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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): | |
# Estrai l'ID del file dal link di Google Drive | |
file_id = url.split('/')[-2] | |
# Crea il link di download diretto | |
download_url = f'https://docs.google.com/uc?export=download&id={file_id}' | |
# Scarica il file | |
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(): | |
# Gradio Interface | |
with gr.Blocks() as app: | |
gr.Markdown( | |
""" | |
# <div align="center"> Ilaria Audio Analyzer π </div> | |
Audio Analyzer Software by Ilaria, Help me on [Ko-Fi!](https://ko-fi.com/ilariaowo)\n | |
Special thanks to Alex Murkoff for helping me coding it! | |
Need help with AI? Join [Join AI Hub!](https://discord.gg/aihub) | |
""" | |
) | |
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): | |
# Clear figure in case it has data in it | |
plt.clf() | |
# Read the audio data from the file | |
audio_data, sample_rate = sf.read(audio_file) | |
# Convert to mono if it's not mono | |
if len(audio_data.shape) > 1: | |
audio_data = np.mean(audio_data, axis=1) | |
# Create the spectrogram | |
plt.specgram(audio_data, Fs=sample_rate / 1, NFFT=4096, sides='onesided', | |
cmap="Reds_r", scale_by_freq=True, scale='dB', mode='magnitude', window=np.hanning(4096)) | |
# Save the spectrogram to a PNG file | |
plt.savefig('spectrogram.png') | |
# Get the audio file info | |
audio_info = sf.info(audio_file) | |
bit_depth = {'PCM_16': 16, 'FLOAT': 32}.get(audio_info.subtype, 0) | |
# Convert duration to minutes, seconds, and milliseconds | |
minutes, seconds = divmod(audio_info.duration, 60) | |
seconds, milliseconds = divmod(seconds, 1) | |
milliseconds *= 1000 # convert from seconds to milliseconds | |
# Convert bitrate to mb/s | |
bitrate = audio_info.samplerate * audio_info.channels * bit_depth / 8 / 1024 / 1024 | |
# Calculate speed in kbps | |
speed_in_kbps = audio_info.samplerate * bit_depth / 1000 | |
# Create a table with the audio file info | |
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() |