diff --git a/lib_v5/mdxnet.py b/lib_v5/mdxnet.py
index c0a61fe..3293c89 100644
--- a/lib_v5/mdxnet.py
+++ b/lib_v5/mdxnet.py
@@ -1,15 +1,11 @@
-from abc import ABCMeta
-
 import torch
 import torch.nn as nn
-from pytorch_lightning import LightningModule
 from .modules import TFC_TDF
+from pytorch_lightning import LightningModule
 
 dim_s = 4
 
 class AbstractMDXNet(LightningModule):
-    __metaclass__ = ABCMeta
-
     def __init__(self, target_name, lr, optimizer, dim_c, dim_f, dim_t, n_fft, hop_length, overlap):
         super().__init__()
         self.target_name = target_name
@@ -24,7 +20,7 @@ class AbstractMDXNet(LightningModule):
         self.window = nn.Parameter(torch.hann_window(window_length=self.n_fft, periodic=True), requires_grad=False)
         self.freq_pad = nn.Parameter(torch.zeros([1, dim_c, self.n_bins - self.dim_f, self.dim_t]), requires_grad=False)
 
-    def configure_optimizers(self):
+    def get_optimizer(self):
         if self.optimizer == 'rmsprop':
             return torch.optim.RMSprop(self.parameters(), self.lr)
         
@@ -37,7 +33,7 @@ class ConvTDFNet(AbstractMDXNet):
 
         super(ConvTDFNet, self).__init__(
             target_name, lr, optimizer, dim_c, dim_f, dim_t, n_fft, hop_length, overlap)
-        self.save_hyperparameters()
+        #self.save_hyperparameters()
 
         self.num_blocks = num_blocks
         self.l = l
diff --git a/lib_v5/spec_utils.py b/lib_v5/spec_utils.py
index 0b12a92..f35a05d 100644
--- a/lib_v5/spec_utils.py
+++ b/lib_v5/spec_utils.py
@@ -1,18 +1,32 @@
+import audioread
 import librosa
 import numpy as np
 import soundfile as sf
 import math
-import random
-import math
 import platform
 import traceback
 from . import pyrb
-#cur
+from scipy.signal import correlate, hilbert
+import io
+
 OPERATING_SYSTEM = platform.system()
 SYSTEM_ARCH = platform.platform()
 SYSTEM_PROC = platform.processor()
 ARM = 'arm'
 
+AUTO_PHASE = "Automatic"
+POSITIVE_PHASE = "Positive Phase"
+NEGATIVE_PHASE = "Negative Phase"
+NONE_P = "None",
+LOW_P = "Shifts: Low",
+MED_P = "Shifts: Medium",
+HIGH_P = "Shifts: High",
+VHIGH_P = "Shifts: Very High"
+MAXIMUM_P = "Shifts: Maximum"
+
+progress_value = 0
+last_update_time = 0
+
 if OPERATING_SYSTEM == 'Windows':
     from pyrubberband import pyrb
 else:
@@ -25,6 +39,11 @@ else:
 
 MAX_SPEC = 'Max Spec'
 MIN_SPEC = 'Min Spec'
+LIN_ENSE = 'Linear Ensemble'
+
+MAX_WAV = MAX_SPEC
+MIN_WAV = MIN_SPEC
+
 AVERAGE = 'Average'
 
 def crop_center(h1, h2):
@@ -57,122 +76,42 @@ def make_padding(width, cropsize, offset):
 
     return left, right, roi_size
 
-def wave_to_spectrogram(wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False):
-    if reverse:
-        wave_left = np.flip(np.asfortranarray(wave[0]))
-        wave_right = np.flip(np.asfortranarray(wave[1]))
-    elif mid_side:
-        wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
-        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
-    elif mid_side_b2:
-        wave_left = np.asfortranarray(np.add(wave[1], wave[0] * .5))
-        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * .5))
-    else:
-        wave_left = np.asfortranarray(wave[0])
-        wave_right = np.asfortranarray(wave[1])
-
-    spec_left = librosa.stft(wave_left, n_fft, hop_length=hop_length)
-    spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
-    
-    spec = np.asfortranarray([spec_left, spec_right])
-
-    return spec
-   
-def wave_to_spectrogram_mt(wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False):
-    import threading
-
-    if reverse:
-        wave_left = np.flip(np.asfortranarray(wave[0]))
-        wave_right = np.flip(np.asfortranarray(wave[1]))
-    elif mid_side:
-        wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
-        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
-    elif mid_side_b2:
-        wave_left = np.asfortranarray(np.add(wave[1], wave[0] * .5))
-        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * .5))
-    else:
-        wave_left = np.asfortranarray(wave[0])
-        wave_right = np.asfortranarray(wave[1])
-   
-    def run_thread(**kwargs):
-        global spec_left
-        spec_left = librosa.stft(**kwargs)
-
-    thread = threading.Thread(target=run_thread, kwargs={'y': wave_left, 'n_fft': n_fft, 'hop_length': hop_length})
-    thread.start()
-    spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
-    thread.join()   
-    
-    spec = np.asfortranarray([spec_left, spec_right])
-
-    return spec
-    
 def normalize(wave, is_normalize=False):
-    """Save output music files"""
+    """Normalize audio"""
+
     maxv = np.abs(wave).max()
     if maxv > 1.0:
-        print(f"\nNormalization Set {is_normalize}: Input above threshold for clipping. Max:{maxv}")
         if is_normalize:
-            print(f"The result was normalized.")
+            print("Above clipping threshold.")
             wave /= maxv
-        else:
-            print(f"The result was not normalized.")
-    else:
-        print(f"\nNormalization Set {is_normalize}: Input not above threshold for clipping. Max:{maxv}")
     
     return wave
     
-def normalize_two_stem(wave, mix, is_normalize=False):
-    """Save output music files"""
-    
-    maxv = np.abs(wave).max()
-    max_mix = np.abs(mix).max()
-    
-    if maxv > 1.0:
-        print(f"\nNormalization Set {is_normalize}: Primary source above threshold for clipping. Max:{maxv}")
-        print(f"\nNormalization Set {is_normalize}: Mixture above threshold for clipping. Max:{max_mix}")
-        if is_normalize:
-            print(f"The result was normalized.")
-            wave /= maxv
-            mix /= maxv
-        else:
-            print(f"The result was not normalized.")
-    else:
-        print(f"\nNormalization Set {is_normalize}: Input not above threshold for clipping. Max:{maxv}")
-    
-    
-    print(f"\nNormalization Set {is_normalize}: Primary source - Max:{np.abs(wave).max()}")
-    print(f"\nNormalization Set {is_normalize}: Mixture - Max:{np.abs(mix).max()}")
-    
-    return wave, mix    
+def auto_transpose(audio_array:np.ndarray):
+    """
+    Ensure that the audio array is in the (channels, samples) format.
 
-def combine_spectrograms(specs, mp):
-    l = min([specs[i].shape[2] for i in specs])    
-    spec_c = np.zeros(shape=(2, mp.param['bins'] + 1, l), dtype=np.complex64)
-    offset = 0
-    bands_n = len(mp.param['band'])
-    
-    for d in range(1, bands_n + 1):
-        h = mp.param['band'][d]['crop_stop'] - mp.param['band'][d]['crop_start']
-        spec_c[:, offset:offset+h, :l] = specs[d][:, mp.param['band'][d]['crop_start']:mp.param['band'][d]['crop_stop'], :l]
-        offset += h
-        
-    if offset > mp.param['bins']:
-        raise ValueError('Too much bins')
-        
-    # lowpass fiter
-    if mp.param['pre_filter_start'] > 0: # and mp.param['band'][bands_n]['res_type'] in ['scipy', 'polyphase']:   
-        if bands_n == 1:
-            spec_c = fft_lp_filter(spec_c, mp.param['pre_filter_start'], mp.param['pre_filter_stop'])
-        else:
-            gp = 1        
-            for b in range(mp.param['pre_filter_start'] + 1, mp.param['pre_filter_stop']):
-                g = math.pow(10, -(b - mp.param['pre_filter_start']) * (3.5 - gp) / 20.0)
-                gp = g
-                spec_c[:, b, :] *= g
-                
-    return np.asfortranarray(spec_c)
+    Parameters:
+        audio_array (ndarray): Input audio array.
+
+    Returns:
+        ndarray: Transposed audio array if necessary.
+    """
     
+    # If the second dimension is 2 (indicating stereo channels), transpose the array
+    if audio_array.shape[1] == 2:
+        return audio_array.T
+    return audio_array
+
+def write_array_to_mem(audio_data, subtype):
+    if isinstance(audio_data, np.ndarray):
+        audio_buffer = io.BytesIO()
+        sf.write(audio_buffer, audio_data, 44100, subtype=subtype, format='WAV')
+        audio_buffer.seek(0)
+        return audio_buffer
+    else:
+        return audio_data
+
 def spectrogram_to_image(spec, mode='magnitude'):
     if mode == 'magnitude':
         if np.iscomplexobj(spec):
@@ -251,7 +190,6 @@ def merge_artifacts(y_mask, thres=0.01, min_range=64, fade_size=32):
         message = f'{error_name}: "{e}"\n{traceback_text}"'
         print('Post Process Failed: ', message)
         
-
     return mask
 
 def align_wave_head_and_tail(a, b):
@@ -259,47 +197,112 @@ def align_wave_head_and_tail(a, b):
     
     return a[:l,:l], b[:l,:l]
     
-def spectrogram_to_wave(spec, hop_length, mid_side, mid_side_b2, reverse, clamp=False):
+def convert_channels(spec, mp, band):
+    cc = mp.param['band'][band].get('convert_channels')
+
+    if 'mid_side_c' == cc:
+        spec_left = np.add(spec[0], spec[1] * .25)
+        spec_right = np.subtract(spec[1], spec[0] * .25)
+    elif 'mid_side' == cc:
+        spec_left = np.add(spec[0], spec[1]) / 2
+        spec_right = np.subtract(spec[0], spec[1])
+    elif 'stereo_n' == cc:
+        spec_left = np.add(spec[0], spec[1] * .25) / 0.9375
+        spec_right = np.add(spec[1], spec[0] * .25) / 0.9375
+    else:
+        return spec
+        
+    return np.asfortranarray([spec_left, spec_right])
+    
+def combine_spectrograms(specs, mp, is_v51_model=False):
+    l = min([specs[i].shape[2] for i in specs])    
+    spec_c = np.zeros(shape=(2, mp.param['bins'] + 1, l), dtype=np.complex64)
+    offset = 0
+    bands_n = len(mp.param['band'])
+    
+    for d in range(1, bands_n + 1):
+        h = mp.param['band'][d]['crop_stop'] - mp.param['band'][d]['crop_start']
+        spec_c[:, offset:offset+h, :l] = specs[d][:, mp.param['band'][d]['crop_start']:mp.param['band'][d]['crop_stop'], :l]
+        offset += h
+        
+    if offset > mp.param['bins']:
+        raise ValueError('Too much bins')
+        
+    # lowpass fiter
+    
+    if mp.param['pre_filter_start'] > 0:
+        if is_v51_model:
+            spec_c *= get_lp_filter_mask(spec_c.shape[1], mp.param['pre_filter_start'], mp.param['pre_filter_stop'])
+        else:
+            if bands_n == 1:
+                spec_c = fft_lp_filter(spec_c, mp.param['pre_filter_start'], mp.param['pre_filter_stop'])
+            else:
+                gp = 1        
+                for b in range(mp.param['pre_filter_start'] + 1, mp.param['pre_filter_stop']):
+                    g = math.pow(10, -(b - mp.param['pre_filter_start']) * (3.5 - gp) / 20.0)
+                    gp = g
+                    spec_c[:, b, :] *= g
+                
+    return np.asfortranarray(spec_c)
+    
+def wave_to_spectrogram(wave, hop_length, n_fft, mp, band, is_v51_model=False):
+
+    if wave.ndim == 1:
+        wave = np.asfortranarray([wave,wave])
+
+    if not is_v51_model:
+        if mp.param['reverse']:
+            wave_left = np.flip(np.asfortranarray(wave[0]))
+            wave_right = np.flip(np.asfortranarray(wave[1]))
+        elif mp.param['mid_side']:
+            wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
+            wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
+        elif mp.param['mid_side_b2']:
+            wave_left = np.asfortranarray(np.add(wave[1], wave[0] * .5))
+            wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * .5))
+        else:
+            wave_left = np.asfortranarray(wave[0])
+            wave_right = np.asfortranarray(wave[1])
+    else:
+        wave_left = np.asfortranarray(wave[0])
+        wave_right = np.asfortranarray(wave[1])
+
+    spec_left = librosa.stft(wave_left, n_fft, hop_length=hop_length)
+    spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
+    
+    spec = np.asfortranarray([spec_left, spec_right])
+
+    if is_v51_model:
+        spec = convert_channels(spec, mp, band)
+
+    return spec
+
+def spectrogram_to_wave(spec, hop_length=1024, mp={}, band=0, is_v51_model=True):
     spec_left = np.asfortranarray(spec[0])
     spec_right = np.asfortranarray(spec[1])
-
+    
     wave_left = librosa.istft(spec_left, hop_length=hop_length)
     wave_right = librosa.istft(spec_right, hop_length=hop_length)
-
-    if reverse:
-        return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
-    elif mid_side:
-        return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])
-    elif mid_side_b2:
-        return np.asfortranarray([np.add(wave_right / 1.25, .4 * wave_left), np.subtract(wave_left / 1.25, .4 * wave_right)])
+    
+    if is_v51_model:
+        cc = mp.param['band'][band].get('convert_channels')
+        if 'mid_side_c' == cc:
+            return np.asfortranarray([np.subtract(wave_left / 1.0625, wave_right / 4.25), np.add(wave_right / 1.0625, wave_left / 4.25)])    
+        elif 'mid_side' == cc:
+            return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])
+        elif 'stereo_n' == cc:
+            return np.asfortranarray([np.subtract(wave_left, wave_right * .25), np.subtract(wave_right, wave_left * .25)])
     else:
-        return np.asfortranarray([wave_left, wave_right])
+        if mp.param['reverse']:
+            return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
+        elif mp.param['mid_side']:
+            return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])
+        elif mp.param['mid_side_b2']:
+            return np.asfortranarray([np.add(wave_right / 1.25, .4 * wave_left), np.subtract(wave_left / 1.25, .4 * wave_right)])
     
-def spectrogram_to_wave_mt(spec, hop_length, mid_side, reverse, mid_side_b2):
-    import threading
-
-    spec_left = np.asfortranarray(spec[0])
-    spec_right = np.asfortranarray(spec[1])
+    return np.asfortranarray([wave_left, wave_right])
     
-    def run_thread(**kwargs):
-        global wave_left
-        wave_left = librosa.istft(**kwargs)
-        
-    thread = threading.Thread(target=run_thread, kwargs={'stft_matrix': spec_left, 'hop_length': hop_length})
-    thread.start()
-    wave_right = librosa.istft(spec_right, hop_length=hop_length)
-    thread.join()   
-    
-    if reverse:
-        return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
-    elif mid_side:
-        return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])
-    elif mid_side_b2:
-        return np.asfortranarray([np.add(wave_right / 1.25, .4 * wave_left), np.subtract(wave_left / 1.25, .4 * wave_right)])
-    else:
-        return np.asfortranarray([wave_left, wave_right])
-    
-def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None):
+def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None, is_v51_model=False):
     bands_n = len(mp.param['band'])    
     offset = 0
 
@@ -308,31 +311,60 @@ def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None):
         spec_s = np.ndarray(shape=(2, bp['n_fft'] // 2 + 1, spec_m.shape[2]), dtype=complex)
         h = bp['crop_stop'] - bp['crop_start']
         spec_s[:, bp['crop_start']:bp['crop_stop'], :] = spec_m[:, offset:offset+h, :]
-        
+                
         offset += h
         if d == bands_n: # higher
             if extra_bins_h: # if --high_end_process bypass
                 max_bin = bp['n_fft'] // 2
                 spec_s[:, max_bin-extra_bins_h:max_bin, :] = extra_bins[:, :extra_bins_h, :]
             if bp['hpf_start'] > 0:
-                spec_s = fft_hp_filter(spec_s, bp['hpf_start'], bp['hpf_stop'] - 1)
+                if is_v51_model:
+                    spec_s *= get_hp_filter_mask(spec_s.shape[1], bp['hpf_start'], bp['hpf_stop'] - 1)
+                else:
+                    spec_s = fft_hp_filter(spec_s, bp['hpf_start'], bp['hpf_stop'] - 1)
             if bands_n == 1:
-                wave = spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse'])
+                wave = spectrogram_to_wave(spec_s, bp['hl'], mp, d, is_v51_model)
             else:
-                wave = np.add(wave, spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse']))
+                wave = np.add(wave, spectrogram_to_wave(spec_s, bp['hl'], mp, d, is_v51_model))
         else:
             sr = mp.param['band'][d+1]['sr']
             if d == 1: # lower
-                spec_s = fft_lp_filter(spec_s, bp['lpf_start'], bp['lpf_stop'])
-                wave = librosa.resample(spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse']), bp['sr'], sr, res_type=wav_resolution)
+                if is_v51_model:
+                    spec_s *= get_lp_filter_mask(spec_s.shape[1], bp['lpf_start'], bp['lpf_stop'])
+                else:
+                    spec_s = fft_lp_filter(spec_s, bp['lpf_start'], bp['lpf_stop'])
+                wave = librosa.resample(spectrogram_to_wave(spec_s, bp['hl'], mp, d, is_v51_model), bp['sr'], sr, res_type=wav_resolution)
             else: # mid
-                spec_s = fft_hp_filter(spec_s, bp['hpf_start'], bp['hpf_stop'] - 1)
-                spec_s = fft_lp_filter(spec_s, bp['lpf_start'], bp['lpf_stop'])
-                wave2 = np.add(wave, spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse']))
+                if is_v51_model:
+                    spec_s *= get_hp_filter_mask(spec_s.shape[1], bp['hpf_start'], bp['hpf_stop'] - 1)
+                    spec_s *= get_lp_filter_mask(spec_s.shape[1], bp['lpf_start'], bp['lpf_stop'])
+                else:
+                    spec_s = fft_hp_filter(spec_s, bp['hpf_start'], bp['hpf_stop'] - 1)
+                    spec_s = fft_lp_filter(spec_s, bp['lpf_start'], bp['lpf_stop'])
+                    
+                wave2 = np.add(wave, spectrogram_to_wave(spec_s, bp['hl'], mp, d, is_v51_model))
                 wave = librosa.resample(wave2, bp['sr'], sr, res_type=wav_resolution)
         
     return wave
 
+def get_lp_filter_mask(n_bins, bin_start, bin_stop):
+    mask = np.concatenate([
+        np.ones((bin_start - 1, 1)),
+        np.linspace(1, 0, bin_stop - bin_start + 1)[:, None],
+        np.zeros((n_bins - bin_stop, 1))
+    ], axis=0)
+
+    return mask
+    
+def get_hp_filter_mask(n_bins, bin_start, bin_stop):
+    mask = np.concatenate([
+        np.zeros((bin_stop + 1, 1)),
+        np.linspace(0, 1, 1 + bin_start - bin_stop)[:, None],
+        np.ones((n_bins - bin_start - 2, 1))
+    ], axis=0)
+
+    return mask
+
 def fft_lp_filter(spec, bin_start, bin_stop):
     g = 1.0
     for b in range(bin_start, bin_stop):
@@ -353,6 +385,30 @@ def fft_hp_filter(spec, bin_start, bin_stop):
 
     return spec
 
+def spectrogram_to_wave_old(spec, hop_length=1024):
+    if spec.ndim == 2:
+        wave = librosa.istft(spec, hop_length=hop_length)
+    elif spec.ndim == 3:
+        spec_left = np.asfortranarray(spec[0])
+        spec_right = np.asfortranarray(spec[1])
+
+        wave_left = librosa.istft(spec_left, hop_length=hop_length)
+        wave_right = librosa.istft(spec_right, hop_length=hop_length)
+        wave = np.asfortranarray([wave_left, wave_right])
+
+    return wave
+    
+def wave_to_spectrogram_old(wave, hop_length, n_fft):
+    wave_left = np.asfortranarray(wave[0])
+    wave_right = np.asfortranarray(wave[1])
+
+    spec_left = librosa.stft(wave_left, n_fft, hop_length=hop_length)
+    spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
+    
+    spec = np.asfortranarray([spec_left, spec_right])
+
+    return spec
+
 def mirroring(a, spec_m, input_high_end, mp):
     if 'mirroring' == a:
         mirror = np.flip(np.abs(spec_m[:, mp.param['pre_filter_start']-10-input_high_end.shape[1]:mp.param['pre_filter_start']-10, :]), 1)
@@ -367,7 +423,7 @@ def mirroring(a, spec_m, input_high_end, mp):
         return np.where(np.abs(input_high_end) <= np.abs(mi), input_high_end, mi)
 
 def adjust_aggr(mask, is_non_accom_stem, aggressiveness):
-    aggr = aggressiveness['value']
+    aggr = aggressiveness['value'] * 2
 
     if aggr != 0:
         if is_non_accom_stem:
@@ -383,9 +439,6 @@ def adjust_aggr(mask, is_non_accom_stem, aggressiveness):
             mask[ch, :aggressiveness['split_bin']] = np.power(mask[ch, :aggressiveness['split_bin']], 1 + aggr[ch] / 3)
             mask[ch, aggressiveness['split_bin']:] = np.power(mask[ch, aggressiveness['split_bin']:], 1 + aggr[ch])
 
-        # if is_non_accom_stem:
-        #     mask = (1.0 - mask)
-        
     return mask
 
 def stft(wave, nfft, hl):
@@ -460,32 +513,51 @@ def invert_audio(specs, invert_p=True):
     return v_spec
 
 def invert_stem(mixture, stem):
-    
     mixture = wave_to_spectrogram_no_mp(mixture)
     stem = wave_to_spectrogram_no_mp(stem)
     output = spectrogram_to_wave_no_mp(invert_audio([mixture, stem]))
 
     return -output.T
 
-def ensembling(a, specs):   
-    for i in range(1, len(specs)):
-        if i == 1:
-            spec = specs[0]
+def ensembling(a, inputs, is_wavs=False): 
 
-        ln = min([spec.shape[2], specs[i].shape[2]])
-        spec = spec[:,:,:ln]
-        specs[i] = specs[i][:,:,:ln]
+    for i in range(1, len(inputs)):
+        if i == 1:
+            input = inputs[0]
+
+        if is_wavs:
+            ln = min([input.shape[1], inputs[i].shape[1]])
+            input = input[:,:ln]
+            inputs[i] = inputs[i][:,:ln]
+        else:
+            ln = min([input.shape[2], inputs[i].shape[2]])
+            input = input[:,:,:ln]
+            inputs[i] = inputs[i][:,:,:ln]
         
         if MIN_SPEC == a:
-            spec = np.where(np.abs(specs[i]) <= np.abs(spec), specs[i], spec)
+            input = np.where(np.abs(inputs[i]) <= np.abs(input), inputs[i], input)
         if MAX_SPEC == a:
-            spec = np.where(np.abs(specs[i]) >= np.abs(spec), specs[i], spec)  
-        if AVERAGE == a:
-            spec = np.where(np.abs(specs[i]) == np.abs(spec), specs[i], spec)  
+            input = np.where(np.abs(inputs[i]) >= np.abs(input), inputs[i], input)  
 
-    return spec
+    #linear_ensemble
+    #input = ensemble_wav(inputs, split_size=1)
 
-def ensemble_inputs(audio_input, algorithm, is_normalization, wav_type_set, save_path):
+    return input
+
+def ensemble_for_align(waves):
+    
+    specs = []
+    
+    for wav in waves:
+        spec = wave_to_spectrogram_no_mp(wav.T)
+        specs.append(spec)
+        
+    wav_aligned = spectrogram_to_wave_no_mp(ensembling(MIN_SPEC, specs)).T
+    wav_aligned = match_array_shapes(wav_aligned, waves[1], is_swap=True)    
+   
+    return wav_aligned
+    
+def ensemble_inputs(audio_input, algorithm, is_normalization, wav_type_set, save_path, is_wave=False, is_array=False):
 
     wavs_ = []
     
@@ -498,13 +570,17 @@ def ensemble_inputs(audio_input, algorithm, is_normalization, wav_type_set, save
         for i in range(len(audio_input)):  
             wave, samplerate = librosa.load(audio_input[i], mono=False, sr=44100)
             wavs_.append(wave)
-            spec = wave_to_spectrogram_no_mp(wave)
+            spec = wave if is_wave else wave_to_spectrogram_no_mp(wave)
             specs.append(spec)
         
         wave_shapes = [w.shape[1] for w in wavs_]
         target_shape = wavs_[wave_shapes.index(max(wave_shapes))]
         
-        output = spectrogram_to_wave_no_mp(ensembling(algorithm, specs))
+        if is_wave:
+            output = ensembling(algorithm, specs, is_wavs=True)
+        else:
+            output = spectrogram_to_wave_no_mp(ensembling(algorithm, specs))
+            
         output = to_shape(output, target_shape.shape)
 
     sf.write(save_path, normalize(output.T, is_normalization), samplerate, subtype=wav_type_set)
@@ -528,26 +604,142 @@ def to_shape_minimize(x: np.ndarray, target_shape):
     
     return np.pad(x, tuple(padding_list), mode='constant')
 
-def augment_audio(export_path, audio_file, rate, is_normalization, wav_type_set, save_format=None, is_pitch=False):
+def detect_leading_silence(audio, sr, silence_threshold=0.007, frame_length=1024):
+    """
+    Detect silence at the beginning of an audio signal.
+
+    :param audio: np.array, audio signal
+    :param sr: int, sample rate
+    :param silence_threshold: float, magnitude threshold below which is considered silence
+    :param frame_length: int, the number of samples to consider for each check
+
+    :return: float, duration of the leading silence in milliseconds
+    """
+    
+    if len(audio.shape) == 2:
+        # If stereo, pick the channel with more energy to determine the silence
+        channel = np.argmax(np.sum(np.abs(audio), axis=1))
+        audio = audio[channel]
+    
+    for i in range(0, len(audio), frame_length):
+        if np.max(np.abs(audio[i:i+frame_length])) > silence_threshold:
+            return (i / sr) * 1000
+
+    return (len(audio) / sr) * 1000
+
+def adjust_leading_silence(target_audio, reference_audio, silence_threshold=0.01, frame_length=1024):
+    """
+    Adjust the leading silence of the target_audio to match the leading silence of the reference_audio.
+
+    :param target_audio: np.array, audio signal that will have its silence adjusted
+    :param reference_audio: np.array, audio signal used as a reference
+    :param sr: int, sample rate
+    :param silence_threshold: float, magnitude threshold below which is considered silence
+    :param frame_length: int, the number of samples to consider for each check
+
+    :return: np.array, target_audio adjusted to have the same leading silence as reference_audio
+    """
+    
+    def find_silence_end(audio):
+        if len(audio.shape) == 2:
+            # If stereo, pick the channel with more energy to determine the silence
+            channel = np.argmax(np.sum(np.abs(audio), axis=1))
+            audio_mono = audio[channel]
+        else:
+            audio_mono = audio
+
+        for i in range(0, len(audio_mono), frame_length):
+            if np.max(np.abs(audio_mono[i:i+frame_length])) > silence_threshold:
+                return i
+        return len(audio_mono)
+
+    ref_silence_end = find_silence_end(reference_audio)
+    target_silence_end = find_silence_end(target_audio)
+    silence_difference = ref_silence_end - target_silence_end
+
+    try:
+        ref_silence_end_p = (ref_silence_end / 44100) * 1000
+        target_silence_end_p = (target_silence_end / 44100) * 1000
+        silence_difference_p = ref_silence_end_p - target_silence_end_p
+        print("silence_difference: ", silence_difference_p)
+    except Exception as e:
+        pass
+
+    if silence_difference > 0:  # Add silence to target_audio
+        if len(target_audio.shape) == 2:  # stereo
+            silence_to_add = np.zeros((target_audio.shape[0], silence_difference))
+        else:  # mono
+            silence_to_add = np.zeros(silence_difference)
+        return np.hstack((silence_to_add, target_audio))
+    elif silence_difference < 0:  # Remove silence from target_audio
+        if len(target_audio.shape) == 2:  # stereo
+            return target_audio[:, -silence_difference:]
+        else:  # mono
+            return target_audio[-silence_difference:]
+    else:  # No adjustment needed
+        return target_audio
+
+def match_array_shapes(array_1:np.ndarray, array_2:np.ndarray, is_swap=False):
+    
+    if is_swap:
+        array_1, array_2 = array_1.T, array_2.T
+    
+    #print("before", array_1.shape, array_2.shape)
+    if array_1.shape[1] > array_2.shape[1]:
+        array_1 = array_1[:,:array_2.shape[1]] 
+    elif array_1.shape[1] < array_2.shape[1]:
+        padding = array_2.shape[1] - array_1.shape[1]
+        array_1 = np.pad(array_1, ((0,0), (0,padding)), 'constant', constant_values=0)
+    
+    #print("after", array_1.shape, array_2.shape)
+    
+    if is_swap:
+        array_1, array_2 = array_1.T, array_2.T
+        
+    return array_1
+
+def match_mono_array_shapes(array_1: np.ndarray, array_2: np.ndarray):
+    
+    if len(array_1) > len(array_2):
+        array_1 = array_1[:len(array_2)]
+    elif len(array_1) < len(array_2):
+        padding = len(array_2) - len(array_1)
+        array_1 = np.pad(array_1, (0, padding), 'constant', constant_values=0)
+        
+    return array_1
+
+def change_pitch_semitones(y, sr, semitone_shift):
+    factor = 2 ** (semitone_shift / 12)  # Convert semitone shift to factor for resampling
+    y_pitch_tuned = []
+    for y_channel in y:
+        y_pitch_tuned.append(librosa.resample(y_channel, sr, sr*factor, res_type=wav_resolution))
+    y_pitch_tuned = np.array(y_pitch_tuned)
+    new_sr = sr * factor
+    return y_pitch_tuned, new_sr
+
+def augment_audio(export_path, audio_file, rate, is_normalization, wav_type_set, save_format=None, is_pitch=False, is_time_correction=True):
 
     wav, sr = librosa.load(audio_file, sr=44100, mono=False)
 
     if wav.ndim == 1:
         wav = np.asfortranarray([wav,wav])
 
-    if is_pitch:
-        wav_1 = pyrb.pitch_shift(wav[0], sr, rate, rbargs=None)
-        wav_2 = pyrb.pitch_shift(wav[1], sr, rate, rbargs=None)
+    if not is_time_correction:
+        wav_mix = change_pitch_semitones(wav, 44100, semitone_shift=-rate)[0]
     else:
-        wav_1 = pyrb.time_stretch(wav[0], sr, rate, rbargs=None)
-        wav_2 = pyrb.time_stretch(wav[1], sr, rate, rbargs=None)
+        if is_pitch:
+            wav_1 = pyrb.pitch_shift(wav[0], sr, rate, rbargs=None)
+            wav_2 = pyrb.pitch_shift(wav[1], sr, rate, rbargs=None)
+        else:
+            wav_1 = pyrb.time_stretch(wav[0], sr, rate, rbargs=None)
+            wav_2 = pyrb.time_stretch(wav[1], sr, rate, rbargs=None)
 
-    if wav_1.shape > wav_2.shape:
-        wav_2 = to_shape(wav_2, wav_1.shape)
-    if wav_1.shape < wav_2.shape:
-        wav_1 = to_shape(wav_1, wav_2.shape)
-        
-    wav_mix = np.asfortranarray([wav_1, wav_2])
+        if wav_1.shape > wav_2.shape:
+            wav_2 = to_shape(wav_2, wav_1.shape)
+        if wav_1.shape < wav_2.shape:
+            wav_1 = to_shape(wav_1, wav_2.shape)
+            
+        wav_mix = np.asfortranarray([wav_1, wav_2])
     
     sf.write(export_path, normalize(wav_mix.T, is_normalization), sr, subtype=wav_type_set)
     save_format(export_path)
@@ -587,7 +779,7 @@ def average_dual_sources(wav_1, wav_2, value):
     wave = (wav_1 * value) + (wav_2 * (1-value))
 
     return wave
-    
+       
 def reshape_sources(wav_1: np.ndarray, wav_2: np.ndarray):
     
     if wav_1.shape > wav_2.shape:
@@ -602,91 +794,434 @@ def reshape_sources(wav_1: np.ndarray, wav_2: np.ndarray):
 
     return wav_2
     
-def align_audio(file1, file2, file2_aligned, file_subtracted, wav_type_set, is_normalization, command_Text, progress_bar_main_var, save_format):
+def reshape_sources_ref(wav_1_shape, wav_2: np.ndarray):
+    
+    if wav_1_shape > wav_2.shape:
+        wav_2 = to_shape(wav_2, wav_1_shape)
+
+    return wav_2
+    
+def combine_arrarys(audio_sources, is_swap=False):
+    source = np.zeros_like(max(audio_sources, key=np.size))
+    
+    for v in audio_sources:
+        v = match_array_shapes(v, source, is_swap=is_swap)
+        source += v
+        
+    return source
+    
+def combine_audio(paths: list, audio_file_base=None, wav_type_set='FLOAT', save_format=None):
+    
+    source = combine_arrarys([load_audio(i) for i in paths])
+    save_path = f"{audio_file_base}_combined.wav"
+    sf.write(save_path, source.T, 44100, subtype=wav_type_set)
+    save_format(save_path)
+    
+def reduce_mix_bv(inst_source, voc_source, reduction_rate=0.9):
+    # Reduce the volume
+    inst_source = inst_source * (1 - reduction_rate)
+
+    mix_reduced = combine_arrarys([inst_source, voc_source], is_swap=True)
+
+    return mix_reduced
+    
+def organize_inputs(inputs):
+    input_list = {
+        "target":None,
+        "reference":None,
+        "reverb":None,
+        "inst":None
+    }
+    
+    for i in inputs:
+        if i.endswith("_(Vocals).wav"):
+            input_list["reference"] = i
+        elif "_RVC_" in i:
+            input_list["target"] = i
+        elif i.endswith("reverbed_stem.wav"):
+            input_list["reverb"] = i
+        elif i.endswith("_(Instrumental).wav"):
+            input_list["inst"] = i
+            
+    return input_list
+      
+def check_if_phase_inverted(wav1, wav2, is_mono=False):
+    # Load the audio files
+    if not is_mono:
+        wav1 = np.mean(wav1, axis=0)
+        wav2 = np.mean(wav2, axis=0)
+    
+    # Compute the correlation
+    correlation = np.corrcoef(wav1[:1000], wav2[:1000])
+    
+    return correlation[0,1] < 0
+         
+def align_audio(file1, 
+                file2, 
+                file2_aligned, 
+                file_subtracted, 
+                wav_type_set, 
+                is_save_aligned, 
+                command_Text, 
+                save_format, 
+                align_window:list, 
+                align_intro_val:list,
+                db_analysis:tuple,
+                set_progress_bar,
+                phase_option,
+                phase_shifts,
+                is_match_silence,
+                is_spec_match):
+    
+    global progress_value
+    progress_value = 0
+    is_mono = False
+    
+    def get_diff(a, b):
+        corr = np.correlate(a, b, "full")
+        diff = corr.argmax() - (b.shape[0] - 1)
+
+        return diff
+
+    def progress_bar(length):
+        global progress_value
+        progress_value += 1
+
+        if (0.90/length*progress_value) >= 0.9:
+            length = progress_value + 1
+
+        set_progress_bar(0.1, (0.9/length*progress_value))
+    
+    # read tracks
+    wav1, sr1 = librosa.load(file1, sr=44100, mono=False)
+    wav2, sr2 = librosa.load(file2, sr=44100, mono=False)
+
+    if wav1.ndim == 1 and wav2.ndim == 1:
+         is_mono = True
+    elif wav1.ndim == 1:
+        wav1 = np.asfortranarray([wav1,wav1])
+    elif wav2.ndim == 1:
+        wav2 = np.asfortranarray([wav2,wav2])
+    
+    # Check if phase is inverted
+    if phase_option == AUTO_PHASE:
+        if check_if_phase_inverted(wav1, wav2, is_mono=is_mono):
+            wav2 = -wav2
+    elif phase_option == POSITIVE_PHASE:
+        wav2 = +wav2
+    elif phase_option == NEGATIVE_PHASE:
+        wav2 = -wav2
+    
+    if is_match_silence:
+        wav2 = adjust_leading_silence(wav2, wav1)
+    
+    wav1_length = int(librosa.get_duration(y=wav1, sr=44100))
+    wav2_length = int(librosa.get_duration(y=wav2, sr=44100))
+    
+    if not is_mono:
+        wav1 = wav1.transpose()
+        wav2 = wav2.transpose()
+
+    wav2_org = wav2.copy()
+    
+    command_Text("Processing files... \n")
+    seconds_length = min(wav1_length, wav2_length)
+    
+    wav2_aligned_sources = []
+    
+    for sec_len in align_intro_val:
+        # pick a position at 1 second in and get diff
+        sec_seg = 1 if sec_len == 1 else int(seconds_length // sec_len)
+        index = sr1*sec_seg  # 1 second in, assuming sr1 = sr2 = 44100
+
+        if is_mono:
+            samp1, samp2 = wav1[index : index + sr1], wav2[index : index + sr1]
+            diff = get_diff(samp1, samp2)
+            #print(f"Estimated difference: {diff}\n")
+        else:
+            index = sr1*sec_seg  # 1 second in, assuming sr1 = sr2 = 44100
+            samp1, samp2 = wav1[index : index + sr1, 0], wav2[index : index + sr1, 0]
+            samp1_r, samp2_r = wav1[index : index + sr1, 1], wav2[index : index + sr1, 1]
+            diff, diff_r = get_diff(samp1, samp2), get_diff(samp1_r, samp2_r)
+            #print(f"Estimated difference Left Channel: {diff}\nEstimated difference Right Channel: {diff_r}\n")
+        
+        # make aligned track 2
+        if diff > 0:
+            zeros_to_append = np.zeros(diff) if is_mono else np.zeros((diff, 2))
+            wav2_aligned = np.append(zeros_to_append, wav2_org, axis=0)
+        elif diff < 0:
+            wav2_aligned = wav2_org[-diff:]
+        else:
+            wav2_aligned = wav2_org
+            #command_Text(f"Audio files already aligned.\n")
+            
+        if not any(np.array_equal(wav2_aligned, source) for source in wav2_aligned_sources):
+            wav2_aligned_sources.append(wav2_aligned)
+
+    #print("Unique Sources: ", len(wav2_aligned_sources))
+    
+    unique_sources = len(wav2_aligned_sources)
+    
+    sub_mapper_big_mapper = {}
+
+    for s in wav2_aligned_sources:
+        wav2_aligned = match_mono_array_shapes(s, wav1) if is_mono else match_array_shapes(s, wav1, is_swap=True)
+        
+        if align_window:
+            wav_sub = time_correction(wav1, wav2_aligned, seconds_length, align_window=align_window, db_analysis=db_analysis, progress_bar=progress_bar, unique_sources=unique_sources, phase_shifts=phase_shifts)
+            wav_sub_size = np.abs(wav_sub).mean()  
+            sub_mapper_big_mapper = {**sub_mapper_big_mapper, **{wav_sub_size:wav_sub}}
+        else:
+            wav2_aligned = wav2_aligned * np.power(10, db_analysis[0] / 20)
+            db_range = db_analysis[1]
+            
+            for db_adjustment in db_range:
+                # Adjust the dB of track2
+                s_adjusted = wav2_aligned * (10 ** (db_adjustment / 20))
+                wav_sub = wav1 - s_adjusted
+                wav_sub_size = np.abs(wav_sub).mean() 
+                sub_mapper_big_mapper = {**sub_mapper_big_mapper, **{wav_sub_size:wav_sub}}
+            
+        #print(sub_mapper_big_mapper.keys(), min(sub_mapper_big_mapper.keys()))
+    
+    sub_mapper_value_list = list(sub_mapper_big_mapper.values())
+    
+    if is_spec_match and len(sub_mapper_value_list) >= 2:
+        #print("using spec ensemble with align")
+        wav_sub = ensemble_for_align(list(sub_mapper_big_mapper.values()))
+    else:
+        #print("using linear ensemble with align")
+        wav_sub = ensemble_wav(list(sub_mapper_big_mapper.values()))
+         
+    #print(f"Mix Mean: {np.abs(wav1).mean()}\nInst Mean: {np.abs(wav2).mean()}")
+    #print('Final: ', np.abs(wav_sub).mean())
+    wav_sub = np.clip(wav_sub, -1, +1)
+    
+    command_Text(f"Saving inverted track... ")
+
+    if is_save_aligned or is_spec_match:
+        wav1 = match_mono_array_shapes(wav1, wav_sub) if is_mono else match_array_shapes(wav1, wav_sub, is_swap=True)
+        wav2_aligned = wav1 - wav_sub
+
+        if is_spec_match:
+            if wav1.ndim == 1 and wav2.ndim == 1:
+                wav2_aligned = np.asfortranarray([wav2_aligned, wav2_aligned]).T
+                wav1 = np.asfortranarray([wav1, wav1]).T
+            
+            wav2_aligned = ensemble_for_align([wav2_aligned, wav1])
+            wav_sub = wav1 - wav2_aligned
+        
+        if is_save_aligned:
+            sf.write(file2_aligned, wav2_aligned, sr1, subtype=wav_type_set)
+            save_format(file2_aligned)
+
+    sf.write(file_subtracted, wav_sub, sr1, subtype=wav_type_set)
+    save_format(file_subtracted)
+
+def phase_shift_hilbert(signal, degree):
+    analytic_signal = hilbert(signal)
+    return np.cos(np.radians(degree)) * analytic_signal.real - np.sin(np.radians(degree)) * analytic_signal.imag
+
+def get_phase_shifted_tracks(track, phase_shift):
+    if phase_shift == 180:
+        return [track, -track]
+
+    step = phase_shift
+    end = 180 - (180 % step) if 180 % step == 0 else 181
+    phase_range = range(step, end, step)
+    
+    flipped_list = [track, -track]
+    for i in phase_range:
+        flipped_list.extend([phase_shift_hilbert(track, i), phase_shift_hilbert(track, -i)])
+
+    return flipped_list
+
+def time_correction(mix:np.ndarray, instrumental:np.ndarray, seconds_length, align_window, db_analysis, sr=44100, progress_bar=None, unique_sources=None, phase_shifts=NONE_P):
+    # Function to align two tracks using cross-correlation
+
+    def align_tracks(track1, track2):
+        # A dictionary to store each version of track2_shifted and its mean absolute value
+        shifted_tracks = {}
+
+        # Loop to adjust dB of track2
+        track2 = track2 * np.power(10, db_analysis[0] / 20)
+        db_range = db_analysis[1]
+        
+        if phase_shifts == 190:
+            track2_flipped = [track2]
+        else:
+            track2_flipped = get_phase_shifted_tracks(track2, phase_shifts)
+            
+        for db_adjustment in db_range:
+            for t in track2_flipped:
+                # Adjust the dB of track2
+                track2_adjusted = t * (10 ** (db_adjustment / 20))
+                corr = correlate(track1, track2_adjusted)
+                delay = np.argmax(np.abs(corr)) - (len(track1) - 1)
+                track2_shifted = np.roll(track2_adjusted, shift=delay)
+
+                # Compute the mean absolute value of track2_shifted
+                track2_shifted_sub = track1 - track2_shifted
+                mean_abs_value = np.abs(track2_shifted_sub).mean()
+
+                # Store track2_shifted and its mean absolute value in the dictionary
+                shifted_tracks[mean_abs_value] = track2_shifted
+
+        # Return the version of track2_shifted with the smallest mean absolute value
+                
+        return shifted_tracks[min(shifted_tracks.keys())]
+
+    # Make sure the audio files have the same shape
+    
+    assert mix.shape == instrumental.shape, f"Audio files must have the same shape - Mix: {mix.shape}, Inst: {instrumental.shape}"
+    
+    seconds_length = seconds_length // 2
+
+    sub_mapper = {}
+    
+    progress_update_interval = 120
+    total_iterations = 0
+    
+    if len(align_window) > 2:
+        progress_update_interval = 320
+    
+    for secs in align_window:
+        step = secs / 2
+        window_size = int(sr * secs)
+        step_size = int(sr * step)
+        
+        if len(mix.shape) == 1:
+            total_mono = (len(range(0, len(mix) - window_size, step_size))//progress_update_interval)*unique_sources
+            total_iterations += total_mono
+        else:
+            total_stereo_ = len(range(0, len(mix[:, 0]) - window_size, step_size))*2
+            total_stereo = (total_stereo_//progress_update_interval) * unique_sources
+            total_iterations += total_stereo
+    
+    #print(total_iterations)
+    
+    for secs in align_window:
+        sub = np.zeros_like(mix)
+        divider = np.zeros_like(mix)
+        step = secs / 2
+        window_size = int(sr * secs)
+        step_size = int(sr * step)
+        window = np.hanning(window_size)
+
+        # For the mono case:
+        if len(mix.shape) == 1:
+            # The files are mono
+            counter = 0
+            for i in range(0, len(mix) - window_size, step_size):
+                counter += 1
+                if counter % progress_update_interval == 0:
+                    progress_bar(total_iterations)
+                window_mix = mix[i:i+window_size] * window
+                window_instrumental = instrumental[i:i+window_size] * window
+                window_instrumental_aligned = align_tracks(window_mix, window_instrumental)
+                sub[i:i+window_size] += window_mix - window_instrumental_aligned
+                divider[i:i+window_size] += window
+        else:
+            # The files are stereo
+            counter = 0
+            for ch in range(mix.shape[1]):
+                for i in range(0, len(mix[:, ch]) - window_size, step_size):
+                    counter += 1
+                    if counter % progress_update_interval == 0:
+                        progress_bar(total_iterations)
+                    window_mix = mix[i:i+window_size, ch] * window
+                    window_instrumental = instrumental[i:i+window_size, ch] * window
+                    window_instrumental_aligned = align_tracks(window_mix, window_instrumental)
+                    sub[i:i+window_size, ch] += window_mix - window_instrumental_aligned
+                    divider[i:i+window_size, ch] += window
+
+        # Normalize the result by the overlap count
+        sub = np.where(divider > 1e-6, sub / divider, sub)
+        sub_size = np.abs(sub).mean()
+        sub_mapper = {**sub_mapper, **{sub_size: sub}}
+
+    #print("SUB_LEN", len(list(sub_mapper.values())))
+
+    sub = ensemble_wav(list(sub_mapper.values()), split_size=12)
+          
+    return sub
+
+def ensemble_wav(waveforms, split_size=240):
+    # Create a dictionary to hold the thirds of each waveform and their mean absolute values
+    waveform_thirds = {i: np.array_split(waveform, split_size) for i, waveform in enumerate(waveforms)}
+
+    # Initialize the final waveform
+    final_waveform = []
+
+    # For chunk
+    for third_idx in range(split_size):
+        # Compute the mean absolute value of each third from each waveform
+        means = [np.abs(waveform_thirds[i][third_idx]).mean() for i in range(len(waveforms))]
+
+        # Find the index of the waveform with the lowest mean absolute value for this third
+        min_index = np.argmin(means)
+
+        # Add the least noisy third to the final waveform
+        final_waveform.append(waveform_thirds[min_index][third_idx])
+
+    # Concatenate all the thirds to create the final waveform
+    final_waveform = np.concatenate(final_waveform)
+
+    return final_waveform
+
+def ensemble_wav_min(waveforms):
+    for i in range(1, len(waveforms)):
+        if i == 1:
+            wave = waveforms[0]
+
+        ln = min(len(wave), len(waveforms[i]))
+        wave = wave[:ln]
+        waveforms[i] = waveforms[i][:ln]
+
+        wave = np.where(np.abs(waveforms[i]) <= np.abs(wave), waveforms[i], wave)
+        
+    return wave
+
+def align_audio_test(wav1, wav2, sr1=44100):
     def get_diff(a, b):
         corr = np.correlate(a, b, "full")
         diff = corr.argmax() - (b.shape[0] - 1)
         return diff
   
-    progress_bar_main_var.set(10)
-    
     # read tracks
-    wav1, sr1 = librosa.load(file1, sr=44100, mono=False)
-    wav2, sr2 = librosa.load(file2, sr=44100, mono=False)
     wav1 = wav1.transpose()
     wav2 = wav2.transpose()
 
-    command_Text(f"Audio file shapes: {wav1.shape} / {wav2.shape}\n")
+    #print(f"Audio file shapes: {wav1.shape} / {wav2.shape}\n")
     
     wav2_org = wav2.copy()
-    progress_bar_main_var.set(20)
     
-    command_Text("Processing files... \n")
-    
-  # pick random position and get diff
-    
-    counts = {}       # counting up for each diff value
-    progress = 20
-    
-    check_range = 64
-
-    base = (64 / check_range)
-
-    for i in range(check_range):
-        index = int(random.uniform(44100 * 2, min(wav1.shape[0], wav2.shape[0]) - 44100 * 2))
-        shift = int(random.uniform(-22050,+22050))
-        samp1 = wav1[index      :index      +44100, 0]          # currently use left channel
-        samp2 = wav2[index+shift:index+shift+44100, 0]
-        progress += 1 * base
-        progress_bar_main_var.set(progress)
-        diff = get_diff(samp1, samp2)
-        diff -= shift
-        
-    if abs(diff) < 22050:
-        if not diff in counts:
-            counts[diff] = 0
-        counts[diff] += 1
-  
-  # use max counted diff value
-    max_count = 0
-    est_diff  = 0
-    for diff in counts.keys():
-        if counts[diff] > max_count:
-            max_count = counts[diff]
-            est_diff = diff
-    
-    command_Text(f"Estimated difference is {est_diff} (count: {max_count})\n")
-
-    progress_bar_main_var.set(90)
-    
-    audio_files = []
-
-    def save_aligned_audio(wav2_aligned):
-        command_Text(f"Aligned File 2 with File 1.\n")
-        command_Text(f"Saving files... ")
-        sf.write(file2_aligned, normalize(wav2_aligned, is_normalization), sr2, subtype=wav_type_set)
-        save_format(file2_aligned)
-        min_len = min(wav1.shape[0], wav2_aligned.shape[0])
-        wav_sub = wav1[:min_len] - wav2_aligned[:min_len]
-        audio_files.append(file2_aligned)
-        return min_len, wav_sub
+    # pick a position at 1 second in and get diff
+    index = sr1#*seconds_length  # 1 second in, assuming sr1 = sr2 = 44100
+    samp1 = wav1[index : index + sr1, 0] # currently use left channel
+    samp2 = wav2[index : index + sr1, 0]
+    diff = get_diff(samp1, samp2)
     
   # make aligned track 2
-    if est_diff > 0:
-        wav2_aligned = np.append(np.zeros((est_diff, 2)), wav2_org, axis=0)
-        min_len, wav_sub = save_aligned_audio(wav2_aligned)
-    elif est_diff < 0:
-        wav2_aligned = wav2_org[-est_diff:]
-        min_len, wav_sub = save_aligned_audio(wav2_aligned)
+    if diff > 0:
+        wav2_aligned = np.append(np.zeros((diff, 1)), wav2_org, axis=0)
+    elif diff < 0:
+        wav2_aligned = wav2_org[-diff:]
     else:
-        command_Text(f"Audio files already aligned.\n")
-        command_Text(f"Saving inverted track... ")
-        min_len = min(wav1.shape[0], wav2.shape[0])
-        wav_sub = wav1[:min_len] - wav2[:min_len]
+        wav2_aligned = wav2_org
+        
+    return wav2_aligned
 
-    wav_sub = np.clip(wav_sub, -1, +1)
-  
-    sf.write(file_subtracted, normalize(wav_sub, is_normalization), sr1, subtype=wav_type_set)
-    save_format(file_subtracted)
-  
-    progress_bar_main_var.set(95)
\ No newline at end of file
+def load_audio(audio_file):
+    wav, sr = librosa.load(audio_file, sr=44100, mono=False)
+
+    if wav.ndim == 1:
+        wav = np.asfortranarray([wav,wav])
+        
+    return wav
+
+def rerun_mp3(audio_file, sample_rate=44100):
+    with audioread.audio_open(audio_file) as f:
+        track_length = int(f.duration)
+
+    return librosa.load(audio_file, duration=track_length, mono=False, sr=sample_rate)[0]
diff --git a/lib_v5/tfc_tdf_v3.py b/lib_v5/tfc_tdf_v3.py
new file mode 100644
index 0000000..8f72595
--- /dev/null
+++ b/lib_v5/tfc_tdf_v3.py
@@ -0,0 +1,234 @@
+import torch
+import torch.nn as nn
+from functools import partial
+
+class STFT:
+    def __init__(self, n_fft, hop_length, dim_f):
+        self.n_fft = n_fft
+        self.hop_length = hop_length
+        self.window = torch.hann_window(window_length=self.n_fft, periodic=True)
+        self.dim_f = dim_f
+
+    def __call__(self, x):
+        window = self.window.to(x.device)
+        batch_dims = x.shape[:-2]
+        c, t = x.shape[-2:]
+        x = x.reshape([-1, t])
+        x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop_length, window=window, center=True,return_complex=False)
+        x = x.permute([0, 3, 1, 2])
+        x = x.reshape([*batch_dims, c, 2, -1, x.shape[-1]]).reshape([*batch_dims, c * 2, -1, x.shape[-1]])
+        return x[..., :self.dim_f, :]
+
+    def inverse(self, x):
+        window = self.window.to(x.device)
+        batch_dims = x.shape[:-3]
+        c, f, t = x.shape[-3:]
+        n = self.n_fft // 2 + 1
+        f_pad = torch.zeros([*batch_dims, c, n - f, t]).to(x.device)
+        x = torch.cat([x, f_pad], -2)
+        x = x.reshape([*batch_dims, c // 2, 2, n, t]).reshape([-1, 2, n, t])
+        x = x.permute([0, 2, 3, 1])
+        x = x[..., 0] + x[..., 1] * 1.j
+        x = torch.istft(x, n_fft=self.n_fft, hop_length=self.hop_length, window=window, center=True)
+        x = x.reshape([*batch_dims, 2, -1])
+        return x
+
+
+def get_norm(norm_type):
+    def norm(c, norm_type):
+        if norm_type == 'BatchNorm':
+            return nn.BatchNorm2d(c)
+        elif norm_type == 'InstanceNorm':
+            return nn.InstanceNorm2d(c, affine=True)
+        elif 'GroupNorm' in norm_type:
+            g = int(norm_type.replace('GroupNorm', ''))
+            return nn.GroupNorm(num_groups=g, num_channels=c)
+        else:
+            return nn.Identity()
+
+    return partial(norm, norm_type=norm_type)
+
+
+def get_act(act_type):
+    if act_type == 'gelu':
+        return nn.GELU()
+    elif act_type == 'relu':
+        return nn.ReLU()
+    elif act_type[:3] == 'elu':
+        alpha = float(act_type.replace('elu', ''))
+        return nn.ELU(alpha)
+    else:
+        raise Exception
+
+
+class Upscale(nn.Module):
+    def __init__(self, in_c, out_c, scale, norm, act):
+        super().__init__()
+        self.conv = nn.Sequential(
+            norm(in_c),
+            act,
+            nn.ConvTranspose2d(in_channels=in_c, out_channels=out_c, kernel_size=scale, stride=scale, bias=False)
+        )
+
+    def forward(self, x):
+        return self.conv(x)
+
+
+class Downscale(nn.Module):
+    def __init__(self, in_c, out_c, scale, norm, act):
+        super().__init__()
+        self.conv = nn.Sequential(
+            norm(in_c),
+            act,
+            nn.Conv2d(in_channels=in_c, out_channels=out_c, kernel_size=scale, stride=scale, bias=False)
+        )
+
+    def forward(self, x):
+        return self.conv(x)
+
+
+class TFC_TDF(nn.Module):
+    def __init__(self, in_c, c, l, f, bn, norm, act):
+        super().__init__()
+
+        self.blocks = nn.ModuleList()
+        for i in range(l):
+            block = nn.Module()
+
+            block.tfc1 = nn.Sequential(
+                norm(in_c),
+                act,
+                nn.Conv2d(in_c, c, 3, 1, 1, bias=False),
+            )
+            block.tdf = nn.Sequential(
+                norm(c),
+                act,
+                nn.Linear(f, f // bn, bias=False),
+                norm(c),
+                act,
+                nn.Linear(f // bn, f, bias=False),
+            )
+            block.tfc2 = nn.Sequential(
+                norm(c),
+                act,
+                nn.Conv2d(c, c, 3, 1, 1, bias=False),
+            )
+            block.shortcut = nn.Conv2d(in_c, c, 1, 1, 0, bias=False)
+
+            self.blocks.append(block)
+            in_c = c
+
+    def forward(self, x):
+        for block in self.blocks:
+            s = block.shortcut(x)
+            x = block.tfc1(x)
+            x = x + block.tdf(x)
+            x = block.tfc2(x)
+            x = x + s
+        return x
+
+
+class TFC_TDF_net(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+
+        norm = get_norm(norm_type=config.model.norm)
+        act = get_act(act_type=config.model.act)
+
+        self.num_target_instruments = 1 if config.training.target_instrument else len(config.training.instruments)
+        self.num_subbands = config.model.num_subbands
+
+        dim_c = self.num_subbands * config.audio.num_channels * 2
+        n = config.model.num_scales
+        scale = config.model.scale
+        l = config.model.num_blocks_per_scale
+        c = config.model.num_channels
+        g = config.model.growth
+        bn = config.model.bottleneck_factor
+        f = config.audio.dim_f // self.num_subbands
+
+        self.first_conv = nn.Conv2d(dim_c, c, 1, 1, 0, bias=False)
+
+        self.encoder_blocks = nn.ModuleList()
+        for i in range(n):
+            block = nn.Module()
+            block.tfc_tdf = TFC_TDF(c, c, l, f, bn, norm, act)
+            block.downscale = Downscale(c, c + g, scale, norm, act)
+            f = f // scale[1]
+            c += g
+            self.encoder_blocks.append(block)
+
+        self.bottleneck_block = TFC_TDF(c, c, l, f, bn, norm, act)
+
+        self.decoder_blocks = nn.ModuleList()
+        for i in range(n):
+            block = nn.Module()
+            block.upscale = Upscale(c, c - g, scale, norm, act)
+            f = f * scale[1]
+            c -= g
+            block.tfc_tdf = TFC_TDF(2 * c, c, l, f, bn, norm, act)
+            self.decoder_blocks.append(block)
+
+        self.final_conv = nn.Sequential(
+            nn.Conv2d(c + dim_c, c, 1, 1, 0, bias=False),
+            act,
+            nn.Conv2d(c, self.num_target_instruments * dim_c, 1, 1, 0, bias=False)
+        )
+
+        self.stft = STFT(config.audio.n_fft, config.audio.hop_length, config.audio.dim_f)
+
+    def cac2cws(self, x):
+        k = self.num_subbands
+        b, c, f, t = x.shape
+        x = x.reshape(b, c, k, f // k, t)
+        x = x.reshape(b, c * k, f // k, t)
+        return x
+
+    def cws2cac(self, x):
+        k = self.num_subbands
+        b, c, f, t = x.shape
+        x = x.reshape(b, c // k, k, f, t)
+        x = x.reshape(b, c // k, f * k, t)
+        return x
+
+    def forward(self, x):
+
+        x = self.stft(x)
+
+        mix = x = self.cac2cws(x)
+
+        first_conv_out = x = self.first_conv(x)
+
+        x = x.transpose(-1, -2)
+
+        encoder_outputs = []
+        for block in self.encoder_blocks:
+            x = block.tfc_tdf(x)
+            encoder_outputs.append(x)
+            x = block.downscale(x)
+
+        x = self.bottleneck_block(x)
+
+        for block in self.decoder_blocks:
+            x = block.upscale(x)
+            x = torch.cat([x, encoder_outputs.pop()], 1)
+            x = block.tfc_tdf(x)
+
+        x = x.transpose(-1, -2)
+
+        x = x * first_conv_out  # reduce artifacts
+
+        x = self.final_conv(torch.cat([mix, x], 1))
+
+        x = self.cws2cac(x)
+
+        if self.num_target_instruments > 1:
+            b, c, f, t = x.shape
+            x = x.reshape(b, self.num_target_instruments, -1, f, t)
+
+        x = self.stft.inverse(x)
+
+        return x
+
+