-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathEmotionClassify.py
105 lines (84 loc) · 3.63 KB
/
EmotionClassify.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
import librosa.display
import numpy as np
from keras.models import model_from_json
import pandas as pd
import librosa
from sklearn.preprocessing import LabelEncoder
from pydub import AudioSegment
from pydub.silence import split_on_silence
loaded_model = None
def splitAudio(audio_path):
sound = AudioSegment.from_mp3(audio_path)
# loudness = sound.dBFS
# print(loudness)
chunks = split_on_silence(sound,
# must be silent for at least three a second,
min_silence_len=1000,
# consider it silent if quieter than -16 dBFS
silence_thresh=-46,
keep_silence=1500
)
print('The number of segments：', len(chunks))
# Abandon the segments whose length < 3s
index = 0
fileStr = audio_path.split('.')
segments_lengths=[]
for i in range(len(chunks)):
if len(chunks[i]) >= 2750 and len(chunks[i]) <= 10000:
chunks[i].export(fileStr[0] + "_" + str(index) + "." + fileStr[1], bitrate='192k', format="wav")
segments_lengths.append(len(chunks[i])-750)
index += 1
print('The number of valid segments：', index)
return segments_lengths
# return chunks
def loadModel(model_path):
json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
global loaded_model
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights(model_path)
loaded_model.predict(np.zeros((1,216,1)), batch_size=32,verbose=1)
print("Loaded model from disk")
def classifyEmotion(path):
lb = LabelEncoder()
feeling_list = ['female_angry', 'female_calm', 'female_fearful', 'female_happy', 'female_sad', 'male_angry',
'male_calm', 'male_fearful', 'male_happy', 'male_sad']
labels = pd.DataFrame(feeling_list)
lb.fit_transform(labels)
X, sample_rate = librosa.load(path, res_type='kaiser_fast', duration=2.5, sr=22050 * 2, offset=0.25)
sample_rate = np.array(sample_rate)
livedf2 = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=13), axis=0)
# featurelive = mfccs
# livedf2 = featurelive
livedf2 = pd.DataFrame(data=livedf2)
livedf2 = livedf2.stack().to_frame().T
twodim = np.expand_dims(livedf2, axis=2)
livepreds = loaded_model.predict(twodim,
batch_size=32,
verbose=1)
livepreds1 = livepreds.argmax(axis=1)
liveabc = livepreds1.astype(int).flatten()
livepredictions = (lb.inverse_transform((liveabc)))
return livepredictions[0]
def getEmotions(audio_path):
segments_lengths = splitAudio(audio_path)
fileStr = audio_path.split('.')
result = []
# loaded_model = loadModel(model_path="saved_models/Emotion_Voice_Detection_Model.h5")
# print(classifyEmotion(path=audio_path,loaded_model=loaded_model))
time_start=0
for i in range(len(segments_lengths)):
path = fileStr[0] + "_" + str(i) + "." + fileStr[1]
emotion = classifyEmotion(path=path)
print(fileStr[0] + "_" + str(i) + "." + fileStr[1], emotion)
result.append([time_start,time_start+segments_lengths[i],emotion])
time_start=time_start+segments_lengths[i]
# print(fileStr[0]+"_"+str(i)+"."+fileStr[1],classifyEmotion(path='03-01-01-01-01-01-03.wav',loaded_model=loaded_model))
return result
audio_path = 'TestAudio/test_4.wav'
# audio_path ='03-01-01-01-01-01-03.wav'
# audio_path ='output10.wav'
# print(getEmotions(audio_path))
# print(liveabc)