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