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| import gradio as gr | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import os | |
| import soundfile as sf | |
| import requests | |
| def download_file(url): | |
| file_id = url.split('/')[-2] | |
| download_url = f'https://docs.google.com/uc?export=download&id={file_id}' | |
| response = requests.get(download_url, allow_redirects=True) | |
| local_filename = url.split('/')[-1] + '.wav' | |
| open(local_filename, 'wb').write(response.content) | |
| return local_filename | |
| def main(): | |
| with gr.Blocks() as app: | |
| gr.Markdown( | |
| """ | |
| Audio Analyzer Software by Ilaria, Help me on Ko-Fi!\n | |
| Special thanks to Alex Murkoff for helping me coding it! | |
| Need help with AI? Join Join AI Hub! | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| audio_input = gr.Audio(type='filepath') | |
| create_spec_butt = gr.Button(value='Create Spectrogram And Get Info', variant='primary') | |
| with gr.Column(): | |
| output_markdown = gr.Markdown(value="", visible=True) | |
| image_output = gr.Image(type='filepath', interactive=False) | |
| with gr.Accordion('Audio Downloader', open=False): | |
| url_input = gr.Textbox(value='', label='Google Drive Audio URL') | |
| download_butt = gr.Button(value='Download audio', variant='primary') | |
| download_butt.click(fn=download_file, inputs=[url_input], outputs=[audio_input]) | |
| create_spec_butt.click(fn=create_spectrogram_and_get_info, inputs=[audio_input], outputs=[output_markdown, image_output]) | |
| download_butt.click(fn=download_file, inputs=[url_input], outputs=[audio_input]) | |
| create_spec_butt.click(fn=create_spectrogram_and_get_info, inputs=[audio_input], outputs=[output_markdown, image_output]) | |
| app.queue(max_size=1022).launch(share=True) | |
| def create_spectrogram_and_get_info(audio_file): | |
| plt.clf() | |
| plt.figure(figsize=(15, 5)) | |
| audio_data, sample_rate = sf.read(audio_file) | |
| if len(audio_data.shape) > 1: | |
| audio_data = np.mean(audio_data, axis=1) | |
| plt.specgram(audio_data, Fs=sample_rate / 1, NFFT=4096, sides='onesided', | |
| cmap="inferno", scale_by_freq=True, scale='dB', mode='magnitude', window=np.hanning(4096)) | |
| plt.savefig('spectrogram.png') | |
| audio_info = sf.info(audio_file) | |
| bit_depth = {'PCM_16': 16, 'FLOAT': 32}.get(audio_info.subtype, 0) | |
| minutes, seconds = divmod(audio_info.duration, 60) | |
| seconds, milliseconds = divmod(seconds, 1) | |
| milliseconds *= 1000 | |
| bitrate = audio_info.samplerate * audio_info.channels * bit_depth / 8 / 1024 / 1024 | |
| speed_in_kbps = audio_info.samplerate * bit_depth / 1000 | |
| filename_without_extension, _ = os.path.splitext(os.path.basename(audio_file)) | |
| info_table = f""" | |
| | Information | Value | | |
| | :---: | :---: | | |
| | File Name | {filename_without_extension} | | |
| | Duration | {int(minutes)} minutes - {int(seconds)} seconds - {int(milliseconds)} milliseconds | | |
| | Bitrate | {speed_in_kbps} kbp/s | | |
| | Audio Channels | {audio_info.channels} | | |
| | Samples per second | {audio_info.samplerate} Hz | | |
| | Bit per second | {audio_info.samplerate * audio_info.channels * bit_depth} bit/s | | |
| """ | |
| # Return the PNG file of the spectrogram and the info table | |
| return info_table, 'spectrogram.png' | |
| # Create the Gradio interface | |
| main() |