README.md

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()