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Anjok07
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@@ -1,37 +1,37 @@
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import os
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from pathlib import Path
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import os.path
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from datetime import datetime
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import pydub
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import shutil
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from random import randrange
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#MDX-Net
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#----------------------------------------
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import soundfile as sf
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import torch
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import numpy as np
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from demucs.pretrained import get_model as _gm
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from demucs.hdemucs import HDemucs
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from demucs.apply import BagOfModels, apply_model
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from demucs.audio import AudioFile
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import time
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import os
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from tqdm import tqdm
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import warnings
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import sys
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import librosa
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import psutil
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#----------------------------------------
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from demucs.hdemucs import HDemucs
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from demucs.model_v2 import Demucs
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from demucs.pretrained import get_model as _gm
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from demucs.tasnet_v2 import ConvTasNet
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from demucs.utils import apply_model_v1
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from demucs.utils import apply_model_v2
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from diffq import DiffQuantizer
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from lib_v5 import spec_utils
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from lib_v5.model_param_init import ModelParameters
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import torch
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# Command line text parsing and widget manipulation
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import tkinter as tk
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import traceback # Error Message Recent Calls
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from pathlib import Path
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from random import randrange
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from tqdm import tqdm
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import gzip
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import io
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import librosa
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import numpy as np
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import os
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import os
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import os.path
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import psutil
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import pydub
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import shutil
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import soundfile as sf
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import sys
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import time
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import time # Timer
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import tkinter as tk
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import torch
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import torch.hub
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import traceback # Error Message Recent Calls
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import warnings
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import zlib
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class Predictor():
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def __init__(self):
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@@ -46,40 +46,62 @@ class Predictor():
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if data['gpu'] == -1:
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device = torch.device('cpu')
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self.demucs = HDemucs(sources=["drums", "bass", "other", "vocals"])
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widget_text.write(base_text + 'Loading Demucs model... ')
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update_progress(**progress_kwargs,
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step=0.05)
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path_d = Path('models/Demucs_Models')
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print('What Demucs model was chosen? ', data['DemucsModel'])
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self.demucs = _gm(name=data['DemucsModel'], repo=path_d)
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widget_text.write('Done!\n')
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if 'UVR' in data['DemucsModel']:
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widget_text.write(base_text + "2 stem model selected.\n")
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if isinstance(self.demucs, BagOfModels):
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widget_text.write(base_text + f"Selected model is a bag of {len(self.demucs.models)} models.\n")
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if data['segment'] == 'None':
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segment = None
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if isinstance(self.demucs, BagOfModels):
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if segment is not None:
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for sub in self.demucs.models:
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sub.segment = segment
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if demucs_model_version == 'v1':
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load_from = "models/Demucs_Models/"f"{demucs_model_set_name}"
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if str(load_from).endswith(".gz"):
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load_from = gzip.open(load_from, "rb")
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klass, args, kwargs, state = torch.load(load_from)
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self.demucs = klass(*args, **kwargs)
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widget_text.write(base_text + 'Loading Demucs v1 model... ')
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update_progress(**progress_kwargs,
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step=0.05)
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self.demucs.to(device)
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self.demucs.load_state_dict(state)
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widget_text.write('Done!\n')
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if not data['segment'] == 'None':
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widget_text.write(base_text + 'Segments is only available in Demucs v3. Please use \"Chunks\" instead.\n')
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else:
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if segment is not None:
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sub.segment = segment
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else:
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try:
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segment = int(data['segment'])
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if isinstance(self.demucs, BagOfModels):
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if segment is not None:
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for sub in self.demucs.models:
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sub.segment = segment
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else:
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if segment is not None:
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sub.segment = segment
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widget_text.write(base_text + "Segments set to "f"{segment}.\n")
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except:
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pass
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if demucs_model_version == 'v2':
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if '48' in demucs_model_set_name:
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channels=48
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elif 'unittest' in demucs_model_set_name:
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channels=4
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else:
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channels=64
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if 'tasnet' in demucs_model_set_name:
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self.demucs = ConvTasNet(sources=["drums", "bass", "other", "vocals"], X=10)
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else:
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self.demucs = Demucs(sources=["drums", "bass", "other", "vocals"], channels=channels)
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widget_text.write(base_text + 'Loading Demucs v2 model... ')
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update_progress(**progress_kwargs,
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step=0.05)
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self.demucs.to(device)
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self.demucs.load_state_dict(torch.load("models/Demucs_Models/"f"{demucs_model_set_name}"))
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widget_text.write('Done!\n')
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if not data['segment'] == 'None':
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widget_text.write(base_text + 'Segments is only available in Demucs v3. Please use \"Chunks\" instead.\n')
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else:
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pass
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self.demucs.eval()
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if demucs_model_version == 'v3':
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self.demucs = HDemucs(sources=["drums", "bass", "other", "vocals"])
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widget_text.write(base_text + 'Loading Demucs model... ')
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update_progress(**progress_kwargs,
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step=0.05)
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path_d = Path('models/Demucs_Models/v3_repo')
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#print('What Demucs model was chosen? ', demucs_model_set_name)
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self.demucs = _gm(name=demucs_model_set_name, repo=path_d)
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widget_text.write('Done!\n')
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if 'UVR' in data['DemucsModel']:
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widget_text.write(base_text + "2 stem model selected.\n")
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if isinstance(self.demucs, BagOfModels):
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widget_text.write(base_text + f"Selected model is a bag of {len(self.demucs.models)} models.\n")
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if data['segment'] == 'None':
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segment = None
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if isinstance(self.demucs, BagOfModels):
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if segment is not None:
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@@ -88,9 +110,29 @@ class Predictor():
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else:
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if segment is not None:
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sub.segment = segment
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self.demucs.to(device)
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self.demucs.eval()
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else:
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try:
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segment = int(data['segment'])
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if isinstance(self.demucs, BagOfModels):
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if segment is not None:
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for sub in self.demucs.models:
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sub.segment = segment
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else:
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if segment is not None:
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sub.segment = segment
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widget_text.write(base_text + "Segments set to "f"{segment}.\n")
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except:
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segment = None
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if isinstance(self.demucs, BagOfModels):
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if segment is not None:
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for sub in self.demucs.models:
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sub.segment = segment
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else:
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if segment is not None:
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sub.segment = segment
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self.demucs.to(device)
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self.demucs.eval()
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update_progress(**progress_kwargs,
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step=0.1)
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@@ -646,7 +688,12 @@ class Predictor():
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if end == samples:
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break
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sources = self.demix_demucs(segmented_mix, margin_size=margin)
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if demucs_model_version == 'v1':
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sources = self.demix_demucs_v1(segmented_mix, margin_size=margin)
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if demucs_model_version == 'v2':
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sources = self.demix_demucs_v2(segmented_mix, margin_size=margin)
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if demucs_model_version == 'v3':
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sources = self.demix_demucs(segmented_mix, margin_size=margin)
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return sources
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@@ -683,31 +730,94 @@ class Predictor():
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sources = np.concatenate(sources, axis=-1)
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widget_text.write('Done!\n')
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return sources
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def demix_demucs_v1(self, mix, margin_size):
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processed = {}
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demucsitera = len(mix)
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demucsitera_calc = demucsitera * 2
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gui_progress_bar_demucs = 0
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widget_text.write(base_text + "Running Demucs v1 Inference...\n")
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widget_text.write(base_text + "Processing "f"{len(mix)} slices... ")
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print(' Running Demucs Inference...')
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for nmix in mix:
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gui_progress_bar_demucs += 1
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update_progress(**progress_kwargs,
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step=(0.35 + (1.05/demucsitera_calc * gui_progress_bar_demucs)))
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cmix = mix[nmix]
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cmix = torch.tensor(cmix, dtype=torch.float32)
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ref = cmix.mean(0)
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cmix = (cmix - ref.mean()) / ref.std()
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with torch.no_grad():
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sources = apply_model_v1(self.demucs, cmix.to(device), split=split_mode, shifts=shift_set)
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sources = (sources * ref.std() + ref.mean()).cpu().numpy()
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sources[[0,1]] = sources[[1,0]]
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start = 0 if nmix == 0 else margin_size
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end = None if nmix == list(mix.keys())[::-1][0] else -margin_size
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if margin_size == 0:
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end = None
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processed[nmix] = sources[:,:,start:end].copy()
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sources = list(processed.values())
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sources = np.concatenate(sources, axis=-1)
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widget_text.write('Done!\n')
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return sources
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def demix_demucs_v2(self, mix, margin_size):
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processed = {}
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demucsitera = len(mix)
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demucsitera_calc = demucsitera * 2
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gui_progress_bar_demucs = 0
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widget_text.write(base_text + "Running Demucs v2 Inference...\n")
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widget_text.write(base_text + "Processing "f"{len(mix)} slices... ")
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print(' Running Demucs Inference...')
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for nmix in mix:
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gui_progress_bar_demucs += 1
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update_progress(**progress_kwargs,
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step=(0.35 + (1.05/demucsitera_calc * gui_progress_bar_demucs)))
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cmix = mix[nmix]
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cmix = torch.tensor(cmix, dtype=torch.float32)
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ref = cmix.mean(0)
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cmix = (cmix - ref.mean()) / ref.std()
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shift_set = 0
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with torch.no_grad():
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sources = apply_model_v2(self.demucs, cmix.to(device), split=split_mode, overlap=overlap_set, shifts=shift_set)
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sources = (sources * ref.std() + ref.mean()).cpu().numpy()
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sources[[0,1]] = sources[[1,0]]
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start = 0 if nmix == 0 else margin_size
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end = None if nmix == list(mix.keys())[::-1][0] else -margin_size
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if margin_size == 0:
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end = None
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processed[nmix] = sources[:,:,start:end].copy()
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sources = list(processed.values())
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sources = np.concatenate(sources, axis=-1)
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widget_text.write('Done!\n')
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return sources
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data = {
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# Paths
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'input_paths': None,
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'export_path': None,
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'saveFormat': 'Wav',
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# Processing Options
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'demucsmodel': True,
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'gpu': -1,
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'audfile': True,
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'chunks_d': 'Full',
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'settest': False,
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'voc_only_b': False,
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'inst_only_b': False,
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'overlap_b': 0.25,
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'shifts_b': 2,
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'segment': 'None',
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'margin': 44100,
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'split_mode': False,
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'normalize': False,
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'compensate': 1.03597672895,
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'demucs_stems': 'All Stems',
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'DemucsModel': 'mdx_extra',
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'audfile': True,
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'wavtype': 'PCM_16',
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'demucsmodel': True,
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'export_path': None,
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'gpu': -1,
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'input_paths': None,
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'inst_only_b': False,
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'margin': 44100,
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'mp3bit': '320k',
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'normalize': False,
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'overlap_b': 0.25,
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'saveFormat': 'Wav',
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'segment': 'None',
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'settest': False,
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'shifts_b': 2,
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'split_mode': False,
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'voc_only_b': False,
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'wavtype': 'PCM_16',
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}
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default_chunks = data['chunks_d']
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@@ -756,6 +866,8 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
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global shift_set
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global source_val
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global split_mode
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global demucs_model_set_name
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global demucs_model_version
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global wav_type_set
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global flac_type_set
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@@ -817,6 +929,69 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
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progress_var.set(0)
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text_widget.clear()
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button_widget.configure(state=tk.DISABLED) # Disable Button
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if data['DemucsModel'] == "Tasnet v1":
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demucs_model_set_name = 'tasnet.th'
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demucs_model_version = 'v1'
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elif data['DemucsModel'] == "Tasnet_extra v1":
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demucs_model_set_name = 'tasnet_extra.th'
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demucs_model_version = 'v1'
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elif data['DemucsModel'] == "Demucs v1":
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demucs_model_set_name = 'demucs.th'
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demucs_model_version = 'v1'
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elif data['DemucsModel'] == "Demucs v1.gz":
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demucs_model_set_name = 'demucs.th.gz'
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demucs_model_version = 'v1'
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elif data['DemucsModel'] == "Demucs_extra v1":
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demucs_model_set_name = 'demucs_extra.th'
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demucs_model_version = 'v1'
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elif data['DemucsModel'] == "Demucs_extra v1.gz":
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demucs_model_set_name = 'demucs_extra.th.gz'
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demucs_model_version = 'v1'
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elif data['DemucsModel'] == "Light v1":
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demucs_model_set_name = 'light.th'
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demucs_model_version = 'v1'
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elif data['DemucsModel'] == "Light v1.gz":
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demucs_model_set_name = 'light.th.gz'
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demucs_model_version = 'v1'
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elif data['DemucsModel'] == "Light_extra v1":
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demucs_model_set_name = 'light_extra.th'
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demucs_model_version = 'v1'
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elif data['DemucsModel'] == "Light_extra v1.gz":
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demucs_model_set_name = 'light_extra.th.gz'
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demucs_model_version = 'v1'
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elif data['DemucsModel'] == "Tasnet v2":
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demucs_model_set_name = 'tasnet-beb46fac.th'
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demucs_model_version = 'v2'
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elif data['DemucsModel'] == "Tasnet_extra v2":
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demucs_model_set_name = 'tasnet_extra-df3777b2.th'
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demucs_model_version = 'v2'
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elif data['DemucsModel'] == "Demucs48_hq v2":
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demucs_model_set_name = 'demucs48_hq-28a1282c.th'
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demucs_model_version = 'v2'
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elif data['DemucsModel'] == "Demucs v2":
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demucs_model_set_name = 'demucs-e07c671f.th'
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demucs_model_version = 'v2'
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elif data['DemucsModel'] == "Demucs_extra v2":
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demucs_model_set_name = 'demucs_extra-3646af93.th'
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demucs_model_version = 'v2'
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elif data['DemucsModel'] == "Demucs_unittest v2":
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demucs_model_set_name = 'demucs_unittest-09ebc15f.th'
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demucs_model_version = 'v2'
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elif '.ckpt' in data['DemucsModel'] and 'v2' in data['DemucsModel']:
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demucs_model_set_name = data['DemucsModel']
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demucs_model_version = 'v2'
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elif '.ckpt' in data['DemucsModel'] and 'v1' in data['DemucsModel']:
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demucs_model_set_name = data['DemucsModel']
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demucs_model_version = 'v1'
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elif '.gz' in data['DemucsModel']:
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demucs_model_set_name = data['DemucsModel']
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demucs_model_version = 'v1'
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else:
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demucs_model_set_name = data['DemucsModel']
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demucs_model_version = 'v3'
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try: #Load File(s)
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for file_num, music_file in tqdm(enumerate(data['input_paths'], start=1)):
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@@ -880,7 +1055,10 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
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channel_set = int(data['channel'])
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margin_set = int(data['margin'])
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shift_set = int(data['shifts_b'])
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split_mode = data['split_mode']
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#print('Split? ', split_mode)
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def determinemusicfileFolderName():
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"""
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