Spaces:
Runtime error
Runtime error
| import gradio as gr | |
| import librosa | |
| import matplotlib.pyplot as plt | |
| import plotly.express as px | |
| from radar_chart import radar_factory | |
| from keras.models import load_model | |
| import os | |
| import numpy as np | |
| model = load_model(os.path.join("model", "Emotion_Voice_Detection_Model_tuned_2.h5")) | |
| def convert_class_to_emotion(pred): | |
| """ | |
| Method to convert the predictions (int) into human readable strings. | |
| """ | |
| # label_conversion = {0: 'neutral', | |
| # 1: 'calm', | |
| # 2: 'happy', | |
| # 3: 'sad', | |
| # 4: 'angry', | |
| # 5: 'fearful', | |
| # 6: 'disgust', | |
| # 7: 'surprised'} | |
| label_conversion = {0: 'kata_sifat', | |
| 1: 'kata_benda', | |
| 2: 'kata_kerja', | |
| 3: 'kata_keterangan} | |
| return label_conversion[int(pred)] | |
| def make_predictions(file, micro=None): | |
| """ | |
| Method to process the files and create your features. | |
| """ | |
| if file is not None and micro is None: | |
| input_audio = file | |
| elif file is None and micro is not None: | |
| input_audio = micro | |
| else: | |
| print("THERE IS A PROBLEM") | |
| input_audio = file | |
| data, sampling_rate = librosa.load(input_audio) | |
| print(data) | |
| print(f"THE SAMPLING RATE IS {sampling_rate}") | |
| mfccs = np.mean(librosa.feature.mfcc(y=data, sr=sampling_rate, n_mfcc=40).T, axis=0) | |
| x = np.expand_dims(mfccs, axis=1) | |
| x = np.expand_dims(x, axis=0) | |
| predictions = np.argmax(model.predict(x), axis=1) | |
| N = 8 | |
| theta = radar_factory(N, frame='polygon') | |
| spoke_labels = np.array(['kata_benda', | |
| 'kata_kerja', | |
| 'kata_keterangan', | |
| 'kata_sifat']) | |
| fig_radar, axs = plt.subplots(figsize=(8, 8), nrows=1, ncols=1, | |
| subplot_kw=dict(projection='radar')) | |
| vec = model.predict(x)[0] | |
| axs.plot(theta, vec, color="b") | |
| axs.fill(theta, vec, alpha=0.3) | |
| axs.set_varlabels(spoke_labels) | |
| fig = plt.figure() | |
| plt.plot(data, alpha=0.8) | |
| plt.xlabel("temps") | |
| plt.ylabel("amplitude") | |
| return convert_class_to_emotion(predictions), fig, fig_radar | |
| # Set the starting state to an empty string | |
| iface = gr.Interface( | |
| fn=make_predictions, | |
| title="identify emotion of a chunk of audio speech", | |
| description="a simple interface to perform emotion recognition from an audio file", | |
| article="Author: <a href=\"https://huggingface.co/poisso\">Poisso</a>.", | |
| inputs=[gr.Audio(source="upload", type="filepath", label="File"), | |
| gr.Audio(source="microphone", type="filepath", streaming=False, label="Microphone")] | |
| , | |
| examples=[[os.path.join("examples", filename)] for filename in os.listdir("examples")], | |
| outputs=[gr.Textbox(label="Text output"), gr.Plot(), gr.Plot()] | |
| ) | |
| iface.launch(debug=True) |