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| import gradio as gr | |
| import wave | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| from extract_features import * | |
| import pickle | |
| import soundfile | |
| import librosa | |
| classifier = pickle.load(open('finalized_rf.sav', 'rb')) | |
| def emotion_predict(input): | |
| input_features = extract_feature(input, mfcc=True, chroma=True, mel=True, contrast=True, tonnetz=True) | |
| rf_prediction = classifier.predict(input_features.reshape(1,-1)) | |
| if rf_prediction == 'happy': | |
| return 'Happy 😎' | |
| elif rf_prediction == 'neutral': | |
| return 'Neutral 😐' | |
| elif rf_prediction == 'sad': | |
| return 'Sad 😢' | |
| else: | |
| return 'Angry 😤' | |
| def plot_fig(input): | |
| wav = wave.open(input, 'r') | |
| raw = wav.readframes(-1) | |
| raw = np.frombuffer(raw, "int16") | |
| sampleRate = wav.getframerate() | |
| Time = np.linspace(0, len(raw)/sampleRate, num=len(raw)) | |
| fig = plt.figure() | |
| plt.rcParams["figure.figsize"] = (50,15) | |
| plt.title("Waveform Of the Audio", fontsize=25) | |
| plt.xticks(fontsize=15) | |
| plt.yticks(fontsize=15) | |
| plt.ylabel("Amplitude", fontsize=25) | |
| plt.plot(Time, raw, color='red') | |
| return fig | |
| with gr.Blocks() as app: | |
| gr.Markdown( | |
| """ | |
| # Speech Emotion Detector 🎵😍 | |
| This application classifies inputted audio 🔊 according to the verbal emotion into four categories: | |
| 1. Happy 😎 | |
| 2. Neutral 😐 | |
| 3. Sad 😢 | |
| 4. Angry 😤 | |
| """ | |
| ) | |
| with gr.Tab("Record Audio"): | |
| record_input = gr.Audio(source="microphone", type="filepath") | |
| with gr.Accordion("Audio Visualization", open=False): | |
| gr.Markdown( | |
| """ | |
| ### Visualization will work only after Audio has been submitted | |
| """ | |
| ) | |
| plot_record = gr.Button("Display Audio Signal") | |
| plot_record_c = gr.Plot(label='Waveform Of the Audio') | |
| record_button = gr.Button("Detect Emotion") | |
| record_output = gr.Text(label = 'Emotion Detected') | |
| with gr.Tab("Upload Audio File"): | |
| gr.Markdown( | |
| """ | |
| ## Uploaded Audio should be of .wav format | |
| """ | |
| ) | |
| upload_input = gr.Audio(type="filepath") | |
| with gr.Accordion("Audio Visualization", open=False): | |
| gr.Markdown( | |
| """ | |
| ### Visualization will work only after Audio has been submitted | |
| """ | |
| ) | |
| plot_upload = gr.Button("Display Audio Signal") | |
| plot_upload_c = gr.Plot(label='Waveform Of the Audio') | |
| upload_button = gr.Button("Detect Emotion") | |
| upload_output = gr.Text(label = 'Emotion Detected') | |
| record_button.click(emotion_predict, inputs=record_input, outputs=record_output) | |
| upload_button.click(emotion_predict, inputs=upload_input, outputs=upload_output) | |
| plot_record.click(plot_fig, inputs=record_input, outputs=plot_record_c) | |
| plot_upload.click(plot_fig, inputs=upload_input, outputs=plot_upload_c) | |
| app.launch() |