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( """ #
Ilaria Audio Analyzer 💖 (BETA)
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"""
| 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 |
""" # Return the PNG file of the spectrogram and the info table return {"value": info_table, "__type__": "update"}, 'spectrogram.png' # Create the Gradio interface main()