app.py

from __future__ import absolute_import, division, print_function, unicode_literals

import gradio as gr
import os
import librosa
import librosa.display
import numpy as np
import shutil
import random
import string
import warnings
import datetime
import tensorflow as tf

from tqdm import tqdm
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import to_categorical
from keras.layers import Flatten, Dropout, Activation
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import BatchNormalization
from sklearn.model_selection import train_test_split
from tqdm import tqdm
from save_data import flag

warnings.filterwarnings("ignore")

timestamp = datetime.datetime.now()
current_date = timestamp.strftime('%d-%m-%Y')
current_time = timestamp.strftime('%I:%M:%S')
IP = ''
cwd = os.getcwd()

classLabels = ('Angry', 'Fear', 'Disgust', 'Happy', 'Sad', 'Surprised', 'Neutral')
numLabels = len(classLabels)
in_shape = (39,216)
model = Sequential()

model.add(Conv2D(8, (13, 13), input_shape=(in_shape[0], in_shape[1], 1)))
model.add(BatchNormalization(axis=-1))
model.add(Activation('relu'))
model.add(Conv2D(8, (13, 13)))
model.add(BatchNormalization(axis=-1))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 1)))
model.add(Conv2D(8, (3, 3)))
model.add(BatchNormalization(axis=-1))
model.add(Activation('relu'))
model.add(Conv2D(8, (1, 1)))
model.add(BatchNormalization(axis=-1))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 1)))
model.add(Flatten())
model.add(Dense(64))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.2))

model.add(Dense(numLabels, activation='softmax'))
model.compile(loss='binary_crossentropy', optimizer='adam',
                           metrics=['accuracy'])

model.load_weights('speech_emotion_detection_ravdess_savee.h5')

def selected_audio(audio):
    try:
        if audio and audio != 'Please select any of the following options':
            post_file_name = audio.lower() + '.wav'
    
            filepath = os.path.join("pre_recoreded",post_file_name)
            if os.path.exists(filepath):
                print("SELECT file name => ",filepath)
                result = predict_speech_emotion(filepath)
                print("result = ",result)
    
            return result
    except Exception as e:
        print(e)
        return "ERROR"

def recorded_audio(audio):
    
    get_audio_name = ''
    final_output = ''
    if audio:
        get_audio_name = ''.join([random.choice(string.ascii_letters + string.digits) for n in range(5)])
        get_audio_name = get_audio_name + '.wav'
        audio_file_path = audio.name
        final_output = predict_speech_emotion(audio_file_path)
        
        flag(audio_file_path,get_audio_name,final_output)
        return final_output
    else:
        raise gr.Error("Please record audio first!!!!")
    
    
def predict_speech_emotion(filepath):
    if os.path.exists(filepath):
        print("last file name => ",filepath)
        X, sample_rate = librosa.load(filepath, res_type='kaiser_best',duration=2.5,sr=22050*2,offset=0.5)
        sample_rate = np.array(sample_rate)
        mfccs = librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=39)
        feature = mfccs
        feature = feature.reshape(39, 216, 1)
        # np_array = np.array([feature])
        np_array = np.array([feature])
        prediction = model.predict(np_array)
        np_argmax = np.argmax(prediction)
        result = classLabels[np_argmax]
        return result


def return_audio_clip(audio_text):
    post_file_name = audio_text.lower() + '.wav'
    filepath = os.path.join("pre_recoreded",post_file_name)
    return filepath

with gr.Blocks(css=".gradio-container {background-color: lightgray;} #btn {background-color: orange;}") as blocks:
    gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>"
                + "Audio Emotion Detection"
                + "</h1>")
    with gr.Row():
        with gr.Column():  
            input_audio_text = gr.Dropdown(label="Input Audio",choices=["Please select any of the following options","Angry", "Happy", "Sad", "Disgust","Fear", "Surprise", "Neutral"],value='Please select any of the following options',interactive=True)
            audio_ui=gr.Audio()
            input_audio_text.change(return_audio_clip,input_audio_text,audio_ui)
            
            output_text = gr.Textbox(label="Detected Emotion!")            
            sub_btn = gr.Button("Detect Emotion",elem_id="btn")
            
        with gr.Column():
            audio=gr.Audio(label="Recored audio",source="microphone", type="file")
            recorded_text = gr.Textbox(label="Detected Emotion!")
            with gr.Column():
                sub_btn2 = gr.Button("Detect Emotion",elem_id="btn")
    gr.Markdown("""<p style='text-align: center;'>Feel free to give us your <a href="https://www.pragnakalp.com/contact/" target="_blank">feedback</a> and contact us 
                    at <a href="mailto:letstalk@pragnakalp.com" target="_blank">letstalk@pragnakalp.com</a> if you want to have your own Speech emotion detection system. 
                    We are just one click away. And don't forget to check out more interesting 
                    <a href="https://www.pragnakalp.com/services/natural-language-processing-services/" target="_blank">NLP services</a> we are offering.</p>
                    <p style='text-align: center;'>Developed by: <a href="https://www.pragnakalp.com" target="_blank">Pragnakalp Techlabs</a></p>""")
    sub_btn.click(selected_audio, inputs=input_audio_text, outputs=output_text)
    sub_btn2.click(recorded_audio, inputs=audio, outputs=recorded_text)

blocks.launch()