirectories
subdirectories = os.listdir(music_parent_directory)
for i in range(0, len(subdirectories)):
if subdirectories[i] == '.DS_Store':
subdirectories.pop(i)
break
subdirectories = [
music_parent_directory + '/' + subdirectory
for subdirectory in subdirectories
]
aT.extract_features_and_train(subdirectories, 1.0, 1.0, aT.shortTermWindow,
aT.shortTermStep, "randomforest",
'data/' + model_name, True)
best_polynomial_model = SVC(**polyKernel_param)
best_polynomial_model.fit(X_train, y_train)
plt.figure()
plt.title("Polynomial Kernel SVC decision boundary", fontsize=16)
plot_decision_boundaries(X_train,
y_train.apply(lambda x: 1 if x == 'cough' else 0),
SVC, **polyKernel_param)
plt.show()
### Storing classifier
import joblib
joblib.dump(best_gaussian_model, 'SVM_cough_classifier.joblib')
'''
Train a classifier with PyAudioAnalysis
'''
from pyAudioAnalysis import audioTrainTest as aT
help(aT.extract_features_and_train)
cough_path = 'C:/Users/Guillem/Desktop/HACKATHON 2020/Unlabeled audio/TRAIN/Cough/'
nocough_path = 'C:/Users/Guillem/Desktop/HACKATHON 2020/Unlabeled audio/TRAIN/No_Cough/'
svm_linear = aT.extract_features_and_train([cough_path, nocough_path],
0.2,
0.2,
aT.shortTermWindow,
aT.shortTermStep,
"svm",
"svm_linear",
False,
train_percentage=0.80)
def parse_arguments():
"""Parse arguments for real time demo.
"""
parser = argparse.ArgumentParser(description="Train audio classifiers")
parser.add_argument("-i",
"--input_audio",
required=True,
nargs='+',
help="List of audio paths")
parser.add_argument("-o",
"--output_model",
required=True,
nargs=None,
help="Output model's path")
return parser.parse_args()
if __name__ == "__main__":
args = parse_arguments()
input_audio = args.input_audio
output_model = args.output_model
mt_win = 3.0
mt_step = 1.0
# this is obviously not optimal in terms of performance but it is quite fast
st_win = st_step = 0.1
aT.extract_features_and_train(input_audio, mt_win, mt_step, st_win,
st_step, "svm_rbf", output_model, False)
"sm", "movie8", "speakers", "speaker-gender",
"music-genre6", "4class"
],
help="Classification task")
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parseArguments()
root_data_path = args.data_folder
classifier_type = args.classifier_type
if args.task == "sm":
aT.extract_features_and_train(
[root_data_path + "SM/speech", root_data_path + "SM/music"], 1.0,
1.0, 0.05, 0.05, classifier_type, classifier_type + "_sm", False)
elif args.task == "movie8":
aT.extract_features_and_train([
root_data_path + "movieSegments/8-class/Speech",
root_data_path + "movieSegments/8-class/Music",
root_data_path + "movieSegments/8-class/Others1",
root_data_path + "movieSegments/8-class/Others2",
root_data_path + "movieSegments/8-class/Others3",
root_data_path + "movieSegments/8-class/Shots",
root_data_path + "movieSegments/8-class/Fights",
root_data_path + "movieSegments/8-class/Screams"
], 1.0, 1.0, 0.05, 0.05, classifier_type,
classifier_type + "_movie8class", False)
elif args.task == "speakers":
aT.extract_features_and_train([
aT.extract_features_and_train([
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 1\\Agressive\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 1\\Boisterous\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 1\\Rowdy\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 1\\Volatile\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 2\\Confident\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 2\\Fiery\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 2\\Intense\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 2\\Passionate\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 2\\Rousing\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 3\\Autumnal\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 3\\Brooding\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 3\\Literate\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 4\\Bittersweet\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 4\\Poignant\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 4\\Wistful\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 5\\Campy\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 5\\whimsical\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 5\\Wry\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 6\\Cheerful\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 6\\Rollicking\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 7\\Fun\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 7\\Humorous\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 7\\Silly\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 7\\Witty\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 8\\Amiable-good natured\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 8\\Sweet\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 9\\Tense - Anxious\\",
"C:\\Users\\asus\\Desktop\\MIREX\\dataset\\Audio\\Cluster 9\\Visceral\\",
], midTerm, midTerm / 4, shortTerm, shortTerm / 4, "extratrees",
"ET_Vybhav_9-July", True)
# Moves all files to temporary directory
os.mkdir("tempTrain")
regions = glob.glob("train/*")
for region in regions:
states = glob.glob(region + "/*")
os.mkdir("tempTrain" + region[region.find("/"):])
for state in states:
paths = glob.glob(state + "/*")
for path in paths:
shutil.move(path, "tempTrain" + region[region.find("/"):])
# Gets list of directories to be trained on
dirs = ["tempTrain/" + directory for directory in os.listdir("tempTrain")]
# Trains model using randomforest
aT.extract_features_and_train(dirs, 1.0, 1.0, 0.1, 0.1, "randomforest", "model", False)
# Moves all files back to original directory
regions = glob.glob("tempTrain/*")
for region in regions:
paths = glob.glob(region + "/*")
for path in paths:
end = path.find(" ")
if not path[end+1:end+2].isdigit():
end = path[end+1:].find(" ") + end + 1
if end != -1:
shutil.move(path, "train" + path[path.find("/"):end])
# Removes temporary directory
regions = glob.glob("tempTrain/*")
for region in regions:
import os
data_dir = 'C:/MachineLearningPractice/emovo/EMOVOdata'
emotion_dirs = [x[0] for x in os.walk(data_dir)]
print(emotion_dirs[1:])
from pyAudioAnalysis import audioTrainTest as aT
aT.extract_features_and_train(emotion_dirs[1:],
1.0,
1.0,
aT.shortTermWindow,
aT.shortTermStep,
"svm",
"svmSMtemp",
False,
train_percentage=0.80)
aT.file_classification(
'C:/MachineLearningPractice/emovo/EMOVOdata/sadness/tri-f1-b1.wav',
"svmSMtemp", "svm")
# Sample code for model training.
from pyAudioAnalysis import audioTrainTest as aT
class1_dir_name = './audio/pyaudio_train/male'
class2_dir_name = './audio/pyaudio_train/female'
# aT.extract_features_and_train([class1_dir_name, class2_dir_name], 1.0, 1.0, aT.shortTermWindow, aT.shortTermStep, 'svm', 'svmMaleFemale', False)
# aT.extract_features_and_train([class1_dir_name, class2_dir_name], 1.0, 1.0, aT.shortTermWindow, aT.shortTermStep, 'knn', 'knnMaleFemale', False)
# aT.extract_features_and_train([class1_dir_name, class2_dir_name], 1.0, 1.0, aT.shortTermWindow, aT.shortTermStep, 'extratrees', 'extraTreesMaleFemale', False)
aT.extract_features_and_train([class1_dir_name, class2_dir_name], 1.0, 1.0,
aT.shortTermWindow, aT.shortTermStep,
'gradientboosting', 'gradientBoostMaleFemale',
False)
# aT.extract_features_and_train([class1_dir_name, class2_dir_name], 1.0, 1.0, aT.shortTermWindow, aT.shortTermStep, 'randomforest', 'rfMaleFemale', False)
from __future__ import print_function
from pyAudioAnalysis import audioBasicIO
from pyAudioAnalysis import MidTermFeatures
from pyAudioAnalysis import audioTrainTest as aT
from pyAudioAnalysis import audioSegmentation as aS
import matplotlib.pyplot as plt
import argparse
def parseArguments():
parser = argparse.ArgumentParser(prog='PROG')
parser.add_argument('-d' , '--data_folder', nargs=None, default="/Users/tyiannak/ResearchData/Audio Dataset/pyAudioAnalysisData/")
parser.add_argument('-c' , '--classifier_type', nargs=None, required=True,
choices = ["knn", "svm", "svm_rbf", "randomforest", "extratrees", "gradientboosting"],
help="Classifier type")
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parseArguments()
root_data_path = args.data_folder
classifier_type = args.classifier_type
classifier_path = "sm_" + classifier_type
aT.extract_features_and_train([root_data_path + "SM/speech", root_data_path + "SM/music"],
1.0, 1.0, 0.2, 0.2, classifier_type,
classifier_path, False)
shortTerm = 0.0625
aT.extract_features_and_train(["D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 1\\Agressive\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 1\\Boisterous\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 1\\Rowdy\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 1\\Volatile\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 2\\Confident\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 2\\Fiery\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 2\\Intense\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 2\\Passionate\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 2\\Rousing\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 3\\Autumnal\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 3\\Brooding\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 3\\Literate\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 4\\Bittersweet\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 4\\Poignant\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 4\\Wistful\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 5\\Campy\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 5\\whimsical\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 5\\Wry\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 6\\Cheerful\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 6\\Rollicking\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 7\\Fun\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 7\\Humorous\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 7\\Silly\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 7\\Witty\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 8\\Amiable-good natured\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 8\\Sweet\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 9\\Tense - Anxious\\",
"D:\\Capstone\\MIREX-like_mood\\dataset\\Audio\\Cluster 9\\Visceral\\,"],
midTerm, midTerm/4, shortTerm, shortTerm/4, "randomforest", "RF_test", True)
"""!
@brief Example 31A
@details: Train a speech-music classifier
@author Theodoros Giannakopoulos {[email protected]}
"""
from pyAudioAnalysis.audioTrainTest import extract_features_and_train
if __name__ == '__main__':
mt = 1.0
st = 0.05
dir_paths = ["../data/speech_music/speech", "../data/speech_music/music"]
extract_features_and_train(dir_paths, mt, mt, st, st,
"svm_rbf", "svm_speech_music")
from pyAudioAnalysis import audioTrainTest as aT
midTerm = 3
shortTerm = 0.0625
aT.extract_features_and_train([
"C:\\Users\\asus\\Desktop\\10sec\\Sad\\",
"C:\\Users\\asus\\Desktop\\10sec\\Devotional\\",
"C:\\Users\\asus\\Desktop\\10sec\\Happy\\",
"C:\\Users\\asus\\Desktop\\10sec\\Party\\",
"C:\\Users\\asus\\Desktop\\10sec\\Romantic\\",
],
midTerm,
midTerm / 4,
shortTerm,
shortTerm / 4,
"extratrees",
"ET_5M_15-July",
compute_beat=True,
train_percentage=1.0)
print(
"******************************************************************************************************************************"
)
print(
"******************************************************************************************************************************"
)
print(
"******************************************************************************************************************************"
)
print(
"******************************************************************************************************************************"
)
print(
# Make sure to change the path
# This will generate 3 files, make sure u dont delete them as they will come in handy
# MAKE LOGS
from pyAudioAnalysis import audioTrainTest as aT
#For Vedansh
aT.extract_features_and_train([
"C:/Users/asus/Desktop/GTZAN/pop/", "C:/Users/asus/Desktop/GTZAN/country/",
"C:/Users/asus/Desktop/GTZAN/blues/", "C:/Users/asus/Desktop/GTZAN/disco/",
"C:/Users/asus/Desktop/GTZAN/jazz/",
"C:/Users/asus/Desktop/GTZAN/classical/",
"C:/Users/asus/Desktop/GTZAN/hiphop/",
"C:/Users/asus/Desktop/GTZAN/metal/",
"C:/Users/asus/Desktop/GTZAN/reggae/", "C:/Users/asus/Desktop/GTZAN/rock/"
], 1.0, 0.5, aT.shortTermWindow, aT.shortTermStep, "svm", "SVM_Linear", True)
#For Arman
aT.extract_features_and_train([
"C:/Users/asus/Desktop/GTZAN/pop/", "C:/Users/asus/Desktop/GTZAN/country/",
"C:/Users/asus/Desktop/GTZAN/blues/", "C:/Users/asus/Desktop/GTZAN/disco/",
"C:/Users/asus/Desktop/GTZAN/jazz/",
"C:/Users/asus/Desktop/GTZAN/classical/",
"C:/Users/asus/Desktop/GTZAN/hiphop/",
"C:/Users/asus/Desktop/GTZAN/metal/",
"C:/Users/asus/Desktop/GTZAN/reggae/", "C:/Users/asus/Desktop/GTZAN/rock/"
], 1.25, 0.6, aT.shortTermWindow, aT.shortTermStep, "knn", "knn_k=3", True)
# I'm gonna train you up, honey!
from pyAudioAnalysis import audioTrainTest as aT
class_dirs = [
# "./wernicke_server_training/burp/",
"./wernicke_server_training/dog/",
"./wernicke_server_training/lover/",
# "./wernicke_server_training/lyriq/",
"./wernicke_server_training/other/",
"./wernicke_server_training/roxy/",
"./wernicke_server_training/sex/",
]
aT.extract_features_and_train(class_dirs, 1.0, 1.0, aT.shortTermWindow,
aT.shortTermStep, "svm", "wernicke_server_model")
from pyAudioAnalysis import audioTrainTest as aT
import numpy as np
import os
'''
subdirectories = ['D:\Audio_Speech_Actors_01-24\Angry',
'D:\Audio_Speech_Actors_01-24\Calm',
'D:\Audio_Speech_Actors_01-24\Disgust',
'D:\Audio_Speech_Actors_01-24\Fearful',
'D:\Audio_Speech_Actors_01-24\Happy',
'D:\Audio_Speech_Actors_01-24\\Neutral',
'D:\Audio_Speech_Actors_01-24\Sad',
'D:\Audio_Speech_Actors_01-24\Surprised']
aT.extract_features_and_train(subdirectories,
1.0, 1.0,
aT.shortTermWindow,
aT.shortTermStep,
"svm", "svmModel", False)
aT.extract_features_and_train(subdirectories,
1.0, 1.0,
aT.shortTermWindow,
aT.shortTermStep,
"knn", "knnModel", False)
aT.extract_features_and_train(subdirectories,
1.0, 1.0,
aT.shortTermWindow,
aT.shortTermStep,
"randomforest", "randomforestModel", False)
'''
Result, P, classNames = aT.file_classification(
from pyAudioAnalysis import audioTrainTest as aT
coyote_data_path = "/Users/2020shatgiskessell/Downloads/coyote_howl_dataset/macaulay_library_audio/split_audio_coyote"
not_coyote_data_path = "/Users/2020shatgiskessell/Downloads/coyote_howl_dataset/macaulay_library_audio/split_audio_bg"
aT.extract_features_and_train([coyote_data_path,not_coyote_data_path], 1.0, 1.0, aT.shortTermWindow, aT.shortTermStep, "svm", "svmSMtemp", False)
# import librosa
# import os
# from scipy.fftpack import fft
#
#
# failures = 0
# for filename in os.listdir("/Users/2020shatgiskessell/Downloads/coyote_howl_dataset/macaulay_library_audio/split_audio_coyote/"):
# try:
# x, sample_rate = librosa.load(filename, sr=None)
# except FileNotFoundError:
# pass
#
# # #train fft
# # X = fft(x, n_fft)
# # X_magnitude, X_phase = librosa.magphase(X)
# # X_magnitude_db = librosa.amplitude_to_db(X_magnitude)
# # # duration = librosa.get_duration(y=clip, sr=sample_rate)
# # # clip = clip[:44,100*duration]
#
# #perform short time fourier transformation (converts audio in time domain to frequency domain)
print("\n\n\n * * * TEST 2 * * * \n\n\n")
[Fs, x] = audioBasicIO.read_audio_file(root_data_path +
"pyAudioAnalysis/data/doremi.wav")
x = audioBasicIO.stereo_to_mono(x)
specgram, TimeAxis, FreqAxis = ShortTermFeatures.spectrogram(
x, Fs, round(Fs * 0.040), round(Fs * 0.040), True)
print("\n\n\n * * * TEST 3 * * * \n\n\n")
[Fs, x] = audioBasicIO.read_audio_file(root_data_path +
"pyAudioAnalysis/data/doremi.wav")
x = audioBasicIO.stereo_to_mono(x)
specgram, TimeAxis, FreqAxis = ShortTermFeatures.chromagram(
x, Fs, round(Fs * 0.040), round(Fs * 0.040), True)
print("\n\n\n * * * TEST 4 * * * \n\n\n")
aT.extract_features_and_train([root_data_path + "1/", root_data_path + "2/"],
1.0, 1.0, 0.2, 0.2, "svm", "temp", True)
print("\n\n\n * * * TEST 5 * * * \n\n\n")
[flagsInd, classesAll, acc, CM] = aS.mid_term_file_classification(
root_data_path + "scottish.wav", root_data_path + "models/svm_rbf_sm",
"svm_rbf", True, root_data_path + 'pyAudioAnalysis/data/scottish.segments')
print("\n\n\n * * * TEST 6 * * * \n\n\n")
aS.train_hmm_from_file(root_data_path + 'radioFinal/train/bbc4A.wav',
root_data_path + 'radioFinal/train/bbc4A.segments',
'hmmTemp1', 1.0, 1.0)
aS.train_hmm_from_directory(root_data_path + 'radioFinal/small', 'hmmTemp2',
1.0, 1.0)
aS.hmm_segmentation(root_data_path + 'pyAudioAnalysis/data//scottish.wav',
'hmmTemp1', True, root_data_path +
'pyAudioAnalysis/data//scottish.segments') # test 1
from pyAudioAnalysis import audioTrainTest as aT
paths = ['C:/Users/MADHUKAR/Desktop/Capstone/samples/GTZAN/blues']
fs = 44100
mid_window = fs
mid_step = 0.5 * fs
short_window = 0.050 * fs
short_step = 0.025 * fs
classifier_type = "knn"
model_name = "knn_train"
aT.extract_features_and_train(paths,
mid_window,
mid_step,
short_window,
short_step,
classifier_type,
model_name,
compute_beat=False,
train_percentage=0.90)
"""!
@brief Example 20_roc
@details General sound classification example, with focus on ROC diagrams
@author Theodoros Giannakopoulos {[email protected]}
"""
from pyAudioAnalysis.audioTrainTest import extract_features_and_train
from pyAudioAnalysis.audioTrainTest import evaluate_model_for_folders
import os
if __name__ == '__main__':
dirs = [
"../data/general/train/animals", "../data/general/train/speech",
"../data/general/train/objects", "../data/general/train/music"
]
class_names = [os.path.basename(d) for d in dirs]
mw, stw = 2, .1
extract_features_and_train(dirs, mw, mw, stw, stw, "svm_rbf",
"svm_general_4")
dirs_test = [
"../data/general/test/animals", "../data/general/test/speech",
"../data/general/test/objects", "../data/general/test/music"
]
evaluate_model_for_folders(dirs_test, "svm_general_4", "svm_rbf",
"animals")
from pyAudioAnalysis import audioTrainTest as aT
from pathlib import Path
directory_in_str = "emotionData/Big4"
pathlist = Path(directory_in_str).glob('**/*.wav')
listOfDirs = []
for path in pathlist:
path_in_str = str(path)
parent = path.parents[0]
if str(parent) not in listOfDirs:
listOfDirs.append(str(parent))
mtWin = 1.0
mtStep = 1.0
stWin = aT.shortTermWindow
stStep = aT.shortTermStep
classifierType = "extratrees"
modelName = "emotionModels/et_big4_1.0"
beat = True
aT.extract_features_and_train(listOfDirs, mtWin, mtStep, stWin, stStep, classifierType, modelName, beat)
# Example6: use pyAudioAnalysis wrapper
# to extract feature and train SVM classifier
# for 20 music (10 classical/10 metal) song samples
from pyAudioAnalysis.audioTrainTest import extract_features_and_train
mt, st = 1.0, 0.05
dirs = ["data/male", "data/female"]
extract_features_and_train(dirs, mt, mt, st, st, "svm_rbf",
"models/svm_male_female")
