Ναρουσέ·μ·γιουμεμί·Χινακάννα
commit
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MIT License
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Copyright (c) 2021 Jingyi Li
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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import os
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import argparse
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from tqdm import tqdm
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from random import shuffle
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import json
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--train_list", type=str, default="./filelists/train.txt", help="path to train list")
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parser.add_argument("--val_list", type=str, default="./filelists/val.txt", help="path to val list")
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parser.add_argument("--test_list", type=str, default="./filelists/test.txt", help="path to test list")
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parser.add_argument("--source_dir", type=str, default="./dataset/32k", help="path to source dir")
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args = parser.parse_args()
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previous_config = json.load(open("configs/config.json", "rb"))
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train = []
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val = []
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test = []
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idx = 0
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spk_dict = previous_config["spk"]
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spk_id = max([i for i in spk_dict.values()]) + 1
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for speaker in tqdm(os.listdir(args.source_dir)):
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if speaker not in spk_dict.keys():
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spk_dict[speaker] = spk_id
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spk_id += 1
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wavs = [os.path.join(args.source_dir, speaker, i)for i in os.listdir(os.path.join(args.source_dir, speaker))]
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wavs = [i for i in wavs if i.endswith("wav")]
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shuffle(wavs)
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train += wavs[2:-10]
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val += wavs[:2]
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test += wavs[-10:]
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assert previous_config["model"]["n_speakers"] > len(spk_dict.keys())
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shuffle(train)
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shuffle(val)
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shuffle(test)
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print("Writing", args.train_list)
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with open(args.train_list, "w") as f:
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for fname in tqdm(train):
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wavpath = fname
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f.write(wavpath + "\n")
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print("Writing", args.val_list)
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with open(args.val_list, "w") as f:
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for fname in tqdm(val):
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wavpath = fname
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f.write(wavpath + "\n")
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print("Writing", args.test_list)
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with open(args.test_list, "w") as f:
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for fname in tqdm(test):
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wavpath = fname
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f.write(wavpath + "\n")
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previous_config["spk"] = spk_dict
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print("Writing configs/config.json")
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with open("configs/config.json", "w") as f:
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json.dump(previous_config, f, indent=2)
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import copy
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import math
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import numpy as np
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import torch
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from torch import nn
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from torch.nn import functional as F
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import commons
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import modules
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from modules import LayerNorm
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class Encoder(nn.Module):
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def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
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super().__init__()
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self.hidden_channels = hidden_channels
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self.filter_channels = filter_channels
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self.n_heads = n_heads
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self.n_layers = n_layers
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.window_size = window_size
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self.drop = nn.Dropout(p_dropout)
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self.attn_layers = nn.ModuleList()
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self.norm_layers_1 = nn.ModuleList()
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self.ffn_layers = nn.ModuleList()
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self.norm_layers_2 = nn.ModuleList()
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for i in range(self.n_layers):
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self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
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self.norm_layers_1.append(LayerNorm(hidden_channels))
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self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
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self.norm_layers_2.append(LayerNorm(hidden_channels))
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def forward(self, x, x_mask):
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attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
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x = x * x_mask
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for i in range(self.n_layers):
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y = self.attn_layers[i](x, x, attn_mask)
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y = self.drop(y)
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x = self.norm_layers_1[i](x + y)
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y = self.ffn_layers[i](x, x_mask)
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y = self.drop(y)
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x = self.norm_layers_2[i](x + y)
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x = x * x_mask
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return x
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class Decoder(nn.Module):
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def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
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super().__init__()
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self.hidden_channels = hidden_channels
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self.filter_channels = filter_channels
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self.n_heads = n_heads
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self.n_layers = n_layers
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.proximal_bias = proximal_bias
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self.proximal_init = proximal_init
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self.drop = nn.Dropout(p_dropout)
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self.self_attn_layers = nn.ModuleList()
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self.norm_layers_0 = nn.ModuleList()
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self.encdec_attn_layers = nn.ModuleList()
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self.norm_layers_1 = nn.ModuleList()
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self.ffn_layers = nn.ModuleList()
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self.norm_layers_2 = nn.ModuleList()
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for i in range(self.n_layers):
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self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
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self.norm_layers_0.append(LayerNorm(hidden_channels))
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self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
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self.norm_layers_1.append(LayerNorm(hidden_channels))
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self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
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self.norm_layers_2.append(LayerNorm(hidden_channels))
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def forward(self, x, x_mask, h, h_mask):
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"""
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x: decoder input
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h: encoder output
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"""
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self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
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encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
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x = x * x_mask
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for i in range(self.n_layers):
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y = self.self_attn_layers[i](x, x, self_attn_mask)
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y = self.drop(y)
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x = self.norm_layers_0[i](x + y)
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y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
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y = self.drop(y)
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x = self.norm_layers_1[i](x + y)
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y = self.ffn_layers[i](x, x_mask)
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y = self.drop(y)
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x = self.norm_layers_2[i](x + y)
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x = x * x_mask
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return x
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class MultiHeadAttention(nn.Module):
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def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
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super().__init__()
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assert channels % n_heads == 0
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self.channels = channels
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self.out_channels = out_channels
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self.n_heads = n_heads
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self.p_dropout = p_dropout
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self.window_size = window_size
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self.heads_share = heads_share
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self.block_length = block_length
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self.proximal_bias = proximal_bias
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self.proximal_init = proximal_init
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self.attn = None
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self.k_channels = channels // n_heads
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self.conv_q = nn.Conv1d(channels, channels, 1)
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self.conv_k = nn.Conv1d(channels, channels, 1)
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self.conv_v = nn.Conv1d(channels, channels, 1)
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self.conv_o = nn.Conv1d(channels, out_channels, 1)
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self.drop = nn.Dropout(p_dropout)
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if window_size is not None:
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n_heads_rel = 1 if heads_share else n_heads
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rel_stddev = self.k_channels**-0.5
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self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
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self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
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nn.init.xavier_uniform_(self.conv_q.weight)
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nn.init.xavier_uniform_(self.conv_k.weight)
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nn.init.xavier_uniform_(self.conv_v.weight)
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if proximal_init:
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with torch.no_grad():
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self.conv_k.weight.copy_(self.conv_q.weight)
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self.conv_k.bias.copy_(self.conv_q.bias)
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def forward(self, x, c, attn_mask=None):
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q = self.conv_q(x)
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k = self.conv_k(c)
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v = self.conv_v(c)
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x, self.attn = self.attention(q, k, v, mask=attn_mask)
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x = self.conv_o(x)
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return x
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def attention(self, query, key, value, mask=None):
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# reshape [b, d, t] -> [b, n_h, t, d_k]
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b, d, t_s, t_t = (*key.size(), query.size(2))
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query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
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key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
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value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
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scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
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if self.window_size is not None:
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assert t_s == t_t, "Relative attention is only available for self-attention."
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key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
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rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
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scores_local = self._relative_position_to_absolute_position(rel_logits)
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scores = scores + scores_local
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if self.proximal_bias:
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assert t_s == t_t, "Proximal bias is only available for self-attention."
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scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
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if mask is not None:
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scores = scores.masked_fill(mask == 0, -1e4)
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if self.block_length is not None:
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assert t_s == t_t, "Local attention is only available for self-attention."
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block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
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scores = scores.masked_fill(block_mask == 0, -1e4)
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p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
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p_attn = self.drop(p_attn)
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output = torch.matmul(p_attn, value)
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if self.window_size is not None:
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relative_weights = self._absolute_position_to_relative_position(p_attn)
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value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
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output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
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output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
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return output, p_attn
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def _matmul_with_relative_values(self, x, y):
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"""
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x: [b, h, l, m]
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y: [h or 1, m, d]
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ret: [b, h, l, d]
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"""
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ret = torch.matmul(x, y.unsqueeze(0))
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return ret
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def _matmul_with_relative_keys(self, x, y):
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"""
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x: [b, h, l, d]
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y: [h or 1, m, d]
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ret: [b, h, l, m]
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"""
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ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
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return ret
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def _get_relative_embeddings(self, relative_embeddings, length):
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max_relative_position = 2 * self.window_size + 1
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# Pad first before slice to avoid using cond ops.
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pad_length = max(length - (self.window_size + 1), 0)
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slice_start_position = max((self.window_size + 1) - length, 0)
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slice_end_position = slice_start_position + 2 * length - 1
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if pad_length > 0:
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padded_relative_embeddings = F.pad(
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relative_embeddings,
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commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
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else:
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padded_relative_embeddings = relative_embeddings
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used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
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return used_relative_embeddings
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def _relative_position_to_absolute_position(self, x):
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"""
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x: [b, h, l, 2*l-1]
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ret: [b, h, l, l]
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"""
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batch, heads, length, _ = x.size()
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# Concat columns of pad to shift from relative to absolute indexing.
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x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
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# Concat extra elements so to add up to shape (len+1, 2*len-1).
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x_flat = x.view([batch, heads, length * 2 * length])
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x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
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# Reshape and slice out the padded elements.
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x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
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return x_final
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def _absolute_position_to_relative_position(self, x):
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"""
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x: [b, h, l, l]
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ret: [b, h, l, 2*l-1]
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"""
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batch, heads, length, _ = x.size()
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# padd along column
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x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
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x_flat = x.view([batch, heads, length**2 + length*(length -1)])
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# add 0's in the beginning that will skew the elements after reshape
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x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
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x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
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return x_final
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def _attention_bias_proximal(self, length):
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"""Bias for self-attention to encourage attention to close positions.
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Args:
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length: an integer scalar.
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Returns:
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a Tensor with shape [1, 1, length, length]
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"""
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r = torch.arange(length, dtype=torch.float32)
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diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
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return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
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class FFN(nn.Module):
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def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
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super().__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.filter_channels = filter_channels
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.activation = activation
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self.causal = causal
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if causal:
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self.padding = self._causal_padding
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else:
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self.padding = self._same_padding
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self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
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self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
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self.drop = nn.Dropout(p_dropout)
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def forward(self, x, x_mask):
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x = self.conv_1(self.padding(x * x_mask))
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if self.activation == "gelu":
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x = x * torch.sigmoid(1.702 * x)
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else:
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x = torch.relu(x)
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x = self.drop(x)
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x = self.conv_2(self.padding(x * x_mask))
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return x * x_mask
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def _causal_padding(self, x):
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if self.kernel_size == 1:
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return x
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pad_l = self.kernel_size - 1
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pad_r = 0
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padding = [[0, 0], [0, 0], [pad_l, pad_r]]
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x = F.pad(x, commons.convert_pad_shape(padding))
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return x
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def _same_padding(self, x):
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if self.kernel_size == 1:
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return x
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pad_l = (self.kernel_size - 1) // 2
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pad_r = self.kernel_size // 2
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padding = [[0, 0], [0, 0], [pad_l, pad_r]]
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x = F.pad(x, commons.convert_pad_shape(padding))
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return x
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@ -0,0 +1,188 @@
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import math
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import numpy as np
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import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
def slice_pitch_segments(x, ids_str, segment_size=4):
|
||||
ret = torch.zeros_like(x[:, :segment_size])
|
||||
for i in range(x.size(0)):
|
||||
idx_str = ids_str[i]
|
||||
idx_end = idx_str + segment_size
|
||||
ret[i] = x[i, idx_str:idx_end]
|
||||
return ret
|
||||
|
||||
def rand_slice_segments_with_pitch(x, pitch, x_lengths=None, segment_size=4):
|
||||
b, d, t = x.size()
|
||||
if x_lengths is None:
|
||||
x_lengths = t
|
||||
ids_str_max = x_lengths - segment_size + 1
|
||||
ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
|
||||
ret = slice_segments(x, ids_str, segment_size)
|
||||
ret_pitch = slice_pitch_segments(pitch, ids_str, segment_size)
|
||||
return ret, ret_pitch, ids_str
|
||||
|
||||
def init_weights(m, mean=0.0, std=0.01):
|
||||
classname = m.__class__.__name__
|
||||
if classname.find("Conv") != -1:
|
||||
m.weight.data.normal_(mean, std)
|
||||
|
||||
|
||||
def get_padding(kernel_size, dilation=1):
|
||||
return int((kernel_size*dilation - dilation)/2)
|
||||
|
||||
|
||||
def convert_pad_shape(pad_shape):
|
||||
l = pad_shape[::-1]
|
||||
pad_shape = [item for sublist in l for item in sublist]
|
||||
return pad_shape
|
||||
|
||||
|
||||
def intersperse(lst, item):
|
||||
result = [item] * (len(lst) * 2 + 1)
|
||||
result[1::2] = lst
|
||||
return result
|
||||
|
||||
|
||||
def kl_divergence(m_p, logs_p, m_q, logs_q):
|
||||
"""KL(P||Q)"""
|
||||
kl = (logs_q - logs_p) - 0.5
|
||||
kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
|
||||
return kl
|
||||
|
||||
|
||||
def rand_gumbel(shape):
|
||||
"""Sample from the Gumbel distribution, protect from overflows."""
|
||||
uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
|
||||
return -torch.log(-torch.log(uniform_samples))
|
||||
|
||||
|
||||
def rand_gumbel_like(x):
|
||||
g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
|
||||
return g
|
||||
|
||||
|
||||
def slice_segments(x, ids_str, segment_size=4):
|
||||
ret = torch.zeros_like(x[:, :, :segment_size])
|
||||
for i in range(x.size(0)):
|
||||
idx_str = ids_str[i]
|
||||
idx_end = idx_str + segment_size
|
||||
ret[i] = x[i, :, idx_str:idx_end]
|
||||
return ret
|
||||
|
||||
|
||||
def rand_slice_segments(x, x_lengths=None, segment_size=4):
|
||||
b, d, t = x.size()
|
||||
if x_lengths is None:
|
||||
x_lengths = t
|
||||
ids_str_max = x_lengths - segment_size + 1
|
||||
ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
|
||||
ret = slice_segments(x, ids_str, segment_size)
|
||||
return ret, ids_str
|
||||
|
||||
|
||||
def rand_spec_segments(x, x_lengths=None, segment_size=4):
|
||||
b, d, t = x.size()
|
||||
if x_lengths is None:
|
||||
x_lengths = t
|
||||
ids_str_max = x_lengths - segment_size
|
||||
ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
|
||||
ret = slice_segments(x, ids_str, segment_size)
|
||||
return ret, ids_str
|
||||
|
||||
|
||||
def get_timing_signal_1d(
|
||||
length, channels, min_timescale=1.0, max_timescale=1.0e4):
|
||||
position = torch.arange(length, dtype=torch.float)
|
||||
num_timescales = channels // 2
|
||||
log_timescale_increment = (
|
||||
math.log(float(max_timescale) / float(min_timescale)) /
|
||||
(num_timescales - 1))
|
||||
inv_timescales = min_timescale * torch.exp(
|
||||
torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
|
||||
scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
|
||||
signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
|
||||
signal = F.pad(signal, [0, 0, 0, channels % 2])
|
||||
signal = signal.view(1, channels, length)
|
||||
return signal
|
||||
|
||||
|
||||
def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
|
||||
b, channels, length = x.size()
|
||||
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
|
||||
return x + signal.to(dtype=x.dtype, device=x.device)
|
||||
|
||||
|
||||
def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
|
||||
b, channels, length = x.size()
|
||||
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
|
||||
return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
|
||||
|
||||
|
||||
def subsequent_mask(length):
|
||||
mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
|
||||
return mask
|
||||
|
||||
|
||||
@torch.jit.script
|
||||
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
|
||||
n_channels_int = n_channels[0]
|
||||
in_act = input_a + input_b
|
||||
t_act = torch.tanh(in_act[:, :n_channels_int, :])
|
||||
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
|
||||
acts = t_act * s_act
|
||||
return acts
|
||||
|
||||
|
||||
def convert_pad_shape(pad_shape):
|
||||
l = pad_shape[::-1]
|
||||
pad_shape = [item for sublist in l for item in sublist]
|
||||
return pad_shape
|
||||
|
||||
|
||||
def shift_1d(x):
|
||||
x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
|
||||
return x
|
||||
|
||||
|
||||
def sequence_mask(length, max_length=None):
|
||||
if max_length is None:
|
||||
max_length = length.max()
|
||||
x = torch.arange(max_length, dtype=length.dtype, device=length.device)
|
||||
return x.unsqueeze(0) < length.unsqueeze(1)
|
||||
|
||||
|
||||
def generate_path(duration, mask):
|
||||
"""
|
||||
duration: [b, 1, t_x]
|
||||
mask: [b, 1, t_y, t_x]
|
||||
"""
|
||||
device = duration.device
|
||||
|
||||
b, _, t_y, t_x = mask.shape
|
||||
cum_duration = torch.cumsum(duration, -1)
|
||||
|
||||
cum_duration_flat = cum_duration.view(b * t_x)
|
||||
path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
|
||||
path = path.view(b, t_x, t_y)
|
||||
path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
|
||||
path = path.unsqueeze(1).transpose(2,3) * mask
|
||||
return path
|
||||
|
||||
|
||||
def clip_grad_value_(parameters, clip_value, norm_type=2):
|
||||
if isinstance(parameters, torch.Tensor):
|
||||
parameters = [parameters]
|
||||
parameters = list(filter(lambda p: p.grad is not None, parameters))
|
||||
norm_type = float(norm_type)
|
||||
if clip_value is not None:
|
||||
clip_value = float(clip_value)
|
||||
|
||||
total_norm = 0
|
||||
for p in parameters:
|
||||
param_norm = p.grad.data.norm(norm_type)
|
||||
total_norm += param_norm.item() ** norm_type
|
||||
if clip_value is not None:
|
||||
p.grad.data.clamp_(min=-clip_value, max=clip_value)
|
||||
total_norm = total_norm ** (1. / norm_type)
|
||||
return total_norm
|
||||
@ -0,0 +1 @@
|
||||
请使用生成的config文件
|
||||
@ -0,0 +1,154 @@
|
||||
import time
|
||||
import os
|
||||
import random
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.utils.data
|
||||
|
||||
import commons
|
||||
from mel_processing import spectrogram_torch, spec_to_mel_torch
|
||||
from utils import load_wav_to_torch, load_filepaths_and_text, transform
|
||||
|
||||
# import h5py
|
||||
|
||||
|
||||
"""Multi speaker version"""
|
||||
|
||||
|
||||
class TextAudioSpeakerLoader(torch.utils.data.Dataset):
|
||||
"""
|
||||
1) loads audio, speaker_id, text pairs
|
||||
2) normalizes text and converts them to sequences of integers
|
||||
3) computes spectrograms from audio files.
|
||||
"""
|
||||
|
||||
def __init__(self, audiopaths, hparams):
|
||||
self.audiopaths = load_filepaths_and_text(audiopaths)
|
||||
self.max_wav_value = hparams.data.max_wav_value
|
||||
self.sampling_rate = hparams.data.sampling_rate
|
||||
self.filter_length = hparams.data.filter_length
|
||||
self.hop_length = hparams.data.hop_length
|
||||
self.win_length = hparams.data.win_length
|
||||
self.sampling_rate = hparams.data.sampling_rate
|
||||
self.use_sr = hparams.train.use_sr
|
||||
self.spec_len = hparams.train.max_speclen
|
||||
self.spk_map = hparams.spk
|
||||
|
||||
random.seed(1234)
|
||||
random.shuffle(self.audiopaths)
|
||||
|
||||
def get_audio(self, filename):
|
||||
filename = filename.replace("\\", "/")
|
||||
audio, sampling_rate = load_wav_to_torch(filename)
|
||||
if sampling_rate != self.sampling_rate:
|
||||
raise ValueError("{} SR doesn't match target {} SR".format(
|
||||
sampling_rate, self.sampling_rate))
|
||||
audio_norm = audio / self.max_wav_value
|
||||
audio_norm = audio_norm.unsqueeze(0)
|
||||
spec_filename = filename.replace(".wav", ".spec.pt")
|
||||
if os.path.exists(spec_filename):
|
||||
spec = torch.load(spec_filename)
|
||||
else:
|
||||
spec = spectrogram_torch(audio_norm, self.filter_length,
|
||||
self.sampling_rate, self.hop_length, self.win_length,
|
||||
center=False)
|
||||
spec = torch.squeeze(spec, 0)
|
||||
torch.save(spec, spec_filename)
|
||||
|
||||
spk = filename.split("/")[-2]
|
||||
spk = torch.LongTensor([self.spk_map[spk]])
|
||||
|
||||
c = torch.load(filename + ".soft.pt").squeeze(0)
|
||||
c = torch.repeat_interleave(c, repeats=2, dim=1)
|
||||
|
||||
f0 = np.load(filename + ".f0.npy")
|
||||
f0 = torch.FloatTensor(f0)
|
||||
lmin = min(c.size(-1), spec.size(-1), f0.shape[0])
|
||||
assert abs(c.size(-1) - spec.size(-1)) < 4, (c.size(-1), spec.size(-1), f0.shape, filename)
|
||||
assert abs(lmin - spec.size(-1)) < 4, (c.size(-1), spec.size(-1), f0.shape)
|
||||
assert abs(lmin - c.size(-1)) < 4, (c.size(-1), spec.size(-1), f0.shape)
|
||||
spec, c, f0 = spec[:, :lmin], c[:, :lmin], f0[:lmin]
|
||||
audio_norm = audio_norm[:, :lmin * self.hop_length]
|
||||
_spec, _c, _audio_norm, _f0 = spec, c, audio_norm, f0
|
||||
while spec.size(-1) < self.spec_len:
|
||||
spec = torch.cat((spec, _spec), -1)
|
||||
c = torch.cat((c, _c), -1)
|
||||
f0 = torch.cat((f0, _f0), -1)
|
||||
audio_norm = torch.cat((audio_norm, _audio_norm), -1)
|
||||
start = random.randint(0, spec.size(-1) - self.spec_len)
|
||||
end = start + self.spec_len
|
||||
spec = spec[:, start:end]
|
||||
c = c[:, start:end]
|
||||
f0 = f0[start:end]
|
||||
audio_norm = audio_norm[:, start * self.hop_length:end * self.hop_length]
|
||||
|
||||
return c, f0, spec, audio_norm, spk
|
||||
|
||||
def __getitem__(self, index):
|
||||
return self.get_audio(self.audiopaths[index][0])
|
||||
|
||||
def __len__(self):
|
||||
return len(self.audiopaths)
|
||||
|
||||
|
||||
class EvalDataLoader(torch.utils.data.Dataset):
|
||||
"""
|
||||
1) loads audio, speaker_id, text pairs
|
||||
2) normalizes text and converts them to sequences of integers
|
||||
3) computes spectrograms from audio files.
|
||||
"""
|
||||
|
||||
def __init__(self, audiopaths, hparams):
|
||||
self.audiopaths = load_filepaths_and_text(audiopaths)
|
||||
self.max_wav_value = hparams.data.max_wav_value
|
||||
self.sampling_rate = hparams.data.sampling_rate
|
||||
self.filter_length = hparams.data.filter_length
|
||||
self.hop_length = hparams.data.hop_length
|
||||
self.win_length = hparams.data.win_length
|
||||
self.sampling_rate = hparams.data.sampling_rate
|
||||
self.use_sr = hparams.train.use_sr
|
||||
self.audiopaths = self.audiopaths[:5]
|
||||
self.spk_map = hparams.spk
|
||||
|
||||
|
||||
def get_audio(self, filename):
|
||||
filename = filename.replace("\\", "/")
|
||||
audio, sampling_rate = load_wav_to_torch(filename)
|
||||
if sampling_rate != self.sampling_rate:
|
||||
raise ValueError("{} SR doesn't match target {} SR".format(
|
||||
sampling_rate, self.sampling_rate))
|
||||
audio_norm = audio / self.max_wav_value
|
||||
audio_norm = audio_norm.unsqueeze(0)
|
||||
spec_filename = filename.replace(".wav", ".spec.pt")
|
||||
if os.path.exists(spec_filename):
|
||||
spec = torch.load(spec_filename)
|
||||
else:
|
||||
spec = spectrogram_torch(audio_norm, self.filter_length,
|
||||
self.sampling_rate, self.hop_length, self.win_length,
|
||||
center=False)
|
||||
spec = torch.squeeze(spec, 0)
|
||||
torch.save(spec, spec_filename)
|
||||
|
||||
spk = filename.split("/")[-2]
|
||||
spk = torch.LongTensor([self.spk_map[spk]])
|
||||
|
||||
c = torch.load(filename + ".soft.pt").squeeze(0)
|
||||
|
||||
c = torch.repeat_interleave(c, repeats=2, dim=1)
|
||||
|
||||
f0 = np.load(filename + ".f0.npy")
|
||||
f0 = torch.FloatTensor(f0)
|
||||
lmin = min(c.size(-1), spec.size(-1), f0.shape[0])
|
||||
assert abs(c.size(-1) - spec.size(-1)) < 4, (c.size(-1), spec.size(-1), f0.shape)
|
||||
assert abs(f0.shape[0] - spec.shape[-1]) < 4, (c.size(-1), spec.size(-1), f0.shape)
|
||||
spec, c, f0 = spec[:, :lmin], c[:, :lmin], f0[:lmin]
|
||||
audio_norm = audio_norm[:, :lmin * self.hop_length]
|
||||
|
||||
return c, f0, spec, audio_norm, spk
|
||||
|
||||
def __getitem__(self, index):
|
||||
return self.get_audio(self.audiopaths[index][0])
|
||||
|
||||
def __len__(self):
|
||||
return len(self.audiopaths)
|
||||
|
||||
@ -0,0 +1,20 @@
|
||||
数据集准备
|
||||
|
||||
raw
|
||||
├───speaker0
|
||||
│ ├───xxx1-xxx1.wav
|
||||
│ ├───...
|
||||
│ └───Lxx-0xx8.wav
|
||||
└───speaker1
|
||||
├───xx2-0xxx2.wav
|
||||
├───...
|
||||
└───xxx7-xxx007.wav
|
||||
|
||||
此外还需要编辑config.json
|
||||
|
||||
"n_speakers": 10
|
||||
|
||||
"spk":{
|
||||
"speaker0": 0,
|
||||
"speaker1": 1,
|
||||
}
|
||||
@ -0,0 +1,7 @@
|
||||
./dataset/32k/yunhao/001829.wav
|
||||
./dataset/32k/yunhao/001827.wav
|
||||
./dataset/32k/jishuang/000104.wav
|
||||
./dataset/32k/nen/kne110_005.wav
|
||||
./dataset/32k/nen/kne110_004.wav
|
||||
./dataset/32k/jishuang/000223.wav
|
||||
./dataset/32k/yunhao/001828.wav
|
||||
@ -0,0 +1,6 @@
|
||||
./dataset/32k/nen/kne110_005.wav
|
||||
./dataset/32k/yunhao/001827.wav
|
||||
./dataset/32k/jishuang/000104.wav
|
||||
./dataset/32k/jishuang/000223.wav
|
||||
./dataset/32k/nen/kne110_004.wav
|
||||
./dataset/32k/yunhao/001828.wav
|
||||
@ -0,0 +1,222 @@
|
||||
import copy
|
||||
import random
|
||||
from typing import Optional, Tuple
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as t_func
|
||||
from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
|
||||
|
||||
|
||||
class Hubert(nn.Module):
|
||||
def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
|
||||
super().__init__()
|
||||
self._mask = mask
|
||||
self.feature_extractor = FeatureExtractor()
|
||||
self.feature_projection = FeatureProjection()
|
||||
self.positional_embedding = PositionalConvEmbedding()
|
||||
self.norm = nn.LayerNorm(768)
|
||||
self.dropout = nn.Dropout(0.1)
|
||||
self.encoder = TransformerEncoder(
|
||||
nn.TransformerEncoderLayer(
|
||||
768, 12, 3072, activation="gelu", batch_first=True
|
||||
),
|
||||
12,
|
||||
)
|
||||
self.proj = nn.Linear(768, 256)
|
||||
|
||||
self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
|
||||
self.label_embedding = nn.Embedding(num_label_embeddings, 256)
|
||||
|
||||
def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
mask = None
|
||||
if self.training and self._mask:
|
||||
mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
|
||||
x[mask] = self.masked_spec_embed.to(x.dtype)
|
||||
return x, mask
|
||||
|
||||
def encode(
|
||||
self, x: torch.Tensor, layer: Optional[int] = None
|
||||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
x = self.feature_extractor(x)
|
||||
x = self.feature_projection(x.transpose(1, 2))
|
||||
x, mask = self.mask(x)
|
||||
x = x + self.positional_embedding(x)
|
||||
x = self.dropout(self.norm(x))
|
||||
x = self.encoder(x, output_layer=layer)
|
||||
return x, mask
|
||||
|
||||
def logits(self, x: torch.Tensor) -> torch.Tensor:
|
||||
logits = torch.cosine_similarity(
|
||||
x.unsqueeze(2),
|
||||
self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
|
||||
dim=-1,
|
||||
)
|
||||
return logits / 0.1
|
||||
|
||||
def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
x, mask = self.encode(x)
|
||||
x = self.proj(x)
|
||||
logits = self.logits(x)
|
||||
return logits, mask
|
||||
|
||||
|
||||
class HubertSoft(Hubert):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
@torch.inference_mode()
|
||||
def units(self, wav: torch.Tensor) -> torch.Tensor:
|
||||
wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
|
||||
x, _ = self.encode(wav)
|
||||
return self.proj(x)
|
||||
|
||||
|
||||
class FeatureExtractor(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
|
||||
self.norm0 = nn.GroupNorm(512, 512)
|
||||
self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
|
||||
self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
|
||||
self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
|
||||
self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
|
||||
self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
|
||||
self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
x = t_func.gelu(self.norm0(self.conv0(x)))
|
||||
x = t_func.gelu(self.conv1(x))
|
||||
x = t_func.gelu(self.conv2(x))
|
||||
x = t_func.gelu(self.conv3(x))
|
||||
x = t_func.gelu(self.conv4(x))
|
||||
x = t_func.gelu(self.conv5(x))
|
||||
x = t_func.gelu(self.conv6(x))
|
||||
return x
|
||||
|
||||
|
||||
class FeatureProjection(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.norm = nn.LayerNorm(512)
|
||||
self.projection = nn.Linear(512, 768)
|
||||
self.dropout = nn.Dropout(0.1)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
x = self.norm(x)
|
||||
x = self.projection(x)
|
||||
x = self.dropout(x)
|
||||
return x
|
||||
|
||||
|
||||
class PositionalConvEmbedding(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.conv = nn.Conv1d(
|
||||
768,
|
||||
768,
|
||||
kernel_size=128,
|
||||
padding=128 // 2,
|
||||
groups=16,
|
||||
)
|
||||
self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
x = self.conv(x.transpose(1, 2))
|
||||
x = t_func.gelu(x[:, :, :-1])
|
||||
return x.transpose(1, 2)
|
||||
|
||||
|
||||
class TransformerEncoder(nn.Module):
|
||||
def __init__(
|
||||
self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
|
||||
) -> None:
|
||||
super(TransformerEncoder, self).__init__()
|
||||
self.layers = nn.ModuleList(
|
||||
[copy.deepcopy(encoder_layer) for _ in range(num_layers)]
|
||||
)
|
||||
self.num_layers = num_layers
|
||||
|
||||
def forward(
|
||||
self,
|
||||
src: torch.Tensor,
|
||||
mask: torch.Tensor = None,
|
||||
src_key_padding_mask: torch.Tensor = None,
|
||||
output_layer: Optional[int] = None,
|
||||
) -> torch.Tensor:
|
||||
output = src
|
||||
for layer in self.layers[:output_layer]:
|
||||
output = layer(
|
||||
output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
|
||||
)
|
||||
return output
|
||||
|
||||
|
||||
def _compute_mask(
|
||||
shape: Tuple[int, int],
|
||||
mask_prob: float,
|
||||
mask_length: int,
|
||||
device: torch.device,
|
||||
min_masks: int = 0,
|
||||
) -> torch.Tensor:
|
||||
batch_size, sequence_length = shape
|
||||
|
||||
if mask_length < 1:
|
||||
raise ValueError("`mask_length` has to be bigger than 0.")
|
||||
|
||||
if mask_length > sequence_length:
|
||||
raise ValueError(
|
||||
f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
|
||||
)
|
||||
|
||||
# compute number of masked spans in batch
|
||||
num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
|
||||
num_masked_spans = max(num_masked_spans, min_masks)
|
||||
|
||||
# make sure num masked indices <= sequence_length
|
||||
if num_masked_spans * mask_length > sequence_length:
|
||||
num_masked_spans = sequence_length // mask_length
|
||||
|
||||
# SpecAugment mask to fill
|
||||
mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
|
||||
|
||||
# uniform distribution to sample from, make sure that offset samples are < sequence_length
|
||||
uniform_dist = torch.ones(
|
||||
(batch_size, sequence_length - (mask_length - 1)), device=device
|
||||
)
|
||||
|
||||
# get random indices to mask
|
||||
mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
|
||||
|
||||
# expand masked indices to masked spans
|
||||
mask_indices = (
|
||||
mask_indices.unsqueeze(dim=-1)
|
||||
.expand((batch_size, num_masked_spans, mask_length))
|
||||
.reshape(batch_size, num_masked_spans * mask_length)
|
||||
)
|
||||
offsets = (
|
||||
torch.arange(mask_length, device=device)[None, None, :]
|
||||
.expand((batch_size, num_masked_spans, mask_length))
|
||||
.reshape(batch_size, num_masked_spans * mask_length)
|
||||
)
|
||||
mask_idxs = mask_indices + offsets
|
||||
|
||||
# scatter indices to mask
|
||||
mask = mask.scatter(1, mask_idxs, True)
|
||||
|
||||
return mask
|
||||
|
||||
|
||||
def hubert_soft(
|
||||
path: str,
|
||||
) -> HubertSoft:
|
||||
r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
|
||||
Args:
|
||||
path (str): path of a pretrained model
|
||||
"""
|
||||
hubert = HubertSoft()
|
||||
checkpoint = torch.load(path)
|
||||
consume_prefix_in_state_dict_if_present(checkpoint, "module.")
|
||||
hubert.load_state_dict(checkpoint)
|
||||
hubert.eval()
|
||||
return hubert
|
||||
@ -0,0 +1,217 @@
|
||||
import copy
|
||||
import random
|
||||
from typing import Optional, Tuple
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as t_func
|
||||
from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
|
||||
|
||||
|
||||
class Hubert(nn.Module):
|
||||
def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
|
||||
super().__init__()
|
||||
self._mask = mask
|
||||
self.feature_extractor = FeatureExtractor()
|
||||
self.feature_projection = FeatureProjection()
|
||||
self.positional_embedding = PositionalConvEmbedding()
|
||||
self.norm = nn.LayerNorm(768)
|
||||
self.dropout = nn.Dropout(0.1)
|
||||
self.encoder = TransformerEncoder(
|
||||
nn.TransformerEncoderLayer(
|
||||
768, 12, 3072, activation="gelu", batch_first=True
|
||||
),
|
||||
12,
|
||||
)
|
||||
self.proj = nn.Linear(768, 256)
|
||||
|
||||
self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
|
||||
self.label_embedding = nn.Embedding(num_label_embeddings, 256)
|
||||
|
||||
def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
mask = None
|
||||
if self.training and self._mask:
|
||||
mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
|
||||
x[mask] = self.masked_spec_embed.to(x.dtype)
|
||||
return x, mask
|
||||
|
||||
def encode(
|
||||
self, x: torch.Tensor, layer: Optional[int] = None
|
||||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
x = self.feature_extractor(x)
|
||||
x = self.feature_projection(x.transpose(1, 2))
|
||||
x, mask = self.mask(x)
|
||||
x = x + self.positional_embedding(x)
|
||||
x = self.dropout(self.norm(x))
|
||||
x = self.encoder(x, output_layer=layer)
|
||||
return x, mask
|
||||
|
||||
def logits(self, x: torch.Tensor) -> torch.Tensor:
|
||||
logits = torch.cosine_similarity(
|
||||
x.unsqueeze(2),
|
||||
self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
|
||||
dim=-1,
|
||||
)
|
||||
return logits / 0.1
|
||||
|
||||
|
||||
class HubertSoft(Hubert):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
def units(self, wav: torch.Tensor) -> torch.Tensor:
|
||||
wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
|
||||
x, _ = self.encode(wav)
|
||||
return self.proj(x)
|
||||
|
||||
def forward(self, x):
|
||||
return self.units(x)
|
||||
|
||||
class FeatureExtractor(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
|
||||
self.norm0 = nn.GroupNorm(512, 512)
|
||||
self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
|
||||
self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
|
||||
self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
|
||||
self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
|
||||
self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
|
||||
self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
x = t_func.gelu(self.norm0(self.conv0(x)))
|
||||
x = t_func.gelu(self.conv1(x))
|
||||
x = t_func.gelu(self.conv2(x))
|
||||
x = t_func.gelu(self.conv3(x))
|
||||
x = t_func.gelu(self.conv4(x))
|
||||
x = t_func.gelu(self.conv5(x))
|
||||
x = t_func.gelu(self.conv6(x))
|
||||
return x
|
||||
|
||||
|
||||
class FeatureProjection(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.norm = nn.LayerNorm(512)
|
||||
self.projection = nn.Linear(512, 768)
|
||||
self.dropout = nn.Dropout(0.1)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
x = self.norm(x)
|
||||
x = self.projection(x)
|
||||
x = self.dropout(x)
|
||||
return x
|
||||
|
||||
|
||||
class PositionalConvEmbedding(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.conv = nn.Conv1d(
|
||||
768,
|
||||
768,
|
||||
kernel_size=128,
|
||||
padding=128 // 2,
|
||||
groups=16,
|
||||
)
|
||||
self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
x = self.conv(x.transpose(1, 2))
|
||||
x = t_func.gelu(x[:, :, :-1])
|
||||
return x.transpose(1, 2)
|
||||
|
||||
|
||||
class TransformerEncoder(nn.Module):
|
||||
def __init__(
|
||||
self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
|
||||
) -> None:
|
||||
super(TransformerEncoder, self).__init__()
|
||||
self.layers = nn.ModuleList(
|
||||
[copy.deepcopy(encoder_layer) for _ in range(num_layers)]
|
||||
)
|
||||
self.num_layers = num_layers
|
||||
|
||||
def forward(
|
||||
self,
|
||||
src: torch.Tensor,
|
||||
mask: torch.Tensor = None,
|
||||
src_key_padding_mask: torch.Tensor = None,
|
||||
output_layer: Optional[int] = None,
|
||||
) -> torch.Tensor:
|
||||
output = src
|
||||
for layer in self.layers[:output_layer]:
|
||||
output = layer(
|
||||
output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
|
||||
)
|
||||
return output
|
||||
|
||||
|
||||
def _compute_mask(
|
||||
shape: Tuple[int, int],
|
||||
mask_prob: float,
|
||||
mask_length: int,
|
||||
device: torch.device,
|
||||
min_masks: int = 0,
|
||||
) -> torch.Tensor:
|
||||
batch_size, sequence_length = shape
|
||||
|
||||
if mask_length < 1:
|
||||
raise ValueError("`mask_length` has to be bigger than 0.")
|
||||
|
||||
if mask_length > sequence_length:
|
||||
raise ValueError(
|
||||
f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
|
||||
)
|
||||
|
||||
# compute number of masked spans in batch
|
||||
num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
|
||||
num_masked_spans = max(num_masked_spans, min_masks)
|
||||
|
||||
# make sure num masked indices <= sequence_length
|
||||
if num_masked_spans * mask_length > sequence_length:
|
||||
num_masked_spans = sequence_length // mask_length
|
||||
|
||||
# SpecAugment mask to fill
|
||||
mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
|
||||
|
||||
# uniform distribution to sample from, make sure that offset samples are < sequence_length
|
||||
uniform_dist = torch.ones(
|
||||
(batch_size, sequence_length - (mask_length - 1)), device=device
|
||||
)
|
||||
|
||||
# get random indices to mask
|
||||
mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
|
||||
|
||||
# expand masked indices to masked spans
|
||||
mask_indices = (
|
||||
mask_indices.unsqueeze(dim=-1)
|
||||
.expand((batch_size, num_masked_spans, mask_length))
|
||||
.reshape(batch_size, num_masked_spans * mask_length)
|
||||
)
|
||||
offsets = (
|
||||
torch.arange(mask_length, device=device)[None, None, :]
|
||||
.expand((batch_size, num_masked_spans, mask_length))
|
||||
.reshape(batch_size, num_masked_spans * mask_length)
|
||||
)
|
||||
mask_idxs = mask_indices + offsets
|
||||
|
||||
# scatter indices to mask
|
||||
mask = mask.scatter(1, mask_idxs, True)
|
||||
|
||||
return mask
|
||||
|
||||
|
||||
def hubert_soft(
|
||||
path: str,
|
||||
) -> HubertSoft:
|
||||
r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
|
||||
Args:
|
||||
path (str): path of a pretrained model
|
||||
"""
|
||||
hubert = HubertSoft()
|
||||
checkpoint = torch.load(path)
|
||||
consume_prefix_in_state_dict_if_present(checkpoint, "module.")
|
||||
hubert.load_state_dict(checkpoint)
|
||||
hubert.eval()
|
||||
return hubert
|
||||
@ -0,0 +1,160 @@
|
||||
import hashlib
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import time
|
||||
from pathlib import Path
|
||||
import io
|
||||
import librosa
|
||||
import maad
|
||||
import numpy as np
|
||||
from inference import slicer
|
||||
import parselmouth
|
||||
import soundfile
|
||||
import torch
|
||||
import torchaudio
|
||||
|
||||
from hubert import hubert_model
|
||||
import utils
|
||||
from models import SynthesizerTrn
|
||||
logging.getLogger('numba').setLevel(logging.WARNING)
|
||||
logging.getLogger('matplotlib').setLevel(logging.WARNING)
|
||||
|
||||
def resize2d_f0(x, target_len):
|
||||
source = np.array(x)
|
||||
source[source < 0.001] = np.nan
|
||||
target = np.interp(np.arange(0, len(source) * target_len, len(source)) / target_len, np.arange(0, len(source)),
|
||||
source)
|
||||
res = np.nan_to_num(target)
|
||||
return res
|
||||
|
||||
def get_f0(x, p_len,f0_up_key=0):
|
||||
|
||||
time_step = 160 / 16000 * 1000
|
||||
f0_min = 50
|
||||
f0_max = 1100
|
||||
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
|
||||
f0_mel_max = 1127 * np.log(1 + f0_max / 700)
|
||||
|
||||
f0 = parselmouth.Sound(x, 16000).to_pitch_ac(
|
||||
time_step=time_step / 1000, voicing_threshold=0.6,
|
||||
pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
|
||||
|
||||
pad_size=(p_len - len(f0) + 1) // 2
|
||||
if(pad_size>0 or p_len - len(f0) - pad_size>0):
|
||||
f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
|
||||
|
||||
f0 *= pow(2, f0_up_key / 12)
|
||||
f0_mel = 1127 * np.log(1 + f0 / 700)
|
||||
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (f0_mel_max - f0_mel_min) + 1
|
||||
f0_mel[f0_mel <= 1] = 1
|
||||
f0_mel[f0_mel > 255] = 255
|
||||
f0_coarse = np.rint(f0_mel).astype(np.int)
|
||||
return f0_coarse, f0
|
||||
|
||||
def clean_pitch(input_pitch):
|
||||
num_nan = np.sum(input_pitch == 1)
|
||||
if num_nan / len(input_pitch) > 0.9:
|
||||
input_pitch[input_pitch != 1] = 1
|
||||
return input_pitch
|
||||
|
||||
|
||||
def plt_pitch(input_pitch):
|
||||
input_pitch = input_pitch.astype(float)
|
||||
input_pitch[input_pitch == 1] = np.nan
|
||||
return input_pitch
|
||||
|
||||
|
||||
def f0_to_pitch(ff):
|
||||
f0_pitch = 69 + 12 * np.log2(ff / 440)
|
||||
return f0_pitch
|
||||
|
||||
|
||||
def fill_a_to_b(a, b):
|
||||
if len(a) < len(b):
|
||||
for _ in range(0, len(b) - len(a)):
|
||||
a.append(a[0])
|
||||
|
||||
|
||||
def mkdir(paths: list):
|
||||
for path in paths:
|
||||
if not os.path.exists(path):
|
||||
os.mkdir(path)
|
||||
|
||||
|
||||
class VitsSvc(object):
|
||||
def __init__(self):
|
||||
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
self.SVCVITS = None
|
||||
self.hps = None
|
||||
self.speakers = None
|
||||
self.hubert_soft = hubert_model.hubert_soft("hubert/model.pt")
|
||||
|
||||
def set_device(self, device):
|
||||
self.device = torch.device(device)
|
||||
self.hubert_soft.to(self.device)
|
||||
if self.SVCVITS != None:
|
||||
self.SVCVITS.to(self.device)
|
||||
|
||||
def loadCheckpoint(self, path):
|
||||
self.hps = utils.get_hparams_from_file(f"checkpoints/{path}/config.json")
|
||||
self.SVCVITS = SynthesizerTrn(
|
||||
self.hps.data.filter_length // 2 + 1,
|
||||
self.hps.train.segment_size // self.hps.data.hop_length,
|
||||
**self.hps.model)
|
||||
_ = utils.load_checkpoint(f"checkpoints/{path}/model.pth", self.SVCVITS, None)
|
||||
_ = self.SVCVITS.eval().to(self.device)
|
||||
self.speakers = self.hps.spk
|
||||
|
||||
def get_units(self, source, sr):
|
||||
source = source.unsqueeze(0).to(self.device)
|
||||
with torch.inference_mode():
|
||||
units = self.hubert_soft.units(source)
|
||||
return units
|
||||
|
||||
|
||||
def get_unit_pitch(self, in_path, tran):
|
||||
source, sr = torchaudio.load(in_path)
|
||||
source = torchaudio.functional.resample(source, sr, 16000)
|
||||
if len(source.shape) == 2 and source.shape[1] >= 2:
|
||||
source = torch.mean(source, dim=0).unsqueeze(0)
|
||||
soft = self.get_units(source, sr).squeeze(0).cpu().numpy()
|
||||
f0_coarse, f0 = get_f0(source.cpu().numpy()[0], soft.shape[0]*2, tran)
|
||||
return soft, f0
|
||||
|
||||
def infer(self, speaker_id, tran, raw_path):
|
||||
speaker_id = self.speakers[speaker_id]
|
||||
sid = torch.LongTensor([int(speaker_id)]).to(self.device).unsqueeze(0)
|
||||
soft, pitch = self.get_unit_pitch(raw_path, tran)
|
||||
f0 = torch.FloatTensor(clean_pitch(pitch)).unsqueeze(0).to(self.device)
|
||||
stn_tst = torch.FloatTensor(soft)
|
||||
with torch.no_grad():
|
||||
x_tst = stn_tst.unsqueeze(0).to(self.device)
|
||||
x_tst = torch.repeat_interleave(x_tst, repeats=2, dim=1).transpose(1, 2)
|
||||
audio = self.SVCVITS.infer(x_tst, f0=f0, g=sid)[0,0].data.float()
|
||||
return audio, audio.shape[-1]
|
||||
|
||||
def inference(self,srcaudio,chara,tran,slice_db):
|
||||
sampling_rate, audio = srcaudio
|
||||
audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
|
||||
if len(audio.shape) > 1:
|
||||
audio = librosa.to_mono(audio.transpose(1, 0))
|
||||
if sampling_rate != 16000:
|
||||
audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
|
||||
soundfile.write("tmpwav.wav", audio, 16000, format="wav")
|
||||
chunks = slicer.cut("tmpwav.wav", db_thresh=slice_db)
|
||||
audio_data, audio_sr = slicer.chunks2audio("tmpwav.wav", chunks)
|
||||
audio = []
|
||||
for (slice_tag, data) in audio_data:
|
||||
length = int(np.ceil(len(data) / audio_sr * self.hps.data.sampling_rate))
|
||||
raw_path = io.BytesIO()
|
||||
soundfile.write(raw_path, data, audio_sr, format="wav")
|
||||
raw_path.seek(0)
|
||||
if slice_tag:
|
||||
_audio = np.zeros(length)
|
||||
else:
|
||||
out_audio, out_sr = self.infer(chara, tran, raw_path)
|
||||
_audio = out_audio.cpu().numpy()
|
||||
audio.extend(list(_audio))
|
||||
audio = (np.array(audio) * 32768.0).astype('int16')
|
||||
return (self.hps.data.sampling_rate,audio)
|
||||
@ -0,0 +1,142 @@
|
||||
import librosa
|
||||
import torch
|
||||
import torchaudio
|
||||
|
||||
|
||||
class Slicer:
|
||||
def __init__(self,
|
||||
sr: int,
|
||||
threshold: float = -40.,
|
||||
min_length: int = 5000,
|
||||
min_interval: int = 300,
|
||||
hop_size: int = 20,
|
||||
max_sil_kept: int = 5000):
|
||||
if not min_length >= min_interval >= hop_size:
|
||||
raise ValueError('The following condition must be satisfied: min_length >= min_interval >= hop_size')
|
||||
if not max_sil_kept >= hop_size:
|
||||
raise ValueError('The following condition must be satisfied: max_sil_kept >= hop_size')
|
||||
min_interval = sr * min_interval / 1000
|
||||
self.threshold = 10 ** (threshold / 20.)
|
||||
self.hop_size = round(sr * hop_size / 1000)
|
||||
self.win_size = min(round(min_interval), 4 * self.hop_size)
|
||||
self.min_length = round(sr * min_length / 1000 / self.hop_size)
|
||||
self.min_interval = round(min_interval / self.hop_size)
|
||||
self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size)
|
||||
|
||||
def _apply_slice(self, waveform, begin, end):
|
||||
if len(waveform.shape) > 1:
|
||||
return waveform[:, begin * self.hop_size: min(waveform.shape[1], end * self.hop_size)]
|
||||
else:
|
||||
return waveform[begin * self.hop_size: min(waveform.shape[0], end * self.hop_size)]
|
||||
|
||||
# @timeit
|
||||
def slice(self, waveform):
|
||||
if len(waveform.shape) > 1:
|
||||
samples = librosa.to_mono(waveform)
|
||||
else:
|
||||
samples = waveform
|
||||
if samples.shape[0] <= self.min_length:
|
||||
return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}}
|
||||
rms_list = librosa.feature.rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0)
|
||||
sil_tags = []
|
||||
silence_start = None
|
||||
clip_start = 0
|
||||
for i, rms in enumerate(rms_list):
|
||||
# Keep looping while frame is silent.
|
||||
if rms < self.threshold:
|
||||
# Record start of silent frames.
|
||||
if silence_start is None:
|
||||
silence_start = i
|
||||
continue
|
||||
# Keep looping while frame is not silent and silence start has not been recorded.
|
||||
if silence_start is None:
|
||||
continue
|
||||
# Clear recorded silence start if interval is not enough or clip is too short
|
||||
is_leading_silence = silence_start == 0 and i > self.max_sil_kept
|
||||
need_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_length
|
||||
if not is_leading_silence and not need_slice_middle:
|
||||
silence_start = None
|
||||
continue
|
||||
# Need slicing. Record the range of silent frames to be removed.
|
||||
if i - silence_start <= self.max_sil_kept:
|
||||
pos = rms_list[silence_start: i + 1].argmin() + silence_start
|
||||
if silence_start == 0:
|
||||
sil_tags.append((0, pos))
|
||||
else:
|
||||
sil_tags.append((pos, pos))
|
||||
clip_start = pos
|
||||
elif i - silence_start <= self.max_sil_kept * 2:
|
||||
pos = rms_list[i - self.max_sil_kept: silence_start + self.max_sil_kept + 1].argmin()
|
||||
pos += i - self.max_sil_kept
|
||||
pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start
|
||||
pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept
|
||||
if silence_start == 0:
|
||||
sil_tags.append((0, pos_r))
|
||||
clip_start = pos_r
|
||||
else:
|
||||
sil_tags.append((min(pos_l, pos), max(pos_r, pos)))
|
||||
clip_start = max(pos_r, pos)
|
||||
else:
|
||||
pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start
|
||||
pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept
|
||||
if silence_start == 0:
|
||||
sil_tags.append((0, pos_r))
|
||||
else:
|
||||
sil_tags.append((pos_l, pos_r))
|
||||
clip_start = pos_r
|
||||
silence_start = None
|
||||
# Deal with trailing silence.
|
||||
total_frames = rms_list.shape[0]
|
||||
if silence_start is not None and total_frames - silence_start >= self.min_interval:
|
||||
silence_end = min(total_frames, silence_start + self.max_sil_kept)
|
||||
pos = rms_list[silence_start: silence_end + 1].argmin() + silence_start
|
||||
sil_tags.append((pos, total_frames + 1))
|
||||
# Apply and return slices.
|
||||
if len(sil_tags) == 0:
|
||||
return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}}
|
||||
else:
|
||||
chunks = []
|
||||
# 第一段静音并非从头开始，补上有声片段
|
||||
if sil_tags[0][0]:
|
||||
chunks.append(
|
||||
{"slice": False, "split_time": f"0,{min(waveform.shape[0], sil_tags[0][0] * self.hop_size)}"})
|
||||
for i in range(0, len(sil_tags)):
|
||||
# 标识有声片段（跳过第一段）
|
||||
if i:
|
||||
chunks.append({"slice": False,
|
||||
"split_time": f"{sil_tags[i - 1][1] * self.hop_size},{min(waveform.shape[0], sil_tags[i][0] * self.hop_size)}"})
|
||||
# 标识所有静音片段
|
||||
chunks.append({"slice": True,
|
||||
"split_time": f"{sil_tags[i][0] * self.hop_size},{min(waveform.shape[0], sil_tags[i][1] * self.hop_size)}"})
|
||||
# 最后一段静音并非结尾，补上结尾片段
|
||||
if sil_tags[-1][1] * self.hop_size < len(waveform):
|
||||
chunks.append({"slice": False, "split_time": f"{sil_tags[-1][1] * self.hop_size},{len(waveform)}"})
|
||||
chunk_dict = {}
|
||||
for i in range(len(chunks)):
|
||||
chunk_dict[str(i)] = chunks[i]
|
||||
return chunk_dict
|
||||
|
||||
|
||||
def cut(audio_path, db_thresh=-30, min_len=5000):
|
||||
audio, sr = librosa.load(audio_path, sr=None)
|
||||
slicer = Slicer(
|
||||
sr=sr,
|
||||
threshold=db_thresh,
|
||||
min_length=min_len
|
||||
)
|
||||
chunks = slicer.slice(audio)
|
||||
return chunks
|
||||
|
||||
|
||||
def chunks2audio(audio_path, chunks):
|
||||
chunks = dict(chunks)
|
||||
audio, sr = torchaudio.load(audio_path)
|
||||
if len(audio.shape) == 2 and audio.shape[1] >= 2:
|
||||
audio = torch.mean(audio, dim=0).unsqueeze(0)
|
||||
audio = audio.cpu().numpy()[0]
|
||||
result = []
|
||||
for k, v in chunks.items():
|
||||
tag = v["split_time"].split(",")
|
||||
if tag[0] != tag[1]:
|
||||
result.append((v["slice"], audio[int(tag[0]):int(tag[1])]))
|
||||
return result, sr
|
||||
@ -0,0 +1,61 @@
|
||||
import torch
|
||||
from torch.nn import functional as F
|
||||
|
||||
import commons
|
||||
|
||||
|
||||
def feature_loss(fmap_r, fmap_g):
|
||||
loss = 0
|
||||
for dr, dg in zip(fmap_r, fmap_g):
|
||||
for rl, gl in zip(dr, dg):
|
||||
rl = rl.float().detach()
|
||||
gl = gl.float()
|
||||
loss += torch.mean(torch.abs(rl - gl))
|
||||
|
||||
return loss * 2
|
||||
|
||||
|
||||
def discriminator_loss(disc_real_outputs, disc_generated_outputs):
|
||||
loss = 0
|
||||
r_losses = []
|
||||
g_losses = []
|
||||
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
|
||||
dr = dr.float()
|
||||
dg = dg.float()
|
||||
r_loss = torch.mean((1-dr)**2)
|
||||
g_loss = torch.mean(dg**2)
|
||||
loss += (r_loss + g_loss)
|
||||
r_losses.append(r_loss.item())
|
||||
g_losses.append(g_loss.item())
|
||||
|
||||
return loss, r_losses, g_losses
|
||||
|
||||
|
||||
def generator_loss(disc_outputs):
|
||||
loss = 0
|
||||
gen_losses = []
|
||||
for dg in disc_outputs:
|
||||
dg = dg.float()
|
||||
l = torch.mean((1-dg)**2)
|
||||
gen_losses.append(l)
|
||||
loss += l
|
||||
|
||||
return loss, gen_losses
|
||||
|
||||
|
||||
def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
|
||||
"""
|
||||
z_p, logs_q: [b, h, t_t]
|
||||
m_p, logs_p: [b, h, t_t]
|
||||
"""
|
||||
z_p = z_p.float()
|
||||
logs_q = logs_q.float()
|
||||
m_p = m_p.float()
|
||||
logs_p = logs_p.float()
|
||||
z_mask = z_mask.float()
|
||||
#print(logs_p)
|
||||
kl = logs_p - logs_q - 0.5
|
||||
kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
|
||||
kl = torch.sum(kl * z_mask)
|
||||
l = kl / torch.sum(z_mask)
|
||||
return l
|
||||
@ -0,0 +1,112 @@
|
||||
import math
|
||||
import os
|
||||
import random
|
||||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
import torch.utils.data
|
||||
import numpy as np
|
||||
import librosa
|
||||
import librosa.util as librosa_util
|
||||
from librosa.util import normalize, pad_center, tiny
|
||||
from scipy.signal import get_window
|
||||
from scipy.io.wavfile import read
|
||||
from librosa.filters import mel as librosa_mel_fn
|
||||
|
||||
MAX_WAV_VALUE = 32768.0
|
||||
|
||||
|
||||
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
|
||||
"""
|
||||
PARAMS
|
||||
------
|
||||
C: compression factor
|
||||
"""
|
||||
return torch.log(torch.clamp(x, min=clip_val) * C)
|
||||
|
||||
|
||||
def dynamic_range_decompression_torch(x, C=1):
|
||||
"""
|
||||
PARAMS
|
||||
------
|
||||
C: compression factor used to compress
|
||||
"""
|
||||
return torch.exp(x) / C
|
||||
|
||||
|
||||
def spectral_normalize_torch(magnitudes):
|
||||
output = dynamic_range_compression_torch(magnitudes)
|
||||
return output
|
||||
|
||||
|
||||
def spectral_de_normalize_torch(magnitudes):
|
||||
output = dynamic_range_decompression_torch(magnitudes)
|
||||
return output
|
||||
|
||||
|
||||
mel_basis = {}
|
||||
hann_window = {}
|
||||
|
||||
|
||||
def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
|
||||
if torch.min(y) < -1.:
|
||||
print('min value is ', torch.min(y))
|
||||
if torch.max(y) > 1.:
|
||||
print('max value is ', torch.max(y))
|
||||
|
||||
global hann_window
|
||||
dtype_device = str(y.dtype) + '_' + str(y.device)
|
||||
wnsize_dtype_device = str(win_size) + '_' + dtype_device
|
||||
if wnsize_dtype_device not in hann_window:
|
||||
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
|
||||
|
||||
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
|
||||
y = y.squeeze(1)
|
||||
|
||||
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
|
||||
center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
|
||||
|
||||
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
|
||||
return spec
|
||||
|
||||
|
||||
def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
|
||||
global mel_basis
|
||||
dtype_device = str(spec.dtype) + '_' + str(spec.device)
|
||||
fmax_dtype_device = str(fmax) + '_' + dtype_device
|
||||
if fmax_dtype_device not in mel_basis:
|
||||
mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
|
||||
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
|
||||
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
|
||||
spec = spectral_normalize_torch(spec)
|
||||
return spec
|
||||
|
||||
|
||||
def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
|
||||
if torch.min(y) < -1.:
|
||||
print('min value is ', torch.min(y))
|
||||
if torch.max(y) > 1.:
|
||||
print('max value is ', torch.max(y))
|
||||
|
||||
global mel_basis, hann_window
|
||||
dtype_device = str(y.dtype) + '_' + str(y.device)
|
||||
fmax_dtype_device = str(fmax) + '_' + dtype_device
|
||||
wnsize_dtype_device = str(win_size) + '_' + dtype_device
|
||||
if fmax_dtype_device not in mel_basis:
|
||||
mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
|
||||
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
|
||||
if wnsize_dtype_device not in hann_window:
|
||||
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
|
||||
|
||||
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
|
||||
y = y.squeeze(1)
|
||||
|
||||
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
|
||||
center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
|
||||
|
||||
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
|
||||
|
||||
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
|
||||
spec = spectral_normalize_torch(spec)
|
||||
|
||||
return spec
|
||||
@ -0,0 +1,328 @@
|
||||
import copy
|
||||
import math
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
import attentions
|
||||
import commons
|
||||
import modules
|
||||
|
||||
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
|
||||
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
|
||||
from commons import init_weights, get_padding
|
||||
from vdecoder.hifigan.models import Generator
|
||||
from utils import f0_to_coarse
|
||||
|
||||
class ResidualCouplingBlock(nn.Module):
|
||||
def __init__(self,
|
||||
channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
n_flows=4,
|
||||
gin_channels=0):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.n_flows = n_flows
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.flows = nn.ModuleList()
|
||||
for i in range(n_flows):
|
||||
self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
|
||||
self.flows.append(modules.Flip())
|
||||
|
||||
def forward(self, x, x_mask, g=None, reverse=False):
|
||||
if not reverse:
|
||||
for flow in self.flows:
|
||||
x, _ = flow(x, x_mask, g=g, reverse=reverse)
|
||||
else:
|
||||
for flow in reversed(self.flows):
|
||||
x = flow(x, x_mask, g=g, reverse=reverse)
|
||||
return x
|
||||
|
||||
|
||||
class Encoder(nn.Module):
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
gin_channels=0):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.out_channels = out_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
|
||||
self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
|
||||
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
||||
|
||||
def forward(self, x, x_lengths, g=None):
|
||||
# print(x.shape,x_lengths.shape)
|
||||
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
||||
x = self.pre(x) * x_mask
|
||||
x = self.enc(x, x_mask, g=g)
|
||||
stats = self.proj(x) * x_mask
|
||||
m, logs = torch.split(stats, self.out_channels, dim=1)
|
||||
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
|
||||
return z, m, logs, x_mask
|
||||
|
||||
|
||||
class TextEncoder(nn.Module):
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
gin_channels=0,
|
||||
filter_channels=None,
|
||||
n_heads=None,
|
||||
p_dropout=None):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.out_channels = out_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.gin_channels = gin_channels
|
||||
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
|
||||
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
||||
self.f0_emb = nn.Embedding(256, hidden_channels)
|
||||
|
||||
self.enc_ = attentions.Encoder(
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout)
|
||||
|
||||
def forward(self, x, x_lengths, f0=None):
|
||||
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
||||
x = self.pre(x) * x_mask
|
||||
x = x + self.f0_emb(f0.long()).transpose(1,2)
|
||||
x = self.enc_(x * x_mask, x_mask)
|
||||
stats = self.proj(x) * x_mask
|
||||
m, logs = torch.split(stats, self.out_channels, dim=1)
|
||||
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
|
||||
|
||||
return z, m, logs, x_mask
|
||||
|
||||
|
||||
|
||||
class DiscriminatorP(torch.nn.Module):
|
||||
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
|
||||
super(DiscriminatorP, self).__init__()
|
||||
self.period = period
|
||||
self.use_spectral_norm = use_spectral_norm
|
||||
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
||||
self.convs = nn.ModuleList([
|
||||
norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
||||
norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
||||
norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
||||
norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
||||
norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
|
||||
])
|
||||
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
|
||||
|
||||
def forward(self, x):
|
||||
fmap = []
|
||||
|
||||
# 1d to 2d
|
||||
b, c, t = x.shape
|
||||
if t % self.period != 0: # pad first
|
||||
n_pad = self.period - (t % self.period)
|
||||
x = F.pad(x, (0, n_pad), "reflect")
|
||||
t = t + n_pad
|
||||
x = x.view(b, c, t // self.period, self.period)
|
||||
|
||||
for l in self.convs:
|
||||
x = l(x)
|
||||
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
||||
fmap.append(x)
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
x = torch.flatten(x, 1, -1)
|
||||
|
||||
return x, fmap
|
||||
|
||||
|
||||
class DiscriminatorS(torch.nn.Module):
|
||||
def __init__(self, use_spectral_norm=False):
|
||||
super(DiscriminatorS, self).__init__()
|
||||
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
||||
self.convs = nn.ModuleList([
|
||||
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
|
||||
norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
|
||||
norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
|
||||
norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
|
||||
norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
|
||||
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
|
||||
])
|
||||
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
|
||||
|
||||
def forward(self, x):
|
||||
fmap = []
|
||||
|
||||
for l in self.convs:
|
||||
x = l(x)
|
||||
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
||||
fmap.append(x)
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
x = torch.flatten(x, 1, -1)
|
||||
|
||||
return x, fmap
|
||||
|
||||
|
||||
class MultiPeriodDiscriminator(torch.nn.Module):
|
||||
def __init__(self, use_spectral_norm=False):
|
||||
super(MultiPeriodDiscriminator, self).__init__()
|
||||
periods = [2,3,5,7,11]
|
||||
|
||||
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
|
||||
discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
|
||||
self.discriminators = nn.ModuleList(discs)
|
||||
|
||||
def forward(self, y, y_hat):
|
||||
y_d_rs = []
|
||||
y_d_gs = []
|
||||
fmap_rs = []
|
||||
fmap_gs = []
|
||||
for i, d in enumerate(self.discriminators):
|
||||
y_d_r, fmap_r = d(y)
|
||||
y_d_g, fmap_g = d(y_hat)
|
||||
y_d_rs.append(y_d_r)
|
||||
y_d_gs.append(y_d_g)
|
||||
fmap_rs.append(fmap_r)
|
||||
fmap_gs.append(fmap_g)
|
||||
|
||||
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
||||
|
||||
|
||||
class SpeakerEncoder(torch.nn.Module):
|
||||
def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
|
||||
super(SpeakerEncoder, self).__init__()
|
||||
self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
|
||||
self.linear = nn.Linear(model_hidden_size, model_embedding_size)
|
||||
self.relu = nn.ReLU()
|
||||
|
||||
def forward(self, mels):
|
||||
self.lstm.flatten_parameters()
|
||||
_, (hidden, _) = self.lstm(mels)
|
||||
embeds_raw = self.relu(self.linear(hidden[-1]))
|
||||
return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
|
||||
|
||||
def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
|
||||
mel_slices = []
|
||||
for i in range(0, total_frames-partial_frames, partial_hop):
|
||||
mel_range = torch.arange(i, i+partial_frames)
|
||||
mel_slices.append(mel_range)
|
||||
|
||||
return mel_slices
|
||||
|
||||
def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
|
||||
mel_len = mel.size(1)
|
||||
last_mel = mel[:,-partial_frames:]
|
||||
|
||||
if mel_len > partial_frames:
|
||||
mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
|
||||
mels = list(mel[:,s] for s in mel_slices)
|
||||
mels.append(last_mel)
|
||||
mels = torch.stack(tuple(mels), 0).squeeze(1)
|
||||
|
||||
with torch.no_grad():
|
||||
partial_embeds = self(mels)
|
||||
embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
|
||||
#embed = embed / torch.linalg.norm(embed, 2)
|
||||
else:
|
||||
with torch.no_grad():
|
||||
embed = self(last_mel)
|
||||
|
||||
return embed
|
||||
|
||||
|
||||
class SynthesizerTrn(nn.Module):
|
||||
"""
|
||||
Synthesizer for Training
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
spec_channels,
|
||||
segment_size,
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
resblock,
|
||||
resblock_kernel_sizes,
|
||||
resblock_dilation_sizes,
|
||||
upsample_rates,
|
||||
upsample_initial_channel,
|
||||
upsample_kernel_sizes,
|
||||
gin_channels,
|
||||
ssl_dim,
|
||||
n_speakers,
|
||||
**kwargs):
|
||||
|
||||
super().__init__()
|
||||
self.spec_channels = spec_channels
|
||||
self.inter_channels = inter_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.n_heads = n_heads
|
||||
self.n_layers = n_layers
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.resblock = resblock
|
||||
self.resblock_kernel_sizes = resblock_kernel_sizes
|
||||
self.resblock_dilation_sizes = resblock_dilation_sizes
|
||||
self.upsample_rates = upsample_rates
|
||||
self.upsample_initial_channel = upsample_initial_channel
|
||||
self.upsample_kernel_sizes = upsample_kernel_sizes
|
||||
self.segment_size = segment_size
|
||||
self.gin_channels = gin_channels
|
||||
self.ssl_dim = ssl_dim
|
||||
self.emb_g = nn.Embedding(n_speakers, gin_channels)
|
||||
|
||||
self.enc_p_ = TextEncoder(ssl_dim, inter_channels, hidden_channels, 5, 1, 16,0, filter_channels, n_heads, p_dropout)
|
||||
hps = {
|
||||
"sampling_rate": 32000,
|
||||
"inter_channels": 192,
|
||||
"resblock": "1",
|
||||
"resblock_kernel_sizes": [3, 7, 11],
|
||||
"resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
|
||||
"upsample_rates": [10, 8, 2, 2],
|
||||
"upsample_initial_channel": 512,
|
||||
"upsample_kernel_sizes": [16, 16, 4, 4],
|
||||
"gin_channels": 256,
|
||||
}
|
||||
self.dec = Generator(h=hps)
|
||||
self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
|
||||
self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
|
||||
|
||||
def forward(self, c, c_lengths, f0, g=None):
|
||||
g = self.emb_g(g.unsqueeze(0)).transpose(1,2)
|
||||
z_p, m_p, logs_p, c_mask = self.enc_p_(c.transpose(1,2), c_lengths, f0=f0_to_coarse(f0))
|
||||
z = self.flow(z_p, c_mask, g=g, reverse=True)
|
||||
o = self.dec(z * c_mask, g=g, f0=f0.float())
|
||||
return o
|
||||
|
||||
@ -0,0 +1,328 @@
|
||||
import copy
|
||||
import math
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
import attentions
|
||||
import commons
|
||||
import modules
|
||||
|
||||
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
|
||||
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
|
||||
from commons import init_weights, get_padding
|
||||
from vdecoder.hifigan.models import Generator
|
||||
from utils import f0_to_coarse
|
||||
|
||||
class ResidualCouplingBlock(nn.Module):
|
||||
def __init__(self,
|
||||
channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
n_flows=4,
|
||||
gin_channels=0):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.n_flows = n_flows
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.flows = nn.ModuleList()
|
||||
for i in range(n_flows):
|
||||
self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
|
||||
self.flows.append(modules.Flip())
|
||||
|
||||
def forward(self, x, x_mask, g=None, reverse=False):
|
||||
if not reverse:
|
||||
for flow in self.flows:
|
||||
x, _ = flow(x, x_mask, g=g, reverse=reverse)
|
||||
else:
|
||||
for flow in reversed(self.flows):
|
||||
x = flow(x, x_mask, g=g, reverse=reverse)
|
||||
return x
|
||||
|
||||
|
||||
class Encoder(nn.Module):
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
gin_channels=0):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.out_channels = out_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
|
||||
self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
|
||||
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
||||
|
||||
def forward(self, x, x_lengths, g=None):
|
||||
# print(x.shape,x_lengths.shape)
|
||||
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
||||
x = self.pre(x) * x_mask
|
||||
x = self.enc(x, x_mask, g=g)
|
||||
stats = self.proj(x) * x_mask
|
||||
m, logs = torch.split(stats, self.out_channels, dim=1)
|
||||
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
|
||||
return z, m, logs, x_mask
|
||||
|
||||
|
||||
class TextEncoder(nn.Module):
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
gin_channels=0,
|
||||
filter_channels=None,
|
||||
n_heads=None,
|
||||
p_dropout=None):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.out_channels = out_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.gin_channels = gin_channels
|
||||
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
|
||||
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
||||
self.f0_emb = nn.Embedding(256, hidden_channels)
|
||||
|
||||
self.enc_ = attentions.Encoder(
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout)
|
||||
|
||||
def forward(self, x, x_lengths, f0=None):
|
||||
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
||||
x = self.pre(x) * x_mask
|
||||
x = x + self.f0_emb(f0.long()).transpose(1,2)
|
||||
x = self.enc_(x * x_mask, x_mask)
|
||||
stats = self.proj(x) * x_mask
|
||||
m, logs = torch.split(stats, self.out_channels, dim=1)
|
||||
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
|
||||
|
||||
return z, m, logs, x_mask
|
||||
|
||||
|
||||
|
||||
class DiscriminatorP(torch.nn.Module):
|
||||
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
|
||||
super(DiscriminatorP, self).__init__()
|
||||
self.period = period
|
||||
self.use_spectral_norm = use_spectral_norm
|
||||
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
||||
self.convs = nn.ModuleList([
|
||||
norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
||||
norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
||||
norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
||||
norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
||||
norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
|
||||
])
|
||||
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
|
||||
|
||||
def forward(self, x):
|
||||
fmap = []
|
||||
|
||||
# 1d to 2d
|
||||
b, c, t = x.shape
|
||||
if t % self.period != 0: # pad first
|
||||
n_pad = self.period - (t % self.period)
|
||||
x = F.pad(x, (0, n_pad), "reflect")
|
||||
t = t + n_pad
|
||||
x = x.view(b, c, t // self.period, self.period)
|
||||
|
||||
for l in self.convs:
|
||||
x = l(x)
|
||||
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
||||
fmap.append(x)
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
x = torch.flatten(x, 1, -1)
|
||||
|
||||
return x, fmap
|
||||
|
||||
|
||||
class DiscriminatorS(torch.nn.Module):
|
||||
def __init__(self, use_spectral_norm=False):
|
||||
super(DiscriminatorS, self).__init__()
|
||||
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
||||
self.convs = nn.ModuleList([
|
||||
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
|
||||
norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
|
||||
norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
|
||||
norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
|
||||
norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
|
||||
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
|
||||
])
|
||||
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
|
||||
|
||||
def forward(self, x):
|
||||
fmap = []
|
||||
|
||||
for l in self.convs:
|
||||
x = l(x)
|
||||
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
||||
fmap.append(x)
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
x = torch.flatten(x, 1, -1)
|
||||
|
||||
return x, fmap
|
||||
|
||||
|
||||
class MultiPeriodDiscriminator(torch.nn.Module):
|
||||
def __init__(self, use_spectral_norm=False):
|
||||
super(MultiPeriodDiscriminator, self).__init__()
|
||||
periods = [2,3,5,7,11]
|
||||
|
||||
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
|
||||
discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
|
||||
self.discriminators = nn.ModuleList(discs)
|
||||
|
||||
def forward(self, y, y_hat):
|
||||
y_d_rs = []
|
||||
y_d_gs = []
|
||||
fmap_rs = []
|
||||
fmap_gs = []
|
||||
for i, d in enumerate(self.discriminators):
|
||||
y_d_r, fmap_r = d(y)
|
||||
y_d_g, fmap_g = d(y_hat)
|
||||
y_d_rs.append(y_d_r)
|
||||
y_d_gs.append(y_d_g)
|
||||
fmap_rs.append(fmap_r)
|
||||
fmap_gs.append(fmap_g)
|
||||
|
||||
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
||||
|
||||
|
||||
class SpeakerEncoder(torch.nn.Module):
|
||||
def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
|
||||
super(SpeakerEncoder, self).__init__()
|
||||
self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
|
||||
self.linear = nn.Linear(model_hidden_size, model_embedding_size)
|
||||
self.relu = nn.ReLU()
|
||||
|
||||
def forward(self, mels):
|
||||
self.lstm.flatten_parameters()
|
||||
_, (hidden, _) = self.lstm(mels)
|
||||
embeds_raw = self.relu(self.linear(hidden[-1]))
|
||||
return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
|
||||
|
||||
def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
|
||||
mel_slices = []
|
||||
for i in range(0, total_frames-partial_frames, partial_hop):
|
||||
mel_range = torch.arange(i, i+partial_frames)
|
||||
mel_slices.append(mel_range)
|
||||
|
||||
return mel_slices
|
||||
|
||||
def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
|
||||
mel_len = mel.size(1)
|
||||
last_mel = mel[:,-partial_frames:]
|
||||
|
||||
if mel_len > partial_frames:
|
||||
mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
|
||||
mels = list(mel[:,s] for s in mel_slices)
|
||||
mels.append(last_mel)
|
||||
mels = torch.stack(tuple(mels), 0).squeeze(1)
|
||||
|
||||
with torch.no_grad():
|
||||
partial_embeds = self(mels)
|
||||
embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
|
||||
#embed = embed / torch.linalg.norm(embed, 2)
|
||||
else:
|
||||
with torch.no_grad():
|
||||
embed = self(last_mel)
|
||||
|
||||
return embed
|
||||
|
||||
|
||||
class SynthesizerTrn(nn.Module):
|
||||
"""
|
||||
Synthesizer for Training
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
spec_channels,
|
||||
segment_size,
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
resblock,
|
||||
resblock_kernel_sizes,
|
||||
resblock_dilation_sizes,
|
||||
upsample_rates,
|
||||
upsample_initial_channel,
|
||||
upsample_kernel_sizes,
|
||||
gin_channels,
|
||||
ssl_dim,
|
||||
n_speakers,
|
||||
**kwargs):
|
||||
|
||||
super().__init__()
|
||||
self.spec_channels = spec_channels
|
||||
self.inter_channels = inter_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.n_heads = n_heads
|
||||
self.n_layers = n_layers
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.resblock = resblock
|
||||
self.resblock_kernel_sizes = resblock_kernel_sizes
|
||||
self.resblock_dilation_sizes = resblock_dilation_sizes
|
||||
self.upsample_rates = upsample_rates
|
||||
self.upsample_initial_channel = upsample_initial_channel
|
||||
self.upsample_kernel_sizes = upsample_kernel_sizes
|
||||
self.segment_size = segment_size
|
||||
self.gin_channels = gin_channels
|
||||
self.ssl_dim = ssl_dim
|
||||
self.emb_g = nn.Embedding(n_speakers, gin_channels)
|
||||
|
||||
self.enc_p_ = TextEncoder(ssl_dim, inter_channels, hidden_channels, 5, 1, 16,0, filter_channels, n_heads, p_dropout)
|
||||
hps = {
|
||||
"sampling_rate": 48000,
|
||||
"inter_channels": 192,
|
||||
"resblock": "1",
|
||||
"resblock_kernel_sizes": [3, 7, 11],
|
||||
"resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
|
||||
"upsample_rates": [10, 8, 2, 2],
|
||||
"upsample_initial_channel": 512,
|
||||
"upsample_kernel_sizes": [16, 16, 4, 4],
|
||||
"gin_channels": 256,
|
||||
}
|
||||
self.dec = Generator(h=hps)
|
||||
self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
|
||||
self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
|
||||
|
||||
def forward(self, c, c_lengths, f0, g=None):
|
||||
g = self.emb_g(g.unsqueeze(0)).transpose(1,2)
|
||||
z_p, m_p, logs_p, c_mask = self.enc_p_(c.transpose(1,2), c_lengths, f0=f0_to_coarse(f0))
|
||||
z = self.flow(z_p, c_mask, g=g, reverse=True)
|
||||
o = self.dec(z * c_mask, g=g, f0=f0.float())
|
||||
return o
|
||||
|
||||
@ -0,0 +1,351 @@
|
||||
import copy
|
||||
import math
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
import attentions
|
||||
import commons
|
||||
import modules
|
||||
|
||||
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
|
||||
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
|
||||
from commons import init_weights, get_padding
|
||||
from vdecoder.hifigan.models import Generator
|
||||
from utils import f0_to_coarse
|
||||
|
||||
class ResidualCouplingBlock(nn.Module):
|
||||
def __init__(self,
|
||||
channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
n_flows=4,
|
||||
gin_channels=0):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.n_flows = n_flows
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.flows = nn.ModuleList()
|
||||
for i in range(n_flows):
|
||||
self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
|
||||
self.flows.append(modules.Flip())
|
||||
|
||||
def forward(self, x, x_mask, g=None, reverse=False):
|
||||
if not reverse:
|
||||
for flow in self.flows:
|
||||
x, _ = flow(x, x_mask, g=g, reverse=reverse)
|
||||
else:
|
||||
for flow in reversed(self.flows):
|
||||
x = flow(x, x_mask, g=g, reverse=reverse)
|
||||
return x
|
||||
|
||||
|
||||
class Encoder(nn.Module):
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
gin_channels=0):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.out_channels = out_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
|
||||
self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
|
||||
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
||||
|
||||
def forward(self, x, x_lengths, g=None):
|
||||
# print(x.shape,x_lengths.shape)
|
||||
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
||||
x = self.pre(x) * x_mask
|
||||
x = self.enc(x, x_mask, g=g)
|
||||
stats = self.proj(x) * x_mask
|
||||
m, logs = torch.split(stats, self.out_channels, dim=1)
|
||||
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
|
||||
return z, m, logs, x_mask
|
||||
|
||||
|
||||
class TextEncoder(nn.Module):
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
gin_channels=0,
|
||||
filter_channels=None,
|
||||
n_heads=None,
|
||||
p_dropout=None):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.out_channels = out_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.gin_channels = gin_channels
|
||||
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
|
||||
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
||||
self.f0_emb = nn.Embedding(256, hidden_channels)
|
||||
|
||||
self.enc_ = attentions.Encoder(
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout)
|
||||
|
||||
def forward(self, x, x_lengths, f0=None):
|
||||
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
||||
x = self.pre(x) * x_mask
|
||||
x = x + self.f0_emb(f0).transpose(1,2)
|
||||
x = self.enc_(x * x_mask, x_mask)
|
||||
stats = self.proj(x) * x_mask
|
||||
m, logs = torch.split(stats, self.out_channels, dim=1)
|
||||
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
|
||||
|
||||
return z, m, logs, x_mask
|
||||
|
||||
|
||||
|
||||
class DiscriminatorP(torch.nn.Module):
|
||||
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
|
||||
super(DiscriminatorP, self).__init__()
|
||||
self.period = period
|
||||
self.use_spectral_norm = use_spectral_norm
|
||||
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
||||
self.convs = nn.ModuleList([
|
||||
norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
||||
norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
||||
norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
||||
norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
||||
norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
|
||||
])
|
||||
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
|
||||
|
||||
def forward(self, x):
|
||||
fmap = []
|
||||
|
||||
# 1d to 2d
|
||||
b, c, t = x.shape
|
||||
if t % self.period != 0: # pad first
|
||||
n_pad = self.period - (t % self.period)
|
||||
x = F.pad(x, (0, n_pad), "reflect")
|
||||
t = t + n_pad
|
||||
x = x.view(b, c, t // self.period, self.period)
|
||||
|
||||
for l in self.convs:
|
||||
x = l(x)
|
||||
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
||||
fmap.append(x)
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
x = torch.flatten(x, 1, -1)
|
||||
|
||||
return x, fmap
|
||||
|
||||
|
||||
class DiscriminatorS(torch.nn.Module):
|
||||
def __init__(self, use_spectral_norm=False):
|
||||
super(DiscriminatorS, self).__init__()
|
||||
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
||||
self.convs = nn.ModuleList([
|
||||
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
|
||||
norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
|
||||
norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
|
||||
norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
|
||||
norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
|
||||
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
|
||||
])
|
||||
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
|
||||
|
||||
def forward(self, x):
|
||||
fmap = []
|
||||
|
||||
for l in self.convs:
|
||||
x = l(x)
|
||||
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
||||
fmap.append(x)
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
x = torch.flatten(x, 1, -1)
|
||||
|
||||
return x, fmap
|
||||
|
||||
|
||||
class MultiPeriodDiscriminator(torch.nn.Module):
|
||||
def __init__(self, use_spectral_norm=False):
|
||||
super(MultiPeriodDiscriminator, self).__init__()
|
||||
periods = [2,3,5,7,11]
|
||||
|
||||
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
|
||||
discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
|
||||
self.discriminators = nn.ModuleList(discs)
|
||||
|
||||
def forward(self, y, y_hat):
|
||||
y_d_rs = []
|
||||
y_d_gs = []
|
||||
fmap_rs = []
|
||||
fmap_gs = []
|
||||
for i, d in enumerate(self.discriminators):
|
||||
y_d_r, fmap_r = d(y)
|
||||
y_d_g, fmap_g = d(y_hat)
|
||||
y_d_rs.append(y_d_r)
|
||||
y_d_gs.append(y_d_g)
|
||||
fmap_rs.append(fmap_r)
|
||||
fmap_gs.append(fmap_g)
|
||||
|
||||
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
||||
|
||||
|
||||
class SpeakerEncoder(torch.nn.Module):
|
||||
def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
|
||||
super(SpeakerEncoder, self).__init__()
|
||||
self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
|
||||
self.linear = nn.Linear(model_hidden_size, model_embedding_size)
|
||||
self.relu = nn.ReLU()
|
||||
|
||||
def forward(self, mels):
|
||||
self.lstm.flatten_parameters()
|
||||
_, (hidden, _) = self.lstm(mels)
|
||||
embeds_raw = self.relu(self.linear(hidden[-1]))
|
||||
return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
|
||||
|
||||
def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
|
||||
mel_slices = []
|
||||
for i in range(0, total_frames-partial_frames, partial_hop):
|
||||
mel_range = torch.arange(i, i+partial_frames)
|
||||
mel_slices.append(mel_range)
|
||||
|
||||
return mel_slices
|
||||
|
||||
def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
|
||||
mel_len = mel.size(1)
|
||||
last_mel = mel[:,-partial_frames:]
|
||||
|
||||
if mel_len > partial_frames:
|
||||
mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
|
||||
mels = list(mel[:,s] for s in mel_slices)
|
||||
mels.append(last_mel)
|
||||
mels = torch.stack(tuple(mels), 0).squeeze(1)
|
||||
|
||||
with torch.no_grad():
|
||||
partial_embeds = self(mels)
|
||||
embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
|
||||
#embed = embed / torch.linalg.norm(embed, 2)
|
||||
else:
|
||||
with torch.no_grad():
|
||||
embed = self(last_mel)
|
||||
|
||||
return embed
|
||||
|
||||
|
||||
class SynthesizerTrn(nn.Module):
|
||||
"""
|
||||
Synthesizer for Training
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
spec_channels,
|
||||
segment_size,
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
resblock,
|
||||
resblock_kernel_sizes,
|
||||
resblock_dilation_sizes,
|
||||
upsample_rates,
|
||||
upsample_initial_channel,
|
||||
upsample_kernel_sizes,
|
||||
gin_channels,
|
||||
ssl_dim,
|
||||
n_speakers,
|
||||
**kwargs):
|
||||
|
||||
super().__init__()
|
||||
self.spec_channels = spec_channels
|
||||
self.inter_channels = inter_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.n_heads = n_heads
|
||||
self.n_layers = n_layers
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.resblock = resblock
|
||||
self.resblock_kernel_sizes = resblock_kernel_sizes
|
||||
self.resblock_dilation_sizes = resblock_dilation_sizes
|
||||
self.upsample_rates = upsample_rates
|
||||
self.upsample_initial_channel = upsample_initial_channel
|
||||
self.upsample_kernel_sizes = upsample_kernel_sizes
|
||||
self.segment_size = segment_size
|
||||
self.gin_channels = gin_channels
|
||||
self.ssl_dim = ssl_dim
|
||||
self.emb_g = nn.Embedding(n_speakers, gin_channels)
|
||||
|
||||
self.enc_p_ = TextEncoder(ssl_dim, inter_channels, hidden_channels, 5, 1, 16,0, filter_channels, n_heads, p_dropout)
|
||||
hps = {
|
||||
"sampling_rate": 32000,
|
||||
"inter_channels": 192,
|
||||
"resblock": "1",
|
||||
"resblock_kernel_sizes": [3, 7, 11],
|
||||
"resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
|
||||
"upsample_rates": [10, 8, 2, 2],
|
||||
"upsample_initial_channel": 512,
|
||||
"upsample_kernel_sizes": [16, 16, 4, 4],
|
||||
"gin_channels": 256,
|
||||
}
|
||||
self.dec = Generator(h=hps)
|
||||
self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
|
||||
self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
|
||||
|
||||
def forward(self, c, f0, spec, g=None, mel=None, c_lengths=None, spec_lengths=None):
|
||||
if c_lengths == None:
|
||||
c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
|
||||
if spec_lengths == None:
|
||||
spec_lengths = (torch.ones(spec.size(0)) * spec.size(-1)).to(spec.device)
|
||||
|
||||
g = self.emb_g(g).transpose(1,2)
|
||||
|
||||
z_ptemp, m_p, logs_p, _ = self.enc_p_(c, c_lengths, f0=f0_to_coarse(f0))
|
||||
z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g)
|
||||
|
||||
z_p = self.flow(z, spec_mask, g=g)
|
||||
z_slice, pitch_slice, ids_slice = commons.rand_slice_segments_with_pitch(z, f0, spec_lengths, self.segment_size)
|
||||
|
||||
# o = self.dec(z_slice, g=g)
|
||||
o = self.dec(z_slice, g=g, f0=pitch_slice)
|
||||
|
||||
return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
|
||||
|
||||
def infer(self, c, f0, g=None, mel=None, c_lengths=None):
|
||||
if c_lengths == None:
|
||||
c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
|
||||
g = self.emb_g(g).transpose(1,2)
|
||||
|
||||
z_p, m_p, logs_p, c_mask = self.enc_p_(c, c_lengths, f0=f0_to_coarse(f0))
|
||||
z = self.flow(z_p, c_mask, g=g, reverse=True)
|
||||
|
||||
o = self.dec(z * c_mask, g=g, f0=f0)
|
||||
|
||||
return o
|
||||
@ -0,0 +1,342 @@
|
||||
import copy
|
||||
import math
|
||||
import numpy as np
|
||||
import scipy
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
|
||||
from torch.nn.utils import weight_norm, remove_weight_norm
|
||||
|
||||
import commons
|
||||
from commons import init_weights, get_padding
|
||||
|
||||
|
||||
LRELU_SLOPE = 0.1
|
||||
|
||||
|
||||
class LayerNorm(nn.Module):
|
||||
def __init__(self, channels, eps=1e-5):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.eps = eps
|
||||
|
||||
self.gamma = nn.Parameter(torch.ones(channels))
|
||||
self.beta = nn.Parameter(torch.zeros(channels))
|
||||
|
||||
def forward(self, x):
|
||||
x = x.transpose(1, -1)
|
||||
x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
|
||||
return x.transpose(1, -1)
|
||||
|
||||
|
||||
class ConvReluNorm(nn.Module):
|
||||
def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.out_channels = out_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.n_layers = n_layers
|
||||
self.p_dropout = p_dropout
|
||||
assert n_layers > 1, "Number of layers should be larger than 0."
|
||||
|
||||
self.conv_layers = nn.ModuleList()
|
||||
self.norm_layers = nn.ModuleList()
|
||||
self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
|
||||
self.norm_layers.append(LayerNorm(hidden_channels))
|
||||
self.relu_drop = nn.Sequential(
|
||||
nn.ReLU(),
|
||||
nn.Dropout(p_dropout))
|
||||
for _ in range(n_layers-1):
|
||||
self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
|
||||
self.norm_layers.append(LayerNorm(hidden_channels))
|
||||
self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
|
||||
self.proj.weight.data.zero_()
|
||||
self.proj.bias.data.zero_()
|
||||
|
||||
def forward(self, x, x_mask):
|
||||
x_org = x
|
||||
for i in range(self.n_layers):
|
||||
x = self.conv_layers[i](x * x_mask)
|
||||
x = self.norm_layers[i](x)
|
||||
x = self.relu_drop(x)
|
||||
x = x_org + self.proj(x)
|
||||
return x * x_mask
|
||||
|
||||
|
||||
class DDSConv(nn.Module):
|
||||
"""
|
||||
Dialted and Depth-Separable Convolution
|
||||
"""
|
||||
def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.kernel_size = kernel_size
|
||||
self.n_layers = n_layers
|
||||
self.p_dropout = p_dropout
|
||||
|
||||
self.drop = nn.Dropout(p_dropout)
|
||||
self.convs_sep = nn.ModuleList()
|
||||
self.convs_1x1 = nn.ModuleList()
|
||||
self.norms_1 = nn.ModuleList()
|
||||
self.norms_2 = nn.ModuleList()
|
||||
for i in range(n_layers):
|
||||
dilation = kernel_size ** i
|
||||
padding = (kernel_size * dilation - dilation) // 2
|
||||
self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
|
||||
groups=channels, dilation=dilation, padding=padding
|
||||
))
|
||||
self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
|
||||
self.norms_1.append(LayerNorm(channels))
|
||||
self.norms_2.append(LayerNorm(channels))
|
||||
|
||||
def forward(self, x, x_mask, g=None):
|
||||
if g is not None:
|
||||
x = x + g
|
||||
for i in range(self.n_layers):
|
||||
y = self.convs_sep[i](x * x_mask)
|
||||
y = self.norms_1[i](y)
|
||||
y = F.gelu(y)
|
||||
y = self.convs_1x1[i](y)
|
||||
y = self.norms_2[i](y)
|
||||
y = F.gelu(y)
|
||||
y = self.drop(y)
|
||||
x = x + y
|
||||
return x * x_mask
|
||||
|
||||
|
||||
class WN(torch.nn.Module):
|
||||
def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
|
||||
super(WN, self).__init__()
|
||||
assert(kernel_size % 2 == 1)
|
||||
self.hidden_channels =hidden_channels
|
||||
self.kernel_size = kernel_size,
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.gin_channels = gin_channels
|
||||
self.p_dropout = p_dropout
|
||||
|
||||
self.in_layers = torch.nn.ModuleList()
|
||||
self.res_skip_layers = torch.nn.ModuleList()
|
||||
self.drop = nn.Dropout(p_dropout)
|
||||
|
||||
if gin_channels != 0:
|
||||
cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
|
||||
self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
|
||||
|
||||
for i in range(n_layers):
|
||||
dilation = dilation_rate ** i
|
||||
padding = int((kernel_size * dilation - dilation) / 2)
|
||||
in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
|
||||
dilation=dilation, padding=padding)
|
||||
in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
|
||||
self.in_layers.append(in_layer)
|
||||
|
||||
# last one is not necessary
|
||||
if i < n_layers - 1:
|
||||
res_skip_channels = 2 * hidden_channels
|
||||
else:
|
||||
res_skip_channels = hidden_channels
|
||||
|
||||
res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
|
||||
res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
|
||||
self.res_skip_layers.append(res_skip_layer)
|
||||
|
||||
def forward(self, x, x_mask, g=None, **kwargs):
|
||||
output = torch.zeros_like(x)
|
||||
n_channels_tensor = torch.IntTensor([self.hidden_channels])
|
||||
|
||||
if g is not None:
|
||||
g = self.cond_layer(g)
|
||||
|
||||
for i in range(self.n_layers):
|
||||
x_in = self.in_layers[i](x)
|
||||
if g is not None:
|
||||
cond_offset = i * 2 * self.hidden_channels
|
||||
g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
|
||||
else:
|
||||
g_l = torch.zeros_like(x_in)
|
||||
|
||||
acts = commons.fused_add_tanh_sigmoid_multiply(
|
||||
x_in,
|
||||
g_l,
|
||||
n_channels_tensor)
|
||||
acts = self.drop(acts)
|
||||
|
||||
res_skip_acts = self.res_skip_layers[i](acts)
|
||||
if i < self.n_layers - 1:
|
||||
res_acts = res_skip_acts[:,:self.hidden_channels,:]
|
||||
x = (x + res_acts) * x_mask
|
||||
output = output + res_skip_acts[:,self.hidden_channels:,:]
|
||||
else:
|
||||
output = output + res_skip_acts
|
||||
return output * x_mask
|
||||
|
||||
def remove_weight_norm(self):
|
||||
if self.gin_channels != 0:
|
||||
torch.nn.utils.remove_weight_norm(self.cond_layer)
|
||||
for l in self.in_layers:
|
||||
torch.nn.utils.remove_weight_norm(l)
|
||||
for l in self.res_skip_layers:
|
||||
torch.nn.utils.remove_weight_norm(l)
|
||||
|
||||
|
||||
class ResBlock1(torch.nn.Module):
|
||||
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
|
||||
super(ResBlock1, self).__init__()
|
||||
self.convs1 = nn.ModuleList([
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
||||
padding=get_padding(kernel_size, dilation[0]))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
||||
padding=get_padding(kernel_size, dilation[1]))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
|
||||
padding=get_padding(kernel_size, dilation[2])))
|
||||
])
|
||||
self.convs1.apply(init_weights)
|
||||
|
||||
self.convs2 = nn.ModuleList([
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
||||
padding=get_padding(kernel_size, 1))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
||||
padding=get_padding(kernel_size, 1))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
||||
padding=get_padding(kernel_size, 1)))
|
||||
])
|
||||
self.convs2.apply(init_weights)
|
||||
|
||||
def forward(self, x, x_mask=None):
|
||||
for c1, c2 in zip(self.convs1, self.convs2):
|
||||
xt = F.leaky_relu(x, LRELU_SLOPE)
|
||||
if x_mask is not None:
|
||||
xt = xt * x_mask
|
||||
xt = c1(xt)
|
||||
xt = F.leaky_relu(xt, LRELU_SLOPE)
|
||||
if x_mask is not None:
|
||||
xt = xt * x_mask
|
||||
xt = c2(xt)
|
||||
x = xt + x
|
||||
if x_mask is not None:
|
||||
x = x * x_mask
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
for l in self.convs1:
|
||||
remove_weight_norm(l)
|
||||
for l in self.convs2:
|
||||
remove_weight_norm(l)
|
||||
|
||||
|
||||
class ResBlock2(torch.nn.Module):
|
||||
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
|
||||
super(ResBlock2, self).__init__()
|
||||
self.convs = nn.ModuleList([
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
||||
padding=get_padding(kernel_size, dilation[0]))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
||||
padding=get_padding(kernel_size, dilation[1])))
|
||||
])
|
||||
self.convs.apply(init_weights)
|
||||
|
||||
def forward(self, x, x_mask=None):
|
||||
for c in self.convs:
|
||||
xt = F.leaky_relu(x, LRELU_SLOPE)
|
||||
if x_mask is not None:
|
||||
xt = xt * x_mask
|
||||
xt = c(xt)
|
||||
x = xt + x
|
||||
if x_mask is not None:
|
||||
x = x * x_mask
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
for l in self.convs:
|
||||
remove_weight_norm(l)
|
||||
|
||||
|
||||
class Log(nn.Module):
|
||||
def forward(self, x, x_mask, reverse=False, **kwargs):
|
||||
if not reverse:
|
||||
y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
|
||||
logdet = torch.sum(-y, [1, 2])
|
||||
return y, logdet
|
||||
else:
|
||||
x = torch.exp(x) * x_mask
|
||||
return x
|
||||
|
||||
|
||||
class Flip(nn.Module):
|
||||
def forward(self, x, *args, reverse=False, **kwargs):
|
||||
x = torch.flip(x, [1])
|
||||
if not reverse:
|
||||
logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
|
||||
return x, logdet
|
||||
else:
|
||||
return x
|
||||
|
||||
|
||||
class ElementwiseAffine(nn.Module):
|
||||
def __init__(self, channels):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.m = nn.Parameter(torch.zeros(channels,1))
|
||||
self.logs = nn.Parameter(torch.zeros(channels,1))
|
||||
|
||||
def forward(self, x, x_mask, reverse=False, **kwargs):
|
||||
if not reverse:
|
||||
y = self.m + torch.exp(self.logs) * x
|
||||
y = y * x_mask
|
||||
logdet = torch.sum(self.logs * x_mask, [1,2])
|
||||
return y, logdet
|
||||
else:
|
||||
x = (x - self.m) * torch.exp(-self.logs) * x_mask
|
||||
return x
|
||||
|
||||
|
||||
class ResidualCouplingLayer(nn.Module):
|
||||
def __init__(self,
|
||||
channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
p_dropout=0,
|
||||
gin_channels=0,
|
||||
mean_only=False):
|
||||
assert channels % 2 == 0, "channels should be divisible by 2"
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.half_channels = channels // 2
|
||||
self.mean_only = mean_only
|
||||
|
||||
self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
|
||||
self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
|
||||
self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
|
||||
self.post.weight.data.zero_()
|
||||
self.post.bias.data.zero_()
|
||||
|
||||
def forward(self, x, x_mask, g=None, reverse=False):
|
||||
x0, x1 = torch.split(x, [self.half_channels]*2, 1)
|
||||
h = self.pre(x0) * x_mask
|
||||
h = self.enc(h, x_mask, g=g)
|
||||
stats = self.post(h) * x_mask
|
||||
if not self.mean_only:
|
||||
m, logs = torch.split(stats, [self.half_channels]*2, 1)
|
||||
else:
|
||||
m = stats
|
||||
logs = torch.zeros_like(m)
|
||||
|
||||
if not reverse:
|
||||
x1 = m + x1 * torch.exp(logs) * x_mask
|
||||
x = torch.cat([x0, x1], 1)
|
||||
logdet = torch.sum(logs, [1,2])
|
||||
return x, logdet
|
||||
else:
|
||||
x1 = (x1 - m) * torch.exp(-logs) * x_mask
|
||||
x = torch.cat([x0, x1], 1)
|
||||
return x
|
||||
@ -0,0 +1,73 @@
|
||||
import argparse
|
||||
import time
|
||||
import numpy as np
|
||||
import onnx
|
||||
from onnxsim import simplify
|
||||
import onnxruntime as ort
|
||||
import onnxoptimizer
|
||||
import torch
|
||||
from model_onnx import SynthesizerTrn
|
||||
import utils
|
||||
from hubert import hubert_model_onnx
|
||||
|
||||
def main(HubertExport,NetExport):
|
||||
|
||||
path = "NyaruTaffy"
|
||||
|
||||
if(HubertExport):
|
||||
device = torch.device("cuda")
|
||||
hubert_soft = hubert_model_onnx.hubert_soft("hubert/model.pt")
|
||||
test_input = torch.rand(1, 1, 16000)
|
||||
input_names = ["source"]
|
||||
output_names = ["embed"]
|
||||
torch.onnx.export(hubert_soft.to(device),
|
||||
test_input.to(device),
|
||||
"hubert3.0.onnx",
|
||||
dynamic_axes={
|
||||
"source": {
|
||||
2: "sample_length"
|
||||
}
|
||||
},
|
||||
verbose=False,
|
||||
opset_version=13,
|
||||
input_names=input_names,
|
||||
output_names=output_names)
|
||||
if(NetExport):
|
||||
device = torch.device("cuda")
|
||||
hps = utils.get_hparams_from_file(f"checkpoints/{path}/config.json")
|
||||
SVCVITS = SynthesizerTrn(
|
||||
hps.data.filter_length // 2 + 1,
|
||||
hps.train.segment_size // hps.data.hop_length,
|
||||
**hps.model)
|
||||
_ = utils.load_checkpoint(f"checkpoints/{path}/model.pth", SVCVITS, None)
|
||||
_ = SVCVITS.eval().to(device)
|
||||
for i in SVCVITS.parameters():
|
||||
i.requires_grad = False
|
||||
test_hidden_unit = torch.rand(1, 50, 256)
|
||||
test_lengths = torch.LongTensor([50])
|
||||
test_pitch = torch.rand(1, 50)
|
||||
test_sid = torch.LongTensor([0])
|
||||
input_names = ["hidden_unit", "lengths", "pitch", "sid"]
|
||||
output_names = ["audio", ]
|
||||
SVCVITS.eval()
|
||||
torch.onnx.export(SVCVITS,
|
||||
(
|
||||
test_hidden_unit.to(device),
|
||||
test_lengths.to(device),
|
||||
test_pitch.to(device),
|
||||
test_sid.to(device)
|
||||
),
|
||||
f"checkpoints/{path}/model.onnx",
|
||||
dynamic_axes={
|
||||
"hidden_unit": [0, 1],
|
||||
"pitch": [1]
|
||||
},
|
||||
do_constant_folding=False,
|
||||
opset_version=16,
|
||||
verbose=False,
|
||||
input_names=input_names,
|
||||
output_names=output_names)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main(False,True)
|
||||
@ -0,0 +1,73 @@
|
||||
import argparse
|
||||
import time
|
||||
import numpy as np
|
||||
import onnx
|
||||
from onnxsim import simplify
|
||||
import onnxruntime as ort
|
||||
import onnxoptimizer
|
||||
import torch
|
||||
from model_onnx_48k import SynthesizerTrn
|
||||
import utils
|
||||
from hubert import hubert_model_onnx
|
||||
|
||||
def main(HubertExport,NetExport):
|
||||
|
||||
path = "NyaruTaffy"
|
||||
|
||||
if(HubertExport):
|
||||
device = torch.device("cuda")
|
||||
hubert_soft = hubert_model_onnx.hubert_soft("hubert/model.pt")
|
||||
test_input = torch.rand(1, 1, 16000)
|
||||
input_names = ["source"]
|
||||
output_names = ["embed"]
|
||||
torch.onnx.export(hubert_soft.to(device),
|
||||
test_input.to(device),
|
||||
"hubert3.0.onnx",
|
||||
dynamic_axes={
|
||||
"source": {
|
||||
2: "sample_length"
|
||||
}
|
||||
},
|
||||
verbose=False,
|
||||
opset_version=13,
|
||||
input_names=input_names,
|
||||
output_names=output_names)
|
||||
if(NetExport):
|
||||
device = torch.device("cuda")
|
||||
hps = utils.get_hparams_from_file(f"checkpoints/{path}/config.json")
|
||||
SVCVITS = SynthesizerTrn(
|
||||
hps.data.filter_length // 2 + 1,
|
||||
hps.train.segment_size // hps.data.hop_length,
|
||||
**hps.model)
|
||||
_ = utils.load_checkpoint(f"checkpoints/{path}/model.pth", SVCVITS, None)
|
||||
_ = SVCVITS.eval().to(device)
|
||||
for i in SVCVITS.parameters():
|
||||
i.requires_grad = False
|
||||
test_hidden_unit = torch.rand(1, 50, 256)
|
||||
test_lengths = torch.LongTensor([50])
|
||||
test_pitch = torch.rand(1, 50)
|
||||
test_sid = torch.LongTensor([0])
|
||||
input_names = ["hidden_unit", "lengths", "pitch", "sid"]
|
||||
output_names = ["audio", ]
|
||||
SVCVITS.eval()
|
||||
torch.onnx.export(SVCVITS,
|
||||
(
|
||||
test_hidden_unit.to(device),
|
||||
test_lengths.to(device),
|
||||
test_pitch.to(device),
|
||||
test_sid.to(device)
|
||||
),
|
||||
f"checkpoints/{path}/model.onnx",
|
||||
dynamic_axes={
|
||||
"hidden_unit": [0, 1],
|
||||
"pitch": [1]
|
||||
},
|
||||
do_constant_folding=False,
|
||||
opset_version=16,
|
||||
verbose=False,
|
||||
input_names=input_names,
|
||||
output_names=output_names)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main(False,True)
|
||||
@ -0,0 +1,106 @@
|
||||
import os
|
||||
import argparse
|
||||
|
||||
import torch
|
||||
import json
|
||||
from glob import glob
|
||||
|
||||
from pyworld import pyworld
|
||||
from tqdm import tqdm
|
||||
from scipy.io import wavfile
|
||||
|
||||
import utils
|
||||
from mel_processing import mel_spectrogram_torch
|
||||
#import h5py
|
||||
import logging
|
||||
logging.getLogger('numba').setLevel(logging.WARNING)
|
||||
|
||||
import parselmouth
|
||||
import librosa
|
||||
import numpy as np
|
||||
|
||||
|
||||
def get_f0(path,p_len=None, f0_up_key=0):
|
||||
x, _ = librosa.load(path, 32000)
|
||||
if p_len is None:
|
||||
p_len = x.shape[0]//320
|
||||
else:
|
||||
assert abs(p_len-x.shape[0]//320) < 3, (path, p_len, x.shape)
|
||||
time_step = 320 / 32000 * 1000
|
||||
f0_min = 50
|
||||
f0_max = 1100
|
||||
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
|
||||
f0_mel_max = 1127 * np.log(1 + f0_max / 700)
|
||||
|
||||
f0 = parselmouth.Sound(x, 32000).to_pitch_ac(
|
||||
time_step=time_step / 1000, voicing_threshold=0.6,
|
||||
pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
|
||||
|
||||
pad_size=(p_len - len(f0) + 1) // 2
|
||||
if(pad_size>0 or p_len - len(f0) - pad_size>0):
|
||||
f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
|
||||
|
||||
f0bak = f0.copy()
|
||||
f0 *= pow(2, f0_up_key / 12)
|
||||
f0_mel = 1127 * np.log(1 + f0 / 700)
|
||||
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (f0_mel_max - f0_mel_min) + 1
|
||||
f0_mel[f0_mel <= 1] = 1
|
||||
f0_mel[f0_mel > 255] = 255
|
||||
f0_coarse = np.rint(f0_mel).astype(np.int)
|
||||
return f0_coarse, f0bak
|
||||
|
||||
def resize2d(x, target_len):
|
||||
source = np.array(x)
|
||||
source[source<0.001] = np.nan
|
||||
target = np.interp(np.arange(0, len(source)*target_len, len(source))/ target_len, np.arange(0, len(source)), source)
|
||||
res = np.nan_to_num(target)
|
||||
return res
|
||||
|
||||
def compute_f0(path, c_len):
|
||||
x, sr = librosa.load(path, sr=32000)
|
||||
f0, t = pyworld.dio(
|
||||
x.astype(np.double),
|
||||
fs=sr,
|
||||
f0_ceil=800,
|
||||
frame_period=1000 * 320 / sr,
|
||||
)
|
||||
f0 = pyworld.stonemask(x.astype(np.double), f0, t, 32000)
|
||||
for index, pitch in enumerate(f0):
|
||||
f0[index] = round(pitch, 1)
|
||||
assert abs(c_len - x.shape[0]//320) < 3, (c_len, f0.shape)
|
||||
|
||||
return None, resize2d(f0, c_len)
|
||||
|
||||
|
||||
def process(filename):
|
||||
print(filename)
|
||||
save_name = filename+".soft.pt"
|
||||
if not os.path.exists(save_name):
|
||||
devive = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
wav, _ = librosa.load(filename, sr=16000)
|
||||
wav = torch.from_numpy(wav).unsqueeze(0).to(devive)
|
||||
c = utils.get_hubert_content(hmodel, wav)
|
||||
torch.save(c.cpu(), save_name)
|
||||
else:
|
||||
c = torch.load(save_name)
|
||||
f0path = filename+".f0.npy"
|
||||
if not os.path.exists(f0path):
|
||||
cf0, f0 = compute_f0(filename, c.shape[-1] * 2)
|
||||
np.save(f0path, f0)
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--in_dir", type=str, default="dataset/32k", help="path to input dir")
|
||||
args = parser.parse_args()
|
||||
|
||||
print("Loading hubert for content...")
|
||||
hmodel = utils.get_hubert_model(0 if torch.cuda.is_available() else None)
|
||||
print("Loaded hubert.")
|
||||
|
||||
filenames = glob(f'{args.in_dir}/*/*.wav', recursive=True)#[:10]
|
||||
|
||||
for filename in tqdm(filenames):
|
||||
process(filename)
|
||||
|
||||
@ -0,0 +1,21 @@
|
||||
Flask==2.1.2
|
||||
Flask_Cors==3.0.10
|
||||
gradio==3.4.1
|
||||
numpy==1.19.2
|
||||
playsound==1.3.0
|
||||
PyAudio==0.2.12
|
||||
pydub==0.25.1
|
||||
pyworld==0.3.0
|
||||
requests==2.28.1
|
||||
scipy==1.7.3
|
||||
sounddevice==0.4.5
|
||||
SoundFile==0.10.3.post1
|
||||
starlette==0.19.1
|
||||
torch==1.10.0+cu113
|
||||
torchaudio==0.10.0+cu113
|
||||
tqdm==4.63.0
|
||||
scikit-maad
|
||||
praat-parselmouth
|
||||
onnx
|
||||
onnxsim
|
||||
onnxoptimizer
|
||||
@ -0,0 +1,48 @@
|
||||
import os
|
||||
import argparse
|
||||
import librosa
|
||||
import numpy as np
|
||||
from multiprocessing import Pool, cpu_count
|
||||
from scipy.io import wavfile
|
||||
from tqdm import tqdm
|
||||
|
||||
|
||||
def process(item):
|
||||
spkdir, wav_name, args = item
|
||||
# speaker 's5', 'p280', 'p315' are excluded,
|
||||
speaker = spkdir.replace("\\", "/").split("/")[-1]
|
||||
wav_path = os.path.join(args.in_dir, speaker, wav_name)
|
||||
if os.path.exists(wav_path) and '.wav' in wav_path:
|
||||
os.makedirs(os.path.join(args.out_dir2, speaker), exist_ok=True)
|
||||
wav, sr = librosa.load(wav_path, None)
|
||||
wav, _ = librosa.effects.trim(wav, top_db=20)
|
||||
peak = np.abs(wav).max()
|
||||
if peak > 1.0:
|
||||
wav = 0.98 * wav / peak
|
||||
wav2 = librosa.resample(wav, orig_sr=sr, target_sr=args.sr2)
|
||||
wav2 /= max(wav2.max(), -wav2.min())
|
||||
save_name = wav_name
|
||||
save_path2 = os.path.join(args.out_dir2, speaker, save_name)
|
||||
wavfile.write(
|
||||
save_path2,
|
||||
args.sr2,
|
||||
(wav2 * np.iinfo(np.int16).max).astype(np.int16)
|
||||
)
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--sr2", type=int, default=32000, help="sampling rate")
|
||||
parser.add_argument("--in_dir", type=str, default="./dataset_raw", help="path to source dir")
|
||||
parser.add_argument("--out_dir2", type=str, default="./dataset/32k", help="path to target dir")
|
||||
args = parser.parse_args()
|
||||
processs = cpu_count()-2 if cpu_count() >4 else 1
|
||||
pool = Pool(processes=processs)
|
||||
|
||||
for speaker in os.listdir(args.in_dir):
|
||||
spk_dir = os.path.join(args.in_dir, speaker)
|
||||
if os.path.isdir(spk_dir):
|
||||
print(spk_dir)
|
||||
for _ in tqdm(pool.imap_unordered(process, [(spk_dir, i, args) for i in os.listdir(spk_dir) if i.endswith("wav")])):
|
||||
pass
|
||||
@ -0,0 +1,47 @@
|
||||
from inference.infer_tool_grad import VitsSvc
|
||||
import gradio as gr
|
||||
import os
|
||||
|
||||
class VitsGradio:
|
||||
def __init__(self):
|
||||
self.so = VitsSvc()
|
||||
self.lspk = []
|
||||
self.modelPaths = []
|
||||
for root,dirs,files in os.walk("checkpoints"):
|
||||
for dir in dirs:
|
||||
self.modelPaths.append(dir)
|
||||
with gr.Blocks() as self.Vits:
|
||||
with gr.Tab("VoiceConversion"):
|
||||
with gr.Row(visible=False) as self.VoiceConversion:
|
||||
with gr.Column():
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
self.srcaudio = gr.Audio(label = "输入音频")
|
||||
self.btnVC = gr.Button("说话人转换")
|
||||
with gr.Column():
|
||||
self.dsid = gr.Dropdown(label = "目标角色", choices = self.lspk)
|
||||
self.tran = gr.Slider(label = "升降调", maximum = 60, minimum = -60, step = 1, value = 0)
|
||||
self.th = gr.Slider(label = "切片阈值", maximum = 32767, minimum = -32768, step = 0.1, value = -40)
|
||||
with gr.Row():
|
||||
self.VCOutputs = gr.Audio()
|
||||
self.btnVC.click(self.so.inference, inputs=[self.srcaudio,self.dsid,self.tran,self.th], outputs=[self.VCOutputs])
|
||||
with gr.Tab("SelectModel"):
|
||||
with gr.Column():
|
||||
modelstrs = gr.Dropdown(label = "模型", choices = self.modelPaths, value = self.modelPaths[0], type = "value")
|
||||
devicestrs = gr.Dropdown(label = "设备", choices = ["cpu","cuda"], value = "cpu", type = "value")
|
||||
btnMod = gr.Button("载入模型")
|
||||
btnMod.click(self.loadModel, inputs=[modelstrs,devicestrs], outputs = [self.dsid,self.VoiceConversion])
|
||||
|
||||
def loadModel(self, path, device):
|
||||
self.lspk = []
|
||||
self.so.set_device(device)
|
||||
self.so.loadCheckpoint(path)
|
||||
for spk, sid in self.so.hps.spk.items():
|
||||
self.lspk.append(spk)
|
||||
VChange = gr.update(visible = True)
|
||||
SDChange = gr.update(choices = self.lspk, value = self.lspk[0])
|
||||
return [SDChange,VChange]
|
||||
|
||||
grVits = VitsGradio()
|
||||
|
||||
grVits.Vits.launch()
|
||||
@ -0,0 +1,22 @@
|
||||
from data_utils import TextAudioSpeakerLoader, EvalDataLoader
|
||||
import json
|
||||
from tqdm import tqdm
|
||||
|
||||
from utils import HParams
|
||||
|
||||
config_path = 'configs/config.json'
|
||||
with open(config_path, "r") as f:
|
||||
data = f.read()
|
||||
config = json.loads(data)
|
||||
hps = HParams(**config)
|
||||
|
||||
train_dataset = TextAudioSpeakerLoader("filelists/train.txt", hps)
|
||||
test_dataset = TextAudioSpeakerLoader("filelists/test.txt", hps)
|
||||
eval_dataset = TextAudioSpeakerLoader("filelists/val.txt", hps)
|
||||
|
||||
for _ in tqdm(train_dataset):
|
||||
pass
|
||||
for _ in tqdm(eval_dataset):
|
||||
pass
|
||||
for _ in tqdm(test_dataset):
|
||||
pass
|
||||
@ -0,0 +1,281 @@
|
||||
import logging
|
||||
logging.getLogger('matplotlib').setLevel(logging.WARNING)
|
||||
import os
|
||||
import json
|
||||
import argparse
|
||||
import itertools
|
||||
import math
|
||||
import torch
|
||||
from torch import nn, optim
|
||||
from torch.nn import functional as F
|
||||
from torch.utils.data import DataLoader
|
||||
from torch.utils.tensorboard import SummaryWriter
|
||||
import torch.multiprocessing as mp
|
||||
import torch.distributed as dist
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
from torch.cuda.amp import autocast, GradScaler
|
||||
|
||||
import commons
|
||||
import utils
|
||||
from data_utils import TextAudioSpeakerLoader, EvalDataLoader
|
||||
from models import (
|
||||
SynthesizerTrn,
|
||||
MultiPeriodDiscriminator,
|
||||
)
|
||||
from losses import (
|
||||
kl_loss,
|
||||
generator_loss, discriminator_loss, feature_loss
|
||||
)
|
||||
|
||||
from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
|
||||
|
||||
torch.backends.cudnn.benchmark = True
|
||||
global_step = 0
|
||||
|
||||
|
||||
# os.environ['TORCH_DISTRIBUTED_DEBUG'] = 'INFO'
|
||||
|
||||
|
||||
def main():
|
||||
"""Assume Single Node Multi GPUs Training Only"""
|
||||
assert torch.cuda.is_available(), "CPU training is not allowed."
|
||||
hps = utils.get_hparams()
|
||||
|
||||
n_gpus = torch.cuda.device_count()
|
||||
os.environ['MASTER_ADDR'] = 'localhost'
|
||||
os.environ['MASTER_PORT'] = hps.train.port
|
||||
|
||||
mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))
|
||||
|
||||
|
||||
def run(rank, n_gpus, hps):
|
||||
global global_step
|
||||
if rank == 0:
|
||||
logger = utils.get_logger(hps.model_dir)
|
||||
logger.info(hps)
|
||||
utils.check_git_hash(hps.model_dir)
|
||||
writer = SummaryWriter(log_dir=hps.model_dir)
|
||||
writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
|
||||
|
||||
dist.init_process_group(backend='nccl', init_method='env://', world_size=n_gpus, rank=rank)
|
||||
torch.manual_seed(hps.train.seed)
|
||||
torch.cuda.set_device(rank)
|
||||
|
||||
train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps)
|
||||
train_loader = DataLoader(train_dataset, num_workers=8, shuffle=False, pin_memory=True,
|
||||
batch_size=hps.train.batch_size)
|
||||
if rank == 0:
|
||||
eval_dataset = EvalDataLoader(hps.data.validation_files, hps)
|
||||
eval_loader = DataLoader(eval_dataset, num_workers=1, shuffle=False,
|
||||
batch_size=1, pin_memory=False,
|
||||
drop_last=False)
|
||||
|
||||
net_g = SynthesizerTrn(
|
||||
hps.data.filter_length // 2 + 1,
|
||||
hps.train.segment_size // hps.data.hop_length,
|
||||
**hps.model).cuda(rank)
|
||||
net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
|
||||
optim_g = torch.optim.AdamW(
|
||||
net_g.parameters(),
|
||||
hps.train.learning_rate,
|
||||
betas=hps.train.betas,
|
||||
eps=hps.train.eps)
|
||||
optim_d = torch.optim.AdamW(
|
||||
net_d.parameters(),
|
||||
hps.train.learning_rate,
|
||||
betas=hps.train.betas,
|
||||
eps=hps.train.eps)
|
||||
net_g = DDP(net_g, device_ids=[rank]) # , find_unused_parameters=True)
|
||||
net_d = DDP(net_d, device_ids=[rank])
|
||||
|
||||
try:
|
||||
_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g,
|
||||
optim_g)
|
||||
_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d,
|
||||
optim_d)
|
||||
global_step = (epoch_str - 1) * len(train_loader)
|
||||
except:
|
||||
epoch_str = 1
|
||||
global_step = 0
|
||||
|
||||
scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
|
||||
scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
|
||||
|
||||
scaler = GradScaler(enabled=hps.train.fp16_run)
|
||||
|
||||
for epoch in range(epoch_str, hps.train.epochs + 1):
|
||||
if rank == 0:
|
||||
train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler,
|
||||
[train_loader, eval_loader], logger, [writer, writer_eval])
|
||||
else:
|
||||
train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler,
|
||||
[train_loader, None], None, None)
|
||||
scheduler_g.step()
|
||||
scheduler_d.step()
|
||||
|
||||
|
||||
def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
|
||||
net_g, net_d = nets
|
||||
optim_g, optim_d = optims
|
||||
scheduler_g, scheduler_d = schedulers
|
||||
train_loader, eval_loader = loaders
|
||||
if writers is not None:
|
||||
writer, writer_eval = writers
|
||||
|
||||
# train_loader.batch_sampler.set_epoch(epoch)
|
||||
global global_step
|
||||
|
||||
net_g.train()
|
||||
net_d.train()
|
||||
for batch_idx, items in enumerate(train_loader):
|
||||
c, f0, spec, y, spk = items
|
||||
g = spk.cuda(rank, non_blocking=True)
|
||||
spec, y = spec.cuda(rank, non_blocking=True), y.cuda(rank, non_blocking=True)
|
||||
c = c.cuda(rank, non_blocking=True)
|
||||
f0 = f0.cuda(rank, non_blocking=True)
|
||||
mel = spec_to_mel_torch(
|
||||
spec,
|
||||
hps.data.filter_length,
|
||||
hps.data.n_mel_channels,
|
||||
hps.data.sampling_rate,
|
||||
hps.data.mel_fmin,
|
||||
hps.data.mel_fmax)
|
||||
|
||||
with autocast(enabled=hps.train.fp16_run):
|
||||
y_hat, ids_slice, z_mask, \
|
||||
(z, z_p, m_p, logs_p, m_q, logs_q) = net_g(c, f0, spec, g=g, mel=mel)
|
||||
|
||||
y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
|
||||
y_hat_mel = mel_spectrogram_torch(
|
||||
y_hat.squeeze(1),
|
||||
hps.data.filter_length,
|
||||
hps.data.n_mel_channels,
|
||||
hps.data.sampling_rate,
|
||||
hps.data.hop_length,
|
||||
hps.data.win_length,
|
||||
hps.data.mel_fmin,
|
||||
hps.data.mel_fmax
|
||||
)
|
||||
y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice
|
||||
|
||||
# Discriminator
|
||||
y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
|
||||
|
||||
with autocast(enabled=False):
|
||||
loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
|
||||
loss_disc_all = loss_disc
|
||||
|
||||
optim_d.zero_grad()
|
||||
scaler.scale(loss_disc_all).backward()
|
||||
scaler.unscale_(optim_d)
|
||||
grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
|
||||
scaler.step(optim_d)
|
||||
|
||||
with autocast(enabled=hps.train.fp16_run):
|
||||
# Generator
|
||||
y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
|
||||
with autocast(enabled=False):
|
||||
loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
|
||||
loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
|
||||
loss_fm = feature_loss(fmap_r, fmap_g)
|
||||
loss_gen, losses_gen = generator_loss(y_d_hat_g)
|
||||
loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl
|
||||
optim_g.zero_grad()
|
||||
scaler.scale(loss_gen_all).backward()
|
||||
scaler.unscale_(optim_g)
|
||||
grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
|
||||
scaler.step(optim_g)
|
||||
scaler.update()
|
||||
|
||||
if rank == 0:
|
||||
if global_step % hps.train.log_interval == 0:
|
||||
lr = optim_g.param_groups[0]['lr']
|
||||
losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_kl]
|
||||
logger.info('Train Epoch: {} [{:.0f}%]'.format(
|
||||
epoch,
|
||||
100. * batch_idx / len(train_loader)))
|
||||
logger.info([x.item() for x in losses] + [global_step, lr])
|
||||
|
||||
scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr,
|
||||
"grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g}
|
||||
scalar_dict.update({"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/kl": loss_kl})
|
||||
|
||||
scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
|
||||
scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
|
||||
scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
|
||||
image_dict = {
|
||||
"slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
|
||||
"slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()),
|
||||
"all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
|
||||
}
|
||||
|
||||
utils.summarize(
|
||||
writer=writer,
|
||||
global_step=global_step,
|
||||
images=image_dict,
|
||||
scalars=scalar_dict
|
||||
)
|
||||
|
||||
if global_step % hps.train.eval_interval == 0:
|
||||
evaluate(hps, net_g, eval_loader, writer_eval)
|
||||
utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch,
|
||||
os.path.join(hps.model_dir, "G_{}.pth".format(global_step)))
|
||||
utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch,
|
||||
os.path.join(hps.model_dir, "D_{}.pth".format(global_step)))
|
||||
global_step += 1
|
||||
|
||||
if rank == 0:
|
||||
logger.info('====> Epoch: {}'.format(epoch))
|
||||
|
||||
|
||||
def evaluate(hps, generator, eval_loader, writer_eval):
|
||||
generator.eval()
|
||||
image_dict = {}
|
||||
audio_dict = {}
|
||||
with torch.no_grad():
|
||||
for batch_idx, items in enumerate(eval_loader):
|
||||
c, f0, spec, y, spk = items
|
||||
g = spk[:1].cuda(0)
|
||||
spec, y = spec[:1].cuda(0), y[:1].cuda(0)
|
||||
c = c[:1].cuda(0)
|
||||
f0 = f0[:1].cuda(0)
|
||||
mel = spec_to_mel_torch(
|
||||
spec,
|
||||
hps.data.filter_length,
|
||||
hps.data.n_mel_channels,
|
||||
hps.data.sampling_rate,
|
||||
hps.data.mel_fmin,
|
||||
hps.data.mel_fmax)
|
||||
y_hat = generator.module.infer(c, f0, g=g, mel=mel)
|
||||
|
||||
y_hat_mel = mel_spectrogram_torch(
|
||||
y_hat.squeeze(1).float(),
|
||||
hps.data.filter_length,
|
||||
hps.data.n_mel_channels,
|
||||
hps.data.sampling_rate,
|
||||
hps.data.hop_length,
|
||||
hps.data.win_length,
|
||||
hps.data.mel_fmin,
|
||||
hps.data.mel_fmax
|
||||
)
|
||||
|
||||
audio_dict.update({
|
||||
f"gen/audio_{batch_idx}": y_hat[0],
|
||||
f"gt/audio_{batch_idx}": y[0]
|
||||
})
|
||||
image_dict.update({
|
||||
f"gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy()),
|
||||
"gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())
|
||||
})
|
||||
utils.summarize(
|
||||
writer=writer_eval,
|
||||
global_step=global_step,
|
||||
images=image_dict,
|
||||
audios=audio_dict,
|
||||
audio_sampling_rate=hps.data.sampling_rate
|
||||
)
|
||||
generator.train()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -0,0 +1,355 @@
|
||||
import os
|
||||
import glob
|
||||
import re
|
||||
import sys
|
||||
import argparse
|
||||
import logging
|
||||
import json
|
||||
import subprocess
|
||||
|
||||
import librosa
|
||||
import numpy as np
|
||||
import torchaudio
|
||||
from scipy.io.wavfile import read
|
||||
import torch
|
||||
import torchvision
|
||||
from torch.nn import functional as F
|
||||
from commons import sequence_mask
|
||||
from hubert import hubert_model
|
||||
MATPLOTLIB_FLAG = False
|
||||
|
||||
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
|
||||
logger = logging
|
||||
|
||||
f0_bin = 256
|
||||
f0_max = 1100.0
|
||||
f0_min = 50.0
|
||||
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
|
||||
f0_mel_max = 1127 * np.log(1 + f0_max / 700)
|
||||
|
||||
def f0_to_coarse(f0):
|
||||
is_torch = isinstance(f0, torch.Tensor)
|
||||
f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700)
|
||||
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1
|
||||
|
||||
f0_mel[f0_mel <= 1] = 1
|
||||
f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
|
||||
f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(np.int)
|
||||
assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min())
|
||||
return f0_coarse
|
||||
|
||||
|
||||
def get_hubert_model(rank=None):
|
||||
|
||||
hubert_soft = hubert_model.hubert_soft("hubert/hubert-soft-0d54a1f4.pt")
|
||||
if rank is not None:
|
||||
hubert_soft = hubert_soft.cuda(rank)
|
||||
return hubert_soft
|
||||
|
||||
def get_hubert_content(hmodel, y=None, path=None):
|
||||
if path is not None:
|
||||
source, sr = torchaudio.load(path)
|
||||
source = torchaudio.functional.resample(source, sr, 16000)
|
||||
if len(source.shape) == 2 and source.shape[1] >= 2:
|
||||
source = torch.mean(source, dim=0).unsqueeze(0)
|
||||
else:
|
||||
source = y
|
||||
source = source.unsqueeze(0)
|
||||
with torch.inference_mode():
|
||||
units = hmodel.units(source)
|
||||
return units.transpose(1,2)
|
||||
|
||||
|
||||
def get_content(cmodel, y):
|
||||
with torch.no_grad():
|
||||
c = cmodel.extract_features(y.squeeze(1))[0]
|
||||
c = c.transpose(1, 2)
|
||||
return c
|
||||
|
||||
|
||||
|
||||
def transform(mel, height): # 68-92
|
||||
#r = np.random.random()
|
||||
#rate = r * 0.3 + 0.85 # 0.85-1.15
|
||||
#height = int(mel.size(-2) * rate)
|
||||
tgt = torchvision.transforms.functional.resize(mel, (height, mel.size(-1)))
|
||||
if height >= mel.size(-2):
|
||||
return tgt[:, :mel.size(-2), :]
|
||||
else:
|
||||
silence = tgt[:,-1:,:].repeat(1,mel.size(-2)-height,1)
|
||||
silence += torch.randn_like(silence) / 10
|
||||
return torch.cat((tgt, silence), 1)
|
||||
|
||||
|
||||
def stretch(mel, width): # 0.5-2
|
||||
return torchvision.transforms.functional.resize(mel, (mel.size(-2), width))
|
||||
|
||||
|
||||
def load_checkpoint(checkpoint_path, model, optimizer=None):
|
||||
assert os.path.isfile(checkpoint_path)
|
||||
checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
|
||||
iteration = checkpoint_dict['iteration']
|
||||
learning_rate = checkpoint_dict['learning_rate']
|
||||
if iteration is None:
|
||||
iteration = 1
|
||||
if learning_rate is None:
|
||||
learning_rate = 0.0002
|
||||
if optimizer is not None and checkpoint_dict['optimizer'] is not None:
|
||||
optimizer.load_state_dict(checkpoint_dict['optimizer'])
|
||||
saved_state_dict = checkpoint_dict['model']
|
||||
if hasattr(model, 'module'):
|
||||
state_dict = model.module.state_dict()
|
||||
else:
|
||||
state_dict = model.state_dict()
|
||||
new_state_dict= {}
|
||||
for k, v in state_dict.items():
|
||||
try:
|
||||
new_state_dict[k] = saved_state_dict[k]
|
||||
except:
|
||||
logger.info("%s is not in the checkpoint" % k)
|
||||
new_state_dict[k] = v
|
||||
if hasattr(model, 'module'):
|
||||
model.module.load_state_dict(new_state_dict)
|
||||
else:
|
||||
model.load_state_dict(new_state_dict)
|
||||
logger.info("Loaded checkpoint '{}' (iteration {})" .format(
|
||||
checkpoint_path, iteration))
|
||||
return model, optimizer, learning_rate, iteration
|
||||
|
||||
|
||||
def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
|
||||
logger.info("Saving model and optimizer state at iteration {} to {}".format(
|
||||
iteration, checkpoint_path))
|
||||
if hasattr(model, 'module'):
|
||||
state_dict = model.module.state_dict()
|
||||
else:
|
||||
state_dict = model.state_dict()
|
||||
torch.save({'model': state_dict,
|
||||
'iteration': iteration,
|
||||
'optimizer': optimizer.state_dict(),
|
||||
'learning_rate': learning_rate}, checkpoint_path)
|
||||
clean_ckpt = False
|
||||
if clean_ckpt:
|
||||
clean_checkpoints(path_to_models='logs/32k/', n_ckpts_to_keep=3, sort_by_time=True)
|
||||
|
||||
def clean_checkpoints(path_to_models='logs/48k/', n_ckpts_to_keep=2, sort_by_time=True):
|
||||
"""Freeing up space by deleting saved ckpts
|
||||
|
||||
Arguments:
|
||||
path_to_models -- Path to the model directory
|
||||
n_ckpts_to_keep -- Number of ckpts to keep, excluding G_0.pth and D_0.pth
|
||||
sort_by_time -- True -> chronologically delete ckpts
|
||||
False -> lexicographically delete ckpts
|
||||
"""
|
||||
ckpts_files = [f for f in os.listdir(path_to_models) if os.path.isfile(os.path.join(path_to_models, f))]
|
||||
name_key = (lambda _f: int(re.compile('._(\d+)\.pth').match(_f).group(1)))
|
||||
time_key = (lambda _f: os.path.getmtime(os.path.join(path_to_models, _f)))
|
||||
sort_key = time_key if sort_by_time else name_key
|
||||
x_sorted = lambda _x: sorted([f for f in ckpts_files if f.startswith(_x) and not f.endswith('_0.pth')], key=sort_key)
|
||||
to_del = [os.path.join(path_to_models, fn) for fn in
|
||||
(x_sorted('G')[:-n_ckpts_to_keep] + x_sorted('D')[:-n_ckpts_to_keep])]
|
||||
del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}")
|
||||
del_routine = lambda x: [os.remove(x), del_info(x)]
|
||||
rs = [del_routine(fn) for fn in to_del]
|
||||
|
||||
def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
|
||||
for k, v in scalars.items():
|
||||
writer.add_scalar(k, v, global_step)
|
||||
for k, v in histograms.items():
|
||||
writer.add_histogram(k, v, global_step)
|
||||
for k, v in images.items():
|
||||
writer.add_image(k, v, global_step, dataformats='HWC')
|
||||
for k, v in audios.items():
|
||||
writer.add_audio(k, v, global_step, audio_sampling_rate)
|
||||
|
||||
|
||||
def latest_checkpoint_path(dir_path, regex="G_*.pth"):
|
||||
f_list = glob.glob(os.path.join(dir_path, regex))
|
||||
f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
|
||||
x = f_list[-1]
|
||||
print(x)
|
||||
return x
|
||||
|
||||
|
||||
def plot_spectrogram_to_numpy(spectrogram):
|
||||
global MATPLOTLIB_FLAG
|
||||
if not MATPLOTLIB_FLAG:
|
||||
import matplotlib
|
||||
matplotlib.use("Agg")
|
||||
MATPLOTLIB_FLAG = True
|
||||
mpl_logger = logging.getLogger('matplotlib')
|
||||
mpl_logger.setLevel(logging.WARNING)
|
||||
import matplotlib.pylab as plt
|
||||
import numpy as np
|
||||
|
||||
fig, ax = plt.subplots(figsize=(10,2))
|
||||
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
|
||||
interpolation='none')
|
||||
plt.colorbar(im, ax=ax)
|
||||
plt.xlabel("Frames")
|
||||
plt.ylabel("Channels")
|
||||
plt.tight_layout()
|
||||
|
||||
fig.canvas.draw()
|
||||
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
|
||||
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
||||
plt.close()
|
||||
return data
|
||||
|
||||
|
||||
def plot_alignment_to_numpy(alignment, info=None):
|
||||
global MATPLOTLIB_FLAG
|
||||
if not MATPLOTLIB_FLAG:
|
||||
import matplotlib
|
||||
matplotlib.use("Agg")
|
||||
MATPLOTLIB_FLAG = True
|
||||
mpl_logger = logging.getLogger('matplotlib')
|
||||
mpl_logger.setLevel(logging.WARNING)
|
||||
import matplotlib.pylab as plt
|
||||
import numpy as np
|
||||
|
||||
fig, ax = plt.subplots(figsize=(6, 4))
|
||||
im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
|
||||
interpolation='none')
|
||||
fig.colorbar(im, ax=ax)
|
||||
xlabel = 'Decoder timestep'
|
||||
if info is not None:
|
||||
xlabel += '\n\n' + info
|
||||
plt.xlabel(xlabel)
|
||||
plt.ylabel('Encoder timestep')
|
||||
plt.tight_layout()
|
||||
|
||||
fig.canvas.draw()
|
||||
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
|
||||
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
||||
plt.close()
|
||||
return data
|
||||
|
||||
|
||||
def load_wav_to_torch(full_path):
|
||||
sampling_rate, data = read(full_path)
|
||||
return torch.FloatTensor(data.astype(np.float32)), sampling_rate
|
||||
|
||||
|
||||
def load_filepaths_and_text(filename, split="|"):
|
||||
with open(filename, encoding='utf-8') as f:
|
||||
filepaths_and_text = [line.strip().split(split) for line in f]
|
||||
return filepaths_and_text
|
||||
|
||||
|
||||
def get_hparams(init=True):
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
|
||||
help='JSON file for configuration')
|
||||
parser.add_argument('-m', '--model', type=str, required=True,
|
||||
help='Model name')
|
||||
|
||||
args = parser.parse_args()
|
||||
model_dir = os.path.join("./logs", args.model)
|
||||
|
||||
if not os.path.exists(model_dir):
|
||||
os.makedirs(model_dir)
|
||||
|
||||
config_path = args.config
|
||||
config_save_path = os.path.join(model_dir, "config.json")
|
||||
if init:
|
||||
with open(config_path, "r") as f:
|
||||
data = f.read()
|
||||
with open(config_save_path, "w") as f:
|
||||
f.write(data)
|
||||
else:
|
||||
with open(config_save_path, "r") as f:
|
||||
data = f.read()
|
||||
config = json.loads(data)
|
||||
|
||||
hparams = HParams(**config)
|
||||
hparams.model_dir = model_dir
|
||||
return hparams
|
||||
|
||||
|
||||
def get_hparams_from_dir(model_dir):
|
||||
config_save_path = os.path.join(model_dir, "config.json")
|
||||
with open(config_save_path, "r") as f:
|
||||
data = f.read()
|
||||
config = json.loads(data)
|
||||
|
||||
hparams =HParams(**config)
|
||||
hparams.model_dir = model_dir
|
||||
return hparams
|
||||
|
||||
|
||||
def get_hparams_from_file(config_path):
|
||||
with open(config_path, "r") as f:
|
||||
data = f.read()
|
||||
config = json.loads(data)
|
||||
|
||||
hparams =HParams(**config)
|
||||
return hparams
|
||||
|
||||
|
||||
def check_git_hash(model_dir):
|
||||
source_dir = os.path.dirname(os.path.realpath(__file__))
|
||||
if not os.path.exists(os.path.join(source_dir, ".git")):
|
||||
logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
|
||||
source_dir
|
||||
))
|
||||
return
|
||||
|
||||
cur_hash = subprocess.getoutput("git rev-parse HEAD")
|
||||
|
||||
path = os.path.join(model_dir, "githash")
|
||||
if os.path.exists(path):
|
||||
saved_hash = open(path).read()
|
||||
if saved_hash != cur_hash:
|
||||
logger.warn("git hash values are different. {}(saved) != {}(current)".format(
|
||||
saved_hash[:8], cur_hash[:8]))
|
||||
else:
|
||||
open(path, "w").write(cur_hash)
|
||||
|
||||
|
||||
def get_logger(model_dir, filename="train.log"):
|
||||
global logger
|
||||
logger = logging.getLogger(os.path.basename(model_dir))
|
||||
logger.setLevel(logging.DEBUG)
|
||||
|
||||
formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
|
||||
if not os.path.exists(model_dir):
|
||||
os.makedirs(model_dir)
|
||||
h = logging.FileHandler(os.path.join(model_dir, filename))
|
||||
h.setLevel(logging.DEBUG)
|
||||
h.setFormatter(formatter)
|
||||
logger.addHandler(h)
|
||||
return logger
|
||||
|
||||
|
||||
class HParams():
|
||||
def __init__(self, **kwargs):
|
||||
for k, v in kwargs.items():
|
||||
if type(v) == dict:
|
||||
v = HParams(**v)
|
||||
self[k] = v
|
||||
|
||||
def keys(self):
|
||||
return self.__dict__.keys()
|
||||
|
||||
def items(self):
|
||||
return self.__dict__.items()
|
||||
|
||||
def values(self):
|
||||
return self.__dict__.values()
|
||||
|
||||
def __len__(self):
|
||||
return len(self.__dict__)
|
||||
|
||||
def __getitem__(self, key):
|
||||
return getattr(self, key)
|
||||
|
||||
def __setitem__(self, key, value):
|
||||
return setattr(self, key, value)
|
||||
|
||||
def __contains__(self, key):
|
||||
return key in self.__dict__
|
||||
|
||||
def __repr__(self):
|
||||
return self.__dict__.__repr__()
|
||||
|
||||
@ -0,0 +1,15 @@
|
||||
import os
|
||||
import shutil
|
||||
|
||||
|
||||
class AttrDict(dict):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super(AttrDict, self).__init__(*args, **kwargs)
|
||||
self.__dict__ = self
|
||||
|
||||
|
||||
def build_env(config, config_name, path):
|
||||
t_path = os.path.join(path, config_name)
|
||||
if config != t_path:
|
||||
os.makedirs(path, exist_ok=True)
|
||||
shutil.copyfile(config, os.path.join(path, config_name))
|
||||
@ -0,0 +1,503 @@
|
||||
import os
|
||||
import json
|
||||
from .env import AttrDict
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import torch.nn as nn
|
||||
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
|
||||
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
|
||||
from .utils import init_weights, get_padding
|
||||
|
||||
LRELU_SLOPE = 0.1
|
||||
|
||||
|
||||
def load_model(model_path, device='cuda'):
|
||||
config_file = os.path.join(os.path.split(model_path)[0], 'config.json')
|
||||
with open(config_file) as f:
|
||||
data = f.read()
|
||||
|
||||
global h
|
||||
json_config = json.loads(data)
|
||||
h = AttrDict(json_config)
|
||||
|
||||
generator = Generator(h).to(device)
|
||||
|
||||
cp_dict = torch.load(model_path)
|
||||
generator.load_state_dict(cp_dict['generator'])
|
||||
generator.eval()
|
||||
generator.remove_weight_norm()
|
||||
del cp_dict
|
||||
return generator, h
|
||||
|
||||
|
||||
class ResBlock1(torch.nn.Module):
|
||||
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
|
||||
super(ResBlock1, self).__init__()
|
||||
self.h = h
|
||||
self.convs1 = nn.ModuleList([
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
||||
padding=get_padding(kernel_size, dilation[0]))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
||||
padding=get_padding(kernel_size, dilation[1]))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
|
||||
padding=get_padding(kernel_size, dilation[2])))
|
||||
])
|
||||
self.convs1.apply(init_weights)
|
||||
|
||||
self.convs2 = nn.ModuleList([
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
||||
padding=get_padding(kernel_size, 1))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
||||
padding=get_padding(kernel_size, 1))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
||||
padding=get_padding(kernel_size, 1)))
|
||||
])
|
||||
self.convs2.apply(init_weights)
|
||||
|
||||
def forward(self, x):
|
||||
for c1, c2 in zip(self.convs1, self.convs2):
|
||||
xt = F.leaky_relu(x, LRELU_SLOPE)
|
||||
xt = c1(xt)
|
||||
xt = F.leaky_relu(xt, LRELU_SLOPE)
|
||||
xt = c2(xt)
|
||||
x = xt + x
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
for l in self.convs1:
|
||||
remove_weight_norm(l)
|
||||
for l in self.convs2:
|
||||
remove_weight_norm(l)
|
||||
|
||||
|
||||
class ResBlock2(torch.nn.Module):
|
||||
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
|
||||
super(ResBlock2, self).__init__()
|
||||
self.h = h
|
||||
self.convs = nn.ModuleList([
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
||||
padding=get_padding(kernel_size, dilation[0]))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
||||
padding=get_padding(kernel_size, dilation[1])))
|
||||
])
|
||||
self.convs.apply(init_weights)
|
||||
|
||||
def forward(self, x):
|
||||
for c in self.convs:
|
||||
xt = F.leaky_relu(x, LRELU_SLOPE)
|
||||
xt = c(xt)
|
||||
x = xt + x
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
for l in self.convs:
|
||||
remove_weight_norm(l)
|
||||
|
||||
|
||||
def padDiff(x):
|
||||
return F.pad(F.pad(x, (0,0,-1,1), 'constant', 0) - x, (0,0,0,-1), 'constant', 0)
|
||||
|
||||
class SineGen(torch.nn.Module):
|
||||
""" Definition of sine generator
|
||||
SineGen(samp_rate, harmonic_num = 0,
|
||||
sine_amp = 0.1, noise_std = 0.003,
|
||||
voiced_threshold = 0,
|
||||
flag_for_pulse=False)
|
||||
samp_rate: sampling rate in Hz
|
||||
harmonic_num: number of harmonic overtones (default 0)
|
||||
sine_amp: amplitude of sine-wavefrom (default 0.1)
|
||||
noise_std: std of Gaussian noise (default 0.003)
|
||||
voiced_thoreshold: F0 threshold for U/V classification (default 0)
|
||||
flag_for_pulse: this SinGen is used inside PulseGen (default False)
|
||||
Note: when flag_for_pulse is True, the first time step of a voiced
|
||||
segment is always sin(np.pi) or cos(0)
|
||||
"""
|
||||
|
||||
def __init__(self, samp_rate, harmonic_num=0,
|
||||
sine_amp=0.1, noise_std=0.003,
|
||||
voiced_threshold=0,
|
||||
flag_for_pulse=False):
|
||||
super(SineGen, self).__init__()
|
||||
self.sine_amp = sine_amp
|
||||
self.noise_std = noise_std
|
||||
self.harmonic_num = harmonic_num
|
||||
self.dim = self.harmonic_num + 1
|
||||
self.sampling_rate = samp_rate
|
||||
self.voiced_threshold = voiced_threshold
|
||||
self.flag_for_pulse = flag_for_pulse
|
||||
|
||||
def _f02uv(self, f0):
|
||||
# generate uv signal
|
||||
uv = (f0 > self.voiced_threshold).type(torch.float32)
|
||||
return uv
|
||||
|
||||
def _f02sine(self, f0_values):
|
||||
""" f0_values: (batchsize, length, dim)
|
||||
where dim indicates fundamental tone and overtones
|
||||
"""
|
||||
# convert to F0 in rad. The interger part n can be ignored
|
||||
# because 2 * np.pi * n doesn't affect phase
|
||||
rad_values = (f0_values / self.sampling_rate) % 1
|
||||
|
||||
# initial phase noise (no noise for fundamental component)
|
||||
rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \
|
||||
device=f0_values.device)
|
||||
rand_ini[:, 0] = 0
|
||||
rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
|
||||
|
||||
# instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad)
|
||||
if not self.flag_for_pulse:
|
||||
# for normal case
|
||||
|
||||
# To prevent torch.cumsum numerical overflow,
|
||||
# it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1.
|
||||
# Buffer tmp_over_one_idx indicates the time step to add -1.
|
||||
# This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi
|
||||
tmp_over_one = torch.cumsum(rad_values, 1) % 1
|
||||
tmp_over_one_idx = (padDiff(tmp_over_one)) < 0
|
||||
cumsum_shift = torch.zeros_like(rad_values)
|
||||
cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
|
||||
|
||||
sines = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1)
|
||||
* 2 * np.pi)
|
||||
else:
|
||||
# If necessary, make sure that the first time step of every
|
||||
# voiced segments is sin(pi) or cos(0)
|
||||
# This is used for pulse-train generation
|
||||
|
||||
# identify the last time step in unvoiced segments
|
||||
uv = self._f02uv(f0_values)
|
||||
uv_1 = torch.roll(uv, shifts=-1, dims=1)
|
||||
uv_1[:, -1, :] = 1
|
||||
u_loc = (uv < 1) * (uv_1 > 0)
|
||||
|
||||
# get the instantanouse phase
|
||||
tmp_cumsum = torch.cumsum(rad_values, dim=1)
|
||||
# different batch needs to be processed differently
|
||||
for idx in range(f0_values.shape[0]):
|
||||
temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :]
|
||||
temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :]
|
||||
# stores the accumulation of i.phase within
|
||||
# each voiced segments
|
||||
tmp_cumsum[idx, :, :] = 0
|
||||
tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum
|
||||
|
||||
# rad_values - tmp_cumsum: remove the accumulation of i.phase
|
||||
# within the previous voiced segment.
|
||||
i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1)
|
||||
|
||||
# get the sines
|
||||
sines = torch.cos(i_phase * 2 * np.pi)
|
||||
return sines
|
||||
|
||||
def forward(self, f0):
|
||||
""" sine_tensor, uv = forward(f0)
|
||||
input F0: tensor(batchsize=1, length, dim=1)
|
||||
f0 for unvoiced steps should be 0
|
||||
output sine_tensor: tensor(batchsize=1, length, dim)
|
||||
output uv: tensor(batchsize=1, length, 1)
|
||||
"""
|
||||
with torch.no_grad():
|
||||
f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim,
|
||||
device=f0.device)
|
||||
# fundamental component
|
||||
fn = torch.multiply(f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device))
|
||||
|
||||
# generate sine waveforms
|
||||
sine_waves = self._f02sine(fn) * self.sine_amp
|
||||
|
||||
# generate uv signal
|
||||
# uv = torch.ones(f0.shape)
|
||||
# uv = uv * (f0 > self.voiced_threshold)
|
||||
uv = self._f02uv(f0)
|
||||
|
||||
# noise: for unvoiced should be similar to sine_amp
|
||||
# std = self.sine_amp/3 -> max value ~ self.sine_amp
|
||||
# . for voiced regions is self.noise_std
|
||||
noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
|
||||
noise = noise_amp * torch.randn_like(sine_waves)
|
||||
|
||||
# first: set the unvoiced part to 0 by uv
|
||||
# then: additive noise
|
||||
sine_waves = sine_waves * uv + noise
|
||||
return sine_waves, uv, noise
|
||||
|
||||
|
||||
class SourceModuleHnNSF(torch.nn.Module):
|
||||
""" SourceModule for hn-nsf
|
||||
SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
|
||||
add_noise_std=0.003, voiced_threshod=0)
|
||||
sampling_rate: sampling_rate in Hz
|
||||
harmonic_num: number of harmonic above F0 (default: 0)
|
||||
sine_amp: amplitude of sine source signal (default: 0.1)
|
||||
add_noise_std: std of additive Gaussian noise (default: 0.003)
|
||||
note that amplitude of noise in unvoiced is decided
|
||||
by sine_amp
|
||||
voiced_threshold: threhold to set U/V given F0 (default: 0)
|
||||
Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
|
||||
F0_sampled (batchsize, length, 1)
|
||||
Sine_source (batchsize, length, 1)
|
||||
noise_source (batchsize, length 1)
|
||||
uv (batchsize, length, 1)
|
||||
"""
|
||||
|
||||
def __init__(self, sampling_rate, harmonic_num=0, sine_amp=0.1,
|
||||
add_noise_std=0.003, voiced_threshod=0):
|
||||
super(SourceModuleHnNSF, self).__init__()
|
||||
|
||||
self.sine_amp = sine_amp
|
||||
self.noise_std = add_noise_std
|
||||
|
||||
# to produce sine waveforms
|
||||
self.l_sin_gen = SineGen(sampling_rate, harmonic_num,
|
||||
sine_amp, add_noise_std, voiced_threshod)
|
||||
|
||||
# to merge source harmonics into a single excitation
|
||||
self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
|
||||
self.l_tanh = torch.nn.Tanh()
|
||||
|
||||
def forward(self, x):
|
||||
"""
|
||||
Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
|
||||
F0_sampled (batchsize, length, 1)
|
||||
Sine_source (batchsize, length, 1)
|
||||
noise_source (batchsize, length 1)
|
||||
"""
|
||||
# source for harmonic branch
|
||||
sine_wavs, uv, _ = self.l_sin_gen(x)
|
||||
sine_merge = self.l_tanh(self.l_linear(sine_wavs))
|
||||
|
||||
# source for noise branch, in the same shape as uv
|
||||
noise = torch.randn_like(uv) * self.sine_amp / 3
|
||||
return sine_merge, noise, uv
|
||||
|
||||
|
||||
class Generator(torch.nn.Module):
|
||||
def __init__(self, h):
|
||||
super(Generator, self).__init__()
|
||||
self.h = h
|
||||
|
||||
self.num_kernels = len(h["resblock_kernel_sizes"])
|
||||
self.num_upsamples = len(h["upsample_rates"])
|
||||
self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h["upsample_rates"]))
|
||||
self.m_source = SourceModuleHnNSF(
|
||||
sampling_rate=h["sampling_rate"],
|
||||
harmonic_num=8)
|
||||
self.noise_convs = nn.ModuleList()
|
||||
self.conv_pre = weight_norm(Conv1d(h["inter_channels"], h["upsample_initial_channel"], 7, 1, padding=3))
|
||||
resblock = ResBlock1 if h["resblock"] == '1' else ResBlock2
|
||||
self.ups = nn.ModuleList()
|
||||
for i, (u, k) in enumerate(zip(h["upsample_rates"], h["upsample_kernel_sizes"])):
|
||||
c_cur = h["upsample_initial_channel"] // (2 ** (i + 1))
|
||||
self.ups.append(weight_norm(
|
||||
ConvTranspose1d(h["upsample_initial_channel"] // (2 ** i), h["upsample_initial_channel"] // (2 ** (i + 1)),
|
||||
k, u, padding=(k - u) // 2)))
|
||||
if i + 1 < len(h["upsample_rates"]): #
|
||||
stride_f0 = np.prod(h["upsample_rates"][i + 1:])
|
||||
self.noise_convs.append(Conv1d(
|
||||
1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2))
|
||||
else:
|
||||
self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
|
||||
self.resblocks = nn.ModuleList()
|
||||
for i in range(len(self.ups)):
|
||||
ch = h["upsample_initial_channel"] // (2 ** (i + 1))
|
||||
for j, (k, d) in enumerate(zip(h["resblock_kernel_sizes"], h["resblock_dilation_sizes"])):
|
||||
self.resblocks.append(resblock(h, ch, k, d))
|
||||
|
||||
self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
|
||||
self.ups.apply(init_weights)
|
||||
self.conv_post.apply(init_weights)
|
||||
self.cond = nn.Conv1d(h['gin_channels'], h['upsample_initial_channel'], 1)
|
||||
|
||||
def forward(self, x, f0, g=None):
|
||||
# print(1,x.shape,f0.shape,f0[:, None].shape)
|
||||
f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t
|
||||
# print(2,f0.shape)
|
||||
har_source, noi_source, uv = self.m_source(f0)
|
||||
har_source = har_source.transpose(1, 2)
|
||||
x = self.conv_pre(x)
|
||||
x = x + self.cond(g)
|
||||
# print(124,x.shape,har_source.shape)
|
||||
for i in range(self.num_upsamples):
|
||||
x = F.leaky_relu(x, LRELU_SLOPE)
|
||||
# print(3,x.shape)
|
||||
x = self.ups[i](x)
|
||||
x_source = self.noise_convs[i](har_source)
|
||||
# print(4,x_source.shape,har_source.shape,x.shape)
|
||||
x = x + x_source
|
||||
xs = None
|
||||
for j in range(self.num_kernels):
|
||||
if xs is None:
|
||||
xs = self.resblocks[i * self.num_kernels + j](x)
|
||||
else:
|
||||
xs += self.resblocks[i * self.num_kernels + j](x)
|
||||
x = xs / self.num_kernels
|
||||
x = F.leaky_relu(x)
|
||||
x = self.conv_post(x)
|
||||
x = torch.tanh(x)
|
||||
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
print('Removing weight norm...')
|
||||
for l in self.ups:
|
||||
remove_weight_norm(l)
|
||||
for l in self.resblocks:
|
||||
l.remove_weight_norm()
|
||||
remove_weight_norm(self.conv_pre)
|
||||
remove_weight_norm(self.conv_post)
|
||||
|
||||
|
||||
class DiscriminatorP(torch.nn.Module):
|
||||
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
|
||||
super(DiscriminatorP, self).__init__()
|
||||
self.period = period
|
||||
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
||||
self.convs = nn.ModuleList([
|
||||
norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
|
||||
norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
|
||||
norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
|
||||
norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
|
||||
norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
|
||||
])
|
||||
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
|
||||
|
||||
def forward(self, x):
|
||||
fmap = []
|
||||
|
||||
# 1d to 2d
|
||||
b, c, t = x.shape
|
||||
if t % self.period != 0: # pad first
|
||||
n_pad = self.period - (t % self.period)
|
||||
x = F.pad(x, (0, n_pad), "reflect")
|
||||
t = t + n_pad
|
||||
x = x.view(b, c, t // self.period, self.period)
|
||||
|
||||
for l in self.convs:
|
||||
x = l(x)
|
||||
x = F.leaky_relu(x, LRELU_SLOPE)
|
||||
fmap.append(x)
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
x = torch.flatten(x, 1, -1)
|
||||
|
||||
return x, fmap
|
||||
|
||||
|
||||
class MultiPeriodDiscriminator(torch.nn.Module):
|
||||
def __init__(self, periods=None):
|
||||
super(MultiPeriodDiscriminator, self).__init__()
|
||||
self.periods = periods if periods is not None else [2, 3, 5, 7, 11]
|
||||
self.discriminators = nn.ModuleList()
|
||||
for period in self.periods:
|
||||
self.discriminators.append(DiscriminatorP(period))
|
||||
|
||||
def forward(self, y, y_hat):
|
||||
y_d_rs = []
|
||||
y_d_gs = []
|
||||
fmap_rs = []
|
||||
fmap_gs = []
|
||||
for i, d in enumerate(self.discriminators):
|
||||
y_d_r, fmap_r = d(y)
|
||||
y_d_g, fmap_g = d(y_hat)
|
||||
y_d_rs.append(y_d_r)
|
||||
fmap_rs.append(fmap_r)
|
||||
y_d_gs.append(y_d_g)
|
||||
fmap_gs.append(fmap_g)
|
||||
|
||||
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
||||
|
||||
|
||||
class DiscriminatorS(torch.nn.Module):
|
||||
def __init__(self, use_spectral_norm=False):
|
||||
super(DiscriminatorS, self).__init__()
|
||||
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
||||
self.convs = nn.ModuleList([
|
||||
norm_f(Conv1d(1, 128, 15, 1, padding=7)),
|
||||
norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
|
||||
norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
|
||||
norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
|
||||
norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
|
||||
norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
|
||||
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
|
||||
])
|
||||
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
|
||||
|
||||
def forward(self, x):
|
||||
fmap = []
|
||||
for l in self.convs:
|
||||
x = l(x)
|
||||
x = F.leaky_relu(x, LRELU_SLOPE)
|
||||
fmap.append(x)
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
x = torch.flatten(x, 1, -1)
|
||||
|
||||
return x, fmap
|
||||
|
||||
|
||||
class MultiScaleDiscriminator(torch.nn.Module):
|
||||
def __init__(self):
|
||||
super(MultiScaleDiscriminator, self).__init__()
|
||||
self.discriminators = nn.ModuleList([
|
||||
DiscriminatorS(use_spectral_norm=True),
|
||||
DiscriminatorS(),
|
||||
DiscriminatorS(),
|
||||
])
|
||||
self.meanpools = nn.ModuleList([
|
||||
AvgPool1d(4, 2, padding=2),
|
||||
AvgPool1d(4, 2, padding=2)
|
||||
])
|
||||
|
||||
def forward(self, y, y_hat):
|
||||
y_d_rs = []
|
||||
y_d_gs = []
|
||||
fmap_rs = []
|
||||
fmap_gs = []
|
||||
for i, d in enumerate(self.discriminators):
|
||||
if i != 0:
|
||||
y = self.meanpools[i - 1](y)
|
||||
y_hat = self.meanpools[i - 1](y_hat)
|
||||
y_d_r, fmap_r = d(y)
|
||||
y_d_g, fmap_g = d(y_hat)
|
||||
y_d_rs.append(y_d_r)
|
||||
fmap_rs.append(fmap_r)
|
||||
y_d_gs.append(y_d_g)
|
||||
fmap_gs.append(fmap_g)
|
||||
|
||||
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
||||
|
||||
|
||||
def feature_loss(fmap_r, fmap_g):
|
||||
loss = 0
|
||||
for dr, dg in zip(fmap_r, fmap_g):
|
||||
for rl, gl in zip(dr, dg):
|
||||
loss += torch.mean(torch.abs(rl - gl))
|
||||
|
||||
return loss * 2
|
||||
|
||||
|
||||
def discriminator_loss(disc_real_outputs, disc_generated_outputs):
|
||||
loss = 0
|
||||
r_losses = []
|
||||
g_losses = []
|
||||
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
|
||||
r_loss = torch.mean((1 - dr) ** 2)
|
||||
g_loss = torch.mean(dg ** 2)
|
||||
loss += (r_loss + g_loss)
|
||||
r_losses.append(r_loss.item())
|
||||
g_losses.append(g_loss.item())
|
||||
|
||||
return loss, r_losses, g_losses
|
||||
|
||||
|
||||
def generator_loss(disc_outputs):
|
||||
loss = 0
|
||||
gen_losses = []
|
||||
for dg in disc_outputs:
|
||||
l = torch.mean((1 - dg) ** 2)
|
||||
gen_losses.append(l)
|
||||
loss += l
|
||||
|
||||
return loss, gen_losses
|
||||
@ -0,0 +1,111 @@
|
||||
import math
|
||||
import os
|
||||
os.environ["LRU_CACHE_CAPACITY"] = "3"
|
||||
import random
|
||||
import torch
|
||||
import torch.utils.data
|
||||
import numpy as np
|
||||
import librosa
|
||||
from librosa.util import normalize
|
||||
from librosa.filters import mel as librosa_mel_fn
|
||||
from scipy.io.wavfile import read
|
||||
import soundfile as sf
|
||||
|
||||
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
|
||||
sampling_rate = None
|
||||
try:
|
||||
data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile.
|
||||
except Exception as ex:
|
||||
print(f"'{full_path}' failed to load.\nException:")
|
||||
print(ex)
|
||||
if return_empty_on_exception:
|
||||
return [], sampling_rate or target_sr or 32000
|
||||
else:
|
||||
raise Exception(ex)
|
||||
|
||||
if len(data.shape) > 1:
|
||||
data = data[:, 0]
|
||||
assert len(data) > 2# check duration of audio file is > 2 samples (because otherwise the slice operation was on the wrong dimension)
|
||||
|
||||
if np.issubdtype(data.dtype, np.integer): # if audio data is type int
|
||||
max_mag = -np.iinfo(data.dtype).min # maximum magnitude = min possible value of intXX
|
||||
else: # if audio data is type fp32
|
||||
max_mag = max(np.amax(data), -np.amin(data))
|
||||
max_mag = (2**31)+1 if max_mag > (2**15) else ((2**15)+1 if max_mag > 1.01 else 1.0) # data should be either 16-bit INT, 32-bit INT or [-1 to 1] float32
|
||||
|
||||
data = torch.FloatTensor(data.astype(np.float32))/max_mag
|
||||
|
||||
if (torch.isinf(data) | torch.isnan(data)).any() and return_empty_on_exception:# resample will crash with inf/NaN inputs. return_empty_on_exception will return empty arr instead of except
|
||||
return [], sampling_rate or target_sr or 32000
|
||||
if target_sr is not None and sampling_rate != target_sr:
|
||||
data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sampling_rate, target_sr=target_sr))
|
||||
sampling_rate = target_sr
|
||||
|
||||
return data, sampling_rate
|
||||
|
||||
def dynamic_range_compression(x, C=1, clip_val=1e-5):
|
||||
return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
|
||||
|
||||
def dynamic_range_decompression(x, C=1):
|
||||
return np.exp(x) / C
|
||||
|
||||
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
|
||||
return torch.log(torch.clamp(x, min=clip_val) * C)
|
||||
|
||||
def dynamic_range_decompression_torch(x, C=1):
|
||||
return torch.exp(x) / C
|
||||
|
||||
class STFT():
|
||||
def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
|
||||
self.target_sr = sr
|
||||
|
||||
self.n_mels = n_mels
|
||||
self.n_fft = n_fft
|
||||
self.win_size = win_size
|
||||
self.hop_length = hop_length
|
||||
self.fmin = fmin
|
||||
self.fmax = fmax
|
||||
self.clip_val = clip_val
|
||||
self.mel_basis = {}
|
||||
self.hann_window = {}
|
||||
|
||||
def get_mel(self, y, center=False):
|
||||
sampling_rate = self.target_sr
|
||||
n_mels = self.n_mels
|
||||
n_fft = self.n_fft
|
||||
win_size = self.win_size
|
||||
hop_length = self.hop_length
|
||||
fmin = self.fmin
|
||||
fmax = self.fmax
|
||||
clip_val = self.clip_val
|
||||
|
||||
if torch.min(y) < -1.:
|
||||
print('min value is ', torch.min(y))
|
||||
if torch.max(y) > 1.:
|
||||
print('max value is ', torch.max(y))
|
||||
|
||||
if fmax not in self.mel_basis:
|
||||
mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
|
||||
self.mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
|
||||
self.hann_window[str(y.device)] = torch.hann_window(self.win_size).to(y.device)
|
||||
|
||||
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_length)/2), int((n_fft-hop_length)/2)), mode='reflect')
|
||||
y = y.squeeze(1)
|
||||
|
||||
spec = torch.stft(y, n_fft, hop_length=hop_length, win_length=win_size, window=self.hann_window[str(y.device)],
|
||||
center=center, pad_mode='reflect', normalized=False, onesided=True)
|
||||
# print(111,spec)
|
||||
spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
|
||||
# print(222,spec)
|
||||
spec = torch.matmul(self.mel_basis[str(fmax)+'_'+str(y.device)], spec)
|
||||
# print(333,spec)
|
||||
spec = dynamic_range_compression_torch(spec, clip_val=clip_val)
|
||||
# print(444,spec)
|
||||
return spec
|
||||
|
||||
def __call__(self, audiopath):
|
||||
audio, sr = load_wav_to_torch(audiopath, target_sr=self.target_sr)
|
||||
spect = self.get_mel(audio.unsqueeze(0)).squeeze(0)
|
||||
return spect
|
||||
|
||||
stft = STFT()
|
||||
@ -0,0 +1,68 @@
|
||||
import glob
|
||||
import os
|
||||
import matplotlib
|
||||
import torch
|
||||
from torch.nn.utils import weight_norm
|
||||
matplotlib.use("Agg")
|
||||
import matplotlib.pylab as plt
|
||||
|
||||
|
||||
def plot_spectrogram(spectrogram):
|
||||
fig, ax = plt.subplots(figsize=(10, 2))
|
||||
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
|
||||
interpolation='none')
|
||||
plt.colorbar(im, ax=ax)
|
||||
|
||||
fig.canvas.draw()
|
||||
plt.close()
|
||||
|
||||
return fig
|
||||
|
||||
|
||||
def init_weights(m, mean=0.0, std=0.01):
|
||||
classname = m.__class__.__name__
|
||||
if classname.find("Conv") != -1:
|
||||
m.weight.data.normal_(mean, std)
|
||||
|
||||
|
||||
def apply_weight_norm(m):
|
||||
classname = m.__class__.__name__
|
||||
if classname.find("Conv") != -1:
|
||||
weight_norm(m)
|
||||
|
||||
|
||||
def get_padding(kernel_size, dilation=1):
|
||||
return int((kernel_size*dilation - dilation)/2)
|
||||
|
||||
|
||||
def load_checkpoint(filepath, device):
|
||||
assert os.path.isfile(filepath)
|
||||
print("Loading '{}'".format(filepath))
|
||||
checkpoint_dict = torch.load(filepath, map_location=device)
|
||||
print("Complete.")
|
||||
return checkpoint_dict
|
||||
|
||||
|
||||
def save_checkpoint(filepath, obj):
|
||||
print("Saving checkpoint to {}".format(filepath))
|
||||
torch.save(obj, filepath)
|
||||
print("Complete.")
|
||||
|
||||
|
||||
def del_old_checkpoints(cp_dir, prefix, n_models=2):
|
||||
pattern = os.path.join(cp_dir, prefix + '????????')
|
||||
cp_list = glob.glob(pattern) # get checkpoint paths
|
||||
cp_list = sorted(cp_list)# sort by iter
|
||||
if len(cp_list) > n_models: # if more than n_models models are found
|
||||
for cp in cp_list[:-n_models]:# delete the oldest models other than lastest n_models
|
||||
open(cp, 'w').close()# empty file contents
|
||||
os.unlink(cp)# delete file (move to trash when using Colab)
|
||||
|
||||
|
||||
def scan_checkpoint(cp_dir, prefix):
|
||||
pattern = os.path.join(cp_dir, prefix + '????????')
|
||||
cp_list = glob.glob(pattern)
|
||||
if len(cp_list) == 0:
|
||||
return None
|
||||
return sorted(cp_list)[-1]
|
||||
|
||||
Loading…
Reference in new issue