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import gradio as gr
import matplotlib.pyplot as plt
import numpy as np
import os
import soundfile as sf
def main():
# Gradio Interface
with gr.Blocks() as app:
gr.Markdown(
"""
# <div align="center"> Ilaria Audio Analyzer 💖 (BETA) </div>
Audio Analyzer Software by Ilaria, Help me on [Ko-Fi](https://ko-fi.com/ilariaowo)\n
Special thanks to [Alex Murkoff](https://github.com/alexlnkp) for helping me coding it!
Need help with AI? [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)
create_spec_butt.click(fn=create_spectrogram_and_get_info, inputs=[audio_input], outputs=[output_markdown, image_output])
audio_input.change(fn=lambda: ({"value": "", "__type__": "update"},
{"value": "", "__type__": "update"}),
inputs=[], outputs=[image_output, output_markdown])
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')
# 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
info_table = f"""
<center>
| Information | Value |
| :---: | :---: |
| File Name | {os.path.basename(audio_file)} |
| 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 |
</center>
"""
# Return the PNG file of the spectrogram and the info table
return {"value": info_table, "__type__": "update"}, 'spectrogram.png'
# Create the Gradio interface
main()
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