Delete lib_v5

lib_v5/mdxnet.py

@@ -1,140 +0,0 @@
-from abc import ABCMeta
-
-import torch
-import torch.nn as nn
-from pytorch_lightning import LightningModule
-from .modules import TFC_TDF
-
-dim_s = 4
-
-class AbstractMDXNet(LightningModule):
-    __metaclass__ = ABCMeta
-
-    def __init__(self, target_name, lr, optimizer, dim_c, dim_f, dim_t, n_fft, hop_length, overlap):
-        super().__init__()
-        self.target_name = target_name
-        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 configure_optimizers(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 sha256:946e03c789160ae4631f7037e54b5de90c32fe2c302fc2e5022696bde6902300
-size 129

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/spec_utils.py

@@ -1,692 +0,0 @@
-import librosa
-import numpy as np
-import soundfile as sf
-import math
-import random
-import math
-import platform
-import traceback
-from . import pyrb
-#cur
-OPERATING_SYSTEM = platform.system()
-SYSTEM_ARCH = platform.platform()
-SYSTEM_PROC = platform.processor()
-ARM = 'arm'
-
-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" 
-else:
-    wav_resolution = "sinc_fastest"
-
-MAX_SPEC = 'Max Spec'
-MIN_SPEC = '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 wave_to_spectrogram(wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False):
-    if reverse:
-        wave_left = np.flip(np.asfortranarray(wave[0]))
-        wave_right = np.flip(np.asfortranarray(wave[1]))
-    elif mid_side:
-        wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
-        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
-    elif mid_side_b2:
-        wave_left = np.asfortranarray(np.add(wave[1], wave[0] * .5))
-        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * .5))
-    else:
-        wave_left = np.asfortranarray(wave[0])
-        wave_right = np.asfortranarray(wave[1])
-
-    spec_left = librosa.stft(wave_left, n_fft, hop_length=hop_length)
-    spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
-    
-    spec = np.asfortranarray([spec_left, spec_right])
-
-    return spec
-   
-def wave_to_spectrogram_mt(wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False):
-    import threading
-
-    if reverse:
-        wave_left = np.flip(np.asfortranarray(wave[0]))
-        wave_right = np.flip(np.asfortranarray(wave[1]))
-    elif mid_side:
-        wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
-        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
-    elif mid_side_b2:
-        wave_left = np.asfortranarray(np.add(wave[1], wave[0] * .5))
-        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * .5))
-    else:
-        wave_left = np.asfortranarray(wave[0])
-        wave_right = np.asfortranarray(wave[1])
-   
-    def run_thread(**kwargs):
-        global spec_left
-        spec_left = librosa.stft(**kwargs)
-
-    thread = threading.Thread(target=run_thread, kwargs={'y': wave_left, 'n_fft': n_fft, 'hop_length': hop_length})
-    thread.start()
-    spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
-    thread.join()   
-    
-    spec = np.asfortranarray([spec_left, spec_right])
-
-    return spec
-    
-def normalize(wave, is_normalize=False):
-    """Save output music files"""
-    maxv = np.abs(wave).max()
-    if maxv > 1.0:
-        print(f"\nNormalization Set {is_normalize}: Input above threshold for clipping. Max:{maxv}")
-        if is_normalize:
-            print(f"The result was normalized.")
-            wave /= maxv
-        else:
-            print(f"The result was not normalized.")
-    else:
-        print(f"\nNormalization Set {is_normalize}: Input not above threshold for clipping. Max:{maxv}")
-    
-    return wave
-    
-def normalize_two_stem(wave, mix, is_normalize=False):
-    """Save output music files"""
-    
-    maxv = np.abs(wave).max()
-    max_mix = np.abs(mix).max()
-    
-    if maxv > 1.0:
-        print(f"\nNormalization Set {is_normalize}: Primary source above threshold for clipping. Max:{maxv}")
-        print(f"\nNormalization Set {is_normalize}: Mixture above threshold for clipping. Max:{max_mix}")
-        if is_normalize:
-            print(f"The result was normalized.")
-            wave /= maxv
-            mix /= maxv
-        else:
-            print(f"The result was not normalized.")
-    else:
-        print(f"\nNormalization Set {is_normalize}: Input not above threshold for clipping. Max:{maxv}")
-    
-    
-    print(f"\nNormalization Set {is_normalize}: Primary source - Max:{np.abs(wave).max()}")
-    print(f"\nNormalization Set {is_normalize}: Mixture - Max:{np.abs(mix).max()}")
-    
-    return wave, mix    
-
-def combine_spectrograms(specs, mp):
-    l = min([specs[i].shape[2] for i in specs])    
-    spec_c = np.zeros(shape=(2, mp.param['bins'] + 1, l), dtype=np.complex64)
-    offset = 0
-    bands_n = len(mp.param['band'])
-    
-    for d in range(1, bands_n + 1):
-        h = mp.param['band'][d]['crop_stop'] - mp.param['band'][d]['crop_start']
-        spec_c[:, offset:offset+h, :l] = specs[d][:, mp.param['band'][d]['crop_start']:mp.param['band'][d]['crop_stop'], :l]
-        offset += h
-        
-    if offset > mp.param['bins']:
-        raise ValueError('Too much bins')
-        
-    # lowpass fiter
-    if mp.param['pre_filter_start'] > 0: # and mp.param['band'][bands_n]['res_type'] in ['scipy', 'polyphase']:   
-        if bands_n == 1:
-            spec_c = fft_lp_filter(spec_c, mp.param['pre_filter_start'], mp.param['pre_filter_stop'])
-        else:
-            gp = 1        
-            for b in range(mp.param['pre_filter_start'] + 1, mp.param['pre_filter_stop']):
-                g = math.pow(10, -(b - mp.param['pre_filter_start']) * (3.5 - gp) / 20.0)
-                gp = g
-                spec_c[:, b, :] *= g
-                
-    return np.asfortranarray(spec_c)
-    
-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 spectrogram_to_wave(spec, hop_length, mid_side, mid_side_b2, reverse, clamp=False):
-    spec_left = np.asfortranarray(spec[0])
-    spec_right = np.asfortranarray(spec[1])
-
-    wave_left = librosa.istft(spec_left, hop_length=hop_length)
-    wave_right = librosa.istft(spec_right, hop_length=hop_length)
-
-    if reverse:
-        return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
-    elif mid_side:
-        return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])
-    elif mid_side_b2:
-        return np.asfortranarray([np.add(wave_right / 1.25, .4 * wave_left), np.subtract(wave_left / 1.25, .4 * wave_right)])
-    else:
-        return np.asfortranarray([wave_left, wave_right])
-    
-def spectrogram_to_wave_mt(spec, hop_length, mid_side, reverse, mid_side_b2):
-    import threading
-
-    spec_left = np.asfortranarray(spec[0])
-    spec_right = np.asfortranarray(spec[1])
-    
-    def run_thread(**kwargs):
-        global wave_left
-        wave_left = librosa.istft(**kwargs)
-        
-    thread = threading.Thread(target=run_thread, kwargs={'stft_matrix': spec_left, 'hop_length': hop_length})
-    thread.start()
-    wave_right = librosa.istft(spec_right, hop_length=hop_length)
-    thread.join()   
-    
-    if reverse:
-        return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
-    elif mid_side:
-        return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])
-    elif mid_side_b2:
-        return np.asfortranarray([np.add(wave_right / 1.25, .4 * wave_left), np.subtract(wave_left / 1.25, .4 * wave_right)])
-    else:
-        return np.asfortranarray([wave_left, wave_right])
-    
-def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None):
-    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:
-                spec_s = fft_hp_filter(spec_s, bp['hpf_start'], bp['hpf_stop'] - 1)
-            if bands_n == 1:
-                wave = spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse'])
-            else:
-                wave = np.add(wave, spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse']))
-        else:
-            sr = mp.param['band'][d+1]['sr']
-            if d == 1: # lower
-                spec_s = fft_lp_filter(spec_s, bp['lpf_start'], bp['lpf_stop'])
-                wave = librosa.resample(spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse']), bp['sr'], sr, res_type=wav_resolution)
-            else: # mid
-                spec_s = fft_hp_filter(spec_s, bp['hpf_start'], bp['hpf_stop'] - 1)
-                spec_s = fft_lp_filter(spec_s, bp['lpf_start'], bp['lpf_stop'])
-                wave2 = np.add(wave, spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse']))
-                wave = librosa.resample(wave2, bp['sr'], sr, res_type=wav_resolution)
-        
-    return wave
-
-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 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']
-
-    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])
-
-        # if is_non_accom_stem:
-        #     mask = (1.0 - mask)
-        
-    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, specs):   
-    for i in range(1, len(specs)):
-        if i == 1:
-            spec = specs[0]
-
-        ln = min([spec.shape[2], specs[i].shape[2]])
-        spec = spec[:,:,:ln]
-        specs[i] = specs[i][:,:,:ln]
-        
-        if MIN_SPEC == a:
-            spec = np.where(np.abs(specs[i]) <= np.abs(spec), specs[i], spec)
-        if MAX_SPEC == a:
-            spec = np.where(np.abs(specs[i]) >= np.abs(spec), specs[i], spec)  
-        if AVERAGE == a:
-            spec = np.where(np.abs(specs[i]) == np.abs(spec), specs[i], spec)  
-
-    return spec
-
-def ensemble_inputs(audio_input, algorithm, is_normalization, wav_type_set, save_path):
-
-    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_to_spectrogram_no_mp(wave)
-            specs.append(spec)
-        
-        wave_shapes = [w.shape[1] for w in wavs_]
-        target_shape = wavs_[wave_shapes.index(max(wave_shapes))]
-        
-        output = spectrogram_to_wave_no_mp(ensembling(algorithm, specs))
-        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 augment_audio(export_path, audio_file, rate, is_normalization, wav_type_set, save_format=None, is_pitch=False):
-
-    wav, sr = librosa.load(audio_file, sr=44100, mono=False)
-
-    if wav.ndim == 1:
-        wav = np.asfortranarray([wav,wav])
-
-    if is_pitch:
-        wav_1 = pyrb.pitch_shift(wav[0], sr, rate, rbargs=None)
-        wav_2 = pyrb.pitch_shift(wav[1], sr, rate, rbargs=None)
-    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 align_audio(file1, file2, file2_aligned, file_subtracted, wav_type_set, is_normalization, command_Text, progress_bar_main_var, save_format):
-    def get_diff(a, b):
-        corr = np.correlate(a, b, "full")
-        diff = corr.argmax() - (b.shape[0] - 1)
-        return diff
-  
-    progress_bar_main_var.set(10)
-    
-    # read tracks
-    wav1, sr1 = librosa.load(file1, sr=44100, mono=False)
-    wav2, sr2 = librosa.load(file2, sr=44100, mono=False)
-    wav1 = wav1.transpose()
-    wav2 = wav2.transpose()
-
-    command_Text(f"Audio file shapes: {wav1.shape} / {wav2.shape}\n")
-    
-    wav2_org = wav2.copy()
-    progress_bar_main_var.set(20)
-    
-    command_Text("Processing files... \n")
-    
-  # pick random position and get diff
-    
-    counts = {}       # counting up for each diff value
-    progress = 20
-    
-    check_range = 64
-
-    base = (64 / check_range)
-
-    for i in range(check_range):
-        index = int(random.uniform(44100 * 2, min(wav1.shape[0], wav2.shape[0]) - 44100 * 2))
-        shift = int(random.uniform(-22050,+22050))
-        samp1 = wav1[index      :index      +44100, 0]          # currently use left channel
-        samp2 = wav2[index+shift:index+shift+44100, 0]
-        progress += 1 * base
-        progress_bar_main_var.set(progress)
-        diff = get_diff(samp1, samp2)
-        diff -= shift
-        
-    if abs(diff) < 22050:
-        if not diff in counts:
-            counts[diff] = 0
-        counts[diff] += 1
-  
-  # use max counted diff value
-    max_count = 0
-    est_diff  = 0
-    for diff in counts.keys():
-        if counts[diff] > max_count:
-            max_count = counts[diff]
-            est_diff = diff
-    
-    command_Text(f"Estimated difference is {est_diff} (count: {max_count})\n")
-
-    progress_bar_main_var.set(90)
-    
-    audio_files = []
-
-    def save_aligned_audio(wav2_aligned):
-        command_Text(f"Aligned File 2 with File 1.\n")
-        command_Text(f"Saving files... ")
-        sf.write(file2_aligned, normalize(wav2_aligned, is_normalization), sr2, subtype=wav_type_set)
-        save_format(file2_aligned)
-        min_len = min(wav1.shape[0], wav2_aligned.shape[0])
-        wav_sub = wav1[:min_len] - wav2_aligned[:min_len]
-        audio_files.append(file2_aligned)
-        return min_len, wav_sub
-    
-  # make aligned track 2
-    if est_diff > 0:
-        wav2_aligned = np.append(np.zeros((est_diff, 2)), wav2_org, axis=0)
-        min_len, wav_sub = save_aligned_audio(wav2_aligned)
-    elif est_diff < 0:
-        wav2_aligned = wav2_org[-est_diff:]
-        min_len, wav_sub = save_aligned_audio(wav2_aligned)
-    else:
-        command_Text(f"Audio files already aligned.\n")
-        command_Text(f"Saving inverted track... ")
-        min_len = min(wav1.shape[0], wav2.shape[0])
-        wav_sub = wav1[:min_len] - wav2[:min_len]
-
-    wav_sub = np.clip(wav_sub, -1, +1)
-  
-    sf.write(file_subtracted, normalize(wav_sub, is_normalization), sr1, subtype=wav_type_set)
-    save_format(file_subtracted)
-  
-    progress_bar_main_var.set(95)

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

@@ -1,59 +0,0 @@
-import json
-import pathlib
-
-default_param = {}
-default_param['bins'] = 768
-default_param['unstable_bins'] = 9 # training only
-default_param['reduction_bins'] = 762 # training only
-default_param['sr'] = 44100
-default_param['pre_filter_start'] = 757
-default_param['pre_filter_stop'] = 768
-default_param['band'] = {}
-
-
-default_param['band'][1] = {
-    'sr': 11025,
-    'hl': 128,
-    'n_fft': 960,
-    'crop_start': 0,
-    'crop_stop': 245,
-    'lpf_start': 61, # inference only
-    'res_type': 'polyphase'
-}
-
-default_param['band'][2] = {
-    'sr': 44100,
-    'hl': 512,
-    'n_fft': 1536,
-    'crop_start': 24,
-    'crop_stop': 547,
-    'hpf_start': 81, # inference only
-    'res_type': 'sinc_best'
-}
-
-
-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=''):
-        if '.pth' == pathlib.Path(config_path).suffix:
-            import zipfile
-            
-            with zipfile.ZipFile(config_path, 'r') as zip:
-                self.param = json.loads(zip.read('param.json'), object_pairs_hook=int_keys)
-        elif '.json' == pathlib.Path(config_path).suffix:
-            with open(config_path, 'r') as f:
-                self.param = json.loads(f.read(), object_pairs_hook=int_keys)
-        else:
-            self.param = default_param
-            
-        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

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/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, 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
-
-        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