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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=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 | |
| def get_title(predictions, categories=CAT6): | |
| title = f"Detected emotion: {categories[predictions.argmax()]} \ | |
| - {predictions.max() * 100:.2f}%" | |
| return title | |
| def color_dict(coldict=COLOR_DICT): | |
| return COLOR_DICT | |
| 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")) | |
| 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, col3 = st.columns(3) | |
| # 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(y=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(y=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.waveshow(wav, sr=44100, color="blue") | |
| 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") | |
| with col3: | |
| st.title("Convert any MP3 audio file to .WAV") | |
| st.subheader("Convert audio file") | |
| link = '[File conversion]' \ | |
| '(https://cloudconvert.com/mp3-to-wav)' | |
| st.markdown(link, unsafe_allow_html=True) | |
| 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("Student Details") | |
| txt = """ | |
| Student information include; | |
| * Student Name: **Adewuyi Gbenga Kolawole** | |
| * Student Matric No: **HNDCOM/22/035** | |
| * Session: **2022/2023** | |
| * Class: **HND 2** | |
| * Level: **400L** | |
| This machine learning web-application PROJECT is a partial fulfillment of requirement in Higher National Diploma (HND) computer science **The Federal College of Animal Health and Production Technology** **FCAHPTIB, 2023**. | |
| """ | |
| st.markdown(txt, unsafe_allow_html=True) | |
| st.subheader("Theory") | |
| link = '[Theory behind - the project(emotion recognition) ]' | |
| st.markdown(link + ":clap::clap::clap:", 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 = """ | |
| Datasets used in this project | |
| * Crowd-sourced Emotional Mutimodal Actors Dataset (**Crema-D**) ("https://www.kaggle.com/code/ejlok1/audio-emotion-part-1-explore-data") | |
| * Ryerson Audio-Visual Database of Emotional Speech and Song (**Ravdess**) ("https://www.kaggle.com/datasets/uwrfkaggler/ravdess-emotional-speech-audio") | |
| * Surrey Audio-Visual Expressed Emotion (**Savee**) ("https://www.kaggle.com/datasets/ejlok1/surrey-audiovisual-expressed-emotion-savee") | |
| * Toronto emotional speech set (**Tess**) | |
| All datasets used can be found on **Kaggle** | |
| The above datasets was used in the model training of this software before deployment | |
| """ | |
| 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) | |
| else: | |
| pass | |
| if __name__ == '__main__': | |
| main() | |