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app.py
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app.py
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import streamlit as st
from audio_feature.audio_featurizer import audio_process, spectrogram_plot, waveplot,lineplot
import numpy as np
import pandas as pd
from pydub import AudioSegment
import os
import pickle
import librosa
import seaborn as sns
import soundfile as sf
from sklearn.svm import SVC
import matplotlib.pyplot as plt
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
#
st.sidebar.markdown(
"""<style>body {background-color: #2C3454; background-image: url('https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRQm90dM3ljhQR0DCcqD8YfPbs2PmKmM2StfA&usqp=CAU'); color:white;}</style><body></body>""",
unsafe_allow_html=True)
st.markdown(
"""<h1 style='text-align: center; color: white;font-size:60px;margin-top:-50px;'>AUDIO EMOTION CLASSIFIER</h1><h1 style='text-align: center; color: white;font-size:30px;margin-top:-30px;'>Using Machine Learning</h1>""",
unsafe_allow_html=True)
check = st.sidebar.checkbox('Do you want a demo')
if check:
rad_test = st.sidebar.radio("Select format of audio file", options=['wav'])
if rad_test == "wav":
rad_file = st.sidebar.radio("Select the Audio Type", ["Angry", "Happy","Sad", "Neutral","Abuse","Non-Abusive"])
if rad_file == "Angry":
rad = st.sidebar.radio("Choose Options", options=["Predict", "Feature-Plots"])
st.audio("Angry.wav")
# rad = st.sidebar.checkbox(label="Do You want to see the spectrogram ?")
if rad == "Predict":
if st.button("Classify Audio"):
def extract_features(x, path):
X, sample_rate = sf.read(path, dtype='float32')
# Mcc
Mcc = librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=47)
Mcc = np.mean(Mcc.T, axis=0)
# chroma_stft
chroma_stft = librosa.feature.chroma_stft(
y=X, sr=sample_rate, n_chroma=12, n_fft=4096)
chroma_stft = np.mean(chroma_stft.T, axis=0)
# chroma_cqt
chroma_cqt = librosa.feature.spectral_bandwidth(y=X, sr=sample_rate)
chroma_cqt = np.mean(chroma_cqt.T, axis=0)
# tonnetz
#tonnetz = librosa.feature.tonnetz(y=librosa.effects.harmonic(X), sr=sample_rate, chroma=chroma_cqt)
# melspectrogram
melspectrogram = librosa.feature.melspectrogram(y=X, sr=sample_rate)
melspectrogram = np.mean(melspectrogram.T, axis=0)
# spectral_centroid
spectral_centroid = librosa.feature.spectral_centroid(y=X, sr=sample_rate)
spectral_centroid = np.mean(spectral_centroid.T, axis=0)
# spectral_contrast
spectral_contrast = librosa.feature.spectral_contrast(y=X, sr=sample_rate)
spectral_contrast = np.mean(spectral_contrast.T, axis=0)
feature = np.hstack((Mcc, chroma_stft, chroma_cqt,
melspectrogram, spectral_centroid, spectral_contrast))
x.append(feature)
def LabelEncoder(arr, le):
le.fit(arr)
print(le.classes_)
Y = le.fit_transform(arr)
return Y
Y = np.array(['Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'])
le = preprocessing.LabelEncoder()
Y = LabelEncoder(Y, le)
print(type(Y))
model_SVC = pickle.load(open('SVC.sav', 'rb'))
X = []
path = "Angry.wav"
extract_features(X, path)
y_pre = model_SVC.predict(X)
le.inverse_transform(y_pre)
print(model_SVC.predict_proba(X))
a = model_SVC.predict_proba(X)
z = ['Angry', 'Happy', 'Neutral', 'Sad']
print(z[np.argmax(a)])
st.markdown(
f"""<h1 style='color:yellow;'>Prediction : <span style='color:white;'>{z[np.argmax(a)]}</span></h1>""",
unsafe_allow_html=True)
elif rad == "Feature-Plots":
fig = spectrogram_plot("Angry.wav")
st.set_option('deprecation.showPyplotGlobalUse', False)
st.markdown(
f"""<h1 style='color:yellow;'>Spectrogram : </h1>""",
unsafe_allow_html=True)
st.pyplot(fig)
fig = waveplot("Angry.wav")
st.set_option('deprecation.showPyplotGlobalUse', False)
st.markdown(
f"""<h1 style='color:yellow;'>WavePlot : </h1>""",
unsafe_allow_html=True)
st.pyplot(fig)
fig = lineplot("Angry.wav")
st.set_option('deprecation.showPyplotGlobalUse', False)
st.markdown(
f"""<h1 style='color:yellow;'>LinePlot : </h1>""",
unsafe_allow_html=True)
st.pyplot(fig)
elif rad_file == "Happy":
rad = st.sidebar.radio("Choose Options", options=[
"Predict", "Feature-Plots"])
st.audio("Happy.wav")
# rad = st.sidebar.checkbox(label="Do You want to see the spectrogram ?")
if rad == "Predict":
if st.button("Classify Audio"):
def extract_features(x, path):
X, sample_rate = sf.read(path, dtype='float32')
# Mcc
Mcc = librosa.feature.mfcc(
y=X, sr=sample_rate, n_mfcc=47)
Mcc = np.mean(Mcc.T, axis=0)
# chroma_stft
chroma_stft = librosa.feature.chroma_stft(
y=X, sr=sample_rate, n_chroma=12, n_fft=4096)
chroma_stft = np.mean(chroma_stft.T, axis=0)
# chroma_cqt
chroma_cqt = librosa.feature.spectral_bandwidth(
y=X, sr=sample_rate)
chroma_cqt = np.mean(chroma_cqt.T, axis=0)
# tonnetz
#tonnetz = librosa.feature.tonnetz(y=librosa.effects.harmonic(X), sr=sample_rate, chroma=chroma_cqt)
# melspectrogram
melspectrogram = librosa.feature.melspectrogram(
y=X, sr=sample_rate)
melspectrogram = np.mean(melspectrogram.T, axis=0)
# spectral_centroid
spectral_centroid = librosa.feature.spectral_centroid(
y=X, sr=sample_rate)
spectral_centroid = np.mean(
spectral_centroid.T, axis=0)
# spectral_contrast
spectral_contrast = librosa.feature.spectral_contrast(
y=X, sr=sample_rate)
spectral_contrast = np.mean(
spectral_contrast.T, axis=0)
feature = np.hstack((Mcc, chroma_stft, chroma_cqt,
melspectrogram, spectral_centroid, spectral_contrast))
x.append(feature)
def LabelEncoder(arr, le):
le.fit(arr)
print(le.classes_)
Y = le.fit_transform(arr)
return Y
Y = np.array(['Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'])
le = preprocessing.LabelEncoder()
Y = LabelEncoder(Y, le)
print(type(Y))
model_SVC = pickle.load(open('SVC.sav', 'rb'))
X = []
path = "Happy.wav"
extract_features(X, path)
y_pre = model_SVC.predict(X)
print(model_SVC.predict_proba(X))
a = model_SVC.predict_proba(X)
z = ['Angry', 'Happy', 'Neutral', 'Sad']
print(z[np.argmax(a)])
st.markdown(
f"""<h1 style='color:yellow;'>Prediction : <span style='color:white;'>{z[np.argmax(a)]}</span></h1>""",
unsafe_allow_html=True)
elif rad == "Feature-Plots":
fig = spectrogram_plot("Happy.wav")
st.set_option('deprecation.showPyplotGlobalUse', False)
st.markdown(
f"""<h1 style='color:yellow;'>Spectrogram : </h1>""",
unsafe_allow_html=True)
st.pyplot(fig)
fig = waveplot("Happy.wav")
st.set_option('deprecation.showPyplotGlobalUse', False)
st.markdown(
f"""<h1 style='color:yellow;'>WavePlot : </h1>""",
unsafe_allow_html=True)
st.pyplot(fig)
fig = lineplot("Happy.wav")
st.set_option('deprecation.showPyplotGlobalUse', False)
st.markdown(
f"""<h1 style='color:yellow;'>LinePlot : </h1>""",
unsafe_allow_html=True)
st.pyplot(fig)
elif rad_file == "Sad":
rad = st.sidebar.radio("Choose Options", options=[
"Predict", "Feature-Plots"])
st.audio("Sad.wav")
# rad = st.sidebar.checkbox(label="Do You want to see the spectrogram ?")
if rad == "Predict":
if st.button("Classify Audio"):
def extract_features(x, path):
X, sample_rate = sf.read(path, dtype='float32')
# Mcc
Mcc = librosa.feature.mfcc(
y=X, sr=sample_rate, n_mfcc=47)
Mcc = np.mean(Mcc.T, axis=0)
# chroma_stft
chroma_stft = librosa.feature.chroma_stft(
y=X, sr=sample_rate, n_chroma=12, n_fft=4096)
chroma_stft = np.mean(chroma_stft.T, axis=0)
# chroma_cqt
chroma_cqt = librosa.feature.spectral_bandwidth(
y=X, sr=sample_rate)
chroma_cqt = np.mean(chroma_cqt.T, axis=0)
# tonnetz
#tonnetz = librosa.feature.tonnetz(y=librosa.effects.harmonic(X), sr=sample_rate, chroma=chroma_cqt)
# melspectrogram
melspectrogram = librosa.feature.melspectrogram(
y=X, sr=sample_rate)
melspectrogram = np.mean(melspectrogram.T, axis=0)
# spectral_centroid
spectral_centroid = librosa.feature.spectral_centroid(
y=X, sr=sample_rate)
spectral_centroid = np.mean(
spectral_centroid.T, axis=0)
# spectral_contrast
spectral_contrast = librosa.feature.spectral_contrast(
y=X, sr=sample_rate)
spectral_contrast = np.mean(
spectral_contrast.T, axis=0)
feature = np.hstack((Mcc, chroma_stft, chroma_cqt,
melspectrogram, spectral_centroid, spectral_contrast))
x.append(feature)
def LabelEncoder(arr, le):
le.fit(arr)
print(le.classes_)
Y = le.fit_transform(arr)
return Y
Y = np.array(['Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'])
le = preprocessing.LabelEncoder()
Y = LabelEncoder(Y, le)
print(type(Y))
model_SVC = pickle.load(open('SVC.sav', 'rb'))
X = []
path = "Sad.wav"
extract_features(X, path)
y_pre = model_SVC.predict(X)
print(model_SVC.predict_proba(X))
a = model_SVC.predict_proba(X)
z = ['Angry', 'Happy', 'Neutral', 'Sad']
print(z[np.argmax(a)])
st.markdown(
f"""<h1 style='color:yellow;'>Prediction : <span style='color:white;'>{z[np.argmax(a)]}</span></h1>""",
unsafe_allow_html=True)
elif rad == "Feature-Plots":
fig = spectrogram_plot("Sad.wav")
st.set_option('deprecation.showPyplotGlobalUse', False)
st.markdown(
f"""<h1 style='color:yellow;'>Spectrogram : </h1>""",
unsafe_allow_html=True)
st.pyplot(fig)
fig = waveplot("Sad.wav")
st.set_option('deprecation.showPyplotGlobalUse', False)
st.markdown(
f"""<h1 style='color:yellow;'>WavePlot : </h1>""",
unsafe_allow_html=True)
st.pyplot(fig)
fig = lineplot("Sad.wav")
st.set_option('deprecation.showPyplotGlobalUse', False)
st.markdown(
f"""<h1 style='color:yellow;'>LinePlot : </h1>""",
unsafe_allow_html=True)
st.pyplot(fig)
elif rad_file == "Neutral":
rad = st.sidebar.radio("Choose Options", options=[
"Predict", "Feature-Plots"])
st.audio("Neutral.wav")
# rad = st.sidebar.checkbox(label="Do You want to see the spectrogram ?")
if rad == "Predict":
if st.button("Classify Audio"):
def extract_features(x, path):
X, sample_rate = sf.read(path, dtype='float32')
# Mcc
Mcc = librosa.feature.mfcc(
y=X, sr=sample_rate, n_mfcc=47)
Mcc = np.mean(Mcc.T, axis=0)
# chroma_stft
chroma_stft = librosa.feature.chroma_stft(
y=X, sr=sample_rate, n_chroma=12, n_fft=4096)
chroma_stft = np.mean(chroma_stft.T, axis=0)
# chroma_cqt
chroma_cqt = librosa.feature.spectral_bandwidth(
y=X, sr=sample_rate)
chroma_cqt = np.mean(chroma_cqt.T, axis=0)
# tonnetz
#tonnetz = librosa.feature.tonnetz(y=librosa.effects.harmonic(X), sr=sample_rate, chroma=chroma_cqt)
# melspectrogram
melspectrogram = librosa.feature.melspectrogram(
y=X, sr=sample_rate)
melspectrogram = np.mean(melspectrogram.T, axis=0)
# spectral_centroid
spectral_centroid = librosa.feature.spectral_centroid(
y=X, sr=sample_rate)
spectral_centroid = np.mean(
spectral_centroid.T, axis=0)
# spectral_contrast
spectral_contrast = librosa.feature.spectral_contrast(
y=X, sr=sample_rate)
spectral_contrast = np.mean(
spectral_contrast.T, axis=0)
feature = np.hstack((Mcc, chroma_stft, chroma_cqt,
melspectrogram, spectral_centroid, spectral_contrast))
x.append(feature)
def LabelEncoder(arr, le):
le.fit(arr)
print(le.classes_)
Y = le.fit_transform(arr)
return Y
Y = np.array(['Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry' 'Angry'
'Angry' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy' 'Happy'
'Happy' 'Happy' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Neutral'
'Neutral' 'Neutral' 'Neutral' 'Neutral' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'
'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad' 'Sad'])
le = preprocessing.LabelEncoder()
Y = LabelEncoder(Y, le)
print(type(Y))
model_SVC = pickle.load(open('SVC.sav', 'rb'))
X = []
path = "Neutral.wav"
extract_features(X, path)
y_pre = model_SVC.predict(X)
print(model_SVC.predict_proba(X))
a = model_SVC.predict_proba(X)
z = ['Angry', 'Happy', 'Neutral', 'Sad']
print(z[np.argmax(a)])
st.markdown(
f"""<h1 style='color:yellow;'>Prediction : <span style='color:white;'>{z[np.argmax(a)]}</span></h1>""",
unsafe_allow_html=True)
elif rad == "Feature-Plots":
fig = spectrogram_plot("Neutral.wav")
st.set_option('deprecation.showPyplotGlobalUse', False)
st.markdown(
f"""<h1 style='color:yellow;'>Spectrogram : </h1>""",
unsafe_allow_html=True)
st.pyplot(fig)
fig = waveplot("Neutral.wav")
st.set_option('deprecation.showPyplotGlobalUse', False)
st.markdown(
f"""<h1 style='color:yellow;'>WavePlot : </h1>""",
unsafe_allow_html=True)
st.pyplot(fig)
fig = lineplot("Neutral.wav")
st.set_option('deprecation.showPyplotGlobalUse', False)
st.markdown(
f"""<h1 style='color:yellow;'>LinePlot : </h1>""",
unsafe_allow_html=True)
st.pyplot(fig)
if rad_file == "Abuse":
rad = st.sidebar.radio("Choose Options", options=[
"Predict", "Feature-Plots"])
st.audio("Abuse.wav")
# rad = st.sidebar.checkbox(label="Do You want to see the spectrogram ?")
if rad == "Predict":
if st.button("Classify Audio"):
def extract_features(x, path):
X, sample_rate = sf.read(path, dtype='float32')
# Mcc
Mcc = librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=47)
Mcc = np.mean(Mcc.T, axis=0)
# chroma_stft
chroma_stft = librosa.feature.chroma_stft(
y=X, sr=sample_rate, n_chroma=12, n_fft=4096)
chroma_stft = np.mean(chroma_stft.T, axis=0)
# chroma_cqt
chroma_cqt = librosa.feature.spectral_bandwidth(y=X, sr=sample_rate)
chroma_cqt = np.mean(chroma_cqt.T, axis=0)
# tonnetz
tonnetz = librosa.feature.tonnetz(
y=librosa.effects.harmonic(X), sr=sample_rate, chroma=chroma_cqt)
# melspectrogram
melspectrogram = librosa.feature.melspectrogram(y=X, sr=sample_rate)
melspectrogram = np.mean(melspectrogram.T, axis=0)
# spectral_centroid
spectral_centroid = librosa.feature.spectral_centroid(y=X, sr=sample_rate)
spectral_centroid = np.mean(spectral_centroid.T, axis=0)
# spectral_contrast
spectral_contrast = librosa.feature.spectral_contrast(y=X, sr=sample_rate)
spectral_contrast = np.mean(spectral_contrast.T, axis=0)
feature = np.hstack((Mcc, chroma_stft, chroma_cqt, melspectrogram,
spectral_centroid, spectral_contrast, tonnetz))
x.append(feature)
def LabelEncoder(arr, le):
le.fit(arr)
print(le.classes_)
Y = le.fit_transform(arr)
return Y
Y = np.array(['No' 'Yes' 'Yes' 'No' 'Yes' 'Yes' 'Yes' 'Yes' 'Yes' 'Yes' 'No' 'No' 'Yes'
'No' 'No' 'Yes' 'Yes' 'Yes' 'No' 'No' 'Yes' 'No' 'No' 'Yes' 'No' 'No'
'No' 'No' 'Yes' 'Yes' 'Yes' 'No' 'Yes' 'No' 'No' 'Yes' 'Yes' 'No' 'No'
'No' 'Yes' 'No' 'No' 'No' 'Yes' 'Yes' 'No' 'No' 'No' 'Yes' 'Yes' 'No'
'No' 'No' 'No' 'Yes' 'Yes' 'No' 'Yes' 'No' 'No' 'Yes' 'No' 'No' 'No' 'No'
'Yes' 'Yes' 'Yes' 'Yes' 'No' 'No' 'Yes' 'Yes' 'Yes' 'Yes' 'Yes' 'No' 'No'
'Yes' 'No' 'Yes' 'No' 'No' 'No' 'Yes' 'No' 'No' 'No' 'Yes' 'No' 'No'
'Yes' 'No' 'No' 'Yes' 'No' 'Yes' 'No' 'No' 'No' 'No' 'No' 'No' 'No' 'No'
'No' 'Yes' 'Yes' 'No' 'Yes' 'Yes' 'Yes' 'Yes' 'No' 'No' 'No' 'No' 'No'
'Yes' 'Yes' 'Yes' 'Yes' 'No' 'Yes' 'No' 'No' 'Yes' 'No' 'No' 'No' 'No'
'No' 'No' 'No' 'Yes' 'No' 'No' 'Yes' 'Yes' 'Yes' 'No' 'Yes' 'No' 'No'
'No' 'No' 'Yes' 'Yes' 'Yes' 'No' 'Yes' 'No' 'Yes' 'No' 'No' 'No' 'Yes'
'No' 'Yes' 'No' 'No' 'Yes' 'No' 'No' 'No' 'Yes' 'No' 'Yes' 'No' 'Yes'
'No' 'No' 'Yes' 'No' 'Yes' 'No' 'No' 'Yes' 'Yes' 'Yes' 'Yes' 'No' 'No'
'No' 'No' 'No' 'No' 'No' 'Yes' 'No' 'Yes' 'Yes' 'Yes' 'Yes' 'No' 'No'
'No' 'No' 'No' 'Yes' 'No' 'No' 'Yes' 'No' 'Yes' 'No' 'No' 'No' 'No' 'No'
'Yes' 'Yes' 'No' 'No' 'Yes' 'No' 'No' 'No' 'No' 'No' 'No' 'Yes' 'No' 'No'
'Yes' 'No' 'No' 'No' 'Yes' 'No' 'Yes' 'No' 'No' 'No' 'No' 'No' 'No' 'No'
'No' 'Yes' 'Yes' 'Yes' 'No' 'No' 'Yes' 'Yes' 'No' 'No' 'No' 'Yes' 'Yes'
'Yes' 'Yes' 'No' 'Yes' 'Yes' 'No' 'No' 'Yes' 'No' 'No' 'Yes' 'Yes' 'No'
'Yes' 'No' 'No' 'No' 'Yes' 'No' 'No' 'No' 'No' 'Yes' 'Yes' 'Yes' 'No'
'No' 'Yes' 'No' 'No' 'No' 'No' 'No' 'No' 'No' 'No' 'Yes' 'No' 'Yes' 'No'
'No' 'Yes' 'No' 'Yes' 'No' 'No' 'Yes' 'No' 'No' 'Yes' 'No' 'No' 'Yes'
'Yes' 'Yes' 'No' 'No' 'No' 'Yes' 'Yes' 'No' 'No' 'Yes' 'No' 'Yes' 'No'
'No' 'No' 'No' 'Yes' 'No' 'Yes' 'Yes' 'No' 'No' 'Yes' 'No' 'No' 'No' 'No'
'No' 'No' 'Yes' 'Yes' 'No' 'No' 'No' 'No' 'Yes' 'No' 'No' 'No' 'Yes' 'No'
'Yes' 'Yes' 'No' 'Yes' 'No' 'Yes' 'No' 'Yes' 'No' 'Yes' 'Yes' 'No' 'Yes'
'Yes' 'Yes' 'Yes' 'No' 'No' 'Yes' 'Yes' 'Yes' 'Yes' 'No' 'Yes' 'No' 'No'
'No' 'Yes' 'No' 'No' 'No' 'Yes' 'No' 'No' 'No' 'No' 'No' 'Yes' 'No' 'Yes'
'No' 'No' 'Yes' 'No' 'No' 'Yes' 'Yes' 'No' 'No' 'No' 'Yes' 'Yes' 'Yes'
'No' 'Yes' 'No' 'No' 'No' 'No' 'Yes' 'No' 'No' 'No' 'Yes' 'No' 'No' 'Yes'
'Yes' 'Yes' 'Yes' 'Yes' 'No' 'No' 'No' 'Yes' 'No' 'No' 'No' 'No' 'Yes'
'No' 'No' 'No' 'Yes' 'Yes' 'No' 'Yes' 'No' 'No' 'No' 'No' 'Yes' 'Yes'
'No' 'No' 'No' 'No' 'Yes' 'No' 'Yes' 'Yes' 'Yes' 'Yes' 'Yes' 'No' 'No'
'Yes' 'Yes' 'No' 'Yes' 'Yes' 'Yes' 'Yes' 'No' 'Yes' 'Yes' 'No' 'No' 'Yes'
'Yes' 'No' 'No' 'Yes' 'Yes' 'No' 'No' 'No' 'No' 'No' 'No' 'No' 'Yes'
'Yes' 'Yes' 'Yes' 'No' 'No' 'Yes' 'Yes' 'No' 'No' 'No' 'No' 'No' 'No'
'No' 'Yes' 'Yes' 'Yes' 'No' 'No' 'Yes' 'No' 'No' 'Yes' 'Yes' 'No' 'Yes'
'Yes' 'No' 'Yes' 'No' 'Yes' 'No' 'No' 'No' 'No' 'No' 'No' 'Yes' 'Yes'
'No' 'No' 'No' 'Yes' 'Yes' 'Yes' 'Yes' 'Yes' 'Yes' 'No' 'Yes' 'Yes' 'No'
'No' 'No' 'No' 'No' 'Yes' 'No' 'No' 'No' 'No' 'No' 'Yes' 'No' 'No' 'No'
'Yes' 'No' 'No' 'No' 'No'])
le = preprocessing.LabelEncoder()
Y = LabelEncoder(Y, le)
Y = np.array(['No' 'Yes'])
model = pickle.load(open('SVC_Model_abuse.sav', 'rb'))
X = []
path = "Abuse.wav"
extract_features(X, path)
y_pre = model.predict(X)
if(y_pre==1):
Result = "Abuse Detected"
st.markdown(
f"""<h1 style='color:yellow;'>Prediction : <span style='color:white;'>{Result}</span></h1>""",
unsafe_allow_html=True)
else:
Result = "Abuse Not Detected"
st.markdown(
f"""<h1 style='color:yellow;'>Prediction : <span style='color:white;'>{Result}</span></h1>""",
unsafe_allow_html=True)
elif rad == "Feature-Plots":
fig = spectrogram_plot("Abuse.wav")
st.set_option('deprecation.showPyplotGlobalUse', False)
st.markdown(
f"""<h1 style='color:yellow;'>Spectrogram : </h1>""",
unsafe_allow_html=True)
st.pyplot(fig)
fig = waveplot("Abuse.wav")
st.set_option('deprecation.showPyplotGlobalUse', False)
st.markdown(
f"""<h1 style='color:yellow;'>WavePlot : </h1>""",
unsafe_allow_html=True)
st.pyplot(fig)
fig = lineplot("Abuse.wav")
st.set_option('deprecation.showPyplotGlobalUse', False)
st.markdown(
f"""<h1 style='color:yellow;'>LinePlot : </h1>""",
unsafe_allow_html=True)
st.pyplot(fig)
if rad_file == "Non-Abusive":
rad = st.sidebar.radio("Choose Options", options=[
"Predict", "Feature-Plots"])
st.audio("No-Abuse.wav")
# rad = st.sidebar.checkbox(label="Do You want to see the spectrogram ?")
if rad == "Predict":
if st.button("Classify Audio"):
def extract_features(x, path):
X, sample_rate = sf.read(path, dtype='float32')
# Mcc
Mcc = librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=47)
Mcc = np.mean(Mcc.T, axis=0)
# chroma_stft
chroma_stft = librosa.feature.chroma_stft(
y=X, sr=sample_rate, n_chroma=12, n_fft=4096)
chroma_stft = np.mean(chroma_stft.T, axis=0)
# chroma_cqt
chroma_cqt = librosa.feature.spectral_bandwidth(y=X, sr=sample_rate)
chroma_cqt = np.mean(chroma_cqt.T, axis=0)
# tonnetz
tonnetz = librosa.feature.tonnetz(
y=librosa.effects.harmonic(X), sr=sample_rate, chroma=chroma_cqt)
# melspectrogram
melspectrogram = librosa.feature.melspectrogram(y=X, sr=sample_rate)
melspectrogram = np.mean(melspectrogram.T, axis=0)
# spectral_centroid
spectral_centroid = librosa.feature.spectral_centroid(y=X, sr=sample_rate)
spectral_centroid = np.mean(spectral_centroid.T, axis=0)
# spectral_contrast
spectral_contrast = librosa.feature.spectral_contrast(y=X, sr=sample_rate)
spectral_contrast = np.mean(spectral_contrast.T, axis=0)
feature = np.hstack((Mcc, chroma_stft, chroma_cqt, melspectrogram,
spectral_centroid, spectral_contrast, tonnetz))
x.append(feature)
def LabelEncoder(arr, le):
le.fit(arr)
print(le.classes_)
Y = le.fit_transform(arr)
return Y
Y = np.array(['No' 'Yes' 'Yes' 'No' 'Yes' 'Yes' 'Yes' 'Yes' 'Yes' 'Yes' 'No' 'No' 'Yes'
'No' 'No' 'Yes' 'Yes' 'Yes' 'No' 'No' 'Yes' 'No' 'No' 'Yes' 'No' 'No'
'No' 'No' 'Yes' 'Yes' 'Yes' 'No' 'Yes' 'No' 'No' 'Yes' 'Yes' 'No' 'No'
'No' 'Yes' 'No' 'No' 'No' 'Yes' 'Yes' 'No' 'No' 'No' 'Yes' 'Yes' 'No'
'No' 'No' 'No' 'Yes' 'Yes' 'No' 'Yes' 'No' 'No' 'Yes' 'No' 'No' 'No' 'No'
'Yes' 'Yes' 'Yes' 'Yes' 'No' 'No' 'Yes' 'Yes' 'Yes' 'Yes' 'Yes' 'No' 'No'
'Yes' 'No' 'Yes' 'No' 'No' 'No' 'Yes' 'No' 'No' 'No' 'Yes' 'No' 'No'
'Yes' 'No' 'No' 'Yes' 'No' 'Yes' 'No' 'No' 'No' 'No' 'No' 'No' 'No' 'No'
'No' 'Yes' 'Yes' 'No' 'Yes' 'Yes' 'Yes' 'Yes' 'No' 'No' 'No' 'No' 'No'
'Yes' 'Yes' 'Yes' 'Yes' 'No' 'Yes' 'No' 'No' 'Yes' 'No' 'No' 'No' 'No'
'No' 'No' 'No' 'Yes' 'No' 'No' 'Yes' 'Yes' 'Yes' 'No' 'Yes' 'No' 'No'
'No' 'No' 'Yes' 'Yes' 'Yes' 'No' 'Yes' 'No' 'Yes' 'No' 'No' 'No' 'Yes'
'No' 'Yes' 'No' 'No' 'Yes' 'No' 'No' 'No' 'Yes' 'No' 'Yes' 'No' 'Yes'
'No' 'No' 'Yes' 'No' 'Yes' 'No' 'No' 'Yes' 'Yes' 'Yes' 'Yes' 'No' 'No'
'No' 'No' 'No' 'No' 'No' 'Yes' 'No' 'Yes' 'Yes' 'Yes' 'Yes' 'No' 'No'
'No' 'No' 'No' 'Yes' 'No' 'No' 'Yes' 'No' 'Yes' 'No' 'No' 'No' 'No' 'No'
'Yes' 'Yes' 'No' 'No' 'Yes' 'No' 'No' 'No' 'No' 'No' 'No' 'Yes' 'No' 'No'
'Yes' 'No' 'No' 'No' 'Yes' 'No' 'Yes' 'No' 'No' 'No' 'No' 'No' 'No' 'No'
'No' 'Yes' 'Yes' 'Yes' 'No' 'No' 'Yes' 'Yes' 'No' 'No' 'No' 'Yes' 'Yes'
'Yes' 'Yes' 'No' 'Yes' 'Yes' 'No' 'No' 'Yes' 'No' 'No' 'Yes' 'Yes' 'No'
'Yes' 'No' 'No' 'No' 'Yes' 'No' 'No' 'No' 'No' 'Yes' 'Yes' 'Yes' 'No'
'No' 'Yes' 'No' 'No' 'No' 'No' 'No' 'No' 'No' 'No' 'Yes' 'No' 'Yes' 'No'
'No' 'Yes' 'No' 'Yes' 'No' 'No' 'Yes' 'No' 'No' 'Yes' 'No' 'No' 'Yes'
'Yes' 'Yes' 'No' 'No' 'No' 'Yes' 'Yes' 'No' 'No' 'Yes' 'No' 'Yes' 'No'
'No' 'No' 'No' 'Yes' 'No' 'Yes' 'Yes' 'No' 'No' 'Yes' 'No' 'No' 'No' 'No'
'No' 'No' 'Yes' 'Yes' 'No' 'No' 'No' 'No' 'Yes' 'No' 'No' 'No' 'Yes' 'No'
'Yes' 'Yes' 'No' 'Yes' 'No' 'Yes' 'No' 'Yes' 'No' 'Yes' 'Yes' 'No' 'Yes'
'Yes' 'Yes' 'Yes' 'No' 'No' 'Yes' 'Yes' 'Yes' 'Yes' 'No' 'Yes' 'No' 'No'
'No' 'Yes' 'No' 'No' 'No' 'Yes' 'No' 'No' 'No' 'No' 'No' 'Yes' 'No' 'Yes'
'No' 'No' 'Yes' 'No' 'No' 'Yes' 'Yes' 'No' 'No' 'No' 'Yes' 'Yes' 'Yes'
'No' 'Yes' 'No' 'No' 'No' 'No' 'Yes' 'No' 'No' 'No' 'Yes' 'No' 'No' 'Yes'
'Yes' 'Yes' 'Yes' 'Yes' 'No' 'No' 'No' 'Yes' 'No' 'No' 'No' 'No' 'Yes'
'No' 'No' 'No' 'Yes' 'Yes' 'No' 'Yes' 'No' 'No' 'No' 'No' 'Yes' 'Yes'
'No' 'No' 'No' 'No' 'Yes' 'No' 'Yes' 'Yes' 'Yes' 'Yes' 'Yes' 'No' 'No'
'Yes' 'Yes' 'No' 'Yes' 'Yes' 'Yes' 'Yes' 'No' 'Yes' 'Yes' 'No' 'No' 'Yes'
'Yes' 'No' 'No' 'Yes' 'Yes' 'No' 'No' 'No' 'No' 'No' 'No' 'No' 'Yes'
'Yes' 'Yes' 'Yes' 'No' 'No' 'Yes' 'Yes' 'No' 'No' 'No' 'No' 'No' 'No'
'No' 'Yes' 'Yes' 'Yes' 'No' 'No' 'Yes' 'No' 'No' 'Yes' 'Yes' 'No' 'Yes'
'Yes' 'No' 'Yes' 'No' 'Yes' 'No' 'No' 'No' 'No' 'No' 'No' 'Yes' 'Yes'
'No' 'No' 'No' 'Yes' 'Yes' 'Yes' 'Yes' 'Yes' 'Yes' 'No' 'Yes' 'Yes' 'No'
'No' 'No' 'No' 'No' 'Yes' 'No' 'No' 'No' 'No' 'No' 'Yes' 'No' 'No' 'No'
'Yes' 'No' 'No' 'No' 'No'])
le = preprocessing.LabelEncoder()
Y = LabelEncoder(Y, le)
Y = np.array(['No' 'Yes'])
model = pickle.load(open('SVC_Model_abuse.sav', 'rb'))
X = []
path = "No-Abuse.wav"
extract_features(X, path)
y_pre = model.predict(X)
if(y_pre==1):
Result = "Abuse Detected"
st.markdown(
f"""<h1 style='color:yellow;'>Prediction : <span style='color:white;'>{Result}</span></h1>""",
unsafe_allow_html=True)
else:
Result = "Abuse Not Detected"
st.markdown(
f"""<h1 style='color:yellow;'>Prediction : <span style='color:white;'>{Result}</span></h1>""",
unsafe_allow_html=True)
elif rad == "Feature-Plots":
fig = spectrogram_plot("No-Abuse.wav")
st.set_option('deprecation.showPyplotGlobalUse', False)
st.markdown(
f"""<h1 style='color:yellow;'>Spectrogram : </h1>""",
unsafe_allow_html=True)
st.pyplot(fig)
fig = waveplot("No-Abuse.wav")
st.set_option('deprecation.showPyplotGlobalUse', False)
st.markdown(
f"""<h1 style='color:yellow;'>WavePlot : </h1>""",
unsafe_allow_html=True)
st.pyplot(fig)
fig = lineplot("No-Abuse.wav")
st.set_option('deprecation.showPyplotGlobalUse', False)
st.markdown(
f"""<h1 style='color:yellow;'>LinePlot : </h1>""",
unsafe_allow_html=True)
st.pyplot(fig)