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| import gradio as gr | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import librosa | |
| import librosa.display | |
| import base64 | |
| from io import BytesIO | |
| def plot_stft(audio_file): | |
| # Load audio file | |
| y, sr = librosa.load(audio_file) | |
| # Compute the Short-Time Fourier Transform (STFT) | |
| D = librosa.stft(y) | |
| S_db = librosa.amplitude_to_db(np.abs(D), ref=np.max) | |
| # Plot the STFT | |
| plt.figure(figsize=(10, 6)) | |
| librosa.display.specshow(S_db, sr=sr, x_axis='time', y_axis='log') | |
| plt.colorbar(format='%+2.0f dB') | |
| plt.title('STFT (Short-Time Fourier Transform)') | |
| # Save the plot to a BytesIO object | |
| buf = BytesIO() | |
| plt.savefig(buf, format='png') | |
| plt.close() | |
| buf.seek(0) | |
| # Encode the image as base64 | |
| image_base64 = base64.b64encode(buf.getvalue()).decode('utf-8') | |
| buf.close() | |
| # Create an HTML img tag with the base64 encoded image | |
| html_img = f'<img src="data:image/png;base64,{image_base64}" alt="STFT plot"/>' | |
| return html_img | |
| # Gradio interface | |
| demo = gr.Interface(fn=plot_stft, | |
| inputs=gr.Audio(type="filepath"), | |
| outputs="html") | |
| demo.launch() | |