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lib_v5/mdxnet.py

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+import torch
+import torch.nn as nn
+from .modules import TFC_TDF
+from pytorch_lightning import LightningModule
+
+dim_s = 4
+
+class AbstractMDXNet(LightningModule):
+    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
+        self.lr = lr
+        self.optimizer = optimizer
+        self.dim_c = dim_c
+        self.dim_f = dim_f
+        self.dim_t = dim_t
+        self.n_fft = n_fft
+        self.n_bins = n_fft // 2 + 1
+        self.hop_length = hop_length
+        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 get_optimizer(self):
+        if self.optimizer == 'rmsprop':
+            return torch.optim.RMSprop(self.parameters(), self.lr)
+        
+        if self.optimizer == 'adamw':
+            return torch.optim.AdamW(self.parameters(), self.lr)
+
+class ConvTDFNet(AbstractMDXNet):
+    def __init__(self, target_name, lr, optimizer, dim_c, dim_f, dim_t, n_fft, hop_length,
+                 num_blocks, l, g, k, bn, bias, overlap):
+
+        super(ConvTDFNet, self).__init__(
+            target_name, lr, optimizer, dim_c, dim_f, dim_t, n_fft, hop_length, overlap)
+        #self.save_hyperparameters()
+
+        self.num_blocks = num_blocks
+        self.l = l
+        self.g = g
+        self.k = k
+        self.bn = bn
+        self.bias = bias
+
+        if optimizer == 'rmsprop':
+            norm = nn.BatchNorm2d
+            
+        if optimizer == 'adamw':
+            norm = lambda input:nn.GroupNorm(2, input)
+            
+        self.n = num_blocks // 2
+        scale = (2, 2)
+
+        self.first_conv = nn.Sequential(
+            nn.Conv2d(in_channels=self.dim_c, out_channels=g, kernel_size=(1, 1)),
+            norm(g),
+            nn.ReLU(),
+        )
+
+        f = self.dim_f
+        c = g
+        self.encoding_blocks = nn.ModuleList()
+        self.ds = nn.ModuleList()
+        for i in range(self.n):
+            self.encoding_blocks.append(TFC_TDF(c, l, f, k, bn, bias=bias, norm=norm))
+            self.ds.append(
+                nn.Sequential(
+                    nn.Conv2d(in_channels=c, out_channels=c + g, kernel_size=scale, stride=scale),
+                    norm(c + g),
+                    nn.ReLU()
+                )
+            )
+            f = f // 2
+            c += g
+
+        self.bottleneck_block = TFC_TDF(c, l, f, k, bn, bias=bias, norm=norm)
+
+        self.decoding_blocks = nn.ModuleList()
+        self.us = nn.ModuleList()
+        for i in range(self.n):
+            self.us.append(
+                nn.Sequential(
+                    nn.ConvTranspose2d(in_channels=c, out_channels=c - g, kernel_size=scale, stride=scale),
+                    norm(c - g),
+                    nn.ReLU()
+                )
+            )
+            f = f * 2
+            c -= g
+
+            self.decoding_blocks.append(TFC_TDF(c, l, f, k, bn, bias=bias, norm=norm))
+
+        self.final_conv = nn.Sequential(
+            nn.Conv2d(in_channels=c, out_channels=self.dim_c, kernel_size=(1, 1)),
+        )
+
+    def forward(self, x):
+
+        x = self.first_conv(x)
+
+        x = x.transpose(-1, -2)
+
+        ds_outputs = []
+        for i in range(self.n):
+            x = self.encoding_blocks[i](x)
+            ds_outputs.append(x)
+            x = self.ds[i](x)
+
+        x = self.bottleneck_block(x)
+
+        for i in range(self.n):
+            x = self.us[i](x)
+            x *= ds_outputs[-i - 1]
+            x = self.decoding_blocks[i](x)
+
+        x = x.transpose(-1, -2)
+
+        x = self.final_conv(x)
+
+        return x
+    
+class Mixer(nn.Module):
+    def __init__(self, device, mixer_path):
+        
+        super(Mixer, self).__init__()
+        
+        self.linear = nn.Linear((dim_s+1)*2, dim_s*2, bias=False)
+        
+        self.load_state_dict(
+            torch.load(mixer_path, map_location=device)
+        )
+
+    def forward(self, x):
+        x = x.reshape(1,(dim_s+1)*2,-1).transpose(-1,-2)
+        x = self.linear(x)
+        return x.transpose(-1,-2).reshape(dim_s,2,-1)

lib_v5/mixer.ckpt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ea781bd52c6a523b825fa6cdbb6189f52e318edd8b17e6fe404f76f7af8caa9c
+size 1208

lib_v5/modules.py

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+import torch
+import torch.nn as nn
+
+
+class TFC(nn.Module):
+    def __init__(self, c, l, k, norm):
+        super(TFC, self).__init__()
+
+        self.H = nn.ModuleList()
+        for i in range(l):
+            self.H.append(
+                nn.Sequential(
+                    nn.Conv2d(in_channels=c, out_channels=c, kernel_size=k, stride=1, padding=k // 2),
+                    norm(c),
+                    nn.ReLU(),
+                )
+            )
+
+    def forward(self, x):
+        for h in self.H:
+            x = h(x)
+        return x
+
+
+class DenseTFC(nn.Module):
+    def __init__(self, c, l, k, norm):
+        super(DenseTFC, self).__init__()
+
+        self.conv = nn.ModuleList()
+        for i in range(l):
+            self.conv.append(
+                nn.Sequential(
+                    nn.Conv2d(in_channels=c, out_channels=c, kernel_size=k, stride=1, padding=k // 2),
+                    norm(c),
+                    nn.ReLU(),
+                )
+            )
+
+    def forward(self, x):
+        for layer in self.conv[:-1]:
+            x = torch.cat([layer(x), x], 1)
+        return self.conv[-1](x)
+
+
+class TFC_TDF(nn.Module):
+    def __init__(self, c, l, f, k, bn, dense=False, bias=True, norm=nn.BatchNorm2d):
+
+        super(TFC_TDF, self).__init__()
+
+        self.use_tdf = bn is not None
+
+        self.tfc = DenseTFC(c, l, k, norm) if dense else TFC(c, l, k, norm)
+
+        if self.use_tdf:
+            if bn == 0:
+                self.tdf = nn.Sequential(
+                    nn.Linear(f, f, bias=bias),
+                    norm(c),
+                    nn.ReLU()
+                )
+            else:
+                self.tdf = nn.Sequential(
+                    nn.Linear(f, f // bn, bias=bias),
+                    norm(c),
+                    nn.ReLU(),
+                    nn.Linear(f // bn, f, bias=bias),
+                    norm(c),
+                    nn.ReLU()
+                )
+
+    def forward(self, x):
+        x = self.tfc(x)
+        return x + self.tdf(x) if self.use_tdf else x
+

lib_v5/pyrb.py

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+import os
+import subprocess
+import tempfile
+import six
+import numpy as np
+import soundfile as sf
+import sys
+
+if getattr(sys, 'frozen', False):
+    BASE_PATH_RUB = sys._MEIPASS
+else:
+    BASE_PATH_RUB = os.path.dirname(os.path.abspath(__file__))
+
+__all__ = ['time_stretch', 'pitch_shift']
+
+__RUBBERBAND_UTIL = os.path.join(BASE_PATH_RUB, 'rubberband')
+
+if six.PY2:
+    DEVNULL = open(os.devnull, 'w')
+else:
+    DEVNULL = subprocess.DEVNULL
+
+def __rubberband(y, sr, **kwargs):
+
+    assert sr > 0
+
+    # Get the input and output tempfile
+    fd, infile = tempfile.mkstemp(suffix='.wav')
+    os.close(fd)
+    fd, outfile = tempfile.mkstemp(suffix='.wav')
+    os.close(fd)
+
+    # dump the audio
+    sf.write(infile, y, sr)
+
+    try:
+        # Execute rubberband
+        arguments = [__RUBBERBAND_UTIL, '-q']
+
+        for key, value in six.iteritems(kwargs):
+            arguments.append(str(key))
+            arguments.append(str(value))
+
+        arguments.extend([infile, outfile])
+
+        subprocess.check_call(arguments, stdout=DEVNULL, stderr=DEVNULL)
+
+        # Load the processed audio.
+        y_out, _ = sf.read(outfile, always_2d=True)
+
+        # make sure that output dimensions matches input
+        if y.ndim == 1:
+            y_out = np.squeeze(y_out)
+
+    except OSError as exc:
+        six.raise_from(RuntimeError('Failed to execute rubberband. '
+                                    'Please verify that rubberband-cli '
+                                    'is installed.'),
+                       exc)
+
+    finally:
+        # Remove temp files
+        os.unlink(infile)
+        os.unlink(outfile)
+
+    return y_out
+
+def time_stretch(y, sr, rate, rbargs=None):
+    if rate <= 0:
+        raise ValueError('rate must be strictly positive')
+
+    if rate == 1.0:
+        return y
+
+    if rbargs is None:
+        rbargs = dict()
+
+    rbargs.setdefault('--tempo', rate)
+
+    return __rubberband(y, sr, **rbargs)
+
+def pitch_shift(y, sr, n_steps, rbargs=None):
+
+    if n_steps == 0:
+        return y
+
+    if rbargs is None:
+        rbargs = dict()
+
+    rbargs.setdefault('--pitch', n_steps)
+
+    return __rubberband(y, sr, **rbargs)

lib_v5/results.py

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+# -*- coding: utf-8 -*-
+
+"""
+Matchering - Audio Matching and Mastering Python Library
+Copyright (C) 2016-2022 Sergree
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program.  If not, see <https://www.gnu.org/licenses/>.
+"""
+
+import os
+import soundfile as sf
+
+
+class Result:
+    def __init__(
+        self, file: str, subtype: str, use_limiter: bool = True, normalize: bool = True
+    ):
+        _, file_ext = os.path.splitext(file)
+        file_ext = file_ext[1:].upper()
+        if not sf.check_format(file_ext):
+            raise TypeError(f"{file_ext} format is not supported")
+        if not sf.check_format(file_ext, subtype):
+            raise TypeError(f"{file_ext} format does not have {subtype} subtype")
+        self.file = file
+        self.subtype = subtype
+        self.use_limiter = use_limiter
+        self.normalize = normalize
+
+
+def pcm16(file: str) -> Result:
+    return Result(file, "PCM_16")
+
+def pcm24(file: str) -> Result:
+    return Result(file, "FLOAT")
+
+def save_audiofile(file: str, wav_set="PCM_16") -> Result:
+    return Result(file, wav_set)

lib_v5/spec_utils.py

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+import audioread
+import librosa
+import numpy as np
+import soundfile as sf
+import math
+import platform
+import traceback
+from . import pyrb
+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
+is_macos = False
+
+if OPERATING_SYSTEM == 'Windows':
+    from pyrubberband import pyrb
+else:
+    from . import pyrb
+
+if OPERATING_SYSTEM == 'Darwin':
+    wav_resolution = "polyphase" if SYSTEM_PROC == ARM or ARM in SYSTEM_ARCH else "sinc_fastest" 
+    wav_resolution_float_resampling = "kaiser_best" if SYSTEM_PROC == ARM or ARM in SYSTEM_ARCH else wav_resolution 
+    is_macos = True
+else:
+    wav_resolution = "sinc_fastest"
+    wav_resolution_float_resampling = wav_resolution 
+
+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):
+    h1_shape = h1.size()
+    h2_shape = h2.size()
+
+    if h1_shape[3] == h2_shape[3]:
+        return h1
+    elif h1_shape[3] < h2_shape[3]:
+        raise ValueError('h1_shape[3] must be greater than h2_shape[3]')
+
+    s_time = (h1_shape[3] - h2_shape[3]) // 2
+    e_time = s_time + h2_shape[3]
+    h1 = h1[:, :, :, s_time:e_time]
+
+    return h1
+
+def preprocess(X_spec):
+    X_mag = np.abs(X_spec)
+    X_phase = np.angle(X_spec)
+
+    return X_mag, X_phase
+
+def make_padding(width, cropsize, offset):
+    left = offset
+    roi_size = cropsize - offset * 2
+    if roi_size == 0:
+        roi_size = cropsize
+    right = roi_size - (width % roi_size) + left
+
+    return left, right, roi_size
+
+def normalize(wave, is_normalize=False):
+    """Normalize audio"""
+
+    maxv = np.abs(wave).max()
+    if maxv > 1.0:
+        if is_normalize:
+            print("Above clipping threshold.")
+            wave /= maxv
+    
+    return wave
+    
+def auto_transpose(audio_array:np.ndarray):
+    """
+    Ensure that the audio array is in the (channels, samples) format.
+
+    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):
+            y = np.abs(spec)
+        else:
+            y = spec
+        y = np.log10(y ** 2 + 1e-8)
+    elif mode == 'phase':
+        if np.iscomplexobj(spec):
+            y = np.angle(spec)
+        else:
+            y = spec
+
+    y -= y.min()
+    y *= 255 / y.max()
+    img = np.uint8(y)
+
+    if y.ndim == 3:
+        img = img.transpose(1, 2, 0)
+        img = np.concatenate([
+            np.max(img, axis=2, keepdims=True), img
+        ], axis=2)
+
+    return img
+
+def reduce_vocal_aggressively(X, y, softmask):
+    v = X - y
+    y_mag_tmp = np.abs(y)
+    v_mag_tmp = np.abs(v)
+
+    v_mask = v_mag_tmp > y_mag_tmp
+    y_mag = np.clip(y_mag_tmp - v_mag_tmp * v_mask * softmask, 0, np.inf)
+
+    return y_mag * np.exp(1.j * np.angle(y))
+
+def merge_artifacts(y_mask, thres=0.01, min_range=64, fade_size=32):
+    mask = y_mask
+    
+    try:
+        if min_range < fade_size * 2:
+            raise ValueError('min_range must be >= fade_size * 2')
+
+        idx = np.where(y_mask.min(axis=(0, 1)) > thres)[0]
+        start_idx = np.insert(idx[np.where(np.diff(idx) != 1)[0] + 1], 0, idx[0])
+        end_idx = np.append(idx[np.where(np.diff(idx) != 1)[0]], idx[-1])
+        artifact_idx = np.where(end_idx - start_idx > min_range)[0]
+        weight = np.zeros_like(y_mask)
+        if len(artifact_idx) > 0:
+            start_idx = start_idx[artifact_idx]
+            end_idx = end_idx[artifact_idx]
+            old_e = None
+            for s, e in zip(start_idx, end_idx):
+                if old_e is not None and s - old_e < fade_size:
+                    s = old_e - fade_size * 2
+
+                if s != 0:
+                    weight[:, :, s:s + fade_size] = np.linspace(0, 1, fade_size)
+                else:
+                    s -= fade_size
+
+                if e != y_mask.shape[2]:
+                    weight[:, :, e - fade_size:e] = np.linspace(1, 0, fade_size)
+                else:
+                    e += fade_size
+
+                weight[:, :, s + fade_size:e - fade_size] = 1
+                old_e = e
+
+        v_mask = 1 - y_mask
+        y_mask += weight * v_mask
+        
+        mask = y_mask
+    except Exception as e:
+        error_name = f'{type(e).__name__}'
+        traceback_text = ''.join(traceback.format_tb(e.__traceback__))
+        message = f'{error_name}: "{e}"\n{traceback_text}"'
+        print('Post Process Failed: ', message)
+        
+    return mask
+
+def align_wave_head_and_tail(a, b):
+    l = min([a[0].size, b[0].size])  
+    
+    return a[:l,:l], b[:l,:l]
+    
+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 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:
+        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)])
+    
+    return np.asfortranarray([wave_left, wave_right])
+    
+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
+
+    for d in range(1, bands_n + 1):
+        bp = mp.param['band'][d]
+        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:
+                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, d, is_v51_model)
+            else:
+                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
+                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
+                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):
+        g -= 1 / (bin_stop - bin_start)
+        spec[:, b, :] = g * spec[:, b, :]
+        
+    spec[:, bin_stop:, :] *= 0
+
+    return spec
+
+def fft_hp_filter(spec, bin_start, bin_stop):
+    g = 1.0
+    for b in range(bin_start, bin_stop, -1):
+        g -= 1 / (bin_start - bin_stop)
+        spec[:, b, :] = g * spec[:, b, :]
+    
+    spec[:, 0:bin_stop+1, :] *= 0
+
+    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)
+        mirror = mirror * np.exp(1.j * np.angle(input_high_end))
+        
+        return np.where(np.abs(input_high_end) <= np.abs(mirror), input_high_end, mirror)
+        
+    if 'mirroring2' == 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)
+        mi = np.multiply(mirror, input_high_end * 1.7)
+        
+        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'] * 2
+
+    if aggr != 0:
+        if is_non_accom_stem:
+            aggr = 1 - aggr
+    
+        aggr = [aggr, aggr]
+    
+        if aggressiveness['aggr_correction'] is not None:
+            aggr[0] += aggressiveness['aggr_correction']['left']
+            aggr[1] += aggressiveness['aggr_correction']['right']
+
+        for ch in range(2):
+            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])
+
+    return mask
+
+def stft(wave, nfft, hl):
+    wave_left = np.asfortranarray(wave[0])
+    wave_right = np.asfortranarray(wave[1])
+    spec_left = librosa.stft(wave_left, nfft, hop_length=hl)
+    spec_right = librosa.stft(wave_right, nfft, hop_length=hl)
+    spec = np.asfortranarray([spec_left, spec_right])
+
+    return spec
+
+def istft(spec, hl):
+    spec_left = np.asfortranarray(spec[0])
+    spec_right = np.asfortranarray(spec[1])
+    wave_left = librosa.istft(spec_left, hop_length=hl)
+    wave_right = librosa.istft(spec_right, hop_length=hl)
+    wave = np.asfortranarray([wave_left, wave_right])
+
+    return wave
+
+def spec_effects(wave, algorithm='Default', value=None):
+    spec = [stft(wave[0],2048,1024), stft(wave[1],2048,1024)]
+    if algorithm == 'Min_Mag':
+        v_spec_m = np.where(np.abs(spec[1]) <= np.abs(spec[0]), spec[1], spec[0])
+        wave = istft(v_spec_m,1024)
+    elif algorithm == 'Max_Mag':
+        v_spec_m = np.where(np.abs(spec[1]) >= np.abs(spec[0]), spec[1], spec[0])
+        wave = istft(v_spec_m,1024)
+    elif algorithm == 'Default':
+        wave = (wave[1] * value) + (wave[0] * (1-value))
+    elif algorithm == 'Invert_p':
+        X_mag = np.abs(spec[0])
+        y_mag = np.abs(spec[1])            
+        max_mag = np.where(X_mag >= y_mag, X_mag, y_mag)  
+        v_spec = spec[1] - max_mag * np.exp(1.j * np.angle(spec[0]))
+        wave = istft(v_spec,1024)
+            
+    return wave      
+
+def spectrogram_to_wave_no_mp(spec, n_fft=2048, hop_length=1024):
+    wave = librosa.istft(spec, n_fft=n_fft, hop_length=hop_length)
+    
+    if wave.ndim == 1:
+        wave = np.asfortranarray([wave,wave])
+
+    return wave
+
+def wave_to_spectrogram_no_mp(wave):
+    
+    spec = librosa.stft(wave, n_fft=2048, hop_length=1024)
+    
+    if spec.ndim == 1:
+        spec = np.asfortranarray([spec,spec])
+
+    return spec
+
+def invert_audio(specs, invert_p=True):
+    
+    ln = min([specs[0].shape[2], specs[1].shape[2]])
+    specs[0] = specs[0][:,:,:ln]
+    specs[1] = specs[1][:,:,:ln]
+        
+    if invert_p:
+        X_mag = np.abs(specs[0])
+        y_mag = np.abs(specs[1])            
+        max_mag = np.where(X_mag >= y_mag, X_mag, y_mag)  
+        v_spec = specs[1] - max_mag * np.exp(1.j * np.angle(specs[0]))
+    else:
+        specs[1] = reduce_vocal_aggressively(specs[0], specs[1], 0.2)
+        v_spec = specs[0] - specs[1]
+
+    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, inputs, is_wavs=False): 
+
+    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:
+            input = np.where(np.abs(inputs[i]) <= np.abs(input), inputs[i], input)
+        if MAX_SPEC == a:
+            input = np.where(np.abs(inputs[i]) >= np.abs(input), inputs[i], input)  
+
+    #linear_ensemble
+    #input = ensemble_wav(inputs, split_size=1)
+
+    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_ = []
+    
+    if algorithm == AVERAGE:
+        output = average_audio(audio_input)
+        samplerate = 44100
+    else:
+        specs = []
+        
+        for i in range(len(audio_input)):  
+            wave, samplerate = librosa.load(audio_input[i], mono=False, sr=44100)
+            wavs_.append(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))]
+        
+        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)
+
+def to_shape(x, target_shape):
+    padding_list = []
+    for x_dim, target_dim in zip(x.shape, target_shape):
+        pad_value = (target_dim - x_dim)
+        pad_tuple = ((0, pad_value))
+        padding_list.append(pad_tuple)
+    
+    return np.pad(x, tuple(padding_list), mode='constant')
+
+def to_shape_minimize(x: np.ndarray, target_shape):
+    
+    padding_list = []
+    for x_dim, target_dim in zip(x.shape, target_shape):
+        pad_value = (target_dim - x_dim)
+        pad_tuple = ((0, pad_value))
+        padding_list.append(pad_tuple)
+    
+    return np.pad(x, tuple(padding_list), mode='constant')
+
+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_float_resampling))
+    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 not is_time_correction:
+        wav_mix = change_pitch_semitones(wav, 44100, semitone_shift=-rate)[0]
+    else:
+        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])
+    
+    sf.write(export_path, normalize(wav_mix.T, is_normalization), sr, subtype=wav_type_set)
+    save_format(export_path)
+    
+def average_audio(audio):
+    
+    waves = []
+    wave_shapes = []
+    final_waves = []
+
+    for i in range(len(audio)):
+        wave = librosa.load(audio[i], sr=44100, mono=False)
+        waves.append(wave[0])
+        wave_shapes.append(wave[0].shape[1])
+
+    wave_shapes_index = wave_shapes.index(max(wave_shapes))
+    target_shape = waves[wave_shapes_index]
+    waves.pop(wave_shapes_index)
+    final_waves.append(target_shape)
+
+    for n_array in waves:
+        wav_target = to_shape(n_array, target_shape.shape)
+        final_waves.append(wav_target)
+
+    waves = sum(final_waves)
+    waves = waves/len(audio)
+
+    return waves
+    
+def average_dual_sources(wav_1, wav_2, value):
+    
+    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)
+
+    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:
+        wav_2 = to_shape(wav_2, wav_1.shape)
+    if wav_1.shape < wav_2.shape:
+        ln = min([wav_1.shape[1], wav_2.shape[1]])
+        wav_2 = wav_2[:,:ln]
+
+    ln = min([wav_1.shape[1], wav_2.shape[1]])
+    wav_1 = wav_1[:,:ln]
+    wav_2 = wav_2[:,:ln]
+
+    return wav_2
+    
+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
+    
+    if file1.endswith(".mp3") and is_macos:
+        length1 = rerun_mp3(file1)
+        wav1, sr1 = librosa.load(file1, duration=length1, sr=44100, mono=False)
+    else:
+        wav1, sr1 = librosa.load(file1, sr=44100, mono=False)
+
+    if file2.endswith(".mp3") and is_macos:
+        length2 = rerun_mp3(file2)
+        wav2, sr2 = librosa.load(file2, duration=length2, sr=44100, mono=False)
+    else:
+        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
+  
+    # read tracks
+    wav1 = wav1.transpose()
+    wav2 = wav2.transpose()
+
+    #print(f"Audio file shapes: {wav1.shape} / {wav2.shape}\n")
+    
+    wav2_org = wav2.copy()
+    
+    # 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 diff > 0:
+        wav2_aligned = np.append(np.zeros((diff, 1)), wav2_org, axis=0)
+    elif diff < 0:
+        wav2_aligned = wav2_org[-diff:]
+    else:
+        wav2_aligned = wav2_org
+        
+    return wav2_aligned
+
+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):
+    with audioread.audio_open(audio_file) as f:
+        track_length = int(f.duration)
+
+    return track_length

lib_v5/tfc_tdf_v3.py

@@ -0,0 +1,253 @@
+import torch
+import torch.nn as nn
+from functools import partial
+
+class STFT:
+    def __init__(self, n_fft, hop_length, dim_f, device):
+        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
+        self.device = device
+
+    def __call__(self, x):
+        
+        x_is_mps = not x.device.type in ["cuda", "cpu"]
+        if x_is_mps:
+            x = x.cpu()
+
+        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]])
+
+        if x_is_mps:
+            x = x.to(self.device)
+
+        return x[..., :self.dim_f, :]
+
+    def inverse(self, x):
+        
+        x_is_mps = not x.device.type in ["cuda", "cpu"]
+        if x_is_mps:
+            x = x.cpu()
+
+        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])
+
+        if x_is_mps:
+            x = x.to(self.device)
+
+        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, device):
+        super().__init__()
+        self.config = config
+        self.device = device
+
+        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, self.device)
+
+    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
+
+

lib_v5/vr_network/__init__.py

@@ -0,0 +1 @@
+# VR init.

lib_v5/vr_network/layers.py

@@ -0,0 +1,143 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+from lib_v5 import spec_utils
+
+class Conv2DBNActiv(nn.Module):
+
+    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
+        super(Conv2DBNActiv, self).__init__()
+        self.conv = nn.Sequential(
+            nn.Conv2d(
+                nin, nout,
+                kernel_size=ksize,
+                stride=stride,
+                padding=pad,
+                dilation=dilation,
+                bias=False),
+            nn.BatchNorm2d(nout),
+            activ()
+        )
+
+    def __call__(self, x):
+        return self.conv(x)
+
+class SeperableConv2DBNActiv(nn.Module):
+
+    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
+        super(SeperableConv2DBNActiv, self).__init__()
+        self.conv = nn.Sequential(
+            nn.Conv2d(
+                nin, nin,
+                kernel_size=ksize,
+                stride=stride,
+                padding=pad,
+                dilation=dilation,
+                groups=nin,
+                bias=False),
+            nn.Conv2d(
+                nin, nout,
+                kernel_size=1,
+                bias=False),
+            nn.BatchNorm2d(nout),
+            activ()
+        )
+
+    def __call__(self, x):
+        return self.conv(x)
+
+
+class Encoder(nn.Module):
+
+    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
+        super(Encoder, self).__init__()
+        self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
+        self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
+
+    def __call__(self, x):
+        skip = self.conv1(x)
+        h = self.conv2(skip)
+
+        return h, skip
+
+
+class Decoder(nn.Module):
+
+    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False):
+        super(Decoder, self).__init__()
+        self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
+        self.dropout = nn.Dropout2d(0.1) if dropout else None
+
+    def __call__(self, x, skip=None):
+        x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True)
+        if skip is not None:
+            skip = spec_utils.crop_center(skip, x)
+            x = torch.cat([x, skip], dim=1)
+        h = self.conv(x)
+
+        if self.dropout is not None:
+            h = self.dropout(h)
+
+        return h
+
+
+class ASPPModule(nn.Module):
+
+    def __init__(self, nn_architecture, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU):
+        super(ASPPModule, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.AdaptiveAvgPool2d((1, None)),
+            Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
+        )
+        
+        self.nn_architecture = nn_architecture
+        self.six_layer = [129605]
+        self.seven_layer = [537238, 537227, 33966]
+        
+        extra_conv = SeperableConv2DBNActiv(
+            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ)
+        
+        self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
+        self.conv3 = SeperableConv2DBNActiv(
+            nin, nin, 3, 1, dilations[0], dilations[0], activ=activ)
+        self.conv4 = SeperableConv2DBNActiv(
+            nin, nin, 3, 1, dilations[1], dilations[1], activ=activ)
+        self.conv5 = SeperableConv2DBNActiv(
+            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ)
+        
+        if self.nn_architecture in self.six_layer:
+            self.conv6 = extra_conv
+            nin_x = 6
+        elif self.nn_architecture in self.seven_layer:
+            self.conv6 = extra_conv
+            self.conv7 = extra_conv
+            nin_x = 7
+        else:
+            nin_x = 5
+            
+        self.bottleneck = nn.Sequential(
+            Conv2DBNActiv(nin * nin_x, nout, 1, 1, 0, activ=activ),
+            nn.Dropout2d(0.1)
+        )
+
+    def forward(self, x):
+        _, _, h, w = x.size()
+        feat1 = F.interpolate(self.conv1(x), size=(h, w), mode='bilinear', align_corners=True)
+        feat2 = self.conv2(x)
+        feat3 = self.conv3(x)
+        feat4 = self.conv4(x)
+        feat5 = self.conv5(x)
+        
+        if self.nn_architecture in self.six_layer:
+            feat6 = self.conv6(x)
+            out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6), dim=1)
+        elif self.nn_architecture in self.seven_layer:
+            feat6 = self.conv6(x)
+            feat7 = self.conv7(x)
+            out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1)
+        else:
+            out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
+            
+        bottle = self.bottleneck(out)
+        return bottle

lib_v5/vr_network/layers_new.py

@@ -0,0 +1,126 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+from lib_v5 import spec_utils
+
+class Conv2DBNActiv(nn.Module):
+
+    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
+        super(Conv2DBNActiv, self).__init__()
+        self.conv = nn.Sequential(
+            nn.Conv2d(
+                nin, nout,
+                kernel_size=ksize,
+                stride=stride,
+                padding=pad,
+                dilation=dilation,
+                bias=False),
+            nn.BatchNorm2d(nout),
+            activ()
+        )
+
+    def __call__(self, x):
+        return self.conv(x)
+
+class Encoder(nn.Module):
+
+    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
+        super(Encoder, self).__init__()
+        self.conv1 = Conv2DBNActiv(nin, nout, ksize, stride, pad, activ=activ)
+        self.conv2 = Conv2DBNActiv(nout, nout, ksize, 1, pad, activ=activ)
+
+    def __call__(self, x):
+        h = self.conv1(x)
+        h = self.conv2(h)
+
+        return h
+
+
+class Decoder(nn.Module):
+
+    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False):
+        super(Decoder, self).__init__()
+        self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
+        # self.conv2 = Conv2DBNActiv(nout, nout, ksize, 1, pad, activ=activ)
+        self.dropout = nn.Dropout2d(0.1) if dropout else None
+
+    def __call__(self, x, skip=None):
+        x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True)
+
+        if skip is not None:
+            skip = spec_utils.crop_center(skip, x)
+            x = torch.cat([x, skip], dim=1)
+
+        h = self.conv1(x)
+        # h = self.conv2(h)
+
+        if self.dropout is not None:
+            h = self.dropout(h)
+
+        return h
+
+
+class ASPPModule(nn.Module):
+
+    def __init__(self, nin, nout, dilations=(4, 8, 12), activ=nn.ReLU, dropout=False):
+        super(ASPPModule, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.AdaptiveAvgPool2d((1, None)),
+            Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ)
+        )
+        self.conv2 = Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ)
+        self.conv3 = Conv2DBNActiv(
+            nin, nout, 3, 1, dilations[0], dilations[0], activ=activ
+        )
+        self.conv4 = Conv2DBNActiv(
+            nin, nout, 3, 1, dilations[1], dilations[1], activ=activ
+        )
+        self.conv5 = Conv2DBNActiv(
+            nin, nout, 3, 1, dilations[2], dilations[2], activ=activ
+        )
+        self.bottleneck = Conv2DBNActiv(nout * 5, nout, 1, 1, 0, activ=activ)
+        self.dropout = nn.Dropout2d(0.1) if dropout else None
+
+    def forward(self, x):
+        _, _, h, w = x.size()
+        feat1 = F.interpolate(self.conv1(x), size=(h, w), mode='bilinear', align_corners=True)
+        feat2 = self.conv2(x)
+        feat3 = self.conv3(x)
+        feat4 = self.conv4(x)
+        feat5 = self.conv5(x)
+        out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
+        out = self.bottleneck(out)
+
+        if self.dropout is not None:
+            out = self.dropout(out)
+
+        return out
+
+
+class LSTMModule(nn.Module):
+
+    def __init__(self, nin_conv, nin_lstm, nout_lstm):
+        super(LSTMModule, self).__init__()
+        self.conv = Conv2DBNActiv(nin_conv, 1, 1, 1, 0)
+        self.lstm = nn.LSTM(
+            input_size=nin_lstm,
+            hidden_size=nout_lstm // 2,
+            bidirectional=True
+        )
+        self.dense = nn.Sequential(
+            nn.Linear(nout_lstm, nin_lstm),
+            nn.BatchNorm1d(nin_lstm),
+            nn.ReLU()
+        )
+
+    def forward(self, x):
+        N, _, nbins, nframes = x.size()
+        h = self.conv(x)[:, 0]  # N, nbins, nframes
+        h = h.permute(2, 0, 1)  # nframes, N, nbins
+        h, _ = self.lstm(h)
+        h = self.dense(h.reshape(-1, h.size()[-1]))  # nframes * N, nbins
+        h = h.reshape(nframes, N, 1, nbins)
+        h = h.permute(1, 2, 3, 0)
+
+        return h

lib_v5/vr_network/model_param_init.py

@@ -0,0 +1,32 @@
+import json
+
+default_param = {}
+default_param['bins'] = -1
+default_param['unstable_bins'] = -1 # training only
+default_param['stable_bins'] = -1 # training only
+default_param['sr'] = 44100
+default_param['pre_filter_start'] = -1
+default_param['pre_filter_stop'] = -1
+default_param['band'] = {}
+
+N_BINS = 'n_bins'
+
+def int_keys(d):
+    r = {}
+    for k, v in d:
+        if k.isdigit():
+            k = int(k)
+        r[k] = v
+    return r
+    
+class ModelParameters(object):
+    def __init__(self, config_path=''):
+        with open(config_path, 'r') as f:
+                self.param = json.loads(f.read(), object_pairs_hook=int_keys)
+                
+        for k in ['mid_side', 'mid_side_b', 'mid_side_b2', 'stereo_w', 'stereo_n', 'reverse']:
+            if not k in self.param:
+                self.param[k] = False
+                
+        if N_BINS in self.param:
+            self.param['bins'] = self.param[N_BINS]

lib_v5/vr_network/modelparams/1band_sr16000_hl512.json

@@ -0,0 +1,19 @@
+{
+	"bins": 1024,
+	"unstable_bins": 0,
+	"reduction_bins": 0,
+	"band": {
+		"1": {
+			"sr": 16000,
+			"hl": 512,
+			"n_fft": 2048,
+			"crop_start": 0,
+			"crop_stop": 1024,
+			"hpf_start": -1,
+			"res_type": "sinc_best"
+		}
+	},
+	"sr": 16000,
+	"pre_filter_start": 1023,
+	"pre_filter_stop": 1024
+}

lib_v5/vr_network/modelparams/1band_sr32000_hl512.json

@@ -0,0 +1,19 @@
+{
+	"bins": 1024,
+	"unstable_bins": 0,
+	"reduction_bins": 0,
+	"band": {
+		"1": {
+			"sr": 32000,
+			"hl": 512,
+			"n_fft": 2048,
+			"crop_start": 0,
+			"crop_stop": 1024,
+			"hpf_start": -1,
+			"res_type": "kaiser_fast"
+		}
+	},
+	"sr": 32000,
+	"pre_filter_start": 1000,
+	"pre_filter_stop": 1021
+}

lib_v5/vr_network/modelparams/1band_sr33075_hl384.json

@@ -0,0 +1,19 @@
+{
+	"bins": 1024,
+	"unstable_bins": 0,
+	"reduction_bins": 0,
+	"band": {
+		"1": {
+			"sr": 33075,
+			"hl": 384,
+			"n_fft": 2048,
+			"crop_start": 0,
+			"crop_stop": 1024,
+			"hpf_start": -1,
+			"res_type": "sinc_best"
+		}
+	},
+	"sr": 33075,
+	"pre_filter_start": 1000,
+	"pre_filter_stop": 1021
+}

lib_v5/vr_network/modelparams/1band_sr44100_hl1024.json

@@ -0,0 +1,19 @@
+{
+	"bins": 1024,
+	"unstable_bins": 0,
+	"reduction_bins": 0,
+	"band": {
+		"1": {
+			"sr": 44100,
+			"hl": 1024,
+			"n_fft": 2048,
+			"crop_start": 0,
+			"crop_stop": 1024,
+			"hpf_start": -1,
+			"res_type": "sinc_best"
+		}
+	},
+	"sr": 44100,
+	"pre_filter_start": 1023,
+	"pre_filter_stop": 1024
+}

lib_v5/vr_network/modelparams/1band_sr44100_hl256.json

@@ -0,0 +1,19 @@
+{
+	"bins": 256,
+	"unstable_bins": 0,
+	"reduction_bins": 0,
+	"band": {
+		"1": {
+			"sr": 44100,
+			"hl": 256,
+			"n_fft": 512,
+			"crop_start": 0,
+			"crop_stop": 256,
+			"hpf_start": -1,
+			"res_type": "sinc_best"
+		}
+	},
+	"sr": 44100,
+	"pre_filter_start": 256,
+	"pre_filter_stop": 256
+}

lib_v5/vr_network/modelparams/1band_sr44100_hl512.json

@@ -0,0 +1,19 @@
+{
+	"bins": 1024,
+	"unstable_bins": 0,
+	"reduction_bins": 0,
+	"band": {
+		"1": {
+			"sr": 44100,
+			"hl": 512,
+			"n_fft": 2048,
+			"crop_start": 0,
+			"crop_stop": 1024,
+			"hpf_start": -1,
+			"res_type": "sinc_best"
+		}
+	},
+	"sr": 44100,
+	"pre_filter_start": 1023,
+	"pre_filter_stop": 1024
+}

lib_v5/vr_network/modelparams/1band_sr44100_hl512_cut.json

@@ -0,0 +1,19 @@
+{
+	"bins": 1024,
+	"unstable_bins": 0,
+	"reduction_bins": 0,
+	"band": {
+		"1": {
+			"sr": 44100,
+			"hl": 512,
+			"n_fft": 2048,
+			"crop_start": 0,
+			"crop_stop": 700,
+			"hpf_start": -1,
+			"res_type": "sinc_best"
+		}
+	},
+	"sr": 44100,
+	"pre_filter_start": 1023,
+	"pre_filter_stop": 700
+}

lib_v5/vr_network/modelparams/1band_sr44100_hl512_nf1024.json

@@ -0,0 +1,19 @@
+{
+	"bins": 512,
+	"unstable_bins": 0,
+	"reduction_bins": 0,
+	"band": {
+		"1": {
+			"sr": 44100,
+			"hl": 512,
+			"n_fft": 1024,
+			"crop_start": 0,
+			"crop_stop": 512,
+			"hpf_start": -1,
+			"res_type": "sinc_best"
+		}
+	},
+	"sr": 44100,
+	"pre_filter_start": 511,
+	"pre_filter_stop": 512
+}

lib_v5/vr_network/modelparams/2band_32000.json

@@ -0,0 +1,30 @@
+{
+	"bins": 768,
+	"unstable_bins": 7,
+	"reduction_bins": 705,
+	"band": {
+		"1": {
+			"sr": 6000,
+			"hl": 66,
+			"n_fft": 512,
+			"crop_start": 0,
+			"crop_stop": 240,
+			"lpf_start": 60,
+			"lpf_stop": 118,
+			"res_type": "sinc_fastest"
+		},
+		"2": {
+			"sr": 32000,
+			"hl": 352,
+			"n_fft": 1024,
+			"crop_start": 22,
+			"crop_stop": 505,
+			"hpf_start": 44,
+			"hpf_stop": 23,
+			"res_type": "sinc_medium"
+		}
+	},
+	"sr": 32000,
+	"pre_filter_start": 710,
+	"pre_filter_stop": 731
+}

lib_v5/vr_network/modelparams/2band_44100_lofi.json

@@ -0,0 +1,30 @@
+{
+	"bins": 512,
+	"unstable_bins": 7,
+	"reduction_bins": 510,
+	"band": {
+		"1": {
+			"sr": 11025,
+			"hl": 160,
+			"n_fft": 768,
+			"crop_start": 0,
+			"crop_stop": 192,
+			"lpf_start": 41,
+			"lpf_stop": 139,
+			"res_type": "sinc_fastest"
+		},
+		"2": {
+			"sr": 44100,
+			"hl": 640,
+			"n_fft": 1024,
+			"crop_start": 10,
+			"crop_stop": 320,
+			"hpf_start": 47,
+			"hpf_stop": 15,
+			"res_type": "sinc_medium"
+		}
+	},
+	"sr": 44100,
+	"pre_filter_start": 510,
+	"pre_filter_stop": 512
+}

lib_v5/vr_network/modelparams/2band_48000.json

@@ -0,0 +1,30 @@
+{
+	"bins": 768,
+	"unstable_bins": 7,
+	"reduction_bins": 705,
+	"band": {
+		"1": {
+			"sr": 6000,
+			"hl": 66,
+			"n_fft": 512,
+			"crop_start": 0,
+			"crop_stop": 240,
+			"lpf_start": 60,
+			"lpf_stop": 240,
+			"res_type": "sinc_fastest"
+		},
+		"2": {
+			"sr": 48000,
+			"hl": 528,
+			"n_fft": 1536,
+			"crop_start": 22,
+			"crop_stop": 505,
+			"hpf_start": 82,
+			"hpf_stop": 22,
+			"res_type": "sinc_medium"
+		}
+	},
+	"sr": 48000,
+	"pre_filter_start": 710,
+	"pre_filter_stop": 731
+}

lib_v5/vr_network/modelparams/3band_44100.json

@@ -0,0 +1,42 @@
+{
+	"bins": 768,
+	"unstable_bins": 5,
+	"reduction_bins": 733,
+	"band": {
+		"1": {
+			"sr": 11025,
+			"hl": 128,
+			"n_fft": 768,
+			"crop_start": 0,
+			"crop_stop": 278,
+			"lpf_start": 28,
+			"lpf_stop": 140,
+			"res_type": "polyphase"
+		},
+		"2": {
+			"sr": 22050,
+			"hl": 256,
+			"n_fft": 768,
+			"crop_start": 14,
+			"crop_stop": 322,
+			"hpf_start": 70,
+			"hpf_stop": 14,
+			"lpf_start": 283,
+			"lpf_stop": 314,
+			"res_type": "polyphase"
+		},	
+		"3": {
+			"sr": 44100,
+			"hl": 512,
+			"n_fft": 768,
+			"crop_start": 131,
+			"crop_stop": 313,
+			"hpf_start": 154,
+			"hpf_stop": 141,
+			"res_type": "sinc_medium"
+		}
+	},
+	"sr": 44100,
+	"pre_filter_start": 757,
+	"pre_filter_stop": 768
+}

lib_v5/vr_network/modelparams/3band_44100_mid.json

@@ -0,0 +1,43 @@
+{
+	"mid_side": true,
+	"bins": 768,
+	"unstable_bins": 5,
+	"reduction_bins": 733,
+	"band": {
+		"1": {
+			"sr": 11025,
+			"hl": 128,
+			"n_fft": 768,
+			"crop_start": 0,
+			"crop_stop": 278,
+			"lpf_start": 28,
+			"lpf_stop": 140,
+			"res_type": "polyphase"
+		},
+		"2": {
+			"sr": 22050,
+			"hl": 256,
+			"n_fft": 768,
+			"crop_start": 14,
+			"crop_stop": 322,
+			"hpf_start": 70,
+			"hpf_stop": 14,
+			"lpf_start": 283,
+			"lpf_stop": 314,
+			"res_type": "polyphase"
+		},	
+		"3": {
+			"sr": 44100,
+			"hl": 512,
+			"n_fft": 768,
+			"crop_start": 131,
+			"crop_stop": 313,
+			"hpf_start": 154,
+			"hpf_stop": 141,
+			"res_type": "sinc_medium"
+		}
+	},
+	"sr": 44100,
+	"pre_filter_start": 757,
+	"pre_filter_stop": 768
+}

lib_v5/vr_network/modelparams/3band_44100_msb2.json

@@ -0,0 +1,43 @@
+{
+	"mid_side_b2": true,
+	"bins": 640,
+	"unstable_bins": 7,
+	"reduction_bins": 565,
+	"band": {
+		"1": {
+			"sr": 11025,
+			"hl": 108,
+			"n_fft": 1024,
+			"crop_start": 0,
+			"crop_stop": 187,
+			"lpf_start": 92,
+			"lpf_stop": 186,
+			"res_type": "polyphase"
+		},
+		"2": {
+			"sr": 22050,
+			"hl": 216,
+			"n_fft": 768,
+			"crop_start": 0,
+			"crop_stop": 212,
+			"hpf_start": 68,
+			"hpf_stop": 34,
+			"lpf_start": 174,
+			"lpf_stop": 209,
+			"res_type": "polyphase"
+		},	
+		"3": {
+			"sr": 44100,
+			"hl": 432,
+			"n_fft": 640,
+			"crop_start": 66,
+			"crop_stop": 307,
+			"hpf_start": 86,
+			"hpf_stop": 72,
+			"res_type": "kaiser_fast"
+		}
+	},
+	"sr": 44100,
+	"pre_filter_start": 639,
+	"pre_filter_stop": 640
+}

lib_v5/vr_network/modelparams/4band_44100.json

@@ -0,0 +1,54 @@
+{
+	"bins": 768,
+	"unstable_bins": 7,
+	"reduction_bins": 668,
+	"band": {
+		"1": {
+			"sr": 11025,
+			"hl": 128,
+			"n_fft": 1024,
+			"crop_start": 0,
+			"crop_stop": 186,
+			"lpf_start": 37,
+			"lpf_stop": 73,
+			"res_type": "polyphase"
+		},
+		"2": {
+			"sr": 11025,
+			"hl": 128,
+			"n_fft": 512,
+			"crop_start": 4,
+			"crop_stop": 185,			
+			"hpf_start": 36,
+			"hpf_stop": 18,
+			"lpf_start": 93,
+			"lpf_stop": 185,
+			"res_type": "polyphase"
+		},
+		"3": {
+			"sr": 22050,
+			"hl": 256,
+			"n_fft": 512,
+			"crop_start": 46,
+			"crop_stop": 186,
+			"hpf_start": 93,
+			"hpf_stop": 46,
+			"lpf_start": 164,
+			"lpf_stop": 186,
+			"res_type": "polyphase"
+		},	
+		"4": {
+			"sr": 44100,
+			"hl": 512,
+			"n_fft": 768,
+			"crop_start": 121,
+			"crop_stop": 382,
+			"hpf_start": 138,
+			"hpf_stop": 123,
+			"res_type": "sinc_medium"
+		}
+	},
+	"sr": 44100,
+	"pre_filter_start": 740,
+	"pre_filter_stop": 768
+}

lib_v5/vr_network/modelparams/4band_44100_mid.json

@@ -0,0 +1,55 @@
+{
+	"bins": 768,
+	"unstable_bins": 7,
+	"mid_side": true,
+	"reduction_bins": 668,
+	"band": {
+		"1": {
+			"sr": 11025,
+			"hl": 128,
+			"n_fft": 1024,
+			"crop_start": 0,
+			"crop_stop": 186,
+			"lpf_start": 37,
+			"lpf_stop": 73,
+			"res_type": "polyphase"
+		},
+		"2": {
+			"sr": 11025,
+			"hl": 128,
+			"n_fft": 512,
+			"crop_start": 4,
+			"crop_stop": 185,			
+			"hpf_start": 36,
+			"hpf_stop": 18,
+			"lpf_start": 93,
+			"lpf_stop": 185,
+			"res_type": "polyphase"
+		},
+		"3": {
+			"sr": 22050,
+			"hl": 256,
+			"n_fft": 512,
+			"crop_start": 46,
+			"crop_stop": 186,
+			"hpf_start": 93,
+			"hpf_stop": 46,
+			"lpf_start": 164,
+			"lpf_stop": 186,
+			"res_type": "polyphase"
+		},	
+		"4": {
+			"sr": 44100,
+			"hl": 512,
+			"n_fft": 768,
+			"crop_start": 121,
+			"crop_stop": 382,
+			"hpf_start": 138,
+			"hpf_stop": 123,
+			"res_type": "sinc_medium"
+		}
+	},
+	"sr": 44100,
+	"pre_filter_start": 740,
+	"pre_filter_stop": 768
+}

lib_v5/vr_network/modelparams/4band_44100_msb.json

@@ -0,0 +1,55 @@
+{
+	"mid_side_b": true,
+	"bins": 768,
+	"unstable_bins": 7,
+	"reduction_bins": 668,
+	"band": {
+		"1": {
+			"sr": 11025,
+			"hl": 128,
+			"n_fft": 1024,
+			"crop_start": 0,
+			"crop_stop": 186,
+			"lpf_start": 37,
+			"lpf_stop": 73,
+			"res_type": "polyphase"
+		},
+		"2": {
+			"sr": 11025,
+			"hl": 128,
+			"n_fft": 512,
+			"crop_start": 4,
+			"crop_stop": 185,			
+			"hpf_start": 36,
+			"hpf_stop": 18,
+			"lpf_start": 93,
+			"lpf_stop": 185,
+			"res_type": "polyphase"
+		},
+		"3": {
+			"sr": 22050,
+			"hl": 256,
+			"n_fft": 512,
+			"crop_start": 46,
+			"crop_stop": 186,
+			"hpf_start": 93,
+			"hpf_stop": 46,
+			"lpf_start": 164,
+			"lpf_stop": 186,
+			"res_type": "polyphase"
+		},	
+		"4": {
+			"sr": 44100,
+			"hl": 512,
+			"n_fft": 768,
+			"crop_start": 121,
+			"crop_stop": 382,
+			"hpf_start": 138,
+			"hpf_stop": 123,
+			"res_type": "sinc_medium"
+		}
+	},
+	"sr": 44100,
+	"pre_filter_start": 740,
+	"pre_filter_stop": 768
+}

lib_v5/vr_network/modelparams/4band_44100_msb2.json

@@ -0,0 +1,55 @@
+{
+	"mid_side_b": true,
+	"bins": 768,
+	"unstable_bins": 7,
+	"reduction_bins": 668,
+	"band": {
+		"1": {
+			"sr": 11025,
+			"hl": 128,
+			"n_fft": 1024,
+			"crop_start": 0,
+			"crop_stop": 186,
+			"lpf_start": 37,
+			"lpf_stop": 73,
+			"res_type": "polyphase"
+		},
+		"2": {
+			"sr": 11025,
+			"hl": 128,
+			"n_fft": 512,
+			"crop_start": 4,
+			"crop_stop": 185,			
+			"hpf_start": 36,
+			"hpf_stop": 18,
+			"lpf_start": 93,
+			"lpf_stop": 185,
+			"res_type": "polyphase"
+		},
+		"3": {
+			"sr": 22050,
+			"hl": 256,
+			"n_fft": 512,
+			"crop_start": 46,
+			"crop_stop": 186,
+			"hpf_start": 93,
+			"hpf_stop": 46,
+			"lpf_start": 164,
+			"lpf_stop": 186,
+			"res_type": "polyphase"
+		},	
+		"4": {
+			"sr": 44100,
+			"hl": 512,
+			"n_fft": 768,
+			"crop_start": 121,
+			"crop_stop": 382,
+			"hpf_start": 138,
+			"hpf_stop": 123,
+			"res_type": "sinc_medium"
+		}
+	},
+	"sr": 44100,
+	"pre_filter_start": 740,
+	"pre_filter_stop": 768
+}

lib_v5/vr_network/modelparams/4band_44100_reverse.json

@@ -0,0 +1,55 @@
+{
+	"reverse": true,
+	"bins": 768,
+	"unstable_bins": 7,
+	"reduction_bins": 668,
+	"band": {
+		"1": {
+			"sr": 11025,
+			"hl": 128,
+			"n_fft": 1024,
+			"crop_start": 0,
+			"crop_stop": 186,
+			"lpf_start": 37,
+			"lpf_stop": 73,
+			"res_type": "polyphase"
+		},
+		"2": {
+			"sr": 11025,
+			"hl": 128,
+			"n_fft": 512,
+			"crop_start": 4,
+			"crop_stop": 185,			
+			"hpf_start": 36,
+			"hpf_stop": 18,
+			"lpf_start": 93,
+			"lpf_stop": 185,
+			"res_type": "polyphase"
+		},
+		"3": {
+			"sr": 22050,
+			"hl": 256,
+			"n_fft": 512,
+			"crop_start": 46,
+			"crop_stop": 186,
+			"hpf_start": 93,
+			"hpf_stop": 46,
+			"lpf_start": 164,
+			"lpf_stop": 186,
+			"res_type": "polyphase"
+		},	
+		"4": {
+			"sr": 44100,
+			"hl": 512,
+			"n_fft": 768,
+			"crop_start": 121,
+			"crop_stop": 382,
+			"hpf_start": 138,
+			"hpf_stop": 123,
+			"res_type": "sinc_medium"
+		}
+	},
+	"sr": 44100,
+	"pre_filter_start": 740,
+	"pre_filter_stop": 768
+}

lib_v5/vr_network/modelparams/4band_44100_sw.json

@@ -0,0 +1,55 @@
+{
+	"stereo_w": true,
+	"bins": 768,
+	"unstable_bins": 7,
+	"reduction_bins": 668,
+	"band": {
+		"1": {
+			"sr": 11025,
+			"hl": 128,
+			"n_fft": 1024,
+			"crop_start": 0,
+			"crop_stop": 186,
+			"lpf_start": 37,
+			"lpf_stop": 73,
+			"res_type": "polyphase"
+		},
+		"2": {
+			"sr": 11025,
+			"hl": 128,
+			"n_fft": 512,
+			"crop_start": 4,
+			"crop_stop": 185,			
+			"hpf_start": 36,
+			"hpf_stop": 18,
+			"lpf_start": 93,
+			"lpf_stop": 185,
+			"res_type": "polyphase"
+		},
+		"3": {
+			"sr": 22050,
+			"hl": 256,
+			"n_fft": 512,
+			"crop_start": 46,
+			"crop_stop": 186,
+			"hpf_start": 93,
+			"hpf_stop": 46,
+			"lpf_start": 164,
+			"lpf_stop": 186,
+			"res_type": "polyphase"
+		},	
+		"4": {
+			"sr": 44100,
+			"hl": 512,
+			"n_fft": 768,
+			"crop_start": 121,
+			"crop_stop": 382,
+			"hpf_start": 138,
+			"hpf_stop": 123,
+			"res_type": "sinc_medium"
+		}
+	},
+	"sr": 44100,
+	"pre_filter_start": 740,
+	"pre_filter_stop": 768
+}

lib_v5/vr_network/modelparams/4band_v2.json

@@ -0,0 +1,54 @@
+{
+	"bins": 672,
+	"unstable_bins": 8,
+	"reduction_bins": 637,
+	"band": {
+		"1": {
+			"sr": 7350,
+			"hl": 80,
+			"n_fft": 640,
+			"crop_start": 0,
+			"crop_stop": 85,
+			"lpf_start": 25,
+			"lpf_stop": 53,
+			"res_type": "polyphase"
+		},
+		"2": {
+			"sr": 7350,
+			"hl": 80,
+			"n_fft": 320,
+			"crop_start": 4,
+			"crop_stop": 87,
+			"hpf_start": 25,
+			"hpf_stop": 12,
+			"lpf_start": 31,
+			"lpf_stop": 62,
+			"res_type": "polyphase"
+		},		
+		"3": {
+			"sr": 14700,
+			"hl": 160,
+			"n_fft": 512,
+			"crop_start": 17,
+			"crop_stop": 216,
+			"hpf_start": 48,
+			"hpf_stop": 24,
+			"lpf_start": 139,
+			"lpf_stop": 210,
+			"res_type": "polyphase"
+		},	
+		"4": {
+			"sr": 44100,
+			"hl": 480,
+			"n_fft": 960,
+			"crop_start": 78,
+			"crop_stop": 383,
+			"hpf_start": 130,
+			"hpf_stop": 86,
+			"res_type": "kaiser_fast"
+		}
+	},
+	"sr": 44100,
+	"pre_filter_start": 668,
+	"pre_filter_stop": 672
+}

lib_v5/vr_network/modelparams/4band_v2_sn.json

@@ -0,0 +1,55 @@
+{
+	"bins": 672,
+	"unstable_bins": 8,
+	"reduction_bins": 637,
+	"band": {
+		"1": {
+			"sr": 7350,
+			"hl": 80,
+			"n_fft": 640,
+			"crop_start": 0,
+			"crop_stop": 85,
+			"lpf_start": 25,
+			"lpf_stop": 53,
+			"res_type": "polyphase"
+		},
+		"2": {
+			"sr": 7350,
+			"hl": 80,
+			"n_fft": 320,
+			"crop_start": 4,
+			"crop_stop": 87,
+			"hpf_start": 25,
+			"hpf_stop": 12,
+			"lpf_start": 31,
+			"lpf_stop": 62,
+			"res_type": "polyphase"
+		},		
+		"3": {
+			"sr": 14700,
+			"hl": 160,
+			"n_fft": 512,
+			"crop_start": 17,
+			"crop_stop": 216,
+			"hpf_start": 48,
+			"hpf_stop": 24,
+			"lpf_start": 139,
+			"lpf_stop": 210,
+			"res_type": "polyphase"
+		},	
+		"4": {
+			"sr": 44100,
+			"hl": 480,
+			"n_fft": 960,
+			"crop_start": 78,
+			"crop_stop": 383,
+			"hpf_start": 130,
+			"hpf_stop": 86,
+			"convert_channels": "stereo_n",
+			"res_type": "kaiser_fast"
+		}
+	},
+	"sr": 44100,
+	"pre_filter_start": 668,
+	"pre_filter_stop": 672
+}

lib_v5/vr_network/modelparams/4band_v3.json

@@ -0,0 +1,54 @@
+{
+	"bins": 672,
+	"unstable_bins": 8,
+	"reduction_bins": 530,
+	"band": {
+		"1": {
+			"sr": 7350,
+			"hl": 80,
+			"n_fft": 640,
+			"crop_start": 0,
+			"crop_stop": 85,
+			"lpf_start": 25,
+			"lpf_stop": 53,
+			"res_type": "polyphase"
+		},
+		"2": {
+			"sr": 7350,
+			"hl": 80,
+			"n_fft": 320,
+			"crop_start": 4,
+			"crop_stop": 87,
+			"hpf_start": 25,
+			"hpf_stop": 12,
+			"lpf_start": 31,
+			"lpf_stop": 62,
+			"res_type": "polyphase"
+		},
+		"3": {
+			"sr": 14700,
+			"hl": 160,
+			"n_fft": 512,
+			"crop_start": 17,
+			"crop_stop": 216,
+			"hpf_start": 48,
+			"hpf_stop": 24,
+			"lpf_start": 139,
+			"lpf_stop": 210,
+			"res_type": "polyphase"
+		},
+		"4": {
+			"sr": 44100,
+			"hl": 480,
+			"n_fft": 960,
+			"crop_start": 78,
+			"crop_stop": 383,
+			"hpf_start": 130,
+			"hpf_stop": 86,
+			"res_type": "kaiser_fast"
+		}
+	},
+	"sr": 44100,
+	"pre_filter_start": 668,
+	"pre_filter_stop": 672
+}

lib_v5/vr_network/modelparams/4band_v3_sn.json

@@ -0,0 +1,55 @@
+{
+	"n_bins": 672,
+	"unstable_bins": 8,
+	"stable_bins": 530,
+	"band": {
+		"1": {
+			"sr": 7350,
+			"hl": 80,
+			"n_fft": 640,
+			"crop_start": 0,
+			"crop_stop": 85,
+			"lpf_start": 25,
+			"lpf_stop": 53,
+			"res_type": "polyphase"
+		},
+		"2": {
+			"sr": 7350,
+			"hl": 80,
+			"n_fft": 320,
+			"crop_start": 4,
+			"crop_stop": 87,
+			"hpf_start": 25,
+			"hpf_stop": 12,
+			"lpf_start": 31,
+			"lpf_stop": 62,
+			"res_type": "polyphase"
+		},
+		"3": {
+			"sr": 14700,
+			"hl": 160,
+			"n_fft": 512,
+			"crop_start": 17,
+			"crop_stop": 216,
+			"hpf_start": 48,
+			"hpf_stop": 24,
+			"lpf_start": 139,
+			"lpf_stop": 210,
+			"res_type": "polyphase"
+		},
+		"4": {
+			"sr": 44100,
+			"hl": 480,
+			"n_fft": 960,
+			"crop_start": 78,
+			"crop_stop": 383,
+			"hpf_start": 130,
+			"hpf_stop": 86,
+			"convert_channels": "stereo_n",
+			"res_type": "kaiser_fast"
+		}
+	},
+	"sr": 44100,
+	"pre_filter_start": 668,
+	"pre_filter_stop": 672
+}

lib_v5/vr_network/modelparams/ensemble.json

@@ -0,0 +1,43 @@
+{
+	"mid_side_b2": true,
+	"bins": 1280,
+	"unstable_bins": 7,
+	"reduction_bins": 565,
+	"band": {
+		"1": {
+			"sr": 11025,
+			"hl": 108,
+			"n_fft": 2048,
+			"crop_start": 0,
+			"crop_stop": 374,
+			"lpf_start": 92,
+			"lpf_stop": 186,
+			"res_type": "polyphase"
+		},
+		"2": {
+			"sr": 22050,
+			"hl": 216,
+			"n_fft": 1536,
+			"crop_start": 0,
+			"crop_stop": 424,
+			"hpf_start": 68,
+			"hpf_stop": 34,
+			"lpf_start": 348,
+			"lpf_stop": 418,
+			"res_type": "polyphase"
+		},	
+		"3": {
+			"sr": 44100,
+			"hl": 432,
+			"n_fft": 1280,
+			"crop_start": 132,
+			"crop_stop": 614,
+			"hpf_start": 172,
+			"hpf_stop": 144,
+			"res_type": "polyphase"
+		}
+	},
+	"sr": 44100,
+	"pre_filter_start": 1280,
+	"pre_filter_stop": 1280
+}

lib_v5/vr_network/nets.py

@@ -0,0 +1,166 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+from . import layers
+
+class BaseASPPNet(nn.Module):
+
+    def __init__(self, nn_architecture, nin, ch, dilations=(4, 8, 16)):
+        super(BaseASPPNet, self).__init__()
+        self.nn_architecture = nn_architecture
+        self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
+        self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
+        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
+        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
+        
+        if self.nn_architecture == 129605:
+            self.enc5 = layers.Encoder(ch * 8, ch * 16, 3, 2, 1)
+            self.aspp = layers.ASPPModule(nn_architecture, ch * 16, ch * 32, dilations)
+            self.dec5 = layers.Decoder(ch * (16 + 32), ch * 16, 3, 1, 1)
+        else:
+            self.aspp = layers.ASPPModule(nn_architecture, ch * 8, ch * 16, dilations)
+            
+        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
+        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
+        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
+        self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
+
+    def __call__(self, x):
+        h, e1 = self.enc1(x)
+        h, e2 = self.enc2(h)
+        h, e3 = self.enc3(h)
+        h, e4 = self.enc4(h)
+        
+        if self.nn_architecture == 129605:
+            h, e5 = self.enc5(h)
+            h = self.aspp(h)
+            h = self.dec5(h, e5)
+        else:
+            h = self.aspp(h)
+            
+        h = self.dec4(h, e4)
+        h = self.dec3(h, e3)
+        h = self.dec2(h, e2)
+        h = self.dec1(h, e1)
+
+        return h
+
+def determine_model_capacity(n_fft_bins, nn_architecture):
+    
+    sp_model_arch = [31191, 33966, 129605]
+    hp_model_arch = [123821, 123812]
+    hp2_model_arch = [537238, 537227]
+    
+    if nn_architecture in sp_model_arch:
+        model_capacity_data = [
+            (2, 16),
+            (2, 16),
+            (18, 8, 1, 1, 0),
+            (8, 16),
+            (34, 16, 1, 1, 0),
+            (16, 32),
+            (32, 2, 1),
+            (16, 2, 1),
+            (16, 2, 1),
+        ]
+    
+    if nn_architecture in hp_model_arch:
+        model_capacity_data = [
+            (2, 32),
+            (2, 32),
+            (34, 16, 1, 1, 0),
+            (16, 32),
+            (66, 32, 1, 1, 0),
+            (32, 64),
+            (64, 2, 1),
+            (32, 2, 1),
+            (32, 2, 1),
+        ]
+       
+    if nn_architecture in hp2_model_arch: 
+        model_capacity_data = [
+            (2, 64),
+            (2, 64),
+            (66, 32, 1, 1, 0),
+            (32, 64),
+            (130, 64, 1, 1, 0),
+            (64, 128),
+            (128, 2, 1),
+            (64, 2, 1),
+            (64, 2, 1),
+        ]
+
+    cascaded = CascadedASPPNet
+    model = cascaded(n_fft_bins, model_capacity_data, nn_architecture)
+    
+    return model
+
+class CascadedASPPNet(nn.Module):
+
+    def __init__(self, n_fft, model_capacity_data, nn_architecture):
+        super(CascadedASPPNet, self).__init__()
+        self.stg1_low_band_net = BaseASPPNet(nn_architecture, *model_capacity_data[0])
+        self.stg1_high_band_net = BaseASPPNet(nn_architecture, *model_capacity_data[1])
+
+        self.stg2_bridge = layers.Conv2DBNActiv(*model_capacity_data[2])
+        self.stg2_full_band_net = BaseASPPNet(nn_architecture, *model_capacity_data[3])
+
+        self.stg3_bridge = layers.Conv2DBNActiv(*model_capacity_data[4])
+        self.stg3_full_band_net = BaseASPPNet(nn_architecture, *model_capacity_data[5])
+
+        self.out = nn.Conv2d(*model_capacity_data[6], bias=False)
+        self.aux1_out = nn.Conv2d(*model_capacity_data[7], bias=False)
+        self.aux2_out = nn.Conv2d(*model_capacity_data[8], bias=False)
+
+        self.max_bin = n_fft // 2
+        self.output_bin = n_fft // 2 + 1
+
+        self.offset = 128
+
+    def forward(self, x):
+        mix = x.detach()
+        x = x.clone()
+
+        x = x[:, :, :self.max_bin]
+
+        bandw = x.size()[2] // 2
+        aux1 = torch.cat([
+            self.stg1_low_band_net(x[:, :, :bandw]),
+            self.stg1_high_band_net(x[:, :, bandw:])
+        ], dim=2)
+
+        h = torch.cat([x, aux1], dim=1)
+        aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
+
+        h = torch.cat([x, aux1, aux2], dim=1)
+        h = self.stg3_full_band_net(self.stg3_bridge(h))
+
+        mask = torch.sigmoid(self.out(h))
+        mask = F.pad(
+            input=mask,
+            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
+            mode='replicate')
+ 
+        if self.training:
+            aux1 = torch.sigmoid(self.aux1_out(aux1))
+            aux1 = F.pad(
+                input=aux1,
+                pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
+                mode='replicate')
+            aux2 = torch.sigmoid(self.aux2_out(aux2))
+            aux2 = F.pad(
+                input=aux2,
+                pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
+                mode='replicate')
+            return mask * mix, aux1 * mix, aux2 * mix
+        else:
+            return mask# * mix
+
+    def predict_mask(self, x):
+        mask = self.forward(x)
+
+        if self.offset > 0:
+            mask = mask[:, :, :, self.offset:-self.offset]
+
+        return mask

lib_v5/vr_network/nets_new.py

@@ -0,0 +1,125 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+from . import layers_new as layers
+
+class BaseNet(nn.Module):
+
+    def __init__(self, nin, nout, nin_lstm, nout_lstm, dilations=((4, 2), (8, 4), (12, 6))):
+        super(BaseNet, self).__init__()
+        self.enc1 = layers.Conv2DBNActiv(nin, nout, 3, 1, 1)
+        self.enc2 = layers.Encoder(nout, nout * 2, 3, 2, 1)
+        self.enc3 = layers.Encoder(nout * 2, nout * 4, 3, 2, 1)
+        self.enc4 = layers.Encoder(nout * 4, nout * 6, 3, 2, 1)
+        self.enc5 = layers.Encoder(nout * 6, nout * 8, 3, 2, 1)
+
+        self.aspp = layers.ASPPModule(nout * 8, nout * 8, dilations, dropout=True)
+
+        self.dec4 = layers.Decoder(nout * (6 + 8), nout * 6, 3, 1, 1)
+        self.dec3 = layers.Decoder(nout * (4 + 6), nout * 4, 3, 1, 1)
+        self.dec2 = layers.Decoder(nout * (2 + 4), nout * 2, 3, 1, 1)
+        self.lstm_dec2 = layers.LSTMModule(nout * 2, nin_lstm, nout_lstm)
+        self.dec1 = layers.Decoder(nout * (1 + 2) + 1, nout * 1, 3, 1, 1)
+
+    def __call__(self, x):
+        e1 = self.enc1(x)
+        e2 = self.enc2(e1)
+        e3 = self.enc3(e2)
+        e4 = self.enc4(e3)
+        e5 = self.enc5(e4)
+
+        h = self.aspp(e5)
+
+        h = self.dec4(h, e4)
+        h = self.dec3(h, e3)
+        h = self.dec2(h, e2)
+        h = torch.cat([h, self.lstm_dec2(h)], dim=1)
+        h = self.dec1(h, e1)
+
+        return h
+
+class CascadedNet(nn.Module):
+
+    def __init__(self, n_fft, nn_arch_size=51000, nout=32, nout_lstm=128):
+        super(CascadedNet, self).__init__()
+        self.max_bin = n_fft // 2
+        self.output_bin = n_fft // 2 + 1
+        self.nin_lstm = self.max_bin // 2
+        self.offset = 64
+        nout = 64 if nn_arch_size == 218409 else nout
+
+        #print(nout, nout_lstm, n_fft)
+
+        self.stg1_low_band_net = nn.Sequential(
+            BaseNet(2, nout // 2, self.nin_lstm // 2, nout_lstm),
+            layers.Conv2DBNActiv(nout // 2, nout // 4, 1, 1, 0)
+        )
+        self.stg1_high_band_net = BaseNet(2, nout // 4, self.nin_lstm // 2, nout_lstm // 2)
+
+        self.stg2_low_band_net = nn.Sequential(
+            BaseNet(nout // 4 + 2, nout, self.nin_lstm // 2, nout_lstm),
+            layers.Conv2DBNActiv(nout, nout // 2, 1, 1, 0)
+        )
+        self.stg2_high_band_net = BaseNet(nout // 4 + 2, nout // 2, self.nin_lstm // 2, nout_lstm // 2)
+
+        self.stg3_full_band_net = BaseNet(3 * nout // 4 + 2, nout, self.nin_lstm, nout_lstm)
+
+        self.out = nn.Conv2d(nout, 2, 1, bias=False)
+        self.aux_out = nn.Conv2d(3 * nout // 4, 2, 1, bias=False)
+
+    def forward(self, x):
+        x = x[:, :, :self.max_bin]
+
+        bandw = x.size()[2] // 2
+        l1_in = x[:, :, :bandw]
+        h1_in = x[:, :, bandw:]
+        l1 = self.stg1_low_band_net(l1_in)
+        h1 = self.stg1_high_band_net(h1_in)
+        aux1 = torch.cat([l1, h1], dim=2)
+
+        l2_in = torch.cat([l1_in, l1], dim=1)
+        h2_in = torch.cat([h1_in, h1], dim=1)
+        l2 = self.stg2_low_band_net(l2_in)
+        h2 = self.stg2_high_band_net(h2_in)
+        aux2 = torch.cat([l2, h2], dim=2)
+
+        f3_in = torch.cat([x, aux1, aux2], dim=1)
+        f3 = self.stg3_full_band_net(f3_in)
+
+        mask = torch.sigmoid(self.out(f3))
+        mask = F.pad(
+            input=mask,
+            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
+            mode='replicate'
+        )
+
+        if self.training:
+            aux = torch.cat([aux1, aux2], dim=1)
+            aux = torch.sigmoid(self.aux_out(aux))
+            aux = F.pad(
+                input=aux,
+                pad=(0, 0, 0, self.output_bin - aux.size()[2]),
+                mode='replicate'
+            )
+            return mask, aux
+        else:
+            return mask
+
+    def predict_mask(self, x):
+        mask = self.forward(x)
+
+        if self.offset > 0:
+            mask = mask[:, :, :, self.offset:-self.offset]
+            assert mask.size()[3] > 0
+
+        return mask
+
+    def predict(self, x):
+        mask = self.forward(x)
+        pred_mag = x * mask
+
+        if self.offset > 0:
+            pred_mag = pred_mag[:, :, :, self.offset:-self.offset]
+            assert pred_mag.size()[3] > 0
+
+        return pred_mag