heimoshuiyu/ultimatevocalremovergui
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This commit is contained in:
Anjok07
2022-07-06 02:57:56 -05:00
committed by GitHub
parent d2d02be33e
commit 4a0b899b15
5 changed files with 602 additions and 208 deletions
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104
inference_MDX.py
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@@ -38,6 +38,7 @@ import torch
import tkinter as tk
import traceback # Error Message Recent Calls
import time # Timer
from random import randrange
from typing import Literal
@@ -213,7 +214,7 @@ class Predictor():
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(
non_reduced_Instrumental_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(
@@ -257,7 +258,7 @@ class Predictor():
else:
widget_text.write(base_text + 'Saving vocals... ')
sf.write(non_reduced_vocal_path, sources[c].T, samplerate)
sf.write(non_reduced_vocal_path, sources[c].T, samplerate, subtype=wav_type_set)
update_progress(**progress_kwargs,
step=(0.9))
widget_text.write('Done!\n')
@@ -279,9 +280,9 @@ class Predictor():
if data['demucs_only']:
if 'UVR' in demucs_model_set:
sf.write(non_reduced_vocal_path, sources[1].T, samplerate)
sf.write(non_reduced_vocal_path, sources[1].T, samplerate, subtype=wav_type_set)
else:
sf.write(non_reduced_vocal_path, sources[source_val].T, samplerate)
sf.write(non_reduced_vocal_path, sources[source_val].T, samplerate, subtype=wav_type_set)
update_progress(**progress_kwargs,
step=(0.9))
widget_text.write('Done!\n')
@@ -304,7 +305,7 @@ class Predictor():
widget_text.write(base_text + 'Preparing Instrumental...')
else:
widget_text.write(base_text + 'Saving Vocals... ')
sf.write(vocal_path, sources[c].T, samplerate)
sf.write(vocal_path, sources[c].T, samplerate, subtype=wav_type_set)
update_progress(**progress_kwargs,
step=(0.9))
widget_text.write('Done!\n')
@@ -316,11 +317,11 @@ class Predictor():
if data['demucs_only']:
if 'UVR' in demucs_model_set:
sf.write(vocal_path, sources[1].T, samplerate)
sf.write(vocal_path, sources[1].T, samplerate, subtype=wav_type_set)
else:
sf.write(vocal_path, sources[source_val].T, samplerate)
sf.write(vocal_path, sources[source_val].T, samplerate, subtype=wav_type_set)
else:
sf.write(vocal_path, sources[source_val].T, samplerate)
sf.write(vocal_path, sources[source_val].T, samplerate, subtype=wav_type_set)
update_progress(**progress_kwargs,
step=(0.9))
@@ -373,7 +374,7 @@ class Predictor():
wave[d] = np.array([wave[d], wave[d]])
else: # lower bands
wave[d] = librosa.resample(wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type'])
spec[d] = spec_utils.wave_to_spectrogram(wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse'])
specs[i] = spec_utils.combine_spectrograms(spec, mp)
@@ -387,12 +388,13 @@ class Predictor():
y_mag = np.abs(specs[1])
max_mag = np.where(X_mag >= y_mag, X_mag, y_mag)
v_spec = specs[1] - max_mag * np.exp(1.j * np.angle(specs[0]))
update_progress(**progress_kwargs,
step=(1))
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'])
sf.write(non_reduced_Instrumental_path, normalization_set(spec_utils.cmb_spectrogram_to_wave(-v_spec, mp)), mp.param['sr'], subtype=wav_type_set)
reduction_sen = float(data['noisereduc_s'])/10
print(noise_pro_set)
@@ -403,9 +405,9 @@ class Predictor():
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'])
sf.write(Instrumental_path, normalization_set(spec_utils.cmb_spectrogram_to_wave(-v_spec, mp)), mp.param['sr'], subtype=wav_type_set)
else:
sf.write(Instrumental_path, spec_utils.cmb_spectrogram_to_wave(-v_spec, mp), mp.param['sr'])
sf.write(Instrumental_path, normalization_set(spec_utils.cmb_spectrogram_to_wave(-v_spec, mp)), mp.param['sr'], subtype=wav_type_set)
if data['inst_only']:
if file_exists_v == 'there':
@@ -427,7 +429,7 @@ class Predictor():
pass
else:
musfile = pydub.AudioSegment.from_wav(non_reduced_Instrumental_path)
musfile.export(non_reduced_Instrumental_path_mp3, format="mp3", bitrate="320k")
musfile.export(non_reduced_Instrumental_path_mp3, format="mp3", bitrate=mp3_bit_set)
try:
os.remove(non_reduced_Instrumental_path)
except:
@@ -435,7 +437,7 @@ class Predictor():
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=mp3_bit_set)
if file_exists_v == 'there':
pass
else:
@@ -451,7 +453,7 @@ class Predictor():
pass
else:
musfile = pydub.AudioSegment.from_wav(non_reduced_Instrumental_path)
musfile.export(non_reduced_Instrumental_path_mp3, format="mp3", bitrate="320k")
musfile.export(non_reduced_Instrumental_path_mp3, format="mp3", bitrate=mp3_bit_set)
if file_exists_n == 'there':
pass
else:
@@ -462,7 +464,7 @@ class Predictor():
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")
musfile.export(non_reduced_vocal_path_mp3, format="mp3", bitrate=mp3_bit_set)
try:
os.remove(non_reduced_vocal_path)
except:
@@ -470,7 +472,7 @@ class Predictor():
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=mp3_bit_set)
if file_exists_i == 'there':
pass
else:
@@ -483,7 +485,7 @@ class Predictor():
pass
else:
musfile = pydub.AudioSegment.from_wav(non_reduced_vocal_path)
musfile.export(non_reduced_vocal_path_mp3, format="mp3", bitrate="320k")
musfile.export(non_reduced_vocal_path_mp3, format="mp3", bitrate=mp3_bit_set)
if file_exists_n == 'there':
pass
else:
@@ -713,7 +715,6 @@ 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)
@@ -742,7 +743,11 @@ class Predictor():
sources[source_val] = (spec_effects(wave=[demucs_out[source_val],base_out[0]],
algorithm=data['mixing'],
value=b[source_val])*float(compensate)) # compensation
return sources
if not data['demucsmodel']:
return sources*float(compensate)
else:
return sources
def demix_base(self, mixes, margin_size):
chunked_sources = []
@@ -882,10 +887,14 @@ data = {
'shifts': 0,
'margin': 44100,
'split_mode': False,
'normalize': False,
'nophaseinst': True,
'compensate': 1.03597672895,
'autocompensate': True,
'demucs_only': False,
'wavtype': 'PCM_16',
'flactype': 'PCM_16',
'mp3bit': '320k',
'mixing': 'Default',
'DemucsModel_MDX': 'UVR_Demucs_Model_1',
# Choose Model
@@ -941,10 +950,8 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
global stemset_n
global noise_pro_set
global demucs_model_set
global autocompensate
global compensate
global channel_set
global margin_set
global overlap_set
@@ -952,10 +959,13 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
global source_val
global split_mode
global demucs_model_set
global wav_type_set
global flac_type_set
global mp3_bit_set
global normalization_set
global demucs_switch
autocompensate = data['autocompensate']
# Update default settings
default_chunks = data['chunks']
@@ -987,6 +997,8 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
data.update(kwargs)
autocompensate = data['autocompensate']
if data['mdxnetModeltype'] == 'Vocals (Custom)':
stemset = 'v'
source_val_set = 3
@@ -1156,7 +1168,22 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
else:
noise_pro_set = data['noise_pro_select']
if data['wavtype'] == '32-bit Float':
wav_type_set = 'FLOAT'
elif data['wavtype'] == '64-bit Float':
wav_type_set = 'DOUBLE'
else:
wav_type_set = data['wavtype']
flac_type_set = data['flactype']
mp3_bit_set = data['mp3bit']
if data['normalize'] == True:
normalization_set = spec_utils.normalize
print('normalization on')
else:
normalization_set = spec_utils.nonormalize
print('normalization off')
print(n_fft_scale_set)
print(dim_f_set)
@@ -1179,6 +1206,22 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
split_mode = data['split_mode']
demucs_switch = data['demucsmodel']
if data['wavtype'] == '64-bit Float':
if data['saveFormat'] == 'Flac':
text_widget.write('Please select \"WAV\" as your save format to use 64-bit Float.\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
if data['wavtype'] == '64-bit Float':
if data['saveFormat'] == 'Mp3':
text_widget.write('Please select \"WAV\" as your save format to use 64-bit Float.\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
if stemset_n == '(Bass)':
if 'UVR' in demucs_model_set:
text_widget.write('The selected Demucs model can only be used with vocal stems.\n')
@@ -1211,8 +1254,17 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
pass
_mixture = f'{data["input_paths"]}'
_basename = f'{data["export_path"]}/{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
timestampnum = round(datetime.utcnow().timestamp())
randomnum = randrange(100000, 1000000)
if data['settest']:
try:
_basename = f'{data["export_path"]}/{str(timestampnum)}_{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
except:
_basename = f'{data["export_path"]}/{str(randomnum)}_{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
else:
_basename = f'{data["export_path"]}/{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
# -Get text and update progress-
base_text = get_baseText(total_files=len(data['input_paths']),
file_num=file_num)