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app.py
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import numpy as np
import streamlit as st
import cv2
import librosa
import librosa.display
from tensorflow.keras.models import load_model
import os
from datetime import datetime
import streamlit.components.v1 as components
import matplotlib.pyplot as plt
from PIL import Image
from melspec import plot_colored_polar, plot_melspec
# load models
model = load_model("model3.h5")
# constants
starttime = datetime.now()
CAT6 = ['fear', 'angry', 'neutral', 'happy', 'sad', 'surprise']
CAT7 = ['fear', 'disgust', 'neutral', 'happy', 'sad', 'surprise', 'angry']
CAT3 = ["positive", "neutral", "negative"]
COLOR_DICT = {"neutral": "grey",
"positive": "green",
"happy": "green",
"surprise": "orange",
"fear": "purple",
"negative": "red",
"angry": "red",
"sad": "lightblue",
"disgust": "brown"}
TEST_CAT = ['fear', 'disgust', 'neutral', 'happy', 'sad', 'surprise', 'angry']
TEST_PRED = np.array([.3, .3, .4, .1, .6, .9, .1])
# page settings
st.set_page_config(page_title="SER web-app", page_icon=":speech_balloon:", layout="wide")
# COLOR = "#1f1f2e"
# BACKGROUND_COLOR = "#d1d1e0"
# @st.cache(hash_funcs={tf_agents.utils.object_identity.ObjectIdentityDictionary: load_model})
# def load_model_cache(model):
# return load_model(model)
# @st.cache
def log_file(txt=None):
with open("log.txt", "a") as f:
datetoday = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
f.write(f"{txt} - {datetoday};\n")
# @st.cache
def save_audio(file):
if file.size > 4000000:
return 1
# if not os.path.exists("audio"):
# os.makedirs("audio")
folder = "audio"
datetoday = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
# clear the folder to avoid storage overload
for filename in os.listdir(folder):
file_path = os.path.join(folder, filename)
try:
if os.path.isfile(file_path) or os.path.islink(file_path):
os.unlink(file_path)
except Exception as e:
print('Failed to delete %s. Reason: %s' % (file_path, e))
try:
with open("log0.txt", "a") as f:
f.write(f"{file.name} - {file.size} - {datetoday};\n")
except:
pass
with open(os.path.join(folder, file.name), "wb") as f:
f.write(file.getbuffer())
return 0
# @st.cache
def get_melspec(audio):
y, sr = librosa.load(audio, sr=44100)
X = librosa.stft(y)
Xdb = librosa.amplitude_to_db(abs(X))
img = np.stack((Xdb,) * 3, -1)
img = img.astype(np.uint8)
grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
grayImage = cv2.resize(grayImage, (224, 224))
rgbImage = np.repeat(grayImage[..., np.newaxis], 3, -1)
return (rgbImage, Xdb)
# @st.cache
def get_mfccs(audio, limit):
y, sr = librosa.load(audio)
a = librosa.feature.mfcc(y, sr=sr, n_mfcc=40)
if a.shape[1] > limit:
mfccs = a[:, :limit]
elif a.shape[1] < limit:
mfccs = np.zeros((a.shape[0], limit))
mfccs[:, :a.shape[1]] = a
return mfccs
@st.cache
def get_title(predictions, categories=CAT6):
title = f"Detected emotion: {categories[predictions.argmax()]} \
- {predictions.max() * 100:.2f}%"
return title
@st.cache
def color_dict(coldict=COLOR_DICT):
return COLOR_DICT
@st.cache
def plot_polar(fig, predictions=TEST_PRED, categories=TEST_CAT,
title="TEST", colors=COLOR_DICT):
# color_sector = "grey"
N = len(predictions)
ind = predictions.argmax()
COLOR = color_sector = colors[categories[ind]]
theta = np.linspace(0.0, 2 * np.pi, N, endpoint=False)
radii = np.zeros_like(predictions)
radii[predictions.argmax()] = predictions.max() * 10
width = np.pi / 1.8 * predictions
fig.set_facecolor("#d1d1e0")
ax = plt.subplot(111, polar="True")
ax.bar(theta, radii, width=width, bottom=0.0, color=color_sector, alpha=0.25)
angles = [i / float(N) * 2 * np.pi for i in range(N)]
angles += angles[:1]
data = list(predictions)
data += data[:1]
plt.polar(angles, data, color=COLOR, linewidth=2)
plt.fill(angles, data, facecolor=COLOR, alpha=0.25)
ax.spines['polar'].set_color('lightgrey')
ax.set_theta_offset(np.pi / 3)
ax.set_theta_direction(-1)
plt.xticks(angles[:-1], categories)
ax.set_rlabel_position(0)
plt.yticks([0, .25, .5, .75, 1], color="grey", size=8)
plt.suptitle(title, color="darkblue", size=12)
plt.title(f"BIG {N}\n", color=COLOR)
plt.ylim(0, 1)
plt.subplots_adjust(top=0.75)
def main():
side_img = Image.open("images/emotion3.jpg")
with st.sidebar:
st.image(side_img, width=300)
st.sidebar.subheader("Menu")
website_menu = st.sidebar.selectbox("Menu", ("Emotion Recognition", "Project description", "Our team",
"Leave feedback", "Relax"))
st.set_option('deprecation.showfileUploaderEncoding', False)
if website_menu == "Emotion Recognition":
st.sidebar.subheader("Model")
model_type = st.sidebar.selectbox("How would you like to predict?", ("mfccs", "mel-specs"))
em3 = em6 = em7 = gender = False
st.sidebar.subheader("Settings")
st.markdown("## Upload the file")
with st.container():
col1, col2 = st.columns(2)
# audio_file = None
# path = None
with col1:
audio_file = st.file_uploader("Upload audio file", type=['wav', 'mp3', 'ogg'])
if audio_file is not None:
if not os.path.exists("audio"):
os.makedirs("audio")
path = os.path.join("audio", audio_file.name)
if_save_audio = save_audio(audio_file)
if if_save_audio == 1:
st.warning("File size is too large. Try another file.")
elif if_save_audio == 0:
# extract features
# display audio
st.audio(audio_file, format='audio/wav', start_time=0)
try:
wav, sr = librosa.load(path, sr=44100)
Xdb = get_melspec(path)[1]
mfccs = librosa.feature.mfcc(wav, sr=sr)
# # display audio
# st.audio(audio_file, format='audio/wav', start_time=0)
except Exception as e:
audio_file = None
st.error(f"Error {e} - wrong format of the file. Try another .wav file.")
else:
st.error("Unknown error")
else:
if st.button("Try test file"):
wav, sr = librosa.load("test.wav", sr=44100)
Xdb = get_melspec("test.wav")[1]
mfccs = librosa.feature.mfcc(wav, sr=sr)
# display audio
st.audio("test.wav", format='audio/wav', start_time=0)
path = "test.wav"
audio_file = "test"
with col2:
if audio_file is not None:
fig = plt.figure(figsize=(10, 2))
fig.set_facecolor('#d1d1e0')
plt.title("Wave-form")
librosa.display.waveplot(wav, sr=44100)
plt.gca().axes.get_yaxis().set_visible(False)
plt.gca().axes.get_xaxis().set_visible(False)
plt.gca().axes.spines["right"].set_visible(False)
plt.gca().axes.spines["left"].set_visible(False)
plt.gca().axes.spines["top"].set_visible(False)
plt.gca().axes.spines["bottom"].set_visible(False)
plt.gca().axes.set_facecolor('#d1d1e0')
st.write(fig)
else:
pass
# st.write("Record audio file")
# if st.button('Record'):
# with st.spinner(f'Recording for 5 seconds ....'):
# st.write("Recording...")
# time.sleep(3)
# st.success("Recording completed")
# st.write("Error while loading the file")
if model_type == "mfccs":
em3 = st.sidebar.checkbox("3 emotions", True)
em6 = st.sidebar.checkbox("6 emotions", True)
em7 = st.sidebar.checkbox("7 emotions")
gender = st.sidebar.checkbox("gender")
elif model_type == "mel-specs":
st.sidebar.warning("This model is temporarily disabled")
else:
st.sidebar.warning("This model is temporarily disabled")
# with st.sidebar.expander("Change colors"):
# st.sidebar.write("Use this options after you got the plots")
# col1, col2, col3, col4, col5, col6, col7 = st.columns(7)
#
# with col1:
# a = st.color_picker("Angry", value="#FF0000")
# with col2:
# f = st.color_picker("Fear", value="#800080")
# with col3:
# d = st.color_picker("Disgust", value="#A52A2A")
# with col4:
# sd = st.color_picker("Sad", value="#ADD8E6")
# with col5:
# n = st.color_picker("Neutral", value="#808080")
# with col6:
# sp = st.color_picker("Surprise", value="#FFA500")
# with col7:
# h = st.color_picker("Happy", value="#008000")
# if st.button("Update colors"):
# global COLOR_DICT
# COLOR_DICT = {"neutral": n,
# "positive": h,
# "happy": h,
# "surprise": sp,
# "fear": f,
# "negative": a,
# "angry": a,
# "sad": sd,
# "disgust": d}
# st.success(COLOR_DICT)
if audio_file is not None:
st.markdown("## Analyzing...")
if not audio_file == "test":
st.sidebar.subheader("Audio file")
file_details = {"Filename": audio_file.name, "FileSize": audio_file.size}
st.sidebar.write(file_details)
with st.container():
col1, col2 = st.columns(2)
with col1:
fig = plt.figure(figsize=(10, 2))
fig.set_facecolor('#d1d1e0')
plt.title("MFCCs")
librosa.display.specshow(mfccs, sr=sr, x_axis='time')
plt.gca().axes.get_yaxis().set_visible(False)
plt.gca().axes.spines["right"].set_visible(False)
plt.gca().axes.spines["left"].set_visible(False)
plt.gca().axes.spines["top"].set_visible(False)
st.write(fig)
with col2:
fig2 = plt.figure(figsize=(10, 2))
fig2.set_facecolor('#d1d1e0')
plt.title("Mel-log-spectrogram")
librosa.display.specshow(Xdb, sr=sr, x_axis='time', y_axis='hz')
plt.gca().axes.get_yaxis().set_visible(False)
plt.gca().axes.spines["right"].set_visible(False)
plt.gca().axes.spines["left"].set_visible(False)
plt.gca().axes.spines["top"].set_visible(False)
st.write(fig2)
if model_type == "mfccs":
st.markdown("## Predictions")
with st.container():
col1, col2, col3, col4 = st.columns(4)
mfccs = get_mfccs(path, model.input_shape[-1])
mfccs = mfccs.reshape(1, *mfccs.shape)
pred = model.predict(mfccs)[0]
with col1:
if em3:
pos = pred[3] + pred[5] * .5
neu = pred[2] + pred[5] * .5 + pred[4] * .5
neg = pred[0] + pred[1] + pred[4] * .5
data3 = np.array([pos, neu, neg])
txt = "MFCCs\n" + get_title(data3, CAT3)
fig = plt.figure(figsize=(5, 5))
COLORS = color_dict(COLOR_DICT)
plot_colored_polar(fig, predictions=data3, categories=CAT3,
title=txt, colors=COLORS)
# plot_polar(fig, predictions=data3, categories=CAT3,
# title=txt, colors=COLORS)
st.write(fig)
with col2:
if em6:
txt = "MFCCs\n" + get_title(pred, CAT6)
fig2 = plt.figure(figsize=(5, 5))
COLORS = color_dict(COLOR_DICT)
plot_colored_polar(fig2, predictions=pred, categories=CAT6,
title=txt, colors=COLORS)
# plot_polar(fig2, predictions=pred, categories=CAT6,
# title=txt, colors=COLORS)
st.write(fig2)
with col3:
if em7:
model_ = load_model("model4.h5")
mfccs_ = get_mfccs(path, model_.input_shape[-2])
mfccs_ = mfccs_.T.reshape(1, *mfccs_.T.shape)
pred_ = model_.predict(mfccs_)[0]
txt = "MFCCs\n" + get_title(pred_, CAT7)
fig3 = plt.figure(figsize=(5, 5))
COLORS = color_dict(COLOR_DICT)
plot_colored_polar(fig3, predictions=pred_, categories=CAT7,
title=txt, colors=COLORS)
# plot_polar(fig3, predictions=pred_, categories=CAT7,
# title=txt, colors=COLORS)
st.write(fig3)
with col4:
if gender:
with st.spinner('Wait for it...'):
gmodel = load_model("model_mw.h5")
gmfccs = get_mfccs(path, gmodel.input_shape[-1])
gmfccs = gmfccs.reshape(1, *gmfccs.shape)
gpred = gmodel.predict(gmfccs)[0]
gdict = [["female", "woman.png"], ["male", "man.png"]]
ind = gpred.argmax()
txt = "Predicted gender: " + gdict[ind][0]
img = Image.open("images/" + gdict[ind][1])
fig4 = plt.figure(figsize=(3, 3))
fig4.set_facecolor('#d1d1e0')
plt.title(txt)
plt.imshow(img)
plt.axis("off")
st.write(fig4)
# if model_type == "mel-specs":
# st.markdown("## Predictions")
# st.warning("The model in test mode. It may not be working properly.")
# if st.checkbox("I'm OK with it"):
# try:
# with st.spinner("Wait... It can take some time"):
# global tmodel
# tmodel = load_model_cache("tmodel_all.h5")
# fig, tpred = plot_melspec(path, tmodel)
# col1, col2, col3 = st.columns(3)
# with col1:
# st.markdown("### Emotional spectrum")
# dimg = Image.open("images/spectrum.png")
# st.image(dimg, use_column_width=True)
# with col2:
# fig_, tpred_ = plot_melspec(path=path,
# tmodel=tmodel,
# three=True)
# st.write(fig_, use_column_width=True)
# with col3:
# st.write(fig, use_column_width=True)
# except Exception as e:
# st.error(f"Error {e}, model is not loaded")
elif website_menu == "Project description":
import pandas as pd
import plotly.express as px
st.title("Project description")
st.subheader("GitHub")
link = '[GitHub repository of the web-application]' \
'(https://github.com/CyberMaryVer/speech-emotion-webapp)'
st.markdown(link, unsafe_allow_html=True)
st.subheader("Theory")
link = '[Theory behind - Medium article]' \
'(https://talbaram3192.medium.com/classifying-emotions-using-audio-recordings-and-python-434e748a95eb)'
st.markdown(link + ":clap::clap::clap: Tal!", unsafe_allow_html=True)
with st.expander("See Wikipedia definition"):
components.iframe("https://en.wikipedia.org/wiki/Emotion_recognition",
height=320, scrolling=True)
st.subheader("Dataset")
txt = """
This web-application is a part of the final **Data Mining** project for **ITC Fellow Program 2020**.
Datasets used in this project
* Crowd-sourced Emotional Mutimodal Actors Dataset (**Crema-D**)
* Ryerson Audio-Visual Database of Emotional Speech and Song (**Ravdess**)
* Surrey Audio-Visual Expressed Emotion (**Savee**)
* Toronto emotional speech set (**Tess**)
"""
st.markdown(txt, unsafe_allow_html=True)
df = pd.read_csv("df_audio.csv")
fig = px.violin(df, y="source", x="emotion4", color="actors", box=True, points="all", hover_data=df.columns)
st.plotly_chart(fig, use_container_width=True)
st.subheader("FYI")
st.write("Since we are currently using a free tier instance of AWS, "
"we disabled mel-spec and ensemble models.\n\n"
"If you want to try them we recommend to clone our GitHub repo")
st.code("git clone https://github.com/CyberMaryVer/speech-emotion-webapp.git", language='bash')
st.write("After that, just uncomment the relevant sections in the app.py file "
"to use these models:")
elif website_menu == "Our team":
st.subheader("Our team")
st.balloons()
col1, col2 = st.columns([3, 2])
with col1:
st.info("maria.s.startseva@gmail.com")
st.info("talbaram3192@gmail.com")
st.info("asherholder123@gmail.com")
with col2:
liimg = Image.open("images/LI-Logo.png")
st.image(liimg)
st.markdown(f""":speech_balloon: [Maria Startseva](https://www.linkedin.com/in/maria-startseva)""",
unsafe_allow_html=True)
st.markdown(f""":speech_balloon: [Tal Baram](https://www.linkedin.com/in/tal-baram-b00b66180)""",
unsafe_allow_html=True)
st.markdown(f""":speech_balloon: [Asher Holder](https://www.linkedin.com/in/asher-holder-526a05173)""",
unsafe_allow_html=True)
elif website_menu == "Leave feedback":
st.subheader("Leave feedback")
user_input = st.text_area("Your feedback is greatly appreciated")
user_name = st.selectbox("Choose your personality", ["checker1", "checker2", "checker3", "checker4"])
if st.button("Submit"):
st.success(f"Message\n\"\"\"{user_input}\"\"\"\nwas sent")
if user_input == "log123456" and user_name == "checker4":
with open("log0.txt", "r", encoding="utf8") as f:
st.text(f.read())
elif user_input == "feedback123456" and user_name == "checker4":
with open("log.txt", "r", encoding="utf8") as f:
st.text(f.read())
else:
log_file(user_name + " " + user_input)
thankimg = Image.open("images/sticky.png")
st.image(thankimg)
else:
import requests
import json
url = 'http://api.quotable.io/random'
if st.button("get random mood"):
with st.container():
col1, col2 = st.columns(2)
n = np.random.randint(1, 1000, 1)[0]
with col1:
quotes = {"Good job and almost done": "checker1",
"Great start!!": "checker2",
"Please make corrections base on the following observation": "checker3",
"DO NOT train with test data": "folk wisdom",
"good work, but no docstrings": "checker4",
"Well done!": "checker3",
"For the sake of reproducibility, I recommend setting the random seed": "checker1"}
if n % 5 == 0:
a = np.random.choice(list(quotes.keys()), 1)[0]
quote, author = a, quotes[a]
else:
try:
r = requests.get(url=url)
text = json.loads(r.text)
quote, author = text['content'], text['author']
except Exception as e:
a = np.random.choice(list(quotes.keys()), 1)[0]
quote, author = a, quotes[a]
st.markdown(f"## *{quote}*")
st.markdown(f"### ***{author}***")
with col2:
st.image(image=f"https://picsum.photos/800/600?random={n}")
if __name__ == '__main__':
main()