55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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import math
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from pydub import AudioSegment
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def create_waveform_from_file(file):
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"""_summary_
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Args:
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file (_type_): _description_
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"""
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# Read the MP3 file
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audio = AudioSegment.from_file(file)
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# Convert to mono and get frame rate and number of channels
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num_channels = 1
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frame_rate = audio.frame_rate
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downsample = math.ceil(frame_rate * num_channels / 1) # /1 = 1 sample per second
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# Process audio data
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process_chunk_size = 600000 - (600000 % frame_rate)
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signal = None
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waveform = np.array([])
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# Convert the audio data to numpy array
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audio_data = np.array(audio.get_array_of_samples())
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# Create waveforms
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start_index = 0
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while start_index < len(audio_data):
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end_index = start_index + process_chunk_size
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signal = audio_data[start_index:end_index].astype(float) # Get chunk and convert to float
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# Take mean of absolute values per 0.5 seconds
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sub_waveform = np.nanmean(
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np.pad(np.absolute(signal), (0, ((downsample - (signal.size % downsample)) % downsample)), mode='constant', constant_values=np.NaN).reshape(-1, downsample),
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axis=1
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)
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waveform = np.concatenate((waveform, sub_waveform))
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start_index = end_index
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# Plot waveforms
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plt.figure(1)
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plt.plot(waveform, color='blue')
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plt.plot(-waveform, color='blue') # Mirrored waveform
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# Fill in area
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plt.fill_between(np.arange(len(waveform)), waveform, -waveform, color='blue', alpha=0.5)
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# Remove decorations, labels, axes, etc
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plt.axis('off')
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# Graph goes to ends of pic
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plt.xlim(0, len(waveform))
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# Save pic
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plt.savefig('assets/now_playing_waveform.png', dpi=64, bbox_inches='tight', pad_inches=0)
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# Show me tho
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# plt.show()
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# create_waveform_from_file(sys.argv[1]) |