2021-08-10 03:31:12 +00:00
|
|
|
from scipy.io import wavfile
|
|
|
|
|
from spleeter.separator import Separator
|
|
|
|
|
from tqdm import tqdm
|
|
|
|
|
|
|
|
|
|
from data.util import find_audio_files
|
|
|
|
|
import os.path as osp
|
|
|
|
|
from spleeter.audio.adapter import AudioAdapter
|
|
|
|
|
import numpy as np
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
|
|
|
src_dir = 'O:\\podcast_dumps'
|
|
|
|
|
#src_dir = 'E:\\audio\\books'
|
|
|
|
|
output_dir = 'D:\\data\\audio\\podcasts-split'
|
|
|
|
|
output_dir_lq = 'D:\\data\\audio\\podcasts-split-with-bg'
|
|
|
|
|
output_dir_garbage = 'D:\\data\\audio\\podcasts-split-garbage'
|
|
|
|
|
#output_dir = 'E:\\audio\\books-clips'
|
|
|
|
|
clip_length = 5 # In seconds
|
|
|
|
|
sparsity = .1 # Only this proportion of the total clips are extracted as wavs.
|
|
|
|
|
output_sample_rate=22050
|
|
|
|
|
|
|
|
|
|
audio_loader = AudioAdapter.default()
|
|
|
|
|
separator = Separator('spleeter:2stems')
|
|
|
|
|
files = find_audio_files(src_dir, include_nonwav=True)
|
|
|
|
|
for e, file in enumerate(tqdm(files)):
|
2021-08-11 14:46:02 +00:00
|
|
|
if e < 3055:
|
2021-08-10 03:31:12 +00:00
|
|
|
continue
|
|
|
|
|
file_basis = osp.relpath(file, src_dir)\
|
|
|
|
|
.replace('/', '_')\
|
|
|
|
|
.replace('\\', '_')\
|
|
|
|
|
.replace('.', '_')\
|
|
|
|
|
.replace(' ', '_')\
|
|
|
|
|
.replace('!', '_')\
|
|
|
|
|
.replace(',', '_')
|
|
|
|
|
if len(file_basis) > 100:
|
|
|
|
|
file_basis = file_basis[:100]
|
|
|
|
|
try:
|
|
|
|
|
wave, sample_rate = audio_loader.load(file, sample_rate=output_sample_rate)
|
|
|
|
|
except:
|
|
|
|
|
print(f"Error with {file}")
|
|
|
|
|
continue
|
|
|
|
|
|
|
|
|
|
#if len(wave.shape) < 2:
|
|
|
|
|
# continue
|
|
|
|
|
|
|
|
|
|
# Calculate how much data we need to extract for each clip.
|
|
|
|
|
clip_sz = sample_rate * clip_length
|
|
|
|
|
interval = int(sample_rate * (clip_length / sparsity))
|
|
|
|
|
i = 0
|
|
|
|
|
while (i+clip_sz) < wave.shape[0]:
|
|
|
|
|
clip = wave[i:i+clip_sz]
|
|
|
|
|
sep = separator.separate(clip)
|
|
|
|
|
vocals = sep['vocals']
|
|
|
|
|
bg = sep['accompaniment']
|
|
|
|
|
vmax = np.abs(vocals).mean()
|
|
|
|
|
bmax = np.abs(bg).mean()
|
|
|
|
|
|
|
|
|
|
# Only output to the "good" sample dir if the ratio of background noise to vocal noise is high enough.
|
2021-08-11 14:46:02 +00:00
|
|
|
ratio = vmax / (bmax+.0000001)
|
2021-08-10 03:31:12 +00:00
|
|
|
if ratio >= 25: # These values were derived empirically
|
|
|
|
|
od = output_dir
|
|
|
|
|
os = clip
|
|
|
|
|
elif ratio >= 1:
|
|
|
|
|
od = output_dir_lq
|
|
|
|
|
os = vocals
|
|
|
|
|
else:
|
|
|
|
|
od = output_dir_garbage
|
|
|
|
|
os = vocals
|
|
|
|
|
|
|
|
|
|
# Strip out channels.
|
|
|
|
|
if len(os.shape) > 1:
|
|
|
|
|
os = os[:, 0] # Just use the first channel.
|
|
|
|
|
|
|
|
|
|
wavfile.write(osp.join(od, f'{e}_{file_basis}_{i}.wav'), output_sample_rate, os)
|
|
|
|
|
i = i + interval
|
