Cool_Tools/ultimatevocalremovergui
1
0
Fork 0
mirror of https://github.com/Anjok07/ultimatevocalremovergui.git synced 2024-11-24 07:20:10 +01:00
Code Issues Packages Projects Releases Wiki Activity

Add files via upload

Browse Source
This commit is contained in:
Anjok07 2022-12-27 04:31:41 -06:00 committed by GitHub
parent 9c25e2c2ab
commit 729f6d6744
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 307 additions and 231 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

516
UVR.py
View File

File diff suppressed because it is too large Load Diff

22
separate.py
View File

@ -586,7 +586,10 @@ class SeperateVR(SeperateAttributes):
else:
self.start_inference()
if self.is_gpu_conversion >= 0:
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
if OPERATING_SYSTEM == 'Darwin':
device = torch.device('mps' if torch.backends.mps.is_available() else 'cpu')
else:
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
else:
device = torch.device('cpu')
@ -599,11 +602,9 @@ class SeperateVR(SeperateAttributes):
nn_architecture = min(nn_arch_sizes, key=lambda x:abs(x-model_size))
if nn_architecture in vr_5_1_models:
print('NEW: ', nn_architecture)
model = nets_new.CascadedNet(self.mp.param['bins'] * 2, nn_architecture)
inference = self.inference_vr_new
else:
print('OLD: ', nn_architecture)
model = nets.determine_model_capacity(self.mp.param['bins'] * 2, nn_architecture)
inference = self.inference_vr
@ -663,14 +664,19 @@ class SeperateVR(SeperateAttributes):
for d in range(bands_n, 0, -1):
bp = self.mp.param['band'][d]
if OPERATING_SYSTEM == 'Windows':
wav_resolution = bp['res_type']
else:
wav_resolution = 'polyphase'
if d == bands_n: # high-end band
X_wave[d], _ = librosa.load(
self.audio_file, bp['sr'], False, dtype=np.float32, res_type=bp['res_type'])
self.audio_file, bp['sr'], False, dtype=np.float32, res_type=wav_resolution)
if X_wave[d].ndim == 1:
X_wave[d] = np.asarray([X_wave[d], X_wave[d]])
else: # lower bands
X_wave[d] = librosa.resample(X_wave[d+1], self.mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type'])
X_wave[d] = librosa.resample(X_wave[d+1], self.mp.param['band'][d+1]['sr'], bp['sr'], res_type=wav_resolution)
X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(X_wave[d], bp['hl'], bp['n_fft'], self.mp.param['mid_side'],
self.mp.param['mid_side_b2'], self.mp.param['reverse'])
@ -844,8 +850,6 @@ def process_secondary_model(secondary_model: ModelData, process_data, main_model
def gather_sources(primary_stem_name, secondary_stem_name, secondary_sources: dict):
print(primary_stem_name, secondary_stem_name)
source_primary = False
source_secondary = False
@ -904,6 +908,10 @@ def save_format(audio_path, save_format, mp3_bit_set):
if not save_format == WAV:
if not OPERATING_SYSTEM == 'Windows':
FFMPEG_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'ffmpeg')
pydub.AudioSegment.converter = FFMPEG_PATH
musfile = pydub.AudioSegment.from_wav(audio_path)
if save_format == FLAC: