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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
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(
"""
<h1><center>Audio Analyzer by Ilaria</center></h1>\n
<h3><center>Help me on <a href="https://ko-fi.com/ilariaowo/shop">Ko-Fi</a>!</center></h3>\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() |