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