mirror of
https://github.com/storytold/storyteller-ml.git
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Delete voice_conversion/uvr5_pack/lib_v5 directory
This commit is contained in:
Justin John
@@ -1,183 +0,0 @@
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import os
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import random
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import numpy as np
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import torch
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import torch.utils.data
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from tqdm import tqdm
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from uvr5_pack.lib_v5 import spec_utils
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class VocalRemoverValidationSet(torch.utils.data.Dataset):
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def __init__(self, patch_list):
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self.patch_list = patch_list
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def __len__(self):
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return len(self.patch_list)
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def __getitem__(self, idx):
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path = self.patch_list[idx]
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data = np.load(path)
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X, y = data["X"], data["y"]
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X_mag = np.abs(X)
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y_mag = np.abs(y)
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return X_mag, y_mag
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def make_pair(mix_dir, inst_dir):
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input_exts = [".wav", ".m4a", ".mp3", ".mp4", ".flac"]
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X_list = sorted(
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[
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os.path.join(mix_dir, fname)
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for fname in os.listdir(mix_dir)
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if os.path.splitext(fname)[1] in input_exts
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]
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)
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y_list = sorted(
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[
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os.path.join(inst_dir, fname)
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for fname in os.listdir(inst_dir)
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if os.path.splitext(fname)[1] in input_exts
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]
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)
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filelist = list(zip(X_list, y_list))
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return filelist
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def train_val_split(dataset_dir, split_mode, val_rate, val_filelist):
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if split_mode == "random":
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filelist = make_pair(
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os.path.join(dataset_dir, "mixtures"),
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os.path.join(dataset_dir, "instruments"),
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)
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random.shuffle(filelist)
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if len(val_filelist) == 0:
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val_size = int(len(filelist) * val_rate)
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train_filelist = filelist[:-val_size]
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val_filelist = filelist[-val_size:]
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else:
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train_filelist = [
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pair for pair in filelist if list(pair) not in val_filelist
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]
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elif split_mode == "subdirs":
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if len(val_filelist) != 0:
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raise ValueError(
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"The `val_filelist` option is not available in `subdirs` mode"
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)
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train_filelist = make_pair(
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os.path.join(dataset_dir, "training/mixtures"),
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os.path.join(dataset_dir, "training/instruments"),
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)
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val_filelist = make_pair(
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os.path.join(dataset_dir, "validation/mixtures"),
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os.path.join(dataset_dir, "validation/instruments"),
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)
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return train_filelist, val_filelist
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def augment(X, y, reduction_rate, reduction_mask, mixup_rate, mixup_alpha):
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perm = np.random.permutation(len(X))
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for i, idx in enumerate(tqdm(perm)):
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if np.random.uniform() < reduction_rate:
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y[idx] = spec_utils.reduce_vocal_aggressively(
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X[idx], y[idx], reduction_mask
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)
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if np.random.uniform() < 0.5:
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# swap channel
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X[idx] = X[idx, ::-1]
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y[idx] = y[idx, ::-1]
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if np.random.uniform() < 0.02:
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# mono
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X[idx] = X[idx].mean(axis=0, keepdims=True)
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y[idx] = y[idx].mean(axis=0, keepdims=True)
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if np.random.uniform() < 0.02:
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# inst
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X[idx] = y[idx]
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if np.random.uniform() < mixup_rate and i < len(perm) - 1:
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lam = np.random.beta(mixup_alpha, mixup_alpha)
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X[idx] = lam * X[idx] + (1 - lam) * X[perm[i + 1]]
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y[idx] = lam * y[idx] + (1 - lam) * y[perm[i + 1]]
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return X, y
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def make_padding(width, cropsize, offset):
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left = offset
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roi_size = cropsize - left * 2
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if roi_size == 0:
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roi_size = cropsize
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right = roi_size - (width % roi_size) + left
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return left, right, roi_size
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def make_training_set(filelist, cropsize, patches, sr, hop_length, n_fft, offset):
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len_dataset = patches * len(filelist)
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X_dataset = np.zeros((len_dataset, 2, n_fft // 2 + 1, cropsize), dtype=np.complex64)
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y_dataset = np.zeros((len_dataset, 2, n_fft // 2 + 1, cropsize), dtype=np.complex64)
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for i, (X_path, y_path) in enumerate(tqdm(filelist)):
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X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length, n_fft)
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coef = np.max([np.abs(X).max(), np.abs(y).max()])
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X, y = X / coef, y / coef
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l, r, roi_size = make_padding(X.shape[2], cropsize, offset)
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X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode="constant")
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y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode="constant")
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starts = np.random.randint(0, X_pad.shape[2] - cropsize, patches)
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ends = starts + cropsize
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for j in range(patches):
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idx = i * patches + j
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X_dataset[idx] = X_pad[:, :, starts[j] : ends[j]]
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y_dataset[idx] = y_pad[:, :, starts[j] : ends[j]]
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return X_dataset, y_dataset
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def make_validation_set(filelist, cropsize, sr, hop_length, n_fft, offset):
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patch_list = []
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patch_dir = "cs{}_sr{}_hl{}_nf{}_of{}".format(
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cropsize, sr, hop_length, n_fft, offset
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)
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os.makedirs(patch_dir, exist_ok=True)
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for i, (X_path, y_path) in enumerate(tqdm(filelist)):
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basename = os.path.splitext(os.path.basename(X_path))[0]
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X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length, n_fft)
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coef = np.max([np.abs(X).max(), np.abs(y).max()])
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X, y = X / coef, y / coef
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l, r, roi_size = make_padding(X.shape[2], cropsize, offset)
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X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode="constant")
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y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode="constant")
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len_dataset = int(np.ceil(X.shape[2] / roi_size))
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for j in range(len_dataset):
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outpath = os.path.join(patch_dir, "{}_p{}.npz".format(basename, j))
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start = j * roi_size
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if not os.path.exists(outpath):
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np.savez(
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outpath,
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X=X_pad[:, :, start : start + cropsize],
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y=y_pad[:, :, start : start + cropsize],
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)
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patch_list.append(outpath)
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return VocalRemoverValidationSet(patch_list)
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@@ -1,118 +0,0 @@
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import torch
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from torch import nn
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import torch.nn.functional as F
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from uvr5_pack.lib_v5 import spec_utils
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class Conv2DBNActiv(nn.Module):
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def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
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super(Conv2DBNActiv, self).__init__()
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self.conv = nn.Sequential(
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nn.Conv2d(
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nin,
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nout,
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kernel_size=ksize,
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stride=stride,
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padding=pad,
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dilation=dilation,
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bias=False,
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),
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nn.BatchNorm2d(nout),
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activ(),
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)
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def __call__(self, x):
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return self.conv(x)
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class SeperableConv2DBNActiv(nn.Module):
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def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
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super(SeperableConv2DBNActiv, self).__init__()
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self.conv = nn.Sequential(
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nn.Conv2d(
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nin,
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nin,
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kernel_size=ksize,
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stride=stride,
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padding=pad,
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dilation=dilation,
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groups=nin,
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bias=False,
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),
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nn.Conv2d(nin, nout, kernel_size=1, bias=False),
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nn.BatchNorm2d(nout),
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activ(),
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)
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def __call__(self, x):
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return self.conv(x)
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class Encoder(nn.Module):
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def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
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super(Encoder, self).__init__()
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self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
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self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
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def __call__(self, x):
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skip = self.conv1(x)
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h = self.conv2(skip)
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return h, skip
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class Decoder(nn.Module):
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def __init__(
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self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False
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):
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super(Decoder, self).__init__()
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self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
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self.dropout = nn.Dropout2d(0.1) if dropout else None
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def __call__(self, x, skip=None):
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x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
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if skip is not None:
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skip = spec_utils.crop_center(skip, x)
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x = torch.cat([x, skip], dim=1)
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h = self.conv(x)
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if self.dropout is not None:
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h = self.dropout(h)
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return h
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class ASPPModule(nn.Module):
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def __init__(self, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU):
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super(ASPPModule, self).__init__()
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self.conv1 = nn.Sequential(
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nn.AdaptiveAvgPool2d((1, None)),
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Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ),
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)
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self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
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self.conv3 = SeperableConv2DBNActiv(
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nin, nin, 3, 1, dilations[0], dilations[0], activ=activ
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)
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self.conv4 = SeperableConv2DBNActiv(
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nin, nin, 3, 1, dilations[1], dilations[1], activ=activ
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)
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self.conv5 = SeperableConv2DBNActiv(
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nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
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)
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self.bottleneck = nn.Sequential(
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Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)
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)
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def forward(self, x):
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_, _, h, w = x.size()
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feat1 = F.interpolate(
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self.conv1(x), size=(h, w), mode="bilinear", align_corners=True
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)
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feat2 = self.conv2(x)
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feat3 = self.conv3(x)
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feat4 = self.conv4(x)
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feat5 = self.conv5(x)
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out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
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bottle = self.bottleneck(out)
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return bottle
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@@ -1,118 +0,0 @@
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import torch
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from torch import nn
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import torch.nn.functional as F
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from uvr5_pack.lib_v5 import spec_utils
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|
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class Conv2DBNActiv(nn.Module):
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def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
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super(Conv2DBNActiv, self).__init__()
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self.conv = nn.Sequential(
|
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nn.Conv2d(
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nin,
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nout,
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kernel_size=ksize,
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stride=stride,
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padding=pad,
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dilation=dilation,
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bias=False,
|
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),
|
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nn.BatchNorm2d(nout),
|
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activ(),
|
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)
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|
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def __call__(self, x):
|
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return self.conv(x)
|
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|
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|
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class SeperableConv2DBNActiv(nn.Module):
|
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def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
|
||||
super(SeperableConv2DBNActiv, self).__init__()
|
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self.conv = nn.Sequential(
|
||||
nn.Conv2d(
|
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nin,
|
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nin,
|
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kernel_size=ksize,
|
||||
stride=stride,
|
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padding=pad,
|
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dilation=dilation,
|
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groups=nin,
|
||||
bias=False,
|
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),
|
||||
nn.Conv2d(nin, nout, kernel_size=1, bias=False),
|
||||
nn.BatchNorm2d(nout),
|
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activ(),
|
||||
)
|
||||
|
||||
def __call__(self, x):
|
||||
return self.conv(x)
|
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|
||||
|
||||
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)
|
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h = self.conv2(skip)
|
||||
|
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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, 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.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
|
||||
)
|
||||
self.bottleneck = nn.Sequential(
|
||||
Conv2DBNActiv(nin * 5, 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)
|
||||
out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
|
||||
bottle = self.bottleneck(out)
|
||||
return bottle
|
||||
@@ -1,118 +0,0 @@
|
||||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from uvr5_pack.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, 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.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
|
||||
)
|
||||
self.bottleneck = nn.Sequential(
|
||||
Conv2DBNActiv(nin * 5, 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)
|
||||
out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
|
||||
bottle = self.bottleneck(out)
|
||||
return bottle
|
||||
@@ -1,126 +0,0 @@
|
||||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from uvr5_pack.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, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU):
|
||||
super(ASPPModule, self).__init__()
|
||||
self.conv1 = nn.Sequential(
|
||||
nn.AdaptiveAvgPool2d((1, None)),
|
||||
Conv2DBNActiv(nin, nin, 1, 1, 0, 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
|
||||
)
|
||||
self.conv6 = SeperableConv2DBNActiv(
|
||||
nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
|
||||
)
|
||||
self.conv7 = SeperableConv2DBNActiv(
|
||||
nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
|
||||
)
|
||||
self.bottleneck = nn.Sequential(
|
||||
Conv2DBNActiv(nin * 7, 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)
|
||||
feat6 = self.conv6(x)
|
||||
feat7 = self.conv7(x)
|
||||
out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1)
|
||||
bottle = self.bottleneck(out)
|
||||
return bottle
|
||||
@@ -1,126 +0,0 @@
|
||||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from uvr5_pack.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, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU):
|
||||
super(ASPPModule, self).__init__()
|
||||
self.conv1 = nn.Sequential(
|
||||
nn.AdaptiveAvgPool2d((1, None)),
|
||||
Conv2DBNActiv(nin, nin, 1, 1, 0, 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
|
||||
)
|
||||
self.conv6 = SeperableConv2DBNActiv(
|
||||
nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
|
||||
)
|
||||
self.conv7 = SeperableConv2DBNActiv(
|
||||
nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
|
||||
)
|
||||
self.bottleneck = nn.Sequential(
|
||||
Conv2DBNActiv(nin * 7, 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)
|
||||
feat6 = self.conv6(x)
|
||||
feat7 = self.conv7(x)
|
||||
out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1)
|
||||
bottle = self.bottleneck(out)
|
||||
return bottle
|
||||
@@ -1,126 +0,0 @@
|
||||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from uvr5_pack.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, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU):
|
||||
super(ASPPModule, self).__init__()
|
||||
self.conv1 = nn.Sequential(
|
||||
nn.AdaptiveAvgPool2d((1, None)),
|
||||
Conv2DBNActiv(nin, nin, 1, 1, 0, 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
|
||||
)
|
||||
self.conv6 = SeperableConv2DBNActiv(
|
||||
nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
|
||||
)
|
||||
self.conv7 = SeperableConv2DBNActiv(
|
||||
nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
|
||||
)
|
||||
self.bottleneck = nn.Sequential(
|
||||
Conv2DBNActiv(nin * 7, 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)
|
||||
feat6 = self.conv6(x)
|
||||
feat7 = self.conv7(x)
|
||||
out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1)
|
||||
bottle = self.bottleneck(out)
|
||||
return bottle
|
||||
@@ -1,125 +0,0 @@
|
||||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from uvr5_pack.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
|
||||
@@ -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
|
||||
}
|
||||
@@ -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
|
||||
}
|
||||
Reference in New Issue
Block a user