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inference_MDX.py
414
inference_MDX.py
@ -1,7 +1,4 @@
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import os
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from pickle import STOP
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from tracemalloc import stop
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from turtle import update
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import subprocess
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from unittest import skip
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from pathlib import Path
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@ -11,14 +8,18 @@ import pydub
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import shutil
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import hashlib
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import gc
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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.model import Demucs
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from demucs.utils import apply_model
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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 pathlib
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from models import get_models, spec_effects
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import onnxruntime as ort
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import time
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@ -37,38 +38,43 @@ import torch
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import tkinter as tk
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import traceback # Error Message Recent Calls
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import time # Timer
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from typing import Literal
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class Predictor():
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def __init__(self):
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pass
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def prediction_setup(self, demucs_name,
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channels=64):
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def prediction_setup(self):
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global device
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print('Print the gpu setting: ', data['gpu'])
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if data['gpu'] >= 0:
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device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
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device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
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if data['gpu'] == -1:
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device = torch.device('cpu')
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if data['demucsmodel']:
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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 model... ')
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if 'UVR' in demucs_model_set:
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self.demucs = HDemucs(sources=["other", "vocals"])
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else:
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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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self.demucs = _gm(name=demucs_model_set, repo=path_d)
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self.demucs.to(device)
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self.demucs.load_state_dict(torch.load(demucs_name))
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widget_text.write('Done!\n')
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self.demucs.eval()
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widget_text.write('Done!\n')
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if isinstance(self.demucs, BagOfModels):
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widget_text.write(base_text + f"Selected Demucs model is a bag of {len(self.demucs.models)} model(s).\n")
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self.onnx_models = {}
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c = 0
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print('stemtype: ', modeltype)
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self.models = get_models('tdf_extra', load=False, device=cpu, stems=modeltype, n_fft_scale=n_fft_scale_set, dim_f=dim_f_set)
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if not data['demucs_only']:
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widget_text.write(base_text + 'Loading ONNX model... ')
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@ -87,19 +93,17 @@ class Predictor():
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elif data['gpu'] == -1:
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run_type = ['CPUExecutionProvider']
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print(run_type)
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print(str(device))
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print('Selected Model: ', model_set)
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self.onnx_models[c] = ort.InferenceSession(os.path.join('models/MDX_Net_Models', str(model_set) + '.onnx'), providers=run_type)
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if not data['demucs_only']:
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widget_text.write('Done!\n')
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def prediction(self, m):
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#mix, rate = sf.read(m)
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mix, rate = librosa.load(m, mono=False, sr=44100)
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mix, samplerate = librosa.load(m, mono=False, sr=44100)
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if mix.ndim == 1:
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mix = np.asfortranarray([mix,mix])
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mix = np.asfortranarray([mix,mix])
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samplerate = samplerate
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mix = mix.T
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sources = self.demix(mix.T)
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widget_text.write(base_text + 'Inferences complete!\n')
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@ -226,13 +230,12 @@ class Predictor():
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c += 1
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if not data['demucsmodel']:
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if data['inst_only']:
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widget_text.write(base_text + 'Preparing to save Instrumental...')
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else:
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widget_text.write(base_text + 'Saving vocals... ')
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sf.write(non_reduced_vocal_path, sources[c].T, rate)
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sf.write(non_reduced_vocal_path, sources[c].T, samplerate)
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update_progress(**progress_kwargs,
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step=(0.9))
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widget_text.write('Done!\n')
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@ -240,7 +243,7 @@ class Predictor():
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reduction_sen = float(int(data['noisereduc_s'])/10)
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subprocess.call("lib_v5\\sox\\sox.exe" + ' "' +
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f"{str(non_reduced_vocal_path)}" + '" "' + f"{str(vocal_path)}" + '" ' +
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"noisered lib_v5\\sox\\mdxnetnoisereduc.prof " + f"{reduction_sen}",
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"noisered lib_v5\\sox\\" + noise_pro_set + ".prof " + f"{reduction_sen}",
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shell=True, stdout=subprocess.PIPE,
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stdin=subprocess.PIPE, stderr=subprocess.PIPE)
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widget_text.write('Done!\n')
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@ -252,7 +255,11 @@ class Predictor():
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else:
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widget_text.write(base_text + 'Saving Vocals... ')
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sf.write(non_reduced_vocal_path, sources[3].T, rate)
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if data['demucs_only']:
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if 'UVR' in demucs_model_set:
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sf.write(non_reduced_vocal_path, sources[1].T, samplerate)
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else:
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sf.write(non_reduced_vocal_path, sources[source_val].T, samplerate)
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update_progress(**progress_kwargs,
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step=(0.9))
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widget_text.write('Done!\n')
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@ -275,7 +282,7 @@ class Predictor():
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widget_text.write(base_text + 'Preparing Instrumental...')
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else:
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widget_text.write(base_text + 'Saving Vocals... ')
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sf.write(vocal_path, sources[c].T, rate)
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sf.write(vocal_path, sources[c].T, samplerate)
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update_progress(**progress_kwargs,
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step=(0.9))
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widget_text.write('Done!\n')
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@ -284,7 +291,15 @@ class Predictor():
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widget_text.write(base_text + 'Preparing Instrumental...')
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else:
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widget_text.write(base_text + 'Saving Vocals... ')
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sf.write(vocal_path, sources[3].T, rate)
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if data['demucs_only']:
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if 'UVR' in demucs_model_set:
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sf.write(vocal_path, sources[1].T, samplerate)
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else:
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sf.write(vocal_path, sources[source_val].T, samplerate)
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else:
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sf.write(vocal_path, sources[source_val].T, samplerate)
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update_progress(**progress_kwargs,
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step=(0.9))
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widget_text.write('Done!\n')
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@ -470,13 +485,6 @@ class Predictor():
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errmessage + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
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except:
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pass
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try:
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print('Is there already a voc file there? ', file_exists_v)
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print('Is there already a non_voc file there? ', file_exists_n)
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except:
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pass
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if data['noisereduc_s'] == 'None':
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pass
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@ -567,23 +575,37 @@ class Predictor():
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segmented_mix[skip] = mix[:,start:end].copy()
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if end == samples:
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break
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if not data['demucsmodel']:
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sources = self.demix_base(segmented_mix, margin_size=margin)
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elif data['demucs_only']:
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sources = self.demix_demucs(segmented_mix, margin_size=margin)
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if split_mode == True:
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sources = self.demix_demucs_split(mix)
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if split_mode == False:
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sources = self.demix_demucs(segmented_mix, margin_size=margin)
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else: # both, apply spec effects
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base_out = self.demix_base(segmented_mix, margin_size=margin)
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demucs_out = self.demix_demucs(segmented_mix, margin_size=margin)
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print(split_mode)
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if split_mode == True:
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demucs_out = self.demix_demucs_split(mix)
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if split_mode == False:
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demucs_out = self.demix_demucs(segmented_mix, margin_size=margin)
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nan_count = np.count_nonzero(np.isnan(demucs_out)) + np.count_nonzero(np.isnan(base_out))
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if nan_count > 0:
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print('Warning: there are {} nan values in the array(s).'.format(nan_count))
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demucs_out, base_out = np.nan_to_num(demucs_out), np.nan_to_num(base_out)
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sources = {}
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print(data['mixing'])
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sources[3] = (spec_effects(wave=[demucs_out[source_val],base_out[0]],
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algorithm=data['mixing'],
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value=b[3])*float(data['compensate'])) # compensation
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if 'UVR' in demucs_model_set:
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sources[source_val] = (spec_effects(wave=[demucs_out[1],base_out[0]],
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algorithm=data['mixing'],
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value=b[source_val])*float(data['compensate'])) # compensation
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else:
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sources[source_val] = (spec_effects(wave=[demucs_out[source_val],base_out[0]],
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algorithm=data['mixing'],
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value=b[source_val])*float(data['compensate'])) # compensation
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return sources
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def demix_base(self, mixes, margin_size):
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@ -594,6 +616,7 @@ class Predictor():
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widget_text.write(base_text + "Running ONNX Inference...\n")
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widget_text.write(base_text + "Processing "f"{onnxitera} slices... ")
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print(' Running ONNX Inference...')
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for mix in mixes:
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gui_progress_bar_onnx += 1
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if data['demucsmodel']:
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@ -602,6 +625,7 @@ class Predictor():
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else:
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update_progress(**progress_kwargs,
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step=(0.1 + (0.9/onnxitera * gui_progress_bar_onnx)))
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cmix = mixes[mix]
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sources = []
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n_sample = cmix.shape[1]
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@ -634,7 +658,6 @@ class Predictor():
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end = None
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sources.append(tar_signal[:,start:end])
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chunked_sources.append(sources)
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_sources = np.concatenate(chunked_sources, axis=-1)
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del self.onnx_models
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@ -647,6 +670,7 @@ class Predictor():
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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 + "Split Mode is off. (Chunks enabled for Demucs Model)\n")
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widget_text.write(base_text + "Running Demucs 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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@ -659,7 +683,8 @@ class Predictor():
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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(self.demucs, cmix.to(device), split=True, overlap=overlap_set, shifts=shift_set)
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print(split_mode)
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sources = apply_model(self.demucs, cmix[None], split=split_mode, device=device, overlap=overlap_set, shifts=shift_set, progress=False)[0]
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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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@ -673,6 +698,27 @@ 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_split(self, mix):
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print('shift_set ', shift_set)
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widget_text.write(base_text + "Split Mode is on. (Chunks disabled for Demucs Model)\n")
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widget_text.write(base_text + "Running Demucs 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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mix = torch.tensor(mix, dtype=torch.float32)
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ref = mix.mean(0)
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mix = (mix - ref.mean()) / ref.std()
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with torch.no_grad():
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sources = apply_model(self.demucs, mix[None], split=split_mode, device=device, overlap=overlap_set, shifts=shift_set, progress=False)[0]
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widget_text.write('Done!\n')
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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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return sources
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data = {
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# Paths
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@ -694,11 +740,11 @@ data = {
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'overlap': 0.5,
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'shifts': 0,
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'margin': 44100,
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'channel': 64,
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'split_mode': False,
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'compensate': 1.03597672895,
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'demucs_only': False,
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'mixing': 'Default',
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'DemucsModel': 'demucs_extra-3646af93_org.th',
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'DemucsModel_MDX': 'UVR_Demucs_Model_1',
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# Choose Model
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'mdxnetModel': 'UVR-MDX-NET 1',
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'mdxnetModeltype': 'Vocals (Custom)',
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@ -751,6 +797,7 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
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global model_set_name
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global stemset_n
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global noise_pro_set
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global demucs_model_set
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global mdx_model_hash
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@ -759,6 +806,9 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
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global overlap_set
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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_switch
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# Update default settings
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default_chunks = data['chunks']
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@ -823,161 +873,90 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
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source_val_set = 0
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stem_name = '(Bass)'
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try:
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if data['mdxnetModel'] == 'UVR-MDX-NET 1':
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if data['mdxnetModel'] == 'UVR-MDX-NET 1':
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if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_1_9703.onnx'):
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model_set = 'UVR_MDXNET_1_9703'
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model_set_name = 'UVR_MDXNET_1_9703'
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modeltype = 'v'
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noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
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stemset_n = '(Vocals)'
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source_val = 3
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n_fft_scale_set=6144
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dim_f_set=2048
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elif data['mdxnetModel'] == 'UVR-MDX-NET 2':
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model_set = 'UVR_MDXNET_2_9682'
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model_set_name = 'UVR_MDXNET_2_9682'
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modeltype = 'v'
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noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
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stemset_n = '(Vocals)'
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source_val = 3
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n_fft_scale_set=6144
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dim_f_set=2048
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elif data['mdxnetModel'] == 'UVR-MDX-NET 3':
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model_set = 'UVR_MDXNET_3_9662'
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model_set_name = 'UVR_MDXNET_3_9662'
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modeltype = 'v'
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noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
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stemset_n = '(Vocals)'
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source_val = 3
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n_fft_scale_set=6144
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dim_f_set=2048
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elif data['mdxnetModel'] == 'UVR-MDX-NET Karaoke':
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model_set = 'UVR_MDXNET_KARA'
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model_set_name = 'UVR_MDXNET_Karaoke'
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modeltype = 'v'
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noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
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stemset_n = '(Vocals)'
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source_val = 3
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n_fft_scale_set=6144
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dim_f_set=2048
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elif data['mdxnetModel'] == 'other':
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model_set = 'other'
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model_set_name = 'other'
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modeltype = 'o'
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noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
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stemset_n = '(Other)'
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source_val = 2
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n_fft_scale_set=8192
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dim_f_set=2048
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elif data['mdxnetModel'] == 'drums':
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model_set = 'drums'
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model_set_name = 'drums'
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modeltype = 'd'
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noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
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stemset_n = '(Drums)'
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source_val = 1
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n_fft_scale_set=4096
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dim_f_set=2048
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elif data['mdxnetModel'] == 'bass':
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model_set = 'bass'
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model_set_name = 'bass'
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modeltype = 'b'
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noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
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stemset_n = '(Bass)'
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source_val = 0
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n_fft_scale_set=16384
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dim_f_set=2048
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else:
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model_set = data['mdxnetModel']
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model_set_name = data['mdxnetModel']
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modeltype = stemset
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noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
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stemset_n = stem_name
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source_val = source_val_set
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n_fft_scale_set=int(data['n_fft_scale'])
|
||||
dim_f_set=int(data['dim_f'])
|
||||
|
||||
MDXModelName=('models/MDX_Net_Models/' + model_set + '.onnx')
|
||||
mdx_model_hash = hashlib.md5(open(MDXModelName, 'rb').read()).hexdigest()
|
||||
print(mdx_model_hash)
|
||||
except:
|
||||
if data['mdxnetModel'] == 'UVR-MDX-NET 1':
|
||||
model_set = 'UVR_MDXNET_9703'
|
||||
model_set_name = 'UVR_MDXNET_9703'
|
||||
modeltype = 'v'
|
||||
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
|
||||
stemset_n = '(Vocals)'
|
||||
source_val = 3
|
||||
n_fft_scale_set=6144
|
||||
dim_f_set=2048
|
||||
elif data['mdxnetModel'] == 'UVR-MDX-NET 2':
|
||||
modeltype = 'v'
|
||||
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
|
||||
stemset_n = '(Vocals)'
|
||||
source_val = 3
|
||||
n_fft_scale_set=6144
|
||||
dim_f_set=2048
|
||||
elif data['mdxnetModel'] == 'UVR-MDX-NET 2':
|
||||
if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_2_9682.onnx'):
|
||||
model_set = 'UVR_MDXNET_2_9682'
|
||||
model_set_name = 'UVR_MDXNET_2_9682'
|
||||
else:
|
||||
model_set = 'UVR_MDXNET_9682'
|
||||
model_set_name = 'UVR_MDXNET_9682'
|
||||
modeltype = 'v'
|
||||
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
|
||||
stemset_n = '(Vocals)'
|
||||
source_val = 3
|
||||
n_fft_scale_set=6144
|
||||
dim_f_set=2048
|
||||
elif data['mdxnetModel'] == 'UVR-MDX-NET 3':
|
||||
modeltype = 'v'
|
||||
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
|
||||
stemset_n = '(Vocals)'
|
||||
source_val = 3
|
||||
n_fft_scale_set=6144
|
||||
dim_f_set=2048
|
||||
elif data['mdxnetModel'] == 'UVR-MDX-NET 3':
|
||||
if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_3_9662.onnx'):
|
||||
model_set = 'UVR_MDXNET_3_9662'
|
||||
model_set_name = 'UVR_MDXNET_3_9662'
|
||||
else:
|
||||
model_set = 'UVR_MDXNET_9662'
|
||||
model_set_name = 'UVR_MDXNET_9662'
|
||||
modeltype = 'v'
|
||||
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
|
||||
stemset_n = '(Vocals)'
|
||||
source_val = 3
|
||||
n_fft_scale_set=6144
|
||||
dim_f_set=2048
|
||||
elif data['mdxnetModel'] == 'UVR-MDX-NET Karaoke':
|
||||
model_set = 'UVR_MDXNET_KARA'
|
||||
model_set_name = 'UVR_MDXNET_Karaoke'
|
||||
modeltype = 'v'
|
||||
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
|
||||
stemset_n = '(Vocals)'
|
||||
source_val = 3
|
||||
n_fft_scale_set=6144
|
||||
dim_f_set=2048
|
||||
elif data['mdxnetModel'] == 'other':
|
||||
model_set = 'other'
|
||||
model_set_name = 'other'
|
||||
modeltype = 'o'
|
||||
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
|
||||
stemset_n = '(Other)'
|
||||
source_val = 2
|
||||
n_fft_scale_set=8192
|
||||
dim_f_set=2048
|
||||
elif data['mdxnetModel'] == 'drums':
|
||||
model_set = 'drums'
|
||||
model_set_name = 'drums'
|
||||
modeltype = 'd'
|
||||
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
|
||||
stemset_n = '(Drums)'
|
||||
source_val = 1
|
||||
n_fft_scale_set=4096
|
||||
dim_f_set=2048
|
||||
elif data['mdxnetModel'] == 'bass':
|
||||
model_set = 'bass'
|
||||
model_set_name = 'bass'
|
||||
modeltype = 'b'
|
||||
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
|
||||
stemset_n = '(Bass)'
|
||||
source_val = 0
|
||||
n_fft_scale_set=16384
|
||||
dim_f_set=2048
|
||||
else:
|
||||
model_set = data['mdxnetModel']
|
||||
model_set_name = data['mdxnetModel']
|
||||
modeltype = stemset
|
||||
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
|
||||
stemset_n = stem_name
|
||||
source_val = source_val_set
|
||||
n_fft_scale_set=int(data['n_fft_scale'])
|
||||
dim_f_set=int(data['dim_f'])
|
||||
|
||||
MDXModelName=('models/MDX_Net_Models/' + model_set_name + '.onnx')
|
||||
mdx_model_hash = hashlib.md5(open(MDXModelName, 'rb').read()).hexdigest()
|
||||
print(mdx_model_hash)
|
||||
modeltype = 'v'
|
||||
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
|
||||
stemset_n = '(Vocals)'
|
||||
source_val = 3
|
||||
n_fft_scale_set=6144
|
||||
dim_f_set=2048
|
||||
elif data['mdxnetModel'] == 'UVR-MDX-NET Karaoke':
|
||||
model_set = 'UVR_MDXNET_KARA'
|
||||
model_set_name = 'UVR_MDXNET_Karaoke'
|
||||
modeltype = 'v'
|
||||
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
|
||||
stemset_n = '(Vocals)'
|
||||
source_val = 3
|
||||
n_fft_scale_set=6144
|
||||
dim_f_set=2048
|
||||
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)'
|
||||
source_val = 2
|
||||
n_fft_scale_set=8192
|
||||
dim_f_set=2048
|
||||
elif 'drums' in data['mdxnetModel']:
|
||||
model_set = 'drums'
|
||||
model_set_name = 'drums'
|
||||
modeltype = 'd'
|
||||
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
|
||||
stemset_n = '(Drums)'
|
||||
source_val = 1
|
||||
n_fft_scale_set=4096
|
||||
dim_f_set=2048
|
||||
elif 'bass' in data['mdxnetModel']:
|
||||
model_set = 'bass'
|
||||
model_set_name = 'bass'
|
||||
modeltype = 'b'
|
||||
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
|
||||
stemset_n = '(Bass)'
|
||||
source_val = 0
|
||||
n_fft_scale_set=16384
|
||||
dim_f_set=2048
|
||||
else:
|
||||
model_set = data['mdxnetModel']
|
||||
model_set_name = data['mdxnetModel']
|
||||
modeltype = stemset
|
||||
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
|
||||
stemset_n = stem_name
|
||||
source_val = source_val_set
|
||||
n_fft_scale_set=int(data['n_fft_scale'])
|
||||
dim_f_set=int(data['dim_f'])
|
||||
|
||||
|
||||
if data['noise_pro_select'] == 'Auto Select':
|
||||
@ -988,12 +967,8 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
|
||||
|
||||
print(n_fft_scale_set)
|
||||
print(dim_f_set)
|
||||
print(data['DemucsModel'])
|
||||
print(data['DemucsModel_MDX'])
|
||||
|
||||
overlap_set = float(data['overlap'])
|
||||
channel_set = int(data['channel'])
|
||||
margin_set = int(data['margin'])
|
||||
shift_set = int(data['shifts'])
|
||||
|
||||
stime = time.perf_counter()
|
||||
progress_var.set(0)
|
||||
@ -1002,7 +977,46 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
|
||||
|
||||
try: #Load File(s)
|
||||
for file_num, music_file in tqdm(enumerate(data['input_paths'], start=1)):
|
||||
|
||||
|
||||
overlap_set = float(data['overlap'])
|
||||
channel_set = int(data['channel'])
|
||||
margin_set = int(data['margin'])
|
||||
shift_set = int(data['shifts'])
|
||||
demucs_model_set = data['DemucsModel_MDX']
|
||||
split_mode = data['split_mode']
|
||||
demucs_switch = data['demucsmodel']
|
||||
|
||||
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')
|
||||
text_widget.write('Please select a 4 stem Demucs model and try again.\n\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
|
||||
else:
|
||||
pass
|
||||
if stemset_n == '(Drums)':
|
||||
if 'UVR' in demucs_model_set:
|
||||
text_widget.write('The selected Demucs model can only be used with vocal stems.\n')
|
||||
text_widget.write('Please select a 4 stem Demucs model and try again.\n\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
|
||||
else:
|
||||
pass
|
||||
if stemset_n == '(Other)':
|
||||
if 'UVR' in demucs_model_set:
|
||||
text_widget.write('The selected Demucs model can only be used with vocal stems.\n')
|
||||
text_widget.write('Please select a 4 stem Demucs model and try again.\n\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
|
||||
else:
|
||||
pass
|
||||
|
||||
_mixture = f'{data["input_paths"]}'
|
||||
_basename = f'{data["export_path"]}/{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
|
||||
|
||||
@ -1063,11 +1077,10 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
|
||||
|
||||
e = os.path.join(data["export_path"])
|
||||
|
||||
demucsmodel = 'models/Demucs_Model/' + str(data['DemucsModel'])
|
||||
demucsmodel = 'models/Demucs_Models/' + str(data['DemucsModel_MDX'])
|
||||
|
||||
pred = Predictor()
|
||||
pred.prediction_setup(demucs_name=demucsmodel,
|
||||
channels=channel_set)
|
||||
pred.prediction_setup()
|
||||
|
||||
print(demucsmodel)
|
||||
|
||||
@ -1373,7 +1386,10 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
|
||||
text_widget.write("\n" + f'Please address the error and try again.' + "\n")
|
||||
text_widget.write(f'If this error persists, please contact the developers with the error details.\n\n')
|
||||
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
|
||||
torch.cuda.empty_cache()
|
||||
try:
|
||||
torch.cuda.empty_cache()
|
||||
except:
|
||||
pass
|
||||
button_widget.configure(state=tk.NORMAL) # Enable Button
|
||||
return
|
||||
|
||||
|
||||
1225
inference_demucs.py
Normal file
1225
inference_demucs.py
Normal file
File diff suppressed because it is too large
Load Diff
@ -1,7 +1,5 @@
|
||||
from functools import total_ordering
|
||||
import os
|
||||
import importlib
|
||||
from statistics import mode
|
||||
import pydub
|
||||
import shutil
|
||||
import hashlib
|
||||
@ -1006,7 +1004,7 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
|
||||
with open('errorlog.txt', 'w') as f:
|
||||
f.write(f'Last Error Received:\n\n' +
|
||||
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
|
||||
f'Process Method: Ensemble Mode\n\n' +
|
||||
f'Process Method: VR Architecture\n\n' +
|
||||
f'Could not write audio file.\n' +
|
||||
f'This could be due to low storage on target device or a system permissions issue.\n' +
|
||||
f'If the error persists, please contact the developers.\n\n' +
|
||||
@ -1031,7 +1029,7 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
|
||||
with open('errorlog.txt', 'w') as f:
|
||||
f.write(f'Last Error Received:\n\n' +
|
||||
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
|
||||
f'Process Method: Ensemble Mode\n\n' +
|
||||
f'Process Method: VR Architecture\n\n' +
|
||||
f'The application was unable to allocate enough system memory to use this model.\n' +
|
||||
f'Please do the following:\n\n1. Restart this application.\n2. Ensure any CPU intensive applications are closed.\n3. Then try again.\n\n' +
|
||||
f'Please Note: Intel Pentium and Intel Celeron processors do not work well with this application.\n\n' +
|
||||
|
||||
@ -11,8 +11,10 @@ import subprocess
|
||||
import soundfile as sf
|
||||
import torch
|
||||
import numpy as np
|
||||
from demucs.model import Demucs
|
||||
from demucs.utils import apply_model
|
||||
from demucs.pretrained import get_model as _gm
|
||||
from demucs.hdemucs import HDemucs
|
||||
from demucs.apply import BagOfModels, apply_model
|
||||
import pathlib
|
||||
from models import get_models, spec_effects
|
||||
import onnxruntime as ort
|
||||
import time
|
||||
@ -47,32 +49,36 @@ class Predictor():
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def prediction_setup(self, demucs_name,
|
||||
channels=64):
|
||||
def prediction_setup(self):
|
||||
|
||||
global device
|
||||
|
||||
print('Print the gpu setting: ', data['gpu'])
|
||||
|
||||
if data['gpu'] >= 0:
|
||||
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
|
||||
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
|
||||
if data['gpu'] == -1:
|
||||
device = torch.device('cpu')
|
||||
|
||||
if data['demucsmodel']:
|
||||
self.demucs = Demucs(sources=["drums", "bass", "other", "vocals"], channels=channels)
|
||||
if demucs_switch == 'on':
|
||||
if 'UVR' in demucs_model_set:
|
||||
self.demucs = HDemucs(sources=["other", "vocals"])
|
||||
else:
|
||||
self.demucs = HDemucs(sources=["drums", "bass", "other", "vocals"])
|
||||
widget_text.write(base_text + 'Loading Demucs model... ')
|
||||
update_progress(**progress_kwargs,
|
||||
step=0.05)
|
||||
path_d = Path('models/Demucs_Models')
|
||||
self.demucs = _gm(name=demucs_model_set, repo=path_d)
|
||||
self.demucs.to(device)
|
||||
self.demucs.load_state_dict(torch.load(demucs_name))
|
||||
widget_text.write('Done!\n')
|
||||
self.demucs.eval()
|
||||
widget_text.write('Done!\n')
|
||||
if isinstance(self.demucs, BagOfModels):
|
||||
widget_text.write(base_text + f"Selected Demucs model is a bag of {len(self.demucs.models)} model(s).\n")
|
||||
|
||||
self.onnx_models = {}
|
||||
c = 0
|
||||
|
||||
self.models = get_models('tdf_extra', load=False, device=cpu, stems=modeltype, n_fft_scale=n_fft_scale_set, dim_f=dim_f_set)
|
||||
if not data['demucs_only']:
|
||||
if demucs_only == 'off':
|
||||
widget_text.write(base_text + 'Loading ONNX model... ')
|
||||
|
||||
update_progress(**progress_kwargs,
|
||||
@ -88,24 +94,26 @@ class Predictor():
|
||||
run_type = ['CPUExecutionProvider']
|
||||
elif data['gpu'] == -1:
|
||||
run_type = ['CPUExecutionProvider']
|
||||
|
||||
print(run_type)
|
||||
print(str(device))
|
||||
|
||||
print('model_set: ', model_set)
|
||||
self.onnx_models[c] = ort.InferenceSession(os.path.join('models/MDX_Net_Models', model_set), providers=run_type)
|
||||
|
||||
if not data['demucs_only']:
|
||||
if demucs_only == 'off':
|
||||
self.onnx_models[c] = ort.InferenceSession(os.path.join('models/MDX_Net_Models', model_set), providers=run_type)
|
||||
print(demucs_model_set)
|
||||
widget_text.write('Done!\n')
|
||||
elif demucs_only == 'on':
|
||||
print(demucs_model_set)
|
||||
pass
|
||||
|
||||
def prediction(self, m):
|
||||
#mix, rate = sf.read(m)
|
||||
mix, rate = librosa.load(m, mono=False, sr=44100)
|
||||
|
||||
mix, samplerate = librosa.load(m, mono=False, sr=44100)
|
||||
if mix.ndim == 1:
|
||||
mix = np.asfortranarray([mix,mix])
|
||||
samplerate = samplerate
|
||||
|
||||
mix = mix.T
|
||||
sources = self.demix(mix.T)
|
||||
widget_text.write(base_text + 'Inferences complete!\n')
|
||||
|
||||
c = -1
|
||||
|
||||
#Main Save Path
|
||||
@ -154,20 +162,22 @@ class Predictor():
|
||||
else:
|
||||
file_exists = 'not_there'
|
||||
|
||||
if demucs_only == 'on':
|
||||
data['noisereduc_s'] == 'None'
|
||||
|
||||
if not data['noisereduc_s'] == 'None':
|
||||
c += 1
|
||||
if not data['demucsmodel']:
|
||||
if demucs_switch == 'off':
|
||||
if data['inst_only'] and not data['voc_only']:
|
||||
widget_text.write(base_text + 'Preparing to save Instrumental...')
|
||||
else:
|
||||
widget_text.write(base_text + 'Saving vocals... ')
|
||||
sf.write(non_reduced_vocal_path, sources[c].T, rate)
|
||||
sf.write(non_reduced_vocal_path, sources[c].T, samplerate)
|
||||
update_progress(**progress_kwargs,
|
||||
step=(0.9))
|
||||
widget_text.write('Done!\n')
|
||||
widget_text.write(base_text + 'Performing Noise Reduction... ')
|
||||
reduction_sen = float(int(data['noisereduc_s'])/10)
|
||||
print(noise_pro_set)
|
||||
subprocess.call("lib_v5\\sox\\sox.exe" + ' "' +
|
||||
f"{str(non_reduced_vocal_path)}" + '" "' + f"{str(vocal_path)}" + '" ' +
|
||||
"noisered lib_v5\\sox\\" + noise_pro_set + ".prof " + f"{reduction_sen}",
|
||||
@ -181,31 +191,49 @@ class Predictor():
|
||||
widget_text.write(base_text + 'Preparing Instrumental...')
|
||||
else:
|
||||
widget_text.write(base_text + 'Saving Vocals... ')
|
||||
sf.write(non_reduced_vocal_path, sources[3].T, rate)
|
||||
update_progress(**progress_kwargs,
|
||||
step=(0.9))
|
||||
widget_text.write('Done!\n')
|
||||
widget_text.write(base_text + 'Performing Noise Reduction... ')
|
||||
reduction_sen = float(data['noisereduc_s'])/10
|
||||
subprocess.call("lib_v5\\sox\\sox.exe" + ' "' +
|
||||
f"{str(non_reduced_vocal_path)}" + '" "' + f"{str(vocal_path)}" + '" ' +
|
||||
"noisered lib_v5\\sox\\" + noise_pro_set + ".prof " + f"{reduction_sen}",
|
||||
shell=True, stdout=subprocess.PIPE,
|
||||
stdin=subprocess.PIPE, stderr=subprocess.PIPE)
|
||||
update_progress(**progress_kwargs,
|
||||
step=(0.95))
|
||||
widget_text.write('Done!\n')
|
||||
if demucs_only == 'on':
|
||||
if 'UVR' in model_set_name:
|
||||
sf.write(vocal_path, sources[1].T, samplerate)
|
||||
update_progress(**progress_kwargs,
|
||||
step=(0.95))
|
||||
widget_text.write('Done!\n')
|
||||
if 'extra' in model_set_name:
|
||||
sf.write(vocal_path, sources[3].T, samplerate)
|
||||
update_progress(**progress_kwargs,
|
||||
step=(0.95))
|
||||
widget_text.write('Done!\n')
|
||||
else:
|
||||
sf.write(non_reduced_vocal_path, sources[3].T, samplerate)
|
||||
update_progress(**progress_kwargs,
|
||||
step=(0.9))
|
||||
widget_text.write('Done!\n')
|
||||
widget_text.write(base_text + 'Performing Noise Reduction... ')
|
||||
reduction_sen = float(data['noisereduc_s'])/10
|
||||
subprocess.call("lib_v5\\sox\\sox.exe" + ' "' +
|
||||
f"{str(non_reduced_vocal_path)}" + '" "' + f"{str(vocal_path)}" + '" ' +
|
||||
"noisered lib_v5\\sox\\" + noise_pro_set + ".prof " + f"{reduction_sen}",
|
||||
shell=True, stdout=subprocess.PIPE,
|
||||
stdin=subprocess.PIPE, stderr=subprocess.PIPE)
|
||||
update_progress(**progress_kwargs,
|
||||
step=(0.95))
|
||||
widget_text.write('Done!\n')
|
||||
else:
|
||||
c += 1
|
||||
if not data['demucsmodel']:
|
||||
if demucs_switch == 'off':
|
||||
widget_text.write(base_text + 'Saving Vocals..')
|
||||
sf.write(vocal_path, sources[c].T, rate)
|
||||
sf.write(vocal_path, sources[c].T, samplerate)
|
||||
update_progress(**progress_kwargs,
|
||||
step=(0.9))
|
||||
widget_text.write('Done!\n')
|
||||
else:
|
||||
widget_text.write(base_text + 'Saving Vocals... ')
|
||||
sf.write(vocal_path, sources[3].T, rate)
|
||||
if demucs_only == 'on':
|
||||
if 'UVR' in model_set_name:
|
||||
sf.write(vocal_path, sources[1].T, samplerate)
|
||||
if 'extra' in model_set_name:
|
||||
sf.write(vocal_path, sources[3].T, samplerate)
|
||||
else:
|
||||
sf.write(vocal_path, sources[3].T, samplerate)
|
||||
update_progress(**progress_kwargs,
|
||||
step=(0.9))
|
||||
widget_text.write('Done!\n')
|
||||
@ -355,23 +383,36 @@ class Predictor():
|
||||
segmented_mix[skip] = mix[:,start:end].copy()
|
||||
if end == samples:
|
||||
break
|
||||
|
||||
|
||||
if not data['demucsmodel']:
|
||||
if demucs_switch == 'off':
|
||||
sources = self.demix_base(segmented_mix, margin_size=margin)
|
||||
elif data['demucs_only']:
|
||||
sources = self.demix_demucs(segmented_mix, margin_size=margin)
|
||||
elif demucs_only == 'on':
|
||||
if split_mode == True:
|
||||
sources = self.demix_demucs_split(mix)
|
||||
if split_mode == False:
|
||||
sources = self.demix_demucs(segmented_mix, margin_size=margin)
|
||||
else: # both, apply spec effects
|
||||
base_out = self.demix_base(segmented_mix, margin_size=margin)
|
||||
demucs_out = self.demix_demucs(segmented_mix, margin_size=margin)
|
||||
if split_mode == True:
|
||||
demucs_out = self.demix_demucs_split(mix)
|
||||
if split_mode == False:
|
||||
demucs_out = self.demix_demucs(segmented_mix, margin_size=margin)
|
||||
nan_count = np.count_nonzero(np.isnan(demucs_out)) + np.count_nonzero(np.isnan(base_out))
|
||||
if nan_count > 0:
|
||||
print('Warning: there are {} nan values in the array(s).'.format(nan_count))
|
||||
demucs_out, base_out = np.nan_to_num(demucs_out), np.nan_to_num(base_out)
|
||||
sources = {}
|
||||
|
||||
sources[3] = (spec_effects(wave=[demucs_out[3],base_out[0]],
|
||||
algorithm=data['mixing'],
|
||||
value=b[3])*float(data['compensate'])) # compensation
|
||||
if 'UVR' in demucs_model_set:
|
||||
sources[3] = (spec_effects(wave=[demucs_out[1],base_out[0]],
|
||||
algorithm=data['mixing'],
|
||||
value=b[3])*float(data['compensate'])) # compensation
|
||||
else:
|
||||
sources[3] = (spec_effects(wave=[demucs_out[3],base_out[0]],
|
||||
algorithm=data['mixing'],
|
||||
value=b[3])*float(data['compensate'])) # compensation
|
||||
|
||||
return sources
|
||||
|
||||
def demix_base(self, mixes, margin_size):
|
||||
@ -384,7 +425,7 @@ class Predictor():
|
||||
print(' Running ONNX Inference...')
|
||||
for mix in mixes:
|
||||
gui_progress_bar_onnx += 1
|
||||
if data['demucsmodel']:
|
||||
if demucs_switch == 'on':
|
||||
update_progress(**progress_kwargs,
|
||||
step=(0.1 + (0.5/onnxitera_calc * gui_progress_bar_onnx)))
|
||||
else:
|
||||
@ -430,13 +471,18 @@ class Predictor():
|
||||
return _sources
|
||||
|
||||
def demix_demucs(self, mix, margin_size):
|
||||
print('shift_set ', shift_set)
|
||||
processed = {}
|
||||
demucsitera = len(mix)
|
||||
demucsitera_calc = demucsitera * 2
|
||||
gui_progress_bar_demucs = 0
|
||||
|
||||
widget_text.write(base_text + "Split Mode is off. (Chunks enabled for Demucs Model)\n")
|
||||
|
||||
widget_text.write(base_text + "Running Demucs Inference...\n")
|
||||
widget_text.write(base_text + "Processing "f"{len(mix)} slices... ")
|
||||
print(' Running Demucs Inference...')
|
||||
print('Running Demucs Inference...')
|
||||
|
||||
for nmix in mix:
|
||||
gui_progress_bar_demucs += 1
|
||||
update_progress(**progress_kwargs,
|
||||
@ -446,7 +492,7 @@ class Predictor():
|
||||
ref = cmix.mean(0)
|
||||
cmix = (cmix - ref.mean()) / ref.std()
|
||||
with torch.no_grad():
|
||||
sources = apply_model(self.demucs, cmix.to(device), split=True, overlap=overlap_set, shifts=shift_set)
|
||||
sources = apply_model(self.demucs, cmix[None], split=split_mode, device=device, overlap=overlap_set, shifts=shift_set, progress=False)[0]
|
||||
sources = (sources * ref.std() + ref.mean()).cpu().numpy()
|
||||
sources[[0,1]] = sources[[1,0]]
|
||||
|
||||
@ -461,6 +507,26 @@ class Predictor():
|
||||
widget_text.write('Done!\n')
|
||||
return sources
|
||||
|
||||
def demix_demucs_split(self, mix):
|
||||
|
||||
print('shift_set ', shift_set)
|
||||
widget_text.write(base_text + "Split Mode is on. (Chunks disabled for Demucs Model)\n")
|
||||
widget_text.write(base_text + "Running Demucs Inference...\n")
|
||||
widget_text.write(base_text + "Processing "f"{len(mix)} slices... ")
|
||||
print(' Running Demucs Inference...')
|
||||
|
||||
mix = torch.tensor(mix, dtype=torch.float32)
|
||||
ref = mix.mean(0)
|
||||
mix = (mix - ref.mean()) / ref.std()
|
||||
|
||||
with torch.no_grad():
|
||||
sources = apply_model(self.demucs, mix[None], split=split_mode, device=device, overlap=overlap_set, shifts=shift_set, progress=False)[0]
|
||||
|
||||
widget_text.write('Done!\n')
|
||||
|
||||
sources = (sources * ref.std() + ref.mean()).cpu().numpy()
|
||||
sources[[0,1]] = sources[[1,0]]
|
||||
return sources
|
||||
|
||||
def update_progress(progress_var, total_files, file_num, step: float = 1):
|
||||
"""Calculate the progress for the progress widget in the GUI"""
|
||||
@ -567,7 +633,7 @@ data = {
|
||||
'chunks': 'auto',
|
||||
'non_red': False,
|
||||
'noisereduc_s': 3,
|
||||
'ensChoose': 'Basic Ensemble',
|
||||
'ensChoose': 'Basic VR Ensemble',
|
||||
'algo': 'Instrumentals (Min Spec)',
|
||||
#Advanced Options
|
||||
'appendensem': False,
|
||||
@ -575,11 +641,11 @@ data = {
|
||||
'overlap': 0.5,
|
||||
'shifts': 0,
|
||||
'margin': 44100,
|
||||
'channel': 64,
|
||||
'split_mode': False,
|
||||
'compensate': 1.03597672895,
|
||||
'demucs_only': False,
|
||||
'mixing': 'Default',
|
||||
'DemucsModel': 'demucs_extra-3646af93_org.th',
|
||||
'DemucsModel_MDX': 'UVR_Demucs_Model_1',
|
||||
|
||||
# Models
|
||||
'instrumentalModel': None,
|
||||
@ -627,17 +693,21 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
|
||||
global ModelName_2
|
||||
global mdx_model_hash
|
||||
|
||||
global demucs_model_set
|
||||
|
||||
global channel_set
|
||||
global margin_set
|
||||
global overlap_set
|
||||
global shift_set
|
||||
|
||||
global noise_pro_set
|
||||
|
||||
|
||||
global n_fft_scale_set
|
||||
global dim_f_set
|
||||
|
||||
global split_mode
|
||||
global demucs_switch
|
||||
global demucs_only
|
||||
|
||||
# Update default settings
|
||||
default_chunks = data['chunks']
|
||||
default_noisereduc_s = data['noisereduc_s']
|
||||
@ -665,12 +735,6 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
|
||||
f'\nLast Conversion Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
|
||||
except:
|
||||
pass
|
||||
|
||||
|
||||
overlap_set = float(data['overlap'])
|
||||
channel_set = int(data['channel'])
|
||||
margin_set = int(data['margin'])
|
||||
shift_set = int(data['shifts'])
|
||||
|
||||
n_fft_scale_set=6144
|
||||
dim_f_set=2048
|
||||
@ -770,7 +834,26 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
|
||||
# Separation Preperation
|
||||
try: #Ensemble Dictionary
|
||||
|
||||
if not data['ensChoose'] == 'User Ensemble':
|
||||
overlap_set = float(data['overlap'])
|
||||
channel_set = int(data['channel'])
|
||||
margin_set = int(data['margin'])
|
||||
shift_set = int(data['shifts'])
|
||||
demucs_model_set = data['DemucsModel_MDX']
|
||||
split_mode = data['split_mode']
|
||||
demucs_switch = data['demucsmodel']
|
||||
|
||||
if data['demucsmodel']:
|
||||
demucs_switch = 'on'
|
||||
else:
|
||||
demucs_switch = 'off'
|
||||
|
||||
if data['demucs_only']:
|
||||
demucs_only = 'on'
|
||||
else:
|
||||
demucs_only = 'off'
|
||||
|
||||
|
||||
if not data['ensChoose'] == 'Manual Ensemble':
|
||||
|
||||
#1st Model
|
||||
|
||||
@ -1219,40 +1302,35 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
|
||||
else:
|
||||
vr_ensem_mdx_c_name = data['vr_ensem_mdx_c']
|
||||
vr_ensem_mdx_c = f'models/Main_Models/{vr_ensem_mdx_c_name}.pth'
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
#MDX-Net Model
|
||||
try:
|
||||
if data['mdx_ensem'] == 'UVR-MDX-NET 1':
|
||||
mdx_ensem = 'UVR_MDXNET_1_9703'
|
||||
if data['mdx_ensem'] == 'UVR-MDX-NET 2':
|
||||
mdx_ensem = 'UVR_MDXNET_2_9682'
|
||||
if data['mdx_ensem'] == 'UVR-MDX-NET 3':
|
||||
mdx_ensem = 'UVR_MDXNET_3_9662'
|
||||
if data['mdx_ensem'] == 'UVR-MDX-NET Karaoke':
|
||||
mdx_ensem = 'UVR_MDXNET_KARA'
|
||||
|
||||
MDXModelName=('models/MDX_Net_Models/' + mdx_ensem + '.onnx')
|
||||
mdx_model_hash = hashlib.md5(open(MDXModelName, 'rb').read()).hexdigest()
|
||||
print(mdx_ensem)
|
||||
except:
|
||||
if data['mdx_ensem'] == 'UVR-MDX-NET 1':
|
||||
mdx_ensem = 'UVR_MDXNET_9703'
|
||||
if data['mdx_ensem'] == 'UVR-MDX-NET 2':
|
||||
mdx_ensem = 'UVR_MDXNET_9682'
|
||||
if data['mdx_ensem'] == 'UVR-MDX-NET 3':
|
||||
mdx_ensem = 'UVR_MDXNET_9662'
|
||||
if data['mdx_ensem'] == 'UVR-MDX-NET Karaoke':
|
||||
mdx_ensem = 'UVR_MDXNET_KARA'
|
||||
|
||||
MDXModelName=('models/MDX_Net_Models/' + mdx_ensem + '.onnx')
|
||||
mdx_model_hash = hashlib.md5(open(MDXModelName, 'rb').read()).hexdigest()
|
||||
print(mdx_model_hash)
|
||||
print(mdx_ensem)
|
||||
|
||||
|
||||
if data['mdx_ensem'] == 'UVR-MDX-NET 1':
|
||||
if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_1_9703.onnx'):
|
||||
mdx_ensem = 'UVR_MDXNET_1_9703'
|
||||
else:
|
||||
mdx_ensem = 'UVR_MDXNET_9703'
|
||||
if data['mdx_ensem'] == 'UVR-MDX-NET 2':
|
||||
if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_2_9682.onnx'):
|
||||
mdx_ensem = 'UVR_MDXNET_2_9682'
|
||||
else:
|
||||
mdx_ensem = 'UVR_MDXNET_9682'
|
||||
if data['mdx_ensem'] == 'UVR-MDX-NET 3':
|
||||
if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_3_9662.onnx'):
|
||||
mdx_ensem = 'UVR_MDXNET_3_9662'
|
||||
else:
|
||||
mdx_ensem = 'UVR_MDXNET_9662'
|
||||
if data['mdx_ensem'] == 'UVR-MDX-NET Karaoke':
|
||||
mdx_ensem = 'UVR_MDXNET_KARA'
|
||||
if data['mdx_ensem'] == 'Demucs UVR Model 1':
|
||||
mdx_ensem = 'UVR_Demucs_Model_1'
|
||||
if data['mdx_ensem'] == 'Demucs UVR Model 2':
|
||||
mdx_ensem = 'UVR_Demucs_Model_2'
|
||||
if data['mdx_ensem'] == 'Demucs mdx_extra':
|
||||
mdx_ensem = 'mdx_extra'
|
||||
if data['mdx_ensem'] == 'Demucs mdx_extra_q':
|
||||
mdx_ensem = 'mdx_extra_q'
|
||||
|
||||
#MDX-Net Model 2
|
||||
|
||||
if data['mdx_ensem_b'] == 'UVR-MDX-NET 1':
|
||||
@ -1263,6 +1341,14 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
|
||||
mdx_ensem_b = 'UVR_MDXNET_3_9662'
|
||||
if data['mdx_ensem_b'] == 'UVR-MDX-NET Karaoke':
|
||||
mdx_ensem_b = 'UVR_MDXNET_KARA'
|
||||
if data['mdx_ensem_b'] == 'Demucs UVR Model 1':
|
||||
mdx_ensem_b = 'UVR_Demucs_Model_1'
|
||||
if data['mdx_ensem_b'] == 'Demucs UVR Model 2':
|
||||
mdx_ensem_b = 'UVR_Demucs_Model_2'
|
||||
if data['mdx_ensem_b'] == 'Demucs mdx_extra':
|
||||
mdx_ensem_b = 'mdx_extra'
|
||||
if data['mdx_ensem_b'] == 'Demucs mdx_extra_q':
|
||||
mdx_ensem_b = 'mdx_extra_q'
|
||||
if data['mdx_ensem_b'] == 'No Model':
|
||||
mdx_ensem_b = 'pass'
|
||||
|
||||
@ -1456,7 +1542,7 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
|
||||
}
|
||||
]
|
||||
|
||||
if data['ensChoose'] == 'Basic Ensemble':
|
||||
if data['ensChoose'] == 'Basic VR Ensemble':
|
||||
loops = Basic_Ensem
|
||||
ensefolder = 'Basic_Ensemble_Outputs'
|
||||
if data['vr_ensem_c'] == 'No Model' and data['vr_ensem_d'] == 'No Model' and data['vr_ensem_e'] == 'No Model':
|
||||
@ -1487,7 +1573,7 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
|
||||
loops = Vocal_Models
|
||||
ensefolder = 'Vocal_Models_Ensemble_Outputs'
|
||||
ensemode = 'Vocal_Models'
|
||||
if data['ensChoose'] == 'MDX-Net/VR Ensemble':
|
||||
if data['ensChoose'] == 'Multi-AI Ensemble':
|
||||
loops = mdx_vr
|
||||
ensefolder = 'MDX_VR_Ensemble_Outputs'
|
||||
if data['vr_ensem'] == 'No Model' and data['vr_ensem_mdx_a'] == 'No Model' and data['vr_ensem_mdx_b'] == 'No Model' and data['vr_ensem_mdx_c'] == 'No Model':
|
||||
@ -1511,7 +1597,6 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
|
||||
|
||||
#Prepare Audiofile(s)
|
||||
for file_num, music_file in enumerate(data['input_paths'], start=1):
|
||||
print(data['input_paths'])
|
||||
# -Get text and update progress-
|
||||
base_text = get_baseText(total_files=len(data['input_paths']),
|
||||
file_num=file_num)
|
||||
@ -1609,9 +1694,9 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
|
||||
presentmodel = Path(c['model_location'])
|
||||
|
||||
if presentmodel.is_file():
|
||||
print(f'The file {presentmodel} exist')
|
||||
print(f'The file {presentmodel} exists')
|
||||
else:
|
||||
if data['ensChoose'] == 'MDX-Net/VR Ensemble':
|
||||
if data['ensChoose'] == 'Multi-AI Ensemble':
|
||||
text_widget.write(base_text + 'Model "' + c['model_name'] + '.pth" is missing.\n')
|
||||
text_widget.write(base_text + 'Installation of v5 Model Expansion Pack required to use this model.\n')
|
||||
text_widget.write(base_text + f'If the error persists, please verify all models are present.\n\n')
|
||||
@ -1963,7 +2048,7 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
|
||||
text_widget.write(base_text + 'Completed Seperation!\n\n')
|
||||
|
||||
|
||||
if data['ensChoose'] == 'MDX-Net/VR Ensemble':
|
||||
if data['ensChoose'] == 'Multi-AI Ensemble':
|
||||
|
||||
mdx_name = c['mdx_model_name']
|
||||
|
||||
@ -1973,46 +2058,77 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
|
||||
text_widget.write('Ensemble Mode - Running Model - ' + mdx_name + '\n\n')
|
||||
|
||||
if mdx_name == 'UVR_MDXNET_1_9703':
|
||||
demucs_only = 'off'
|
||||
model_set = 'UVR_MDXNET_1_9703.onnx'
|
||||
model_set_name = 'UVR_MDXNET_1_9703'
|
||||
modeltype = 'v'
|
||||
demucs_model_set = data['DemucsModel_MDX']
|
||||
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
|
||||
if mdx_name == 'UVR_MDXNET_2_9682':
|
||||
demucs_only = 'off'
|
||||
model_set = 'UVR_MDXNET_2_9682.onnx'
|
||||
model_set_name = 'UVR_MDXNET_2_9682'
|
||||
modeltype = 'v'
|
||||
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
|
||||
if mdx_name == 'UVR_MDXNET_3_9662':
|
||||
demucs_only = 'off'
|
||||
model_set = 'UVR_MDXNET_3_9662.onnx'
|
||||
model_set_name = 'UVR_MDXNET_3_9662'
|
||||
modeltype = 'v'
|
||||
demucs_model_set = data['DemucsModel_MDX']
|
||||
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
|
||||
if mdx_name == 'UVR_MDXNET_KARA':
|
||||
demucs_only = 'off'
|
||||
model_set = 'UVR_MDXNET_KARA.onnx'
|
||||
model_set_name = 'UVR_MDXNET_KARA'
|
||||
modeltype = 'v'
|
||||
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
|
||||
if mdx_name == 'UVR_MDXNET_9703':
|
||||
demucs_only = 'off'
|
||||
model_set = 'UVR_MDXNET_9703.onnx'
|
||||
model_set_name = 'UVR_MDXNET_9703'
|
||||
modeltype = 'v'
|
||||
demucs_model_set = data['DemucsModel_MDX']
|
||||
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
|
||||
if mdx_name == 'UVR_MDXNET_9682':
|
||||
demucs_only = 'off'
|
||||
model_set = 'UVR_MDXNET_9682.onnx'
|
||||
model_set_name = 'UVR_MDXNET_9682'
|
||||
modeltype = 'v'
|
||||
demucs_model_set = data['DemucsModel_MDX']
|
||||
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
|
||||
if mdx_name == 'UVR_MDXNET_9662':
|
||||
demucs_only = 'off'
|
||||
model_set = 'UVR_MDXNET_9662.onnx'
|
||||
model_set_name = 'UVR_MDXNET_9662'
|
||||
modeltype = 'v'
|
||||
demucs_model_set = data['DemucsModel_MDX']
|
||||
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
|
||||
if mdx_name == 'UVR_MDXNET_KARA':
|
||||
demucs_only = 'off'
|
||||
model_set = 'UVR_MDXNET_KARA.onnx'
|
||||
model_set_name = 'UVR_MDXNET_KARA'
|
||||
modeltype = 'v'
|
||||
demucs_model_set = data['DemucsModel_MDX']
|
||||
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
|
||||
if 'Demucs' in mdx_name:
|
||||
demucs_only = 'on'
|
||||
demucs_switch = 'on'
|
||||
demucs_model_set = mdx_name
|
||||
model_set = ''
|
||||
model_set_name = 'UVR'
|
||||
modeltype = 'v'
|
||||
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
|
||||
if 'extra' in mdx_name:
|
||||
demucs_only = 'on'
|
||||
demucs_switch = 'on'
|
||||
demucs_model_set = mdx_name
|
||||
model_set = ''
|
||||
model_set_name = 'extra'
|
||||
modeltype = 'v'
|
||||
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
|
||||
|
||||
print('demucs_only? ', demucs_only)
|
||||
|
||||
if data['noise_pro_select'] == 'Auto Select':
|
||||
noise_pro_set = noise_pro
|
||||
@ -2033,12 +2149,9 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
|
||||
widget_text.write(base_text + 'Noise Reduction will be disabled until SoX is available.\n\n')
|
||||
|
||||
e = os.path.join(data["export_path"])
|
||||
|
||||
demucsmodel = 'models/Demucs_Model/' + str(data['DemucsModel'])
|
||||
|
||||
pred = Predictor()
|
||||
pred.prediction_setup(demucs_name=demucsmodel,
|
||||
channels=channel_set)
|
||||
pred.prediction_setup()
|
||||
|
||||
# split
|
||||
pred.prediction(
|
||||
@ -2502,7 +2615,7 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
|
||||
try:
|
||||
with open('errorlog.txt', 'w') as f:
|
||||
f.write(f'Last Error Received:\n\n' +
|
||||
f'Error Received while attempting to run user ensemble:\n' +
|
||||
f'Error Received while attempting to run Manual Ensemble:\n' +
|
||||
f'Process Method: Ensemble Mode\n\n' +
|
||||
f'FFmpeg might be missing or corrupted.\n\n' +
|
||||
f'If this error persists, please contact the developers.\n\n' +
|
||||
@ -2530,7 +2643,7 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
|
||||
try:
|
||||
with open('errorlog.txt', 'w') as f:
|
||||
f.write(f'Last Error Received:\n\n' +
|
||||
f'Error Received while attempting to run user ensemble:\n' +
|
||||
f'Error Received while attempting to run Manual Ensemble:\n' +
|
||||
f'Process Method: Ensemble Mode\n\n' +
|
||||
f'FFmpeg might be missing or corrupted.\n\n' +
|
||||
f'If this error persists, please contact the developers.\n\n' +
|
||||
@ -2899,11 +3012,10 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
|
||||
update_progress(**progress_kwargs,
|
||||
step=1)
|
||||
|
||||
|
||||
print('Done!')
|
||||
|
||||
progress_var.set(0)
|
||||
if not data['ensChoose'] == 'User Ensemble':
|
||||
if not data['ensChoose'] == 'Manual Ensemble':
|
||||
text_widget.write(base_text + f'Conversions Completed!\n')
|
||||
elif data['algo'] == 'Instrumentals (Min Spec)' and len(data['input_paths']) <= 1 or data['algo'] == 'Vocals (Max Spec)' and len(data['input_paths']) <= 1:
|
||||
text_widget.write(base_text + f'Please select 2 or more files to use this feature and try again.\n')
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user