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This commit is contained in:
Anjok07
2022-07-03 18:47:33 -05:00
committed by GitHub
parent 7cce5e303a
commit 04f29d3bb7
3 changed files with 489 additions and 77 deletions
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237
inference_MDX.py
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@@ -101,6 +101,7 @@ class Predictor():
def prediction(self, m):
mix, samplerate = librosa.load(m, mono=False, sr=44100)
print('print mix: ', mix)
if mix.ndim == 1:
mix = np.asfortranarray([mix,mix])
samplerate = samplerate
@@ -208,6 +209,27 @@ class Predictor():
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{vocal_name}_No_Reduction',)
if data['modelFolder']:
non_reduced_Instrumental_path = '{save_path}/{file_name}.wav'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_{model_set_name}_No_Reduction',)
non_reduced_path_mp3 = '{save_path}/{file_name}.mp3'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_{model_set_name}_No_Reduction',)
non_reduced_Instrumental_path_flac = '{save_path}/{file_name}.flac'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_{model_set_name}_No_Reduction',)
else:
non_reduced_Instrumental_path = '{save_path}/{file_name}.wav'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_No_Reduction',)
non_reduced_Instrumental_path_mp3 = '{save_path}/{file_name}.mp3'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_No_Reduction',)
non_reduced_Instrumental_path_flac = '{save_path}/{file_name}.flac'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_No_Reduction',)
if os.path.isfile(non_reduced_vocal_path):
file_exists_n = 'there'
@@ -306,14 +328,30 @@ class Predictor():
if data['voc_only'] and not data['inst_only']:
pass
else:
finalfiles = [
{
'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json',
'files':[str(music_file), vocal_path],
}
]
if not data['noisereduc_s'] == 'None':
if data['nophaseinst']:
finalfiles = [
{
'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json',
'files':[str(music_file), non_reduced_vocal_path],
}
]
else:
finalfiles = [
{
'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json',
'files':[str(music_file), vocal_path],
}
]
else:
finalfiles = [
{
'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json',
'files':[str(music_file), vocal_path],
}
]
widget_text.write(base_text + 'Saving Instrumental... ')
for i, e in tqdm(enumerate(finalfiles)):
@@ -351,9 +389,24 @@ class Predictor():
v_spec = specs[1] - max_mag * np.exp(1.j * np.angle(specs[0]))
update_progress(**progress_kwargs,
step=(1))
sf.write(Instrumental_path, spec_utils.cmb_spectrogram_to_wave(-v_spec, mp), mp.param['sr'])
if not data['noisereduc_s'] == 'None':
if data['nophaseinst']:
sf.write(non_reduced_Instrumental_path, spec_utils.cmb_spectrogram_to_wave(-v_spec, mp), mp.param['sr'])
reduction_sen = float(data['noisereduc_s'])/10
print(noise_pro_set)
subprocess.call("lib_v5\\sox\\sox.exe" + ' "' +
f"{str(non_reduced_Instrumental_path)}" + '" "' + f"{str(Instrumental_path)}" + '" ' +
"noisered lib_v5\\sox\\" + noise_pro_set + ".prof " + f"{reduction_sen}",
shell=True, stdout=subprocess.PIPE,
stdin=subprocess.PIPE, stderr=subprocess.PIPE)
else:
sf.write(Instrumental_path, spec_utils.cmb_spectrogram_to_wave(-v_spec, mp), mp.param['sr'])
else:
sf.write(Instrumental_path, spec_utils.cmb_spectrogram_to_wave(-v_spec, mp), mp.param['sr'])
if data['inst_only']:
if file_exists_v == 'there':
pass
@@ -365,14 +418,24 @@ class Predictor():
widget_text.write('Done!\n')
if data['saveFormat'] == 'Mp3':
try:
if data['inst_only'] == True:
if data['non_red'] == True:
if not data['nophaseinst']:
pass
else:
musfile = pydub.AudioSegment.from_wav(non_reduced_Instrumental_path)
musfile.export(non_reduced_Instrumental_path_mp3, format="mp3", bitrate="320k")
try:
os.remove(non_reduced_Instrumental_path)
except:
pass
pass
else:
musfile = pydub.AudioSegment.from_wav(vocal_path)
musfile.export(vocal_path_mp3, format="mp3", bitrate="320k")
musfile.export(vocal_path_mp3, format="mp3", bitrate="320k")
if file_exists_v == 'there':
pass
else:
@@ -380,21 +443,47 @@ class Predictor():
os.remove(vocal_path)
except:
pass
if data['non_red'] == True:
if not data['nophaseinst']:
pass
else:
if data['voc_only'] == True:
pass
else:
musfile = pydub.AudioSegment.from_wav(non_reduced_Instrumental_path)
musfile.export(non_reduced_Instrumental_path_mp3, format="mp3", bitrate="320k")
if file_exists_n == 'there':
pass
else:
try:
os.remove(non_reduced_Instrumental_path)
except:
pass
if data['voc_only'] == True:
if data['non_red'] == True:
musfile = pydub.AudioSegment.from_wav(non_reduced_vocal_path)
musfile.export(non_reduced_vocal_path_mp3, format="mp3", bitrate="320k")
try:
os.remove(non_reduced_vocal_path)
except:
pass
pass
else:
musfile = pydub.AudioSegment.from_wav(Instrumental_path)
musfile.export(Instrumental_path_mp3, format="mp3", bitrate="320k")
musfile.export(Instrumental_path_mp3, format="mp3", bitrate="320k")
if file_exists_i == 'there':
pass
else:
try:
os.remove(Instrumental_path)
except:
pass
pass
if data['non_red'] == True:
musfile = pydub.AudioSegment.from_wav(non_reduced_vocal_path)
musfile.export(non_reduced_vocal_path_mp3, format="mp3", bitrate="320k")
if data['inst_only'] == True:
pass
else:
musfile = pydub.AudioSegment.from_wav(non_reduced_vocal_path)
musfile.export(non_reduced_vocal_path_mp3, format="mp3", bitrate="320k")
if file_exists_n == 'there':
pass
else:
@@ -429,6 +518,16 @@ class Predictor():
if data['saveFormat'] == 'Flac':
try:
if data['inst_only'] == True:
if data['non_red'] == True:
if not data['nophaseinst']:
pass
else:
musfile = pydub.AudioSegment.from_wav(non_reduced_Instrumental_path)
musfile.export(non_reduced_Instrumental_path_flac, format="flac")
try:
os.remove(non_reduced_Instrumental_path)
except:
pass
pass
else:
musfile = pydub.AudioSegment.from_wav(vocal_path)
@@ -440,7 +539,30 @@ class Predictor():
os.remove(vocal_path)
except:
pass
if data['non_red'] == True:
if not data['nophaseinst']:
pass
else:
if data['voc_only'] == True:
pass
else:
musfile = pydub.AudioSegment.from_wav(non_reduced_Instrumental_path)
musfile.export(non_reduced_Instrumental_path_flac, format="flac")
if file_exists_n == 'there':
pass
else:
try:
os.remove(non_reduced_Instrumental_path)
except:
pass
if data['voc_only'] == True:
if data['non_red'] == True:
musfile = pydub.AudioSegment.from_wav(non_reduced_vocal_path)
musfile.export(non_reduced_vocal_path_flac, format="flac")
try:
os.remove(non_reduced_vocal_path)
except:
pass
pass
else:
musfile = pydub.AudioSegment.from_wav(Instrumental_path)
@@ -453,8 +575,11 @@ class Predictor():
except:
pass
if data['non_red'] == True:
musfile = pydub.AudioSegment.from_wav(non_reduced_vocal_path)
musfile.export(non_reduced_vocal_path_flac, format="flac")
if data['inst_only'] == True:
pass
else:
musfile = pydub.AudioSegment.from_wav(non_reduced_vocal_path)
musfile.export(non_reduced_vocal_path_flac, format="flac")
if file_exists_n == 'there':
pass
else:
@@ -489,6 +614,14 @@ class Predictor():
if data['noisereduc_s'] == 'None':
pass
elif data['non_red'] == True:
if data['inst_only']:
if file_exists_n == 'there':
pass
else:
try:
os.remove(non_reduced_vocal_path)
except:
pass
pass
elif data['inst_only']:
if file_exists_n == 'there':
@@ -501,6 +634,7 @@ class Predictor():
else:
try:
os.remove(non_reduced_vocal_path)
os.remove(non_reduced_Instrumental_path)
except:
pass
@@ -579,6 +713,7 @@ class Predictor():
if not data['demucsmodel']:
sources = self.demix_base(segmented_mix, margin_size=margin)
#value=float(0.9)*float(compensate)
elif data['demucs_only']:
if split_mode == True:
sources = self.demix_demucs_split(mix)
@@ -599,13 +734,14 @@ class Predictor():
print(data['mixing'])
if 'UVR' in demucs_model_set:
sources[source_val] = (spec_effects(wave=[demucs_out[1],base_out[0]],
algorithm=data['mixing'],
value=b[source_val])*float(data['compensate'])) # compensation
value=b[source_val])*float(compensate)) # compensation
else:
sources[source_val] = (spec_effects(wave=[demucs_out[source_val],base_out[0]],
algorithm=data['mixing'],
value=b[source_val])*float(data['compensate'])) # compensation
value=b[source_val])*float(compensate)) # compensation
return sources
def demix_base(self, mixes, margin_size):
@@ -697,6 +833,8 @@ class Predictor():
sources = list(processed.values())
sources = np.concatenate(sources, axis=-1)
widget_text.write('Done!\n')
print('the demucs model is done running')
return sources
def demix_demucs_split(self, mix):
@@ -718,6 +856,9 @@ class Predictor():
sources = (sources * ref.std() + ref.mean()).cpu().numpy()
sources[[0,1]] = sources[[1,0]]
print('the demucs model is done running')
return sources
data = {
@@ -741,12 +882,14 @@ data = {
'shifts': 0,
'margin': 44100,
'split_mode': False,
'nophaseinst': True,
'compensate': 1.03597672895,
'autocompensate': True,
'demucs_only': False,
'mixing': 'Default',
'DemucsModel_MDX': 'UVR_Demucs_Model_1',
# Choose Model
'mdxnetModel': 'UVR-MDX-NET 1',
'mdxnetModel': 'UVR-MDX-NET Main',
'mdxnetModeltype': 'Vocals (Custom)',
}
default_chunks = data['chunks']
@@ -799,7 +942,8 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
global noise_pro_set
global demucs_model_set
global mdx_model_hash
global autocompensate
global compensate
global channel_set
global margin_set
@@ -807,9 +951,12 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
global shift_set
global source_val
global split_mode
global demucs_model_set
global demucs_switch
autocompensate = data['autocompensate']
# Update default settings
default_chunks = data['chunks']
default_noisereduc_s = data['noisereduc_s']
@@ -883,6 +1030,10 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
modeltype = 'v'
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
stemset_n = '(Vocals)'
if autocompensate == True:
compensate = 1.03597672895
else:
compensate = data['compensate']
source_val = 3
n_fft_scale_set=6144
dim_f_set=2048
@@ -896,6 +1047,10 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
modeltype = 'v'
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
stemset_n = '(Vocals)'
if autocompensate == True:
compensate = 1.03597672895
else:
compensate = data['compensate']
source_val = 3
n_fft_scale_set=6144
dim_f_set=2048
@@ -909,6 +1064,10 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
modeltype = 'v'
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
stemset_n = '(Vocals)'
if autocompensate == True:
compensate = 1.03597672895
else:
compensate = data['compensate']
source_val = 3
n_fft_scale_set=6144
dim_f_set=2048
@@ -918,15 +1077,36 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
modeltype = 'v'
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
stemset_n = '(Vocals)'
if autocompensate == True:
compensate = 1.03597672895
else:
compensate = data['compensate']
source_val = 3
n_fft_scale_set=6144
dim_f_set=2048
elif data['mdxnetModel'] == 'UVR-MDX-NET Main':
model_set = 'UVR_MDXNET_Main'
model_set_name = 'UVR_MDXNET_Main'
modeltype = 'v'
noise_pro = 'MDX-NET_Noise_Profile_17_kHz'
stemset_n = '(Vocals)'
if autocompensate == True:
compensate = 1.08
else:
compensate = data['compensate']
source_val = 3
n_fft_scale_set=7680
dim_f_set=3072
elif 'other' in data['mdxnetModel']:
model_set = 'other'
model_set_name = 'other'
modeltype = 'o'
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
stemset_n = '(Other)'
if autocompensate == True:
compensate = 1.03597672895
else:
compensate = data['compensate']
source_val = 2
n_fft_scale_set=8192
dim_f_set=2048
@@ -936,6 +1116,10 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
modeltype = 'd'
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
stemset_n = '(Drums)'
if autocompensate == True:
compensate = 1.03597672895
else:
compensate = data['compensate']
source_val = 1
n_fft_scale_set=4096
dim_f_set=2048
@@ -945,6 +1129,10 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
modeltype = 'b'
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
stemset_n = '(Bass)'
if autocompensate == True:
compensate = 1.03597672895
else:
compensate = data['compensate']
source_val = 0
n_fft_scale_set=16384
dim_f_set=2048
@@ -954,6 +1142,10 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
modeltype = stemset
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
stemset_n = stem_name
if autocompensate == True:
compensate = 1.03597672895
else:
compensate = data['compensate']
source_val = source_val_set
n_fft_scale_set=int(data['n_fft_scale'])
dim_f_set=int(data['dim_f'])
@@ -963,7 +1155,8 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
noise_pro_set = noise_pro
else:
noise_pro_set = data['noise_pro_select']
print(n_fft_scale_set)
print(dim_f_set)
@@ -1031,7 +1224,7 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
try:
if float(data['noisereduc_s']) >= 10:
if float(data['noisereduc_s']) >= 11:
text_widget.write('Error: Noise Reduction only supports values between 0-10.\nPlease set a value between 0-10 (with or without decimals) and try again.')
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button