Delete lib_v5

lib_v5/mdxnet.py

@@ -1,136 +0,0 @@
-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

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ea781bd52c6a523b825fa6cdbb6189f52e318edd8b17e6fe404f76f7af8caa9c
-size 1208

lib_v5/modules.py

@@ -1,74 +0,0 @@
-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

@@ -1,92 +0,0 @@
-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

@@ -1,48 +0,0 @@
-# -*- 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

@@ -1,1241 +0,0 @@
-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

@@ -1,253 +0,0 @@
-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

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

lib_v5/vr_network/layers.py

@@ -1,143 +0,0 @@
-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

@@ -1,126 +0,0 @@
-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

@@ -1,32 +0,0 @@
-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

@@ -1,19 +0,0 @@
-{
-	"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

@@ -1,19 +0,0 @@
-{
-	"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

@@ -1,19 +0,0 @@
-{
-	"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

@@ -1,19 +0,0 @@
-{
-	"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

@@ -1,19 +0,0 @@
-{
-	"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

@@ -1,19 +0,0 @@
-{
-	"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

@@ -1,19 +0,0 @@
-{
-	"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

@@ -1,19 +0,0 @@
-{
-	"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

@@ -1,30 +0,0 @@
-{
-	"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

@@ -1,30 +0,0 @@
-{
-	"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

@@ -1,30 +0,0 @@
-{
-	"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

@@ -1,42 +0,0 @@
-{
-	"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

@@ -1,43 +0,0 @@
-{
-	"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

@@ -1,43 +0,0 @@
-{
-	"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

@@ -1,54 +0,0 @@
-{
-	"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

@@ -1,55 +0,0 @@
-{
-	"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

@@ -1,55 +0,0 @@
-{
-	"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

@@ -1,55 +0,0 @@
-{
-	"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

@@ -1,55 +0,0 @@
-{
-	"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

@@ -1,55 +0,0 @@
-{
-	"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

@@ -1,54 +0,0 @@
-{
-	"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

@@ -1,55 +0,0 @@
-{
-	"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

@@ -1,54 +0,0 @@
-{
-	"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

@@ -1,55 +0,0 @@
-{
-	"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

@@ -1,43 +0,0 @@
-{
-	"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

@@ -1,166 +0,0 @@
-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

@@ -1,125 +0,0 @@
-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