import gradio as gr import matplotlib.pyplot as plt import numpy as np import os import soundfile as sf import requests import librosa.display 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 by Ilaria

\n

Help me on Ko-Fi!

\n ## Special thanks to Alex Murkoff for helping me code it! #### Need help with AI? Join [AI Hub](https://discord.gg/aihub)!\n **Note**: Try to keep the audio length under **2 minutes**, since long audio files dont work well with a static spectrogram """ ) with gr.Row(): image_output = gr.Image(type='filepath', interactive=False) 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) 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() y, sr = librosa.load(audio_file, sr=None) S = librosa.feature.melspectrogram(y, sr=sr, n_mels=256) log_S = librosa.amplitude_to_db(S, ref=np.max, top_db=256) plt.figure(figsize=(12, 5.5)) librosa.display.specshow(log_S, sr=sr, x_axis='time') plt.colorbar(format='%+2.0f dB', pad=0.01) plt.tight_layout(pad=0.5) plt.savefig('spectrogram.png', dpi=500) 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 # this bitrate one doesnt seem to be used anywhere so i just removed it 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()