def trainRegressionWrapper(method, beat_feats, dirName, model_name):
aT.feature_extraction_train_regression(dirName,
1,
1,
aT.shortTermWindow,
aT.shortTermStep,
method.lower(),
model_name,
compute_beat=beat_feats)
def auto_test_regression(path, mode_path, mode_type, answer_name):
path_v1 = os.path.join(path, "*")
path_list = glob.glob(path_v1)
path_list.pop(0)
wav_file_list = [[] for i in range(len(path_list))]
f = open(answer_name + ".txt", 'w')
# for num, i in enumerate(path_list):
# for string in step_params.container().get_music_type():
# x = os.path.join(i, string)
# y = glob.glob(x)
# wav_file_list[num].extend(y)
audioTrainTest.feature_extraction_train_regression(path_list[0], 10, 10,
0.2, 0, 2, mode_type,
"svm1")
f.close()
from pyAudioAnalysis import ShortTermFeatures as stf
from pyAudioAnalysis import audioSegmentation
import pyAudioAnalysis
import sounddevice as sd
from scipy.io.wavfile import write
from pyAudioAnalysis import audioTrainTest as aT
import boto3
import time
fs = 44100 # Sample rate
seconds = 30 # Duration of recording It likes long recordings better for speaker diarization
result = "whatever you want, I don't know yet"
aT.feature_extraction_train_regression("data/speechEmotion/", 1, 1,
aT.shortTermWindow, aT.shortTermStep,
"svm", "data/svmSpeechEmotion", False)
def GenrePicker(valence, arousal, people):
print(valence, arousal, people)
if people <= 3:
if valence > 0.25: #Very Happy
if arousal > 0.25: #very Excited
return ("Pop")
elif arousal > -0.25: #neutral Exitement
return ("Hip-Hop")
else:
return ("Lofi Beats") #Low Energy
elif valence > -0.25: #Neutral Happy
if arousal > 0.25:
'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
aS.hmm_segmentation(root_data_path + 'pyAudioAnalysis/data//scottish.wav',
'hmmTemp2', True, root_data_path +
'pyAudioAnalysis/data//scottish.segments') # test 2
print("\n\n\n * * * TEST 7 * * * \n\n\n")
aT.feature_extraction_train_regression(root_data_path +
"pyAudioAnalysis/data/speechEmotion",
1,
1,
0.050,
0.050,
"svm_rbf",
"temp.mod",
compute_beat=False)
print(
aT.file_regression(
root_data_path + "pyAudioAnalysis/data/speechEmotion/01.wav",
"temp.mod", "svm_rbf"))
print("\n\n\n * * * TEST 8 * * * \n\n\n")
aT.feature_extraction_train_regression(root_data_path +
"pyAudioAnalysis/data/speechEmotion",
1,
1,
0.050,