Spectrogram too Dense about audio-to-spectrogram

[adibyte95]

use the below script to convert .au file into .wav file

genres = 0
genre_names = ['blues','classical','country','disco','hiphop','jazz','metal','pop','reggae','rock']

cur_time = time.time()
while genres<10:
folder_name = genre_names[genres]
songs = os.listdir('data/' + folder_name)
print('processing folder: ', folder_name)
song_number = 0
while song_number<100:
filename = songs[song_number]
path = 'data/'+folder_name+'/'+filename
song = AudioSegment.from_file(path,'au')
path_export = 'data_wav/'+folder_name+'/'+str(song_number)+'.wav'
song.export(path_export,format='wav')
song_number += 1
genres +=1
final_time = time.time()
time_taken = final_time - cur_time
print('time taken : ', time_taken/60)

and then generate images using below script

genres = 0
genre_names = ['blues','classical','country','disco','hiphop','jazz','metal','pop','reggae','rock']

cur_time = time.time()
while genres<10:
folder_name = genre_names[genres]
songs = os.listdir('data_wav/' + folder_name)
print('processing folder: ', folder_name)
song_number = 0
while song_number<100:
filename = songs[song_number]
path = 'data_wav/'+folder_name+'/'+filename
graph_spectrogram(path,folder_name,song_number)
song_number += 1
genres +=1
final_time = time.time()
time_taken = final_time - cur_time
print('time taken : ', time_taken/60)

[adibyte95]

more on this here ---> https://github.com/adibyte95/music-genre-classification

[adibyte95]

@jayrodge i don't think there is any difference ...not that i know of

[jayrodge]

Thanks @adibyte95!
just wanted to know that is there any difference between converting .au-> spectrogram and .au->.wav->spectrogram?