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Duplicate from l1006986533/vits-chinese
Browse filesCo-authored-by: liao <l1006986533@users.noreply.huggingface.co>
- .gitattributes +34 -0
- .gitignore +2 -0
- G_98000.pth +3 -0
- README.md +11 -0
- app.py +54 -0
- attentions.py +303 -0
- commons.py +161 -0
- configs/config.json +52 -0
- models.py +534 -0
- modules.py +390 -0
- monotonic_align/__init__.py +19 -0
- monotonic_align/core.c +0 -0
- monotonic_align/core.pyx +42 -0
- monotonic_align/setup.py +9 -0
- requirements.txt +6 -0
- text/__init__.py +64 -0
- text/cleaners.py +138 -0
- text/symbols.py +24 -0
- transforms.py +193 -0
- utils.py +259 -0
.gitattributes
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.arrow filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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*.rar filter=lfs diff=lfs merge=lfs -text
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*.safetensors filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.tar.* filter=lfs diff=lfs merge=lfs -text
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*.xz filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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.gitignore
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.DS_Store
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G_98000.pth
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G_98000.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:8229f5dfdc52704016fe1cd8a58d0c37ddabfe04266ea71a41fdce686fb30509
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size 436704977
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README.md
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---
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title: Vits Chinese
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emoji: 🏃
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colorFrom: red
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colorTo: yellow
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sdk: gradio
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sdk_version: 3.9
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app_file: app.py
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pinned: false
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duplicated_from: l1006986533/vits-chinese
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---
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app.py
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import gradio as gr
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import os
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os.system('cd monotonic_align && python setup.py build_ext --inplace && cd ..')
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import torch
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import commons
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import utils
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from models import SynthesizerTrn
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from text.symbols import symbols
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from text import text_to_sequence
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def get_text(text, hps):
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text_norm = text_to_sequence(text, hps.data.text_cleaners)
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if hps.data.add_blank:
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text_norm = commons.intersperse(text_norm, 0)
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text_norm = torch.LongTensor(text_norm)
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return text_norm
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hps = utils.get_hparams_from_file("configs/config.json")
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net_g = SynthesizerTrn(
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len(symbols),
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hps.data.filter_length // 2 + 1,
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hps.train.segment_size // hps.data.hop_length,
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**hps.model)
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_ = net_g.eval()
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# _ = utils.load_checkpoint("logs/woman_csmsc/G_100000.pth", net_g, None)
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_ = utils.load_checkpoint("G_98000.pth", net_g, None)
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def vc_fn(input):
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stn_tst = get_text(input, hps)
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with torch.no_grad():
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x_tst = stn_tst.unsqueeze(0)
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x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
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# x_tst = stn_tst.cpu().unsqueeze(0)
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# x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).cpu()
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audio = net_g.infer(x_tst, x_tst_lengths, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][0,0].data.cpu().float().numpy()
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sampling_rate = 44100
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return (sampling_rate, audio)
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app = gr.Blocks()
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with app:
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with gr.Tabs():
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with gr.TabItem("Basic"):
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vc_input = gr.Textbox(label="Input Message")
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vc_submit = gr.Button("Convert", variant="primary")
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vc_output = gr.Audio(label="Output Audio")
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vc_submit.click(vc_fn, [ vc_input], [vc_output])
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app.launch()
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attentions.py
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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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118 |
+
self.conv_q = nn.Conv1d(channels, channels, 1)
|
119 |
+
self.conv_k = nn.Conv1d(channels, channels, 1)
|
120 |
+
self.conv_v = nn.Conv1d(channels, channels, 1)
|
121 |
+
self.conv_o = nn.Conv1d(channels, out_channels, 1)
|
122 |
+
self.drop = nn.Dropout(p_dropout)
|
123 |
+
|
124 |
+
if window_size is not None:
|
125 |
+
n_heads_rel = 1 if heads_share else n_heads
|
126 |
+
rel_stddev = self.k_channels**-0.5
|
127 |
+
self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
|
128 |
+
self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
|
129 |
+
|
130 |
+
nn.init.xavier_uniform_(self.conv_q.weight)
|
131 |
+
nn.init.xavier_uniform_(self.conv_k.weight)
|
132 |
+
nn.init.xavier_uniform_(self.conv_v.weight)
|
133 |
+
if proximal_init:
|
134 |
+
with torch.no_grad():
|
135 |
+
self.conv_k.weight.copy_(self.conv_q.weight)
|
136 |
+
self.conv_k.bias.copy_(self.conv_q.bias)
|
137 |
+
|
138 |
+
def forward(self, x, c, attn_mask=None):
|
139 |
+
q = self.conv_q(x)
|
140 |
+
k = self.conv_k(c)
|
141 |
+
v = self.conv_v(c)
|
142 |
+
|
143 |
+
x, self.attn = self.attention(q, k, v, mask=attn_mask)
|
144 |
+
|
145 |
+
x = self.conv_o(x)
|
146 |
+
return x
|
147 |
+
|
148 |
+
def attention(self, query, key, value, mask=None):
|
149 |
+
# reshape [b, d, t] -> [b, n_h, t, d_k]
|
150 |
+
b, d, t_s, t_t = (*key.size(), query.size(2))
|
151 |
+
query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
|
152 |
+
key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
|
153 |
+
value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
|
154 |
+
|
155 |
+
scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
|
156 |
+
if self.window_size is not None:
|
157 |
+
assert t_s == t_t, "Relative attention is only available for self-attention."
|
158 |
+
key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
|
159 |
+
rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
|
160 |
+
scores_local = self._relative_position_to_absolute_position(rel_logits)
|
161 |
+
scores = scores + scores_local
|
162 |
+
if self.proximal_bias:
|
163 |
+
assert t_s == t_t, "Proximal bias is only available for self-attention."
|
164 |
+
scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
|
165 |
+
if mask is not None:
|
166 |
+
scores = scores.masked_fill(mask == 0, -1e4)
|
167 |
+
if self.block_length is not None:
|
168 |
+
assert t_s == t_t, "Local attention is only available for self-attention."
|
169 |
+
block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
|
170 |
+
scores = scores.masked_fill(block_mask == 0, -1e4)
|
171 |
+
p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
|
172 |
+
p_attn = self.drop(p_attn)
|
173 |
+
output = torch.matmul(p_attn, value)
|
174 |
+
if self.window_size is not None:
|
175 |
+
relative_weights = self._absolute_position_to_relative_position(p_attn)
|
176 |
+
value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
|
177 |
+
output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
|
178 |
+
output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
|
179 |
+
return output, p_attn
|
180 |
+
|
181 |
+
def _matmul_with_relative_values(self, x, y):
|
182 |
+
"""
|
183 |
+
x: [b, h, l, m]
|
184 |
+
y: [h or 1, m, d]
|
185 |
+
ret: [b, h, l, d]
|
186 |
+
"""
|
187 |
+
ret = torch.matmul(x, y.unsqueeze(0))
|
188 |
+
return ret
|
189 |
+
|
190 |
+
def _matmul_with_relative_keys(self, x, y):
|
191 |
+
"""
|
192 |
+
x: [b, h, l, d]
|
193 |
+
y: [h or 1, m, d]
|
194 |
+
ret: [b, h, l, m]
|
195 |
+
"""
|
196 |
+
ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
|
197 |
+
return ret
|
198 |
+
|
199 |
+
def _get_relative_embeddings(self, relative_embeddings, length):
|
200 |
+
max_relative_position = 2 * self.window_size + 1
|
201 |
+
# Pad first before slice to avoid using cond ops.
|
202 |
+
pad_length = max(length - (self.window_size + 1), 0)
|
203 |
+
slice_start_position = max((self.window_size + 1) - length, 0)
|
204 |
+
slice_end_position = slice_start_position + 2 * length - 1
|
205 |
+
if pad_length > 0:
|
206 |
+
padded_relative_embeddings = F.pad(
|
207 |
+
relative_embeddings,
|
208 |
+
commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
|
209 |
+
else:
|
210 |
+
padded_relative_embeddings = relative_embeddings
|
211 |
+
used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
|
212 |
+
return used_relative_embeddings
|
213 |
+
|
214 |
+
def _relative_position_to_absolute_position(self, x):
|
215 |
+
"""
|
216 |
+
x: [b, h, l, 2*l-1]
|
217 |
+
ret: [b, h, l, l]
|
218 |
+
"""
|
219 |
+
batch, heads, length, _ = x.size()
|
220 |
+
# Concat columns of pad to shift from relative to absolute indexing.
|
221 |
+
x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
|
222 |
+
|
223 |
+
# Concat extra elements so to add up to shape (len+1, 2*len-1).
|
224 |
+
x_flat = x.view([batch, heads, length * 2 * length])
|
225 |
+
x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
|
226 |
+
|
227 |
+
# Reshape and slice out the padded elements.
|
228 |
+
x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
|
229 |
+
return x_final
|
230 |
+
|
231 |
+
def _absolute_position_to_relative_position(self, x):
|
232 |
+
"""
|
233 |
+
x: [b, h, l, l]
|
234 |
+
ret: [b, h, l, 2*l-1]
|
235 |
+
"""
|
236 |
+
batch, heads, length, _ = x.size()
|
237 |
+
# padd along column
|
238 |
+
x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
|
239 |
+
x_flat = x.view([batch, heads, length**2 + length*(length -1)])
|
240 |
+
# add 0's in the beginning that will skew the elements after reshape
|
241 |
+
x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
|
242 |
+
x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
|
243 |
+
return x_final
|
244 |
+
|
245 |
+
def _attention_bias_proximal(self, length):
|
246 |
+
"""Bias for self-attention to encourage attention to close positions.
|
247 |
+
Args:
|
248 |
+
length: an integer scalar.
|
249 |
+
Returns:
|
250 |
+
a Tensor with shape [1, 1, length, length]
|
251 |
+
"""
|
252 |
+
r = torch.arange(length, dtype=torch.float32)
|
253 |
+
diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
|
254 |
+
return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
|
255 |
+
|
256 |
+
|
257 |
+
class FFN(nn.Module):
|
258 |
+
def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
|
259 |
+
super().__init__()
|
260 |
+
self.in_channels = in_channels
|
261 |
+
self.out_channels = out_channels
|
262 |
+
self.filter_channels = filter_channels
|
263 |
+
self.kernel_size = kernel_size
|
264 |
+
self.p_dropout = p_dropout
|
265 |
+
self.activation = activation
|
266 |
+
self.causal = causal
|
267 |
+
|
268 |
+
if causal:
|
269 |
+
self.padding = self._causal_padding
|
270 |
+
else:
|
271 |
+
self.padding = self._same_padding
|
272 |
+
|
273 |
+
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
|
274 |
+
self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
|
275 |
+
self.drop = nn.Dropout(p_dropout)
|
276 |
+
|
277 |
+
def forward(self, x, x_mask):
|
278 |
+
x = self.conv_1(self.padding(x * x_mask))
|
279 |
+
if self.activation == "gelu":
|
280 |
+
x = x * torch.sigmoid(1.702 * x)
|
281 |
+
else:
|
282 |
+
x = torch.relu(x)
|
283 |
+
x = self.drop(x)
|
284 |
+
x = self.conv_2(self.padding(x * x_mask))
|
285 |
+
return x * x_mask
|
286 |
+
|
287 |
+
def _causal_padding(self, x):
|
288 |
+
if self.kernel_size == 1:
|
289 |
+
return x
|
290 |
+
pad_l = self.kernel_size - 1
|
291 |
+
pad_r = 0
|
292 |
+
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
|
293 |
+
x = F.pad(x, commons.convert_pad_shape(padding))
|
294 |
+
return x
|
295 |
+
|
296 |
+
def _same_padding(self, x):
|
297 |
+
if self.kernel_size == 1:
|
298 |
+
return x
|
299 |
+
pad_l = (self.kernel_size - 1) // 2
|
300 |
+
pad_r = self.kernel_size // 2
|
301 |
+
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
|
302 |
+
x = F.pad(x, commons.convert_pad_shape(padding))
|
303 |
+
return x
|
commons.py
ADDED
@@ -0,0 +1,161 @@
|
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|
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|
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|
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|
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|
|
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|
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|
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|
|
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|
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|
|
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|
|
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|
|
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|
|
|
|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import math
|
2 |
+
import numpy as np
|
3 |
+
import torch
|
4 |
+
from torch import nn
|
5 |
+
from torch.nn import functional as F
|
6 |
+
|
7 |
+
|
8 |
+
def init_weights(m, mean=0.0, std=0.01):
|
9 |
+
classname = m.__class__.__name__
|
10 |
+
if classname.find("Conv") != -1:
|
11 |
+
m.weight.data.normal_(mean, std)
|
12 |
+
|
13 |
+
|
14 |
+
def get_padding(kernel_size, dilation=1):
|
15 |
+
return int((kernel_size*dilation - dilation)/2)
|
16 |
+
|
17 |
+
|
18 |
+
def convert_pad_shape(pad_shape):
|
19 |
+
l = pad_shape[::-1]
|
20 |
+
pad_shape = [item for sublist in l for item in sublist]
|
21 |
+
return pad_shape
|
22 |
+
|
23 |
+
|
24 |
+
def intersperse(lst, item):
|
25 |
+
result = [item] * (len(lst) * 2 + 1)
|
26 |
+
result[1::2] = lst
|
27 |
+
return result
|
28 |
+
|
29 |
+
|
30 |
+
def kl_divergence(m_p, logs_p, m_q, logs_q):
|
31 |
+
"""KL(P||Q)"""
|
32 |
+
kl = (logs_q - logs_p) - 0.5
|
33 |
+
kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
|
34 |
+
return kl
|
35 |
+
|
36 |
+
|
37 |
+
def rand_gumbel(shape):
|
38 |
+
"""Sample from the Gumbel distribution, protect from overflows."""
|
39 |
+
uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
|
40 |
+
return -torch.log(-torch.log(uniform_samples))
|
41 |
+
|
42 |
+
|
43 |
+
def rand_gumbel_like(x):
|
44 |
+
g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
|
45 |
+
return g
|
46 |
+
|
47 |
+
|
48 |
+
def slice_segments(x, ids_str, segment_size=4):
|
49 |
+
ret = torch.zeros_like(x[:, :, :segment_size])
|
50 |
+
for i in range(x.size(0)):
|
51 |
+
idx_str = ids_str[i]
|
52 |
+
idx_end = idx_str + segment_size
|
53 |
+
ret[i] = x[i, :, idx_str:idx_end]
|
54 |
+
return ret
|
55 |
+
|
56 |
+
|
57 |
+
def rand_slice_segments(x, x_lengths=None, segment_size=4):
|
58 |
+
b, d, t = x.size()
|
59 |
+
if x_lengths is None:
|
60 |
+
x_lengths = t
|
61 |
+
ids_str_max = x_lengths - segment_size + 1
|
62 |
+
ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
|
63 |
+
ret = slice_segments(x, ids_str, segment_size)
|
64 |
+
return ret, ids_str
|
65 |
+
|
66 |
+
|
67 |
+
def get_timing_signal_1d(
|
68 |
+
length, channels, min_timescale=1.0, max_timescale=1.0e4):
|
69 |
+
position = torch.arange(length, dtype=torch.float)
|
70 |
+
num_timescales = channels // 2
|
71 |
+
log_timescale_increment = (
|
72 |
+
math.log(float(max_timescale) / float(min_timescale)) /
|
73 |
+
(num_timescales - 1))
|
74 |
+
inv_timescales = min_timescale * torch.exp(
|
75 |
+
torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
|
76 |
+
scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
|
77 |
+
signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
|
78 |
+
signal = F.pad(signal, [0, 0, 0, channels % 2])
|
79 |
+
signal = signal.view(1, channels, length)
|
80 |
+
return signal
|
81 |
+
|
82 |
+
|
83 |
+
def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
|
84 |
+
b, channels, length = x.size()
|
85 |
+
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
|
86 |
+
return x + signal.to(dtype=x.dtype, device=x.device)
|
87 |
+
|
88 |
+
|
89 |
+
def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
|
90 |
+
b, channels, length = x.size()
|
91 |
+
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
|
92 |
+
return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
|
93 |
+
|
94 |
+
|
95 |
+
def subsequent_mask(length):
|
96 |
+
mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
|
97 |
+
return mask
|
98 |
+
|
99 |
+
|
100 |
+
@torch.jit.script
|
101 |
+
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
|
102 |
+
n_channels_int = n_channels[0]
|
103 |
+
in_act = input_a + input_b
|
104 |
+
t_act = torch.tanh(in_act[:, :n_channels_int, :])
|
105 |
+
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
|
106 |
+
acts = t_act * s_act
|
107 |
+
return acts
|
108 |
+
|
109 |
+
|
110 |
+
def convert_pad_shape(pad_shape):
|
111 |
+
l = pad_shape[::-1]
|
112 |
+
pad_shape = [item for sublist in l for item in sublist]
|
113 |
+
return pad_shape
|
114 |
+
|
115 |
+
|
116 |
+
def shift_1d(x):
|
117 |
+
x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
|
118 |
+
return x
|
119 |
+
|
120 |
+
|
121 |
+
def sequence_mask(length, max_length=None):
|
122 |
+
if max_length is None:
|
123 |
+
max_length = length.max()
|
124 |
+
x = torch.arange(max_length, dtype=length.dtype, device=length.device)
|
125 |
+
return x.unsqueeze(0) < length.unsqueeze(1)
|
126 |
+
|
127 |
+
|
128 |
+
def generate_path(duration, mask):
|
129 |
+
"""
|
130 |
+
duration: [b, 1, t_x]
|
131 |
+
mask: [b, 1, t_y, t_x]
|
132 |
+
"""
|
133 |
+
device = duration.device
|
134 |
+
|
135 |
+
b, _, t_y, t_x = mask.shape
|
136 |
+
cum_duration = torch.cumsum(duration, -1)
|
137 |
+
|
138 |
+
cum_duration_flat = cum_duration.view(b * t_x)
|
139 |
+
path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
|
140 |
+
path = path.view(b, t_x, t_y)
|
141 |
+
path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
|
142 |
+
path = path.unsqueeze(1).transpose(2,3) * mask
|
143 |
+
return path
|
144 |
+
|
145 |
+
|
146 |
+
def clip_grad_value_(parameters, clip_value, norm_type=2):
|
147 |
+
if isinstance(parameters, torch.Tensor):
|
148 |
+
parameters = [parameters]
|
149 |
+
parameters = list(filter(lambda p: p.grad is not None, parameters))
|
150 |
+
norm_type = float(norm_type)
|
151 |
+
if clip_value is not None:
|
152 |
+
clip_value = float(clip_value)
|
153 |
+
|
154 |
+
total_norm = 0
|
155 |
+
for p in parameters:
|
156 |
+
param_norm = p.grad.data.norm(norm_type)
|
157 |
+
total_norm += param_norm.item() ** norm_type
|
158 |
+
if clip_value is not None:
|
159 |
+
p.grad.data.clamp_(min=-clip_value, max=clip_value)
|
160 |
+
total_norm = total_norm ** (1. / norm_type)
|
161 |
+
return total_norm
|
configs/config.json
ADDED
@@ -0,0 +1,52 @@
|
|
|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"train": {
|
3 |
+
"log_interval": 200,
|
4 |
+
"eval_interval": 2000,
|
5 |
+
"seed": 1234,
|
6 |
+
"epochs": 20000,
|
7 |
+
"learning_rate": 2e-4,
|
8 |
+
"betas": [0.8, 0.99],
|
9 |
+
"eps": 1e-9,
|
10 |
+
"batch_size": 24,
|
11 |
+
"fp16_run": true,
|
12 |
+
"lr_decay": 0.999875,
|
13 |
+
"segment_size": 8192,
|
14 |
+
"init_lr_ratio": 1,
|
15 |
+
"warmup_epochs": 0,
|
16 |
+
"c_mel": 45,
|
17 |
+
"c_kl": 1.0
|
18 |
+
},
|
19 |
+
"data": {
|
20 |
+
"training_files":"test/dsq.txt.cleaned",
|
21 |
+
"validation_files":"test/dsq_val.txt.cleaned",
|
22 |
+
"text_cleaners":["chinese_cleaners1"],
|
23 |
+
"max_wav_value": 32768.0,
|
24 |
+
"sampling_rate": 44100,
|
25 |
+
"filter_length": 1024,
|
26 |
+
"hop_length": 256,
|
27 |
+
"win_length": 1024,
|
28 |
+
"n_mel_channels": 80,
|
29 |
+
"mel_fmin": 0.0,
|
30 |
+
"mel_fmax": null,
|
31 |
+
"add_blank": true,
|
32 |
+
"n_speakers": 0,
|
33 |
+
"cleaned_text": true
|
34 |
+
},
|
35 |
+
"model": {
|
36 |
+
"inter_channels": 192,
|
37 |
+
"hidden_channels": 192,
|
38 |
+
"filter_channels": 768,
|
39 |
+
"n_heads": 2,
|
40 |
+
"n_layers": 6,
|
41 |
+
"kernel_size": 3,
|
42 |
+
"p_dropout": 0.1,
|
43 |
+
"resblock": "1",
|
44 |
+
"resblock_kernel_sizes": [3,7,11],
|
45 |
+
"resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
|
46 |
+
"upsample_rates": [8,8,2,2],
|
47 |
+
"upsample_initial_channel": 512,
|
48 |
+
"upsample_kernel_sizes": [16,16,4,4],
|
49 |
+
"n_layers_q": 3,
|
50 |
+
"use_spectral_norm": false
|
51 |
+
}
|
52 |
+
}
|
models.py
ADDED
@@ -0,0 +1,534 @@
|
|
|
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|
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|
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|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import copy
|
2 |
+
import math
|
3 |
+
import torch
|
4 |
+
from torch import nn
|
5 |
+
from torch.nn import functional as F
|
6 |
+
|
7 |
+
import commons
|
8 |
+
import modules
|
9 |
+
import attentions
|
10 |
+
import monotonic_align
|
11 |
+
|
12 |
+
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
|
13 |
+
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
|
14 |
+
from commons import init_weights, get_padding
|
15 |
+
|
16 |
+
|
17 |
+
class StochasticDurationPredictor(nn.Module):
|
18 |
+
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
|
19 |
+
super().__init__()
|
20 |
+
filter_channels = in_channels # it needs to be removed from future version.
|
21 |
+
self.in_channels = in_channels
|
22 |
+
self.filter_channels = filter_channels
|
23 |
+
self.kernel_size = kernel_size
|
24 |
+
self.p_dropout = p_dropout
|
25 |
+
self.n_flows = n_flows
|
26 |
+
self.gin_channels = gin_channels
|
27 |
+
|
28 |
+
self.log_flow = modules.Log()
|
29 |
+
self.flows = nn.ModuleList()
|
30 |
+
self.flows.append(modules.ElementwiseAffine(2))
|
31 |
+
for i in range(n_flows):
|
32 |
+
self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
|
33 |
+
self.flows.append(modules.Flip())
|
34 |
+
|
35 |
+
self.post_pre = nn.Conv1d(1, filter_channels, 1)
|
36 |
+
self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
|
37 |
+
self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
|
38 |
+
self.post_flows = nn.ModuleList()
|
39 |
+
self.post_flows.append(modules.ElementwiseAffine(2))
|
40 |
+
for i in range(4):
|
41 |
+
self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
|
42 |
+
self.post_flows.append(modules.Flip())
|
43 |
+
|
44 |
+
self.pre = nn.Conv1d(in_channels, filter_channels, 1)
|
45 |
+
self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
|
46 |
+
self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
|
47 |
+
if gin_channels != 0:
|
48 |
+
self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
|
49 |
+
|
50 |
+
def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
|
51 |
+
x = torch.detach(x)
|
52 |
+
x = self.pre(x)
|
53 |
+
if g is not None:
|
54 |
+
g = torch.detach(g)
|
55 |
+
x = x + self.cond(g)
|
56 |
+
x = self.convs(x, x_mask)
|
57 |
+
x = self.proj(x) * x_mask
|
58 |
+
|
59 |
+
if not reverse:
|
60 |
+
flows = self.flows
|
61 |
+
assert w is not None
|
62 |
+
|
63 |
+
logdet_tot_q = 0
|
64 |
+
h_w = self.post_pre(w)
|
65 |
+
h_w = self.post_convs(h_w, x_mask)
|
66 |
+
h_w = self.post_proj(h_w) * x_mask
|
67 |
+
e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
|
68 |
+
z_q = e_q
|
69 |
+
for flow in self.post_flows:
|
70 |
+
z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
|
71 |
+
logdet_tot_q += logdet_q
|
72 |
+
z_u, z1 = torch.split(z_q, [1, 1], 1)
|
73 |
+
u = torch.sigmoid(z_u) * x_mask
|
74 |
+
z0 = (w - u) * x_mask
|
75 |
+
logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
|
76 |
+
logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
|
77 |
+
|
78 |
+
logdet_tot = 0
|
79 |
+
z0, logdet = self.log_flow(z0, x_mask)
|
80 |
+
logdet_tot += logdet
|
81 |
+
z = torch.cat([z0, z1], 1)
|
82 |
+
for flow in flows:
|
83 |
+
z, logdet = flow(z, x_mask, g=x, reverse=reverse)
|
84 |
+
logdet_tot = logdet_tot + logdet
|
85 |
+
nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
|
86 |
+
return nll + logq # [b]
|
87 |
+
else:
|
88 |
+
flows = list(reversed(self.flows))
|
89 |
+
flows = flows[:-2] + [flows[-1]] # remove a useless vflow
|
90 |
+
z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
|
91 |
+
for flow in flows:
|
92 |
+
z = flow(z, x_mask, g=x, reverse=reverse)
|
93 |
+
z0, z1 = torch.split(z, [1, 1], 1)
|
94 |
+
logw = z0
|
95 |
+
return logw
|
96 |
+
|
97 |
+
|
98 |
+
class DurationPredictor(nn.Module):
|
99 |
+
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
|
100 |
+
super().__init__()
|
101 |
+
|
102 |
+
self.in_channels = in_channels
|
103 |
+
self.filter_channels = filter_channels
|
104 |
+
self.kernel_size = kernel_size
|
105 |
+
self.p_dropout = p_dropout
|
106 |
+
self.gin_channels = gin_channels
|
107 |
+
|
108 |
+
self.drop = nn.Dropout(p_dropout)
|
109 |
+
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
|
110 |
+
self.norm_1 = modules.LayerNorm(filter_channels)
|
111 |
+
self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
|
112 |
+
self.norm_2 = modules.LayerNorm(filter_channels)
|
113 |
+
self.proj = nn.Conv1d(filter_channels, 1, 1)
|
114 |
+
|
115 |
+
if gin_channels != 0:
|
116 |
+
self.cond = nn.Conv1d(gin_channels, in_channels, 1)
|
117 |
+
|
118 |
+
def forward(self, x, x_mask, g=None):
|
119 |
+
x = torch.detach(x)
|
120 |
+
if g is not None:
|
121 |
+
g = torch.detach(g)
|
122 |
+
x = x + self.cond(g)
|
123 |
+
x = self.conv_1(x * x_mask)
|
124 |
+
x = torch.relu(x)
|
125 |
+
x = self.norm_1(x)
|
126 |
+
x = self.drop(x)
|
127 |
+
x = self.conv_2(x * x_mask)
|
128 |
+
x = torch.relu(x)
|
129 |
+
x = self.norm_2(x)
|
130 |
+
x = self.drop(x)
|
131 |
+
x = self.proj(x * x_mask)
|
132 |
+
return x * x_mask
|
133 |
+
|
134 |
+
|
135 |
+
class TextEncoder(nn.Module):
|
136 |
+
def __init__(self,
|
137 |
+
n_vocab,
|
138 |
+
out_channels,
|
139 |
+
hidden_channels,
|
140 |
+
filter_channels,
|
141 |
+
n_heads,
|
142 |
+
n_layers,
|
143 |
+
kernel_size,
|
144 |
+
p_dropout):
|
145 |
+
super().__init__()
|
146 |
+
self.n_vocab = n_vocab
|
147 |
+
self.out_channels = out_channels
|
148 |
+
self.hidden_channels = hidden_channels
|
149 |
+
self.filter_channels = filter_channels
|
150 |
+
self.n_heads = n_heads
|
151 |
+
self.n_layers = n_layers
|
152 |
+
self.kernel_size = kernel_size
|
153 |
+
self.p_dropout = p_dropout
|
154 |
+
|
155 |
+
self.emb = nn.Embedding(n_vocab, hidden_channels)
|
156 |
+
nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
|
157 |
+
|
158 |
+
self.encoder = attentions.Encoder(
|
159 |
+
hidden_channels,
|
160 |
+
filter_channels,
|
161 |
+
n_heads,
|
162 |
+
n_layers,
|
163 |
+
kernel_size,
|
164 |
+
p_dropout)
|
165 |
+
self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
166 |
+
|
167 |
+
def forward(self, x, x_lengths):
|
168 |
+
x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
|
169 |
+
x = torch.transpose(x, 1, -1) # [b, h, t]
|
170 |
+
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
171 |
+
|
172 |
+
x = self.encoder(x * x_mask, x_mask)
|
173 |
+
stats = self.proj(x) * x_mask
|
174 |
+
|
175 |
+
m, logs = torch.split(stats, self.out_channels, dim=1)
|
176 |
+
return x, m, logs, x_mask
|
177 |
+
|
178 |
+
|
179 |
+
class ResidualCouplingBlock(nn.Module):
|
180 |
+
def __init__(self,
|
181 |
+
channels,
|
182 |
+
hidden_channels,
|
183 |
+
kernel_size,
|
184 |
+
dilation_rate,
|
185 |
+
n_layers,
|
186 |
+
n_flows=4,
|
187 |
+
gin_channels=0):
|
188 |
+
super().__init__()
|
189 |
+
self.channels = channels
|
190 |
+
self.hidden_channels = hidden_channels
|
191 |
+
self.kernel_size = kernel_size
|
192 |
+
self.dilation_rate = dilation_rate
|
193 |
+
self.n_layers = n_layers
|
194 |
+
self.n_flows = n_flows
|
195 |
+
self.gin_channels = gin_channels
|
196 |
+
|
197 |
+
self.flows = nn.ModuleList()
|
198 |
+
for i in range(n_flows):
|
199 |
+
self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
|
200 |
+
self.flows.append(modules.Flip())
|
201 |
+
|
202 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
203 |
+
if not reverse:
|
204 |
+
for flow in self.flows:
|
205 |
+
x, _ = flow(x, x_mask, g=g, reverse=reverse)
|
206 |
+
else:
|
207 |
+
for flow in reversed(self.flows):
|
208 |
+
x = flow(x, x_mask, g=g, reverse=reverse)
|
209 |
+
return x
|
210 |
+
|
211 |
+
|
212 |
+
class PosteriorEncoder(nn.Module):
|
213 |
+
def __init__(self,
|
214 |
+
in_channels,
|
215 |
+
out_channels,
|
216 |
+
hidden_channels,
|
217 |
+
kernel_size,
|
218 |
+
dilation_rate,
|
219 |
+
n_layers,
|
220 |
+
gin_channels=0):
|
221 |
+
super().__init__()
|
222 |
+
self.in_channels = in_channels
|
223 |
+
self.out_channels = out_channels
|
224 |
+
self.hidden_channels = hidden_channels
|
225 |
+
self.kernel_size = kernel_size
|
226 |
+
self.dilation_rate = dilation_rate
|
227 |
+
self.n_layers = n_layers
|
228 |
+
self.gin_channels = gin_channels
|
229 |
+
|
230 |
+
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
|
231 |
+
self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
|
232 |
+
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
233 |
+
|
234 |
+
def forward(self, x, x_lengths, g=None):
|
235 |
+
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
236 |
+
x = self.pre(x) * x_mask
|
237 |
+
x = self.enc(x, x_mask, g=g)
|
238 |
+
stats = self.proj(x) * x_mask
|
239 |
+
m, logs = torch.split(stats, self.out_channels, dim=1)
|
240 |
+
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
|
241 |
+
return z, m, logs, x_mask
|
242 |
+
|
243 |
+
|
244 |
+
class Generator(torch.nn.Module):
|
245 |
+
def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
|
246 |
+
super(Generator, self).__init__()
|
247 |
+
self.num_kernels = len(resblock_kernel_sizes)
|
248 |
+
self.num_upsamples = len(upsample_rates)
|
249 |
+
self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
|
250 |
+
resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
|
251 |
+
|
252 |
+
self.ups = nn.ModuleList()
|
253 |
+
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
|
254 |
+
self.ups.append(weight_norm(
|
255 |
+
ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
|
256 |
+
k, u, padding=(k-u)//2)))
|
257 |
+
|
258 |
+
self.resblocks = nn.ModuleList()
|
259 |
+
for i in range(len(self.ups)):
|
260 |
+
ch = upsample_initial_channel//(2**(i+1))
|
261 |
+
for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
|
262 |
+
self.resblocks.append(resblock(ch, k, d))
|
263 |
+
|
264 |
+
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
|
265 |
+
self.ups.apply(init_weights)
|
266 |
+
|
267 |
+
if gin_channels != 0:
|
268 |
+
self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
|
269 |
+
|
270 |
+
def forward(self, x, g=None):
|
271 |
+
x = self.conv_pre(x)
|
272 |
+
if g is not None:
|
273 |
+
x = x + self.cond(g)
|
274 |
+
|
275 |
+
for i in range(self.num_upsamples):
|
276 |
+
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
277 |
+
x = self.ups[i](x)
|
278 |
+
xs = None
|
279 |
+
for j in range(self.num_kernels):
|
280 |
+
if xs is None:
|
281 |
+
xs = self.resblocks[i*self.num_kernels+j](x)
|
282 |
+
else:
|
283 |
+
xs += self.resblocks[i*self.num_kernels+j](x)
|
284 |
+
x = xs / self.num_kernels
|
285 |
+
x = F.leaky_relu(x)
|
286 |
+
x = self.conv_post(x)
|
287 |
+
x = torch.tanh(x)
|
288 |
+
|
289 |
+
return x
|
290 |
+
|
291 |
+
def remove_weight_norm(self):
|
292 |
+
print('Removing weight norm...')
|
293 |
+
for l in self.ups:
|
294 |
+
remove_weight_norm(l)
|
295 |
+
for l in self.resblocks:
|
296 |
+
l.remove_weight_norm()
|
297 |
+
|
298 |
+
|
299 |
+
class DiscriminatorP(torch.nn.Module):
|
300 |
+
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
|
301 |
+
super(DiscriminatorP, self).__init__()
|
302 |
+
self.period = period
|
303 |
+
self.use_spectral_norm = use_spectral_norm
|
304 |
+
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
305 |
+
self.convs = nn.ModuleList([
|
306 |
+
norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
307 |
+
norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
308 |
+
norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
309 |
+
norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
310 |
+
norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
|
311 |
+
])
|
312 |
+
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
|
313 |
+
|
314 |
+
def forward(self, x):
|
315 |
+
fmap = []
|
316 |
+
|
317 |
+
# 1d to 2d
|
318 |
+
b, c, t = x.shape
|
319 |
+
if t % self.period != 0: # pad first
|
320 |
+
n_pad = self.period - (t % self.period)
|
321 |
+
x = F.pad(x, (0, n_pad), "reflect")
|
322 |
+
t = t + n_pad
|
323 |
+
x = x.view(b, c, t // self.period, self.period)
|
324 |
+
|
325 |
+
for l in self.convs:
|
326 |
+
x = l(x)
|
327 |
+
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
328 |
+
fmap.append(x)
|
329 |
+
x = self.conv_post(x)
|
330 |
+
fmap.append(x)
|
331 |
+
x = torch.flatten(x, 1, -1)
|
332 |
+
|
333 |
+
return x, fmap
|
334 |
+
|
335 |
+
|
336 |
+
class DiscriminatorS(torch.nn.Module):
|
337 |
+
def __init__(self, use_spectral_norm=False):
|
338 |
+
super(DiscriminatorS, self).__init__()
|
339 |
+
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
340 |
+
self.convs = nn.ModuleList([
|
341 |
+
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
|
342 |
+
norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
|
343 |
+
norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
|
344 |
+
norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
|
345 |
+
norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
|
346 |
+
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
|
347 |
+
])
|
348 |
+
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
|
349 |
+
|
350 |
+
def forward(self, x):
|
351 |
+
fmap = []
|
352 |
+
|
353 |
+
for l in self.convs:
|
354 |
+
x = l(x)
|
355 |
+
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
356 |
+
fmap.append(x)
|
357 |
+
x = self.conv_post(x)
|
358 |
+
fmap.append(x)
|
359 |
+
x = torch.flatten(x, 1, -1)
|
360 |
+
|
361 |
+
return x, fmap
|
362 |
+
|
363 |
+
|
364 |
+
class MultiPeriodDiscriminator(torch.nn.Module):
|
365 |
+
def __init__(self, use_spectral_norm=False):
|
366 |
+
super(MultiPeriodDiscriminator, self).__init__()
|
367 |
+
periods = [2,3,5,7,11]
|
368 |
+
|
369 |
+
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
|
370 |
+
discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
|
371 |
+
self.discriminators = nn.ModuleList(discs)
|
372 |
+
|
373 |
+
def forward(self, y, y_hat):
|
374 |
+
y_d_rs = []
|
375 |
+
y_d_gs = []
|
376 |
+
fmap_rs = []
|
377 |
+
fmap_gs = []
|
378 |
+
for i, d in enumerate(self.discriminators):
|
379 |
+
y_d_r, fmap_r = d(y)
|
380 |
+
y_d_g, fmap_g = d(y_hat)
|
381 |
+
y_d_rs.append(y_d_r)
|
382 |
+
y_d_gs.append(y_d_g)
|
383 |
+
fmap_rs.append(fmap_r)
|
384 |
+
fmap_gs.append(fmap_g)
|
385 |
+
|
386 |
+
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
387 |
+
|
388 |
+
|
389 |
+
|
390 |
+
class SynthesizerTrn(nn.Module):
|
391 |
+
"""
|
392 |
+
Synthesizer for Training
|
393 |
+
"""
|
394 |
+
|
395 |
+
def __init__(self,
|
396 |
+
n_vocab,
|
397 |
+
spec_channels,
|
398 |
+
segment_size,
|
399 |
+
inter_channels,
|
400 |
+
hidden_channels,
|
401 |
+
filter_channels,
|
402 |
+
n_heads,
|
403 |
+
n_layers,
|
404 |
+
kernel_size,
|
405 |
+
p_dropout,
|
406 |
+
resblock,
|
407 |
+
resblock_kernel_sizes,
|
408 |
+
resblock_dilation_sizes,
|
409 |
+
upsample_rates,
|
410 |
+
upsample_initial_channel,
|
411 |
+
upsample_kernel_sizes,
|
412 |
+
n_speakers=0,
|
413 |
+
gin_channels=0,
|
414 |
+
use_sdp=True,
|
415 |
+
**kwargs):
|
416 |
+
|
417 |
+
super().__init__()
|
418 |
+
self.n_vocab = n_vocab
|
419 |
+
self.spec_channels = spec_channels
|
420 |
+
self.inter_channels = inter_channels
|
421 |
+
self.hidden_channels = hidden_channels
|
422 |
+
self.filter_channels = filter_channels
|
423 |
+
self.n_heads = n_heads
|
424 |
+
self.n_layers = n_layers
|
425 |
+
self.kernel_size = kernel_size
|
426 |
+
self.p_dropout = p_dropout
|
427 |
+
self.resblock = resblock
|
428 |
+
self.resblock_kernel_sizes = resblock_kernel_sizes
|
429 |
+
self.resblock_dilation_sizes = resblock_dilation_sizes
|
430 |
+
self.upsample_rates = upsample_rates
|
431 |
+
self.upsample_initial_channel = upsample_initial_channel
|
432 |
+
self.upsample_kernel_sizes = upsample_kernel_sizes
|
433 |
+
self.segment_size = segment_size
|
434 |
+
self.n_speakers = n_speakers
|
435 |
+
self.gin_channels = gin_channels
|
436 |
+
|
437 |
+
self.use_sdp = use_sdp
|
438 |
+
|
439 |
+
self.enc_p = TextEncoder(n_vocab,
|
440 |
+
inter_channels,
|
441 |
+
hidden_channels,
|
442 |
+
filter_channels,
|
443 |
+
n_heads,
|
444 |
+
n_layers,
|
445 |
+
kernel_size,
|
446 |
+
p_dropout)
|
447 |
+
self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
|
448 |
+
self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
|
449 |
+
self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
|
450 |
+
|
451 |
+
if use_sdp:
|
452 |
+
self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
|
453 |
+
else:
|
454 |
+
self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
|
455 |
+
|
456 |
+
if n_speakers > 1:
|
457 |
+
self.emb_g = nn.Embedding(n_speakers, gin_channels)
|
458 |
+
|
459 |
+
def forward(self, x, x_lengths, y, y_lengths, sid=None):
|
460 |
+
|
461 |
+
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
|
462 |
+
if self.n_speakers > 0:
|
463 |
+
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
|
464 |
+
else:
|
465 |
+
g = None
|
466 |
+
|
467 |
+
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
|
468 |
+
z_p = self.flow(z, y_mask, g=g)
|
469 |
+
|
470 |
+
with torch.no_grad():
|
471 |
+
# negative cross-entropy
|
472 |
+
s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
|
473 |
+
neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
|
474 |
+
neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
|
475 |
+
neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
|
476 |
+
neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
|
477 |
+
neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
|
478 |
+
|
479 |
+
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
|
480 |
+
attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
|
481 |
+
|
482 |
+
w = attn.sum(2)
|
483 |
+
if self.use_sdp:
|
484 |
+
l_length = self.dp(x, x_mask, w, g=g)
|
485 |
+
l_length = l_length / torch.sum(x_mask)
|
486 |
+
else:
|
487 |
+
logw_ = torch.log(w + 1e-6) * x_mask
|
488 |
+
logw = self.dp(x, x_mask, g=g)
|
489 |
+
l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging
|
490 |
+
|
491 |
+
# expand prior
|
492 |
+
m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
|
493 |
+
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
|
494 |
+
|
495 |
+
z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
|
496 |
+
o = self.dec(z_slice, g=g)
|
497 |
+
return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
|
498 |
+
|
499 |
+
def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
|
500 |
+
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
|
501 |
+
if self.n_speakers > 0:
|
502 |
+
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
|
503 |
+
else:
|
504 |
+
g = None
|
505 |
+
|
506 |
+
if self.use_sdp:
|
507 |
+
logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
|
508 |
+
else:
|
509 |
+
logw = self.dp(x, x_mask, g=g)
|
510 |
+
w = torch.exp(logw) * x_mask * length_scale
|
511 |
+
w_ceil = torch.ceil(w)
|
512 |
+
y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
|
513 |
+
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
|
514 |
+
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
|
515 |
+
attn = commons.generate_path(w_ceil, attn_mask)
|
516 |
+
|
517 |
+
m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
|
518 |
+
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
|
519 |
+
|
520 |
+
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
|
521 |
+
z = self.flow(z_p, y_mask, g=g, reverse=True)
|
522 |
+
o = self.dec((z * y_mask)[:,:,:max_len], g=g)
|
523 |
+
return o, attn, y_mask, (z, z_p, m_p, logs_p)
|
524 |
+
|
525 |
+
def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
|
526 |
+
assert self.n_speakers > 0, "n_speakers have to be larger than 0."
|
527 |
+
g_src = self.emb_g(sid_src).unsqueeze(-1)
|
528 |
+
g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
|
529 |
+
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
|
530 |
+
z_p = self.flow(z, y_mask, g=g_src)
|
531 |
+
z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
|
532 |
+
o_hat = self.dec(z_hat * y_mask, g=g_tgt)
|
533 |
+
return o_hat, y_mask, (z, z_p, z_hat)
|
534 |
+
|
modules.py
ADDED
@@ -0,0 +1,390 @@
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|
|
|
|
|
|
1 |
+
import copy
|
2 |
+
import math
|
3 |
+
import numpy as np
|
4 |
+
import scipy
|
5 |
+
import torch
|
6 |
+
from torch import nn
|
7 |
+
from torch.nn import functional as F
|
8 |
+
|
9 |
+
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
|
10 |
+
from torch.nn.utils import weight_norm, remove_weight_norm
|
11 |
+
|
12 |
+
import commons
|
13 |
+
from commons import init_weights, get_padding
|
14 |
+
from transforms import piecewise_rational_quadratic_transform
|
15 |
+
|
16 |
+
|
17 |
+
LRELU_SLOPE = 0.1
|
18 |
+
|
19 |
+
|
20 |
+
class LayerNorm(nn.Module):
|
21 |
+
def __init__(self, channels, eps=1e-5):
|
22 |
+
super().__init__()
|
23 |
+
self.channels = channels
|
24 |
+
self.eps = eps
|
25 |
+
|
26 |
+
self.gamma = nn.Parameter(torch.ones(channels))
|
27 |
+
self.beta = nn.Parameter(torch.zeros(channels))
|
28 |
+
|
29 |
+
def forward(self, x):
|
30 |
+
x = x.transpose(1, -1)
|
31 |
+
x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
|
32 |
+
return x.transpose(1, -1)
|
33 |
+
|
34 |
+
|
35 |
+
class ConvReluNorm(nn.Module):
|
36 |
+
def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
|
37 |
+
super().__init__()
|
38 |
+
self.in_channels = in_channels
|
39 |
+
self.hidden_channels = hidden_channels
|
40 |
+
self.out_channels = out_channels
|
41 |
+
self.kernel_size = kernel_size
|
42 |
+
self.n_layers = n_layers
|
43 |
+
self.p_dropout = p_dropout
|
44 |
+
assert n_layers > 1, "Number of layers should be larger than 0."
|
45 |
+
|
46 |
+
self.conv_layers = nn.ModuleList()
|
47 |
+
self.norm_layers = nn.ModuleList()
|
48 |
+
self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
|
49 |
+
self.norm_layers.append(LayerNorm(hidden_channels))
|
50 |
+
self.relu_drop = nn.Sequential(
|
51 |
+
nn.ReLU(),
|
52 |
+
nn.Dropout(p_dropout))
|
53 |
+
for _ in range(n_layers-1):
|
54 |
+
self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
|
55 |
+
self.norm_layers.append(LayerNorm(hidden_channels))
|
56 |
+
self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
|
57 |
+
self.proj.weight.data.zero_()
|
58 |
+
self.proj.bias.data.zero_()
|
59 |
+
|
60 |
+
def forward(self, x, x_mask):
|
61 |
+
x_org = x
|
62 |
+
for i in range(self.n_layers):
|
63 |
+
x = self.conv_layers[i](x * x_mask)
|
64 |
+
x = self.norm_layers[i](x)
|
65 |
+
x = self.relu_drop(x)
|
66 |
+
x = x_org + self.proj(x)
|
67 |
+
return x * x_mask
|
68 |
+
|
69 |
+
|
70 |
+
class DDSConv(nn.Module):
|
71 |
+
"""
|
72 |
+
Dialted and Depth-Separable Convolution
|
73 |
+
"""
|
74 |
+
def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
|
75 |
+
super().__init__()
|
76 |
+
self.channels = channels
|
77 |
+
self.kernel_size = kernel_size
|
78 |
+
self.n_layers = n_layers
|
79 |
+
self.p_dropout = p_dropout
|
80 |
+
|
81 |
+
self.drop = nn.Dropout(p_dropout)
|
82 |
+
self.convs_sep = nn.ModuleList()
|
83 |
+
self.convs_1x1 = nn.ModuleList()
|
84 |
+
self.norms_1 = nn.ModuleList()
|
85 |
+
self.norms_2 = nn.ModuleList()
|
86 |
+
for i in range(n_layers):
|
87 |
+
dilation = kernel_size ** i
|
88 |
+
padding = (kernel_size * dilation - dilation) // 2
|
89 |
+
self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
|
90 |
+
groups=channels, dilation=dilation, padding=padding
|
91 |
+
))
|
92 |
+
self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
|
93 |
+
self.norms_1.append(LayerNorm(channels))
|
94 |
+
self.norms_2.append(LayerNorm(channels))
|
95 |
+
|
96 |
+
def forward(self, x, x_mask, g=None):
|
97 |
+
if g is not None:
|
98 |
+
x = x + g
|
99 |
+
for i in range(self.n_layers):
|
100 |
+
y = self.convs_sep[i](x * x_mask)
|
101 |
+
y = self.norms_1[i](y)
|
102 |
+
y = F.gelu(y)
|
103 |
+
y = self.convs_1x1[i](y)
|
104 |
+
y = self.norms_2[i](y)
|
105 |
+
y = F.gelu(y)
|
106 |
+
y = self.drop(y)
|
107 |
+
x = x + y
|
108 |
+
return x * x_mask
|
109 |
+
|
110 |
+
|
111 |
+
class WN(torch.nn.Module):
|
112 |
+
def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
|
113 |
+
super(WN, self).__init__()
|
114 |
+
assert(kernel_size % 2 == 1)
|
115 |
+
self.hidden_channels =hidden_channels
|
116 |
+
self.kernel_size = kernel_size,
|
117 |
+
self.dilation_rate = dilation_rate
|
118 |
+
self.n_layers = n_layers
|
119 |
+
self.gin_channels = gin_channels
|
120 |
+
self.p_dropout = p_dropout
|
121 |
+
|
122 |
+
self.in_layers = torch.nn.ModuleList()
|
123 |
+
self.res_skip_layers = torch.nn.ModuleList()
|
124 |
+
self.drop = nn.Dropout(p_dropout)
|
125 |
+
|
126 |
+
if gin_channels != 0:
|
127 |
+
cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
|
128 |
+
self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
|
129 |
+
|
130 |
+
for i in range(n_layers):
|
131 |
+
dilation = dilation_rate ** i
|
132 |
+
padding = int((kernel_size * dilation - dilation) / 2)
|
133 |
+
in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
|
134 |
+
dilation=dilation, padding=padding)
|
135 |
+
in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
|
136 |
+
self.in_layers.append(in_layer)
|
137 |
+
|
138 |
+
# last one is not necessary
|
139 |
+
if i < n_layers - 1:
|
140 |
+
res_skip_channels = 2 * hidden_channels
|
141 |
+
else:
|
142 |
+
res_skip_channels = hidden_channels
|
143 |
+
|
144 |
+
res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
|
145 |
+
res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
|
146 |
+
self.res_skip_layers.append(res_skip_layer)
|
147 |
+
|
148 |
+
def forward(self, x, x_mask, g=None, **kwargs):
|
149 |
+
output = torch.zeros_like(x)
|
150 |
+
n_channels_tensor = torch.IntTensor([self.hidden_channels])
|
151 |
+
|
152 |
+
if g is not None:
|
153 |
+
g = self.cond_layer(g)
|
154 |
+
|
155 |
+
for i in range(self.n_layers):
|
156 |
+
x_in = self.in_layers[i](x)
|
157 |
+
if g is not None:
|
158 |
+
cond_offset = i * 2 * self.hidden_channels
|
159 |
+
g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
|
160 |
+
else:
|
161 |
+
g_l = torch.zeros_like(x_in)
|
162 |
+
|
163 |
+
acts = commons.fused_add_tanh_sigmoid_multiply(
|
164 |
+
x_in,
|
165 |
+
g_l,
|
166 |
+
n_channels_tensor)
|
167 |
+
acts = self.drop(acts)
|
168 |
+
|
169 |
+
res_skip_acts = self.res_skip_layers[i](acts)
|
170 |
+
if i < self.n_layers - 1:
|
171 |
+
res_acts = res_skip_acts[:,:self.hidden_channels,:]
|
172 |
+
x = (x + res_acts) * x_mask
|
173 |
+
output = output + res_skip_acts[:,self.hidden_channels:,:]
|
174 |
+
else:
|
175 |
+
output = output + res_skip_acts
|
176 |
+
return output * x_mask
|
177 |
+
|
178 |
+
def remove_weight_norm(self):
|
179 |
+
if self.gin_channels != 0:
|
180 |
+
torch.nn.utils.remove_weight_norm(self.cond_layer)
|
181 |
+
for l in self.in_layers:
|
182 |
+
torch.nn.utils.remove_weight_norm(l)
|
183 |
+
for l in self.res_skip_layers:
|
184 |
+
torch.nn.utils.remove_weight_norm(l)
|
185 |
+
|
186 |
+
|
187 |
+
class ResBlock1(torch.nn.Module):
|
188 |
+
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
|
189 |
+
super(ResBlock1, self).__init__()
|
190 |
+
self.convs1 = nn.ModuleList([
|
191 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
192 |
+
padding=get_padding(kernel_size, dilation[0]))),
|
193 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
194 |
+
padding=get_padding(kernel_size, dilation[1]))),
|
195 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
|
196 |
+
padding=get_padding(kernel_size, dilation[2])))
|
197 |
+
])
|
198 |
+
self.convs1.apply(init_weights)
|
199 |
+
|
200 |
+
self.convs2 = nn.ModuleList([
|
201 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
202 |
+
padding=get_padding(kernel_size, 1))),
|
203 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
204 |
+
padding=get_padding(kernel_size, 1))),
|
205 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
206 |
+
padding=get_padding(kernel_size, 1)))
|
207 |
+
])
|
208 |
+
self.convs2.apply(init_weights)
|
209 |
+
|
210 |
+
def forward(self, x, x_mask=None):
|
211 |
+
for c1, c2 in zip(self.convs1, self.convs2):
|
212 |
+
xt = F.leaky_relu(x, LRELU_SLOPE)
|
213 |
+
if x_mask is not None:
|
214 |
+
xt = xt * x_mask
|
215 |
+
xt = c1(xt)
|
216 |
+
xt = F.leaky_relu(xt, LRELU_SLOPE)
|
217 |
+
if x_mask is not None:
|
218 |
+
xt = xt * x_mask
|
219 |
+
xt = c2(xt)
|
220 |
+
x = xt + x
|
221 |
+
if x_mask is not None:
|
222 |
+
x = x * x_mask
|
223 |
+
return x
|
224 |
+
|
225 |
+
def remove_weight_norm(self):
|
226 |
+
for l in self.convs1:
|
227 |
+
remove_weight_norm(l)
|
228 |
+
for l in self.convs2:
|
229 |
+
remove_weight_norm(l)
|
230 |
+
|
231 |
+
|
232 |
+
class ResBlock2(torch.nn.Module):
|
233 |
+
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
|
234 |
+
super(ResBlock2, self).__init__()
|
235 |
+
self.convs = nn.ModuleList([
|
236 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
237 |
+
padding=get_padding(kernel_size, dilation[0]))),
|
238 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
239 |
+
padding=get_padding(kernel_size, dilation[1])))
|
240 |
+
])
|
241 |
+
self.convs.apply(init_weights)
|
242 |
+
|
243 |
+
def forward(self, x, x_mask=None):
|
244 |
+
for c in self.convs:
|
245 |
+
xt = F.leaky_relu(x, LRELU_SLOPE)
|
246 |
+
if x_mask is not None:
|
247 |
+
xt = xt * x_mask
|
248 |
+
xt = c(xt)
|
249 |
+
x = xt + x
|
250 |
+
if x_mask is not None:
|
251 |
+
x = x * x_mask
|
252 |
+
return x
|
253 |
+
|
254 |
+
def remove_weight_norm(self):
|
255 |
+
for l in self.convs:
|
256 |
+
remove_weight_norm(l)
|
257 |
+
|
258 |
+
|
259 |
+
class Log(nn.Module):
|
260 |
+
def forward(self, x, x_mask, reverse=False, **kwargs):
|
261 |
+
if not reverse:
|
262 |
+
y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
|
263 |
+
logdet = torch.sum(-y, [1, 2])
|
264 |
+
return y, logdet
|
265 |
+
else:
|
266 |
+
x = torch.exp(x) * x_mask
|
267 |
+
return x
|
268 |
+
|
269 |
+
|
270 |
+
class Flip(nn.Module):
|
271 |
+
def forward(self, x, *args, reverse=False, **kwargs):
|
272 |
+
x = torch.flip(x, [1])
|
273 |
+
if not reverse:
|
274 |
+
logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
|
275 |
+
return x, logdet
|
276 |
+
else:
|
277 |
+
return x
|
278 |
+
|
279 |
+
|
280 |
+
class ElementwiseAffine(nn.Module):
|
281 |
+
def __init__(self, channels):
|
282 |
+
super().__init__()
|
283 |
+
self.channels = channels
|
284 |
+
self.m = nn.Parameter(torch.zeros(channels,1))
|
285 |
+
self.logs = nn.Parameter(torch.zeros(channels,1))
|
286 |
+
|
287 |
+
def forward(self, x, x_mask, reverse=False, **kwargs):
|
288 |
+
if not reverse:
|
289 |
+
y = self.m + torch.exp(self.logs) * x
|
290 |
+
y = y * x_mask
|
291 |
+
logdet = torch.sum(self.logs * x_mask, [1,2])
|
292 |
+
return y, logdet
|
293 |
+
else:
|
294 |
+
x = (x - self.m) * torch.exp(-self.logs) * x_mask
|
295 |
+
return x
|
296 |
+
|
297 |
+
|
298 |
+
class ResidualCouplingLayer(nn.Module):
|
299 |
+
def __init__(self,
|
300 |
+
channels,
|
301 |
+
hidden_channels,
|
302 |
+
kernel_size,
|
303 |
+
dilation_rate,
|
304 |
+
n_layers,
|
305 |
+
p_dropout=0,
|
306 |
+
gin_channels=0,
|
307 |
+
mean_only=False):
|
308 |
+
assert channels % 2 == 0, "channels should be divisible by 2"
|
309 |
+
super().__init__()
|
310 |
+
self.channels = channels
|
311 |
+
self.hidden_channels = hidden_channels
|
312 |
+
self.kernel_size = kernel_size
|
313 |
+
self.dilation_rate = dilation_rate
|
314 |
+
self.n_layers = n_layers
|
315 |
+
self.half_channels = channels // 2
|
316 |
+
self.mean_only = mean_only
|
317 |
+
|
318 |
+
self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
|
319 |
+
self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
|
320 |
+
self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
|
321 |
+
self.post.weight.data.zero_()
|
322 |
+
self.post.bias.data.zero_()
|
323 |
+
|
324 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
325 |
+
x0, x1 = torch.split(x, [self.half_channels]*2, 1)
|
326 |
+
h = self.pre(x0) * x_mask
|
327 |
+
h = self.enc(h, x_mask, g=g)
|
328 |
+
stats = self.post(h) * x_mask
|
329 |
+
if not self.mean_only:
|
330 |
+
m, logs = torch.split(stats, [self.half_channels]*2, 1)
|
331 |
+
else:
|
332 |
+
m = stats
|
333 |
+
logs = torch.zeros_like(m)
|
334 |
+
|
335 |
+
if not reverse:
|
336 |
+
x1 = m + x1 * torch.exp(logs) * x_mask
|
337 |
+
x = torch.cat([x0, x1], 1)
|
338 |
+
logdet = torch.sum(logs, [1,2])
|
339 |
+
return x, logdet
|
340 |
+
else:
|
341 |
+
x1 = (x1 - m) * torch.exp(-logs) * x_mask
|
342 |
+
x = torch.cat([x0, x1], 1)
|
343 |
+
return x
|
344 |
+
|
345 |
+
|
346 |
+
class ConvFlow(nn.Module):
|
347 |
+
def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
|
348 |
+
super().__init__()
|
349 |
+
self.in_channels = in_channels
|
350 |
+
self.filter_channels = filter_channels
|
351 |
+
self.kernel_size = kernel_size
|
352 |
+
self.n_layers = n_layers
|
353 |
+
self.num_bins = num_bins
|
354 |
+
self.tail_bound = tail_bound
|
355 |
+
self.half_channels = in_channels // 2
|
356 |
+
|
357 |
+
self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
|
358 |
+
self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
|
359 |
+
self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
|
360 |
+
self.proj.weight.data.zero_()
|
361 |
+
self.proj.bias.data.zero_()
|
362 |
+
|
363 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
364 |
+
x0, x1 = torch.split(x, [self.half_channels]*2, 1)
|
365 |
+
h = self.pre(x0)
|
366 |
+
h = self.convs(h, x_mask, g=g)
|
367 |
+
h = self.proj(h) * x_mask
|
368 |
+
|
369 |
+
b, c, t = x0.shape
|
370 |
+
h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
|
371 |
+
|
372 |
+
unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
|
373 |
+
unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
|
374 |
+
unnormalized_derivatives = h[..., 2 * self.num_bins:]
|
375 |
+
|
376 |
+
x1, logabsdet = piecewise_rational_quadratic_transform(x1,
|
377 |
+
unnormalized_widths,
|
378 |
+
unnormalized_heights,
|
379 |
+
unnormalized_derivatives,
|
380 |
+
inverse=reverse,
|
381 |
+
tails='linear',
|
382 |
+
tail_bound=self.tail_bound
|
383 |
+
)
|
384 |
+
|
385 |
+
x = torch.cat([x0, x1], 1) * x_mask
|
386 |
+
logdet = torch.sum(logabsdet * x_mask, [1,2])
|
387 |
+
if not reverse:
|
388 |
+
return x, logdet
|
389 |
+
else:
|
390 |
+
return x
|
monotonic_align/__init__.py
ADDED
@@ -0,0 +1,19 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import numpy as np
|
2 |
+
import torch
|
3 |
+
from .monotonic_align.core import maximum_path_c
|
4 |
+
|
5 |
+
|
6 |
+
def maximum_path(neg_cent, mask):
|
7 |
+
""" Cython optimized version.
|
8 |
+
neg_cent: [b, t_t, t_s]
|
9 |
+
mask: [b, t_t, t_s]
|
10 |
+
"""
|
11 |
+
device = neg_cent.device
|
12 |
+
dtype = neg_cent.dtype
|
13 |
+
neg_cent = neg_cent.data.cpu().numpy().astype(np.float32)
|
14 |
+
path = np.zeros(neg_cent.shape, dtype=np.int32)
|
15 |
+
|
16 |
+
t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32)
|
17 |
+
t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32)
|
18 |
+
maximum_path_c(path, neg_cent, t_t_max, t_s_max)
|
19 |
+
return torch.from_numpy(path).to(device=device, dtype=dtype)
|
monotonic_align/core.c
ADDED
The diff for this file is too large to render.
See raw diff
|
|
monotonic_align/core.pyx
ADDED
@@ -0,0 +1,42 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
cimport cython
|
2 |
+
from cython.parallel import prange
|
3 |
+
|
4 |
+
|
5 |
+
@cython.boundscheck(False)
|
6 |
+
@cython.wraparound(False)
|
7 |
+
cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_y, int t_x, float max_neg_val=-1e9) nogil:
|
8 |
+
cdef int x
|
9 |
+
cdef int y
|
10 |
+
cdef float v_prev
|
11 |
+
cdef float v_cur
|
12 |
+
cdef float tmp
|
13 |
+
cdef int index = t_x - 1
|
14 |
+
|
15 |
+
for y in range(t_y):
|
16 |
+
for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
|
17 |
+
if x == y:
|
18 |
+
v_cur = max_neg_val
|
19 |
+
else:
|
20 |
+
v_cur = value[y-1, x]
|
21 |
+
if x == 0:
|
22 |
+
if y == 0:
|
23 |
+
v_prev = 0.
|
24 |
+
else:
|
25 |
+
v_prev = max_neg_val
|
26 |
+
else:
|
27 |
+
v_prev = value[y-1, x-1]
|
28 |
+
value[y, x] += max(v_prev, v_cur)
|
29 |
+
|
30 |
+
for y in range(t_y - 1, -1, -1):
|
31 |
+
path[y, index] = 1
|
32 |
+
if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):
|
33 |
+
index = index - 1
|
34 |
+
|
35 |
+
|
36 |
+
@cython.boundscheck(False)
|
37 |
+
@cython.wraparound(False)
|
38 |
+
cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_ys, int[::1] t_xs) nogil:
|
39 |
+
cdef int b = paths.shape[0]
|
40 |
+
cdef int i
|
41 |
+
for i in prange(b, nogil=True):
|
42 |
+
maximum_path_each(paths[i], values[i], t_ys[i], t_xs[i])
|
monotonic_align/setup.py
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from distutils.core import setup
|
2 |
+
from Cython.Build import cythonize
|
3 |
+
import numpy
|
4 |
+
|
5 |
+
setup(
|
6 |
+
name = 'monotonic_align',
|
7 |
+
ext_modules = cythonize("core.pyx"),
|
8 |
+
include_dirs=[numpy.get_include()]
|
9 |
+
)
|
requirements.txt
ADDED
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
torch
|
2 |
+
scipy
|
3 |
+
Cython
|
4 |
+
unidecode
|
5 |
+
pypinyin
|
6 |
+
phonemizer
|
text/__init__.py
ADDED
@@ -0,0 +1,64 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
""" from https://github.com/keithito/tacotron """
|
2 |
+
from text import cleaners
|
3 |
+
from text.symbols import symbols,symbols_zh
|
4 |
+
|
5 |
+
|
6 |
+
# Mappings from symbol to numeric ID and vice versa:
|
7 |
+
# _symbol_to_id = {s: i for i, s in enumerate(symbols)}
|
8 |
+
# _id_to_symbol = {i: s for i, s in enumerate(symbols)}
|
9 |
+
|
10 |
+
chinese_mode = True
|
11 |
+
if chinese_mode:
|
12 |
+
_symbol_to_id = {s: i for i, s in enumerate(symbols_zh)}
|
13 |
+
_id_to_symbol = {i: s for i, s in enumerate(symbols_zh)}
|
14 |
+
else:
|
15 |
+
_symbol_to_id = {s: i for i, s in enumerate(symbols)}
|
16 |
+
_id_to_symbol = {i: s for i, s in enumerate(symbols)}
|
17 |
+
|
18 |
+
def text_to_sequence(text, cleaner_names, ):
|
19 |
+
'''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
|
20 |
+
Args:
|
21 |
+
text: string to convert to a sequence
|
22 |
+
cleaner_names: names of the cleaner functions to run the text through
|
23 |
+
Returns:
|
24 |
+
List of integers corresponding to the symbols in the text
|
25 |
+
'''
|
26 |
+
sequence = []
|
27 |
+
|
28 |
+
clean_text = _clean_text(text, cleaner_names)
|
29 |
+
for symbol in clean_text:
|
30 |
+
symbol_id = _symbol_to_id[symbol]
|
31 |
+
sequence += [symbol_id]
|
32 |
+
return sequence
|
33 |
+
|
34 |
+
|
35 |
+
def cleaned_text_to_sequence(cleaned_text, chinese_mode=True):
|
36 |
+
'''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
|
37 |
+
Args:
|
38 |
+
text: string to convert to a sequence
|
39 |
+
Returns:
|
40 |
+
List of integers corresponding to the symbols in the text
|
41 |
+
'''
|
42 |
+
# if chinese_mode:
|
43 |
+
# sequence = [_symbol_to_id_zh[symbol] for symbol in cleaned_text]
|
44 |
+
# else:
|
45 |
+
sequence = [_symbol_to_id[symbol] for symbol in cleaned_text]
|
46 |
+
return sequence
|
47 |
+
|
48 |
+
|
49 |
+
def sequence_to_text(sequence):
|
50 |
+
'''Converts a sequence of IDs back to a string'''
|
51 |
+
result = ''
|
52 |
+
for symbol_id in sequence:
|
53 |
+
s = _id_to_symbol[symbol_id]
|
54 |
+
result += s
|
55 |
+
return result
|
56 |
+
|
57 |
+
|
58 |
+
def _clean_text(text, cleaner_names):
|
59 |
+
for name in cleaner_names:
|
60 |
+
cleaner = getattr(cleaners, name)
|
61 |
+
if not cleaner:
|
62 |
+
raise Exception('Unknown cleaner: %s' % name)
|
63 |
+
text = cleaner(text)
|
64 |
+
return text
|
text/cleaners.py
ADDED
@@ -0,0 +1,138 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
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|
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|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
""" from https://github.com/keithito/tacotron """
|
2 |
+
|
3 |
+
'''
|
4 |
+
Cleaners are transformations that run over the input text at both training and eval time.
|
5 |
+
|
6 |
+
Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
|
7 |
+
hyperparameter. Some cleaners are English-specific. You'll typically want to use:
|
8 |
+
1. "english_cleaners" for English text
|
9 |
+
2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
|
10 |
+
the Unidecode library (https://pypi.python.org/pypi/Unidecode)
|
11 |
+
3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
|
12 |
+
the symbols in symbols.py to match your data).
|
13 |
+
'''
|
14 |
+
|
15 |
+
import re
|
16 |
+
from unidecode import unidecode
|
17 |
+
from phonemizer import phonemize
|
18 |
+
from pypinyin import Style, pinyin
|
19 |
+
from pypinyin.style._utils import get_finals, get_initials
|
20 |
+
# Regular expression matching whitespace:
|
21 |
+
_whitespace_re = re.compile(r'\s+')
|
22 |
+
|
23 |
+
# List of (regular expression, replacement) pairs for abbreviations:
|
24 |
+
_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
|
25 |
+
('mrs', 'misess'),
|
26 |
+
('mr', 'mister'),
|
27 |
+
('dr', 'doctor'),
|
28 |
+
('st', 'saint'),
|
29 |
+
('co', 'company'),
|
30 |
+
('jr', 'junior'),
|
31 |
+
('maj', 'major'),
|
32 |
+
('gen', 'general'),
|
33 |
+
('drs', 'doctors'),
|
34 |
+
('rev', 'reverend'),
|
35 |
+
('lt', 'lieutenant'),
|
36 |
+
('hon', 'honorable'),
|
37 |
+
('sgt', 'sergeant'),
|
38 |
+
('capt', 'captain'),
|
39 |
+
('esq', 'esquire'),
|
40 |
+
('ltd', 'limited'),
|
41 |
+
('col', 'colonel'),
|
42 |
+
('ft', 'fort'),
|
43 |
+
]]
|
44 |
+
|
45 |
+
|
46 |
+
def expand_abbreviations(text):
|
47 |
+
for regex, replacement in _abbreviations:
|
48 |
+
text = re.sub(regex, replacement, text)
|
49 |
+
return text
|
50 |
+
|
51 |
+
|
52 |
+
def expand_numbers(text):
|
53 |
+
return normalize_numbers(text)
|
54 |
+
|
55 |
+
|
56 |
+
def lowercase(text):
|
57 |
+
return text.lower()
|
58 |
+
|
59 |
+
|
60 |
+
def collapse_whitespace(text):
|
61 |
+
return re.sub(_whitespace_re, ' ', text)
|
62 |
+
|
63 |
+
|
64 |
+
def convert_to_ascii(text):
|
65 |
+
return unidecode(text)
|
66 |
+
|
67 |
+
|
68 |
+
def basic_cleaners(text):
|
69 |
+
'''Basic pipeline that lowercases and collapses whitespace without transliteration.'''
|
70 |
+
text = lowercase(text)
|
71 |
+
text = collapse_whitespace(text)
|
72 |
+
return text
|
73 |
+
|
74 |
+
|
75 |
+
def transliteration_cleaners(text):
|
76 |
+
'''Pipeline for non-English text that transliterates to ASCII.'''
|
77 |
+
text = convert_to_ascii(text)
|
78 |
+
text = lowercase(text)
|
79 |
+
text = collapse_whitespace(text)
|
80 |
+
return text
|
81 |
+
|
82 |
+
|
83 |
+
def english_cleaners(text):
|
84 |
+
'''Pipeline for English text, including abbreviation expansion.'''
|
85 |
+
text = convert_to_ascii(text)
|
86 |
+
text = lowercase(text)
|
87 |
+
text = expand_abbreviations(text)
|
88 |
+
phonemes = phonemize(text, language='en-us', backend='espeak', strip=True)
|
89 |
+
phonemes = collapse_whitespace(phonemes)
|
90 |
+
return phonemes
|
91 |
+
|
92 |
+
|
93 |
+
def english_cleaners2(text):
|
94 |
+
'''Pipeline for English text, including abbreviation expansion. + punctuation + stress'''
|
95 |
+
text = convert_to_ascii(text)
|
96 |
+
text = lowercase(text)
|
97 |
+
text = expand_abbreviations(text)
|
98 |
+
phonemes = phonemize(text, language='en-us', backend='espeak', strip=True, preserve_punctuation=True, with_stress=True)
|
99 |
+
phonemes = collapse_whitespace(phonemes)
|
100 |
+
return phonemes
|
101 |
+
|
102 |
+
|
103 |
+
|
104 |
+
|
105 |
+
def chinese_cleaners1(text):
|
106 |
+
from pypinyin import Style, pinyin
|
107 |
+
|
108 |
+
phones = [phone[0] for phone in pinyin(text, style=Style.TONE3)]
|
109 |
+
return ' '.join(phones)
|
110 |
+
|
111 |
+
|
112 |
+
def chinese_cleaners2(text):
|
113 |
+
phones = [
|
114 |
+
p
|
115 |
+
for phone in pinyin(text, style=Style.TONE3)
|
116 |
+
for p in [
|
117 |
+
get_initials(phone[0], strict=True),
|
118 |
+
get_finals(phone[0][:-1], strict=True) + phone[0][-1]
|
119 |
+
if phone[0][-1].isdigit()
|
120 |
+
else get_finals(phone[0], strict=True)
|
121 |
+
if phone[0][-1].isalnum()
|
122 |
+
else phone[0],
|
123 |
+
]
|
124 |
+
# Remove the case of individual tones as a phoneme
|
125 |
+
if len(p) != 0 and not p.isdigit()
|
126 |
+
]
|
127 |
+
return phones
|
128 |
+
# return phonemes
|
129 |
+
|
130 |
+
if __name__ == '__main__':
|
131 |
+
res = chinese_cleaners2('这是语音测试!')
|
132 |
+
print(res)
|
133 |
+
res = chinese_cleaners1('"第一,南京不是发展的不行,是大家对他期望很高,')
|
134 |
+
print(res)
|
135 |
+
|
136 |
+
|
137 |
+
res = english_cleaners2('this is a club test for one train.GDP')
|
138 |
+
print(res)
|
text/symbols.py
ADDED
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
""" from https://github.com/keithito/tacotron """
|
2 |
+
|
3 |
+
'''
|
4 |
+
Defines the set of symbols used in text input to the model.
|
5 |
+
'''
|
6 |
+
_pad = '_'
|
7 |
+
_punctuation = ';:,.!?¡¿—…"«»“” '
|
8 |
+
|
9 |
+
_punctuation_zh = ';:,。!?-“”《》、 '
|
10 |
+
_letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
|
11 |
+
|
12 |
+
_numbers = '123450'
|
13 |
+
_others = ''
|
14 |
+
|
15 |
+
_letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
|
16 |
+
|
17 |
+
|
18 |
+
# Export all symbols:
|
19 |
+
symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa)
|
20 |
+
|
21 |
+
symbols_zh = [_pad] + list(_punctuation_zh) + list(_letters) + list(_numbers)
|
22 |
+
|
23 |
+
# Special symbol ids
|
24 |
+
SPACE_ID = symbols.index(" ")
|
transforms.py
ADDED
@@ -0,0 +1,193 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
from torch.nn import functional as F
|
3 |
+
|
4 |
+
import numpy as np
|
5 |
+
|
6 |
+
|
7 |
+
DEFAULT_MIN_BIN_WIDTH = 1e-3
|
8 |
+
DEFAULT_MIN_BIN_HEIGHT = 1e-3
|
9 |
+
DEFAULT_MIN_DERIVATIVE = 1e-3
|
10 |
+
|
11 |
+
|
12 |
+
def piecewise_rational_quadratic_transform(inputs,
|
13 |
+
unnormalized_widths,
|
14 |
+
unnormalized_heights,
|
15 |
+
unnormalized_derivatives,
|
16 |
+
inverse=False,
|
17 |
+
tails=None,
|
18 |
+
tail_bound=1.,
|
19 |
+
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
20 |
+
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
21 |
+
min_derivative=DEFAULT_MIN_DERIVATIVE):
|
22 |
+
|
23 |
+
if tails is None:
|
24 |
+
spline_fn = rational_quadratic_spline
|
25 |
+
spline_kwargs = {}
|
26 |
+
else:
|
27 |
+
spline_fn = unconstrained_rational_quadratic_spline
|
28 |
+
spline_kwargs = {
|
29 |
+
'tails': tails,
|
30 |
+
'tail_bound': tail_bound
|
31 |
+
}
|
32 |
+
|
33 |
+
outputs, logabsdet = spline_fn(
|
34 |
+
inputs=inputs,
|
35 |
+
unnormalized_widths=unnormalized_widths,
|
36 |
+
unnormalized_heights=unnormalized_heights,
|
37 |
+
unnormalized_derivatives=unnormalized_derivatives,
|
38 |
+
inverse=inverse,
|
39 |
+
min_bin_width=min_bin_width,
|
40 |
+
min_bin_height=min_bin_height,
|
41 |
+
min_derivative=min_derivative,
|
42 |
+
**spline_kwargs
|
43 |
+
)
|
44 |
+
return outputs, logabsdet
|
45 |
+
|
46 |
+
|
47 |
+
def searchsorted(bin_locations, inputs, eps=1e-6):
|
48 |
+
bin_locations[..., -1] += eps
|
49 |
+
return torch.sum(
|
50 |
+
inputs[..., None] >= bin_locations,
|
51 |
+
dim=-1
|
52 |
+
) - 1
|
53 |
+
|
54 |
+
|
55 |
+
def unconstrained_rational_quadratic_spline(inputs,
|
56 |
+
unnormalized_widths,
|
57 |
+
unnormalized_heights,
|
58 |
+
unnormalized_derivatives,
|
59 |
+
inverse=False,
|
60 |
+
tails='linear',
|
61 |
+
tail_bound=1.,
|
62 |
+
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
63 |
+
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
64 |
+
min_derivative=DEFAULT_MIN_DERIVATIVE):
|
65 |
+
inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
|
66 |
+
outside_interval_mask = ~inside_interval_mask
|
67 |
+
|
68 |
+
outputs = torch.zeros_like(inputs)
|
69 |
+
logabsdet = torch.zeros_like(inputs)
|
70 |
+
|
71 |
+
if tails == 'linear':
|
72 |
+
unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
|
73 |
+
constant = np.log(np.exp(1 - min_derivative) - 1)
|
74 |
+
unnormalized_derivatives[..., 0] = constant
|
75 |
+
unnormalized_derivatives[..., -1] = constant
|
76 |
+
|
77 |
+
outputs[outside_interval_mask] = inputs[outside_interval_mask]
|
78 |
+
logabsdet[outside_interval_mask] = 0
|
79 |
+
else:
|
80 |
+
raise RuntimeError('{} tails are not implemented.'.format(tails))
|
81 |
+
|
82 |
+
outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
|
83 |
+
inputs=inputs[inside_interval_mask],
|
84 |
+
unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
|
85 |
+
unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
|
86 |
+
unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
|
87 |
+
inverse=inverse,
|
88 |
+
left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
|
89 |
+
min_bin_width=min_bin_width,
|
90 |
+
min_bin_height=min_bin_height,
|
91 |
+
min_derivative=min_derivative
|
92 |
+
)
|
93 |
+
|
94 |
+
return outputs, logabsdet
|
95 |
+
|
96 |
+
def rational_quadratic_spline(inputs,
|
97 |
+
unnormalized_widths,
|
98 |
+
unnormalized_heights,
|
99 |
+
unnormalized_derivatives,
|
100 |
+
inverse=False,
|
101 |
+
left=0., right=1., bottom=0., top=1.,
|
102 |
+
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
103 |
+
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
104 |
+
min_derivative=DEFAULT_MIN_DERIVATIVE):
|
105 |
+
if torch.min(inputs) < left or torch.max(inputs) > right:
|
106 |
+
raise ValueError('Input to a transform is not within its domain')
|
107 |
+
|
108 |
+
num_bins = unnormalized_widths.shape[-1]
|
109 |
+
|
110 |
+
if min_bin_width * num_bins > 1.0:
|
111 |
+
raise ValueError('Minimal bin width too large for the number of bins')
|
112 |
+
if min_bin_height * num_bins > 1.0:
|
113 |
+
raise ValueError('Minimal bin height too large for the number of bins')
|
114 |
+
|
115 |
+
widths = F.softmax(unnormalized_widths, dim=-1)
|
116 |
+
widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
|
117 |
+
cumwidths = torch.cumsum(widths, dim=-1)
|
118 |
+
cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
|
119 |
+
cumwidths = (right - left) * cumwidths + left
|
120 |
+
cumwidths[..., 0] = left
|
121 |
+
cumwidths[..., -1] = right
|
122 |
+
widths = cumwidths[..., 1:] - cumwidths[..., :-1]
|
123 |
+
|
124 |
+
derivatives = min_derivative + F.softplus(unnormalized_derivatives)
|
125 |
+
|
126 |
+
heights = F.softmax(unnormalized_heights, dim=-1)
|
127 |
+
heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
|
128 |
+
cumheights = torch.cumsum(heights, dim=-1)
|
129 |
+
cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
|
130 |
+
cumheights = (top - bottom) * cumheights + bottom
|
131 |
+
cumheights[..., 0] = bottom
|
132 |
+
cumheights[..., -1] = top
|
133 |
+
heights = cumheights[..., 1:] - cumheights[..., :-1]
|
134 |
+
|
135 |
+
if inverse:
|
136 |
+
bin_idx = searchsorted(cumheights, inputs)[..., None]
|
137 |
+
else:
|
138 |
+
bin_idx = searchsorted(cumwidths, inputs)[..., None]
|
139 |
+
|
140 |
+
input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
|
141 |
+
input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
|
142 |
+
|
143 |
+
input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
|
144 |
+
delta = heights / widths
|
145 |
+
input_delta = delta.gather(-1, bin_idx)[..., 0]
|
146 |
+
|
147 |
+
input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
|
148 |
+
input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
|
149 |
+
|
150 |
+
input_heights = heights.gather(-1, bin_idx)[..., 0]
|
151 |
+
|
152 |
+
if inverse:
|
153 |
+
a = (((inputs - input_cumheights) * (input_derivatives
|
154 |
+
+ input_derivatives_plus_one
|
155 |
+
- 2 * input_delta)
|
156 |
+
+ input_heights * (input_delta - input_derivatives)))
|
157 |
+
b = (input_heights * input_derivatives
|
158 |
+
- (inputs - input_cumheights) * (input_derivatives
|
159 |
+
+ input_derivatives_plus_one
|
160 |
+
- 2 * input_delta))
|
161 |
+
c = - input_delta * (inputs - input_cumheights)
|
162 |
+
|
163 |
+
discriminant = b.pow(2) - 4 * a * c
|
164 |
+
assert (discriminant >= 0).all()
|
165 |
+
|
166 |
+
root = (2 * c) / (-b - torch.sqrt(discriminant))
|
167 |
+
outputs = root * input_bin_widths + input_cumwidths
|
168 |
+
|
169 |
+
theta_one_minus_theta = root * (1 - root)
|
170 |
+
denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
|
171 |
+
* theta_one_minus_theta)
|
172 |
+
derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
|
173 |
+
+ 2 * input_delta * theta_one_minus_theta
|
174 |
+
+ input_derivatives * (1 - root).pow(2))
|
175 |
+
logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
|
176 |
+
|
177 |
+
return outputs, -logabsdet
|
178 |
+
else:
|
179 |
+
theta = (inputs - input_cumwidths) / input_bin_widths
|
180 |
+
theta_one_minus_theta = theta * (1 - theta)
|
181 |
+
|
182 |
+
numerator = input_heights * (input_delta * theta.pow(2)
|
183 |
+
+ input_derivatives * theta_one_minus_theta)
|
184 |
+
denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
|
185 |
+
* theta_one_minus_theta)
|
186 |
+
outputs = input_cumheights + numerator / denominator
|
187 |
+
|
188 |
+
derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
|
189 |
+
+ 2 * input_delta * theta_one_minus_theta
|
190 |
+
+ input_derivatives * (1 - theta).pow(2))
|
191 |
+
logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
|
192 |
+
|
193 |
+
return outputs, logabsdet
|
utils.py
ADDED
@@ -0,0 +1,259 @@
|
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|
|
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|
|
|
|
|
|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
import glob
|
3 |
+
import sys
|
4 |
+
import argparse
|
5 |
+
import logging
|
6 |
+
import json
|
7 |
+
import subprocess
|
8 |
+
import numpy as np
|
9 |
+
from scipy.io.wavfile import read
|
10 |
+
import torch
|
11 |
+
|
12 |
+
MATPLOTLIB_FLAG = False
|
13 |
+
|
14 |
+
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
|
15 |
+
logger = logging
|
16 |
+
|
17 |
+
|
18 |
+
def load_checkpoint(checkpoint_path, model, optimizer=None):
|
19 |
+
assert os.path.isfile(checkpoint_path)
|
20 |
+
checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
|
21 |
+
iteration = checkpoint_dict['iteration']
|
22 |
+
learning_rate = checkpoint_dict['learning_rate']
|
23 |
+
if optimizer is not None:
|
24 |
+
optimizer.load_state_dict(checkpoint_dict['optimizer'])
|
25 |
+
saved_state_dict = checkpoint_dict['model']
|
26 |
+
if hasattr(model, 'module'):
|
27 |
+
state_dict = model.module.state_dict()
|
28 |
+
else:
|
29 |
+
state_dict = model.state_dict()
|
30 |
+
new_state_dict= {}
|
31 |
+
for k, v in state_dict.items():
|
32 |
+
try:
|
33 |
+
new_state_dict[k] = saved_state_dict[k]
|
34 |
+
except:
|
35 |
+
logger.info("%s is not in the checkpoint" % k)
|
36 |
+
new_state_dict[k] = v
|
37 |
+
if hasattr(model, 'module'):
|
38 |
+
model.module.load_state_dict(new_state_dict)
|
39 |
+
else:
|
40 |
+
model.load_state_dict(new_state_dict)
|
41 |
+
logger.info("Loaded checkpoint '{}' (iteration {})" .format(
|
42 |
+
checkpoint_path, iteration))
|
43 |
+
return model, optimizer, learning_rate, iteration
|
44 |
+
|
45 |
+
|
46 |
+
def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
|
47 |
+
logger.info("Saving model and optimizer state at iteration {} to {}".format(
|
48 |
+
iteration, checkpoint_path))
|
49 |
+
if hasattr(model, 'module'):
|
50 |
+
state_dict = model.module.state_dict()
|
51 |
+
else:
|
52 |
+
state_dict = model.state_dict()
|
53 |
+
torch.save({'model': state_dict,
|
54 |
+
'iteration': iteration,
|
55 |
+
'optimizer': optimizer.state_dict(),
|
56 |
+
'learning_rate': learning_rate}, checkpoint_path)
|
57 |
+
|
58 |
+
|
59 |
+
def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
|
60 |
+
for k, v in scalars.items():
|
61 |
+
writer.add_scalar(k, v, global_step)
|
62 |
+
for k, v in histograms.items():
|
63 |
+
writer.add_histogram(k, v, global_step)
|
64 |
+
for k, v in images.items():
|
65 |
+
writer.add_image(k, v, global_step, dataformats='HWC')
|
66 |
+
for k, v in audios.items():
|
67 |
+
writer.add_audio(k, v, global_step, audio_sampling_rate)
|
68 |
+
|
69 |
+
|
70 |
+
def latest_checkpoint_path(dir_path, regex="G_*.pth"):
|
71 |
+
f_list = glob.glob(os.path.join(dir_path, regex))
|
72 |
+
f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
|
73 |
+
x = f_list[-1]
|
74 |
+
print(x)
|
75 |
+
return x
|
76 |
+
|
77 |
+
|
78 |
+
def plot_spectrogram_to_numpy(spectrogram):
|
79 |
+
global MATPLOTLIB_FLAG
|
80 |
+
if not MATPLOTLIB_FLAG:
|
81 |
+
import matplotlib
|
82 |
+
matplotlib.use("Agg")
|
83 |
+
MATPLOTLIB_FLAG = True
|
84 |
+
mpl_logger = logging.getLogger('matplotlib')
|
85 |
+
mpl_logger.setLevel(logging.WARNING)
|
86 |
+
import matplotlib.pylab as plt
|
87 |
+
import numpy as np
|
88 |
+
|
89 |
+
fig, ax = plt.subplots(figsize=(10,2))
|
90 |
+
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
|
91 |
+
interpolation='none')
|
92 |
+
plt.colorbar(im, ax=ax)
|
93 |
+
plt.xlabel("Frames")
|
94 |
+
plt.ylabel("Channels")
|
95 |
+
plt.tight_layout()
|
96 |
+
|
97 |
+
fig.canvas.draw()
|
98 |
+
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
|
99 |
+
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
100 |
+
plt.close()
|
101 |
+
return data
|
102 |
+
|
103 |
+
|
104 |
+
def plot_alignment_to_numpy(alignment, info=None):
|
105 |
+
global MATPLOTLIB_FLAG
|
106 |
+
if not MATPLOTLIB_FLAG:
|
107 |
+
import matplotlib
|
108 |
+
matplotlib.use("Agg")
|
109 |
+
MATPLOTLIB_FLAG = True
|
110 |
+
mpl_logger = logging.getLogger('matplotlib')
|
111 |
+
mpl_logger.setLevel(logging.WARNING)
|
112 |
+
import matplotlib.pylab as plt
|
113 |
+
import numpy as np
|
114 |
+
|
115 |
+
fig, ax = plt.subplots(figsize=(6, 4))
|
116 |
+
im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
|
117 |
+
interpolation='none')
|
118 |
+
fig.colorbar(im, ax=ax)
|
119 |
+
xlabel = 'Decoder timestep'
|
120 |
+
if info is not None:
|
121 |
+
xlabel += '\n\n' + info
|
122 |
+
plt.xlabel(xlabel)
|
123 |
+
plt.ylabel('Encoder timestep')
|
124 |
+
plt.tight_layout()
|
125 |
+
|
126 |
+
fig.canvas.draw()
|
127 |
+
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
|
128 |
+
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
129 |
+
plt.close()
|
130 |
+
return data
|
131 |
+
|
132 |
+
|
133 |
+
def load_wav_to_torch(full_path):
|
134 |
+
sampling_rate, data = read(full_path)
|
135 |
+
return torch.FloatTensor(data.astype(np.float32)), sampling_rate
|
136 |
+
|
137 |
+
|
138 |
+
def load_filepaths_and_text(filename, split="|"):
|
139 |
+
with open(filename, encoding='utf-8') as f:
|
140 |
+
filepaths_and_text = [line.strip().split(split) for line in f]
|
141 |
+
return filepaths_and_text
|
142 |
+
|
143 |
+
|
144 |
+
def get_hparams(init=True):
|
145 |
+
parser = argparse.ArgumentParser()
|
146 |
+
parser.add_argument('-c', '--config', type=str, default="./configs/woman_csmsc.json",
|
147 |
+
help='JSON file for configuration')
|
148 |
+
parser.add_argument('-m', '--model', type=str, default="./woman_csmsc",
|
149 |
+
# required=True,
|
150 |
+
help='Model name')
|
151 |
+
|
152 |
+
args = parser.parse_args()
|
153 |
+
model_dir = os.path.join("./logs", args.model)
|
154 |
+
|
155 |
+
if not os.path.exists(model_dir):
|
156 |
+
os.makedirs(model_dir)
|
157 |
+
|
158 |
+
config_path = args.config
|
159 |
+
config_save_path = os.path.join(model_dir, "config.json")
|
160 |
+
if init:
|
161 |
+
with open(config_path, "r") as f:
|
162 |
+
data = f.read()
|
163 |
+
with open(config_save_path, "w") as f:
|
164 |
+
f.write(data)
|
165 |
+
else:
|
166 |
+
with open(config_save_path, "r") as f:
|
167 |
+
data = f.read()
|
168 |
+
config = json.loads(data)
|
169 |
+
|
170 |
+
hparams = HParams(**config)
|
171 |
+
hparams.model_dir = model_dir
|
172 |
+
return hparams
|
173 |
+
|
174 |
+
|
175 |
+
def get_hparams_from_dir(model_dir):
|
176 |
+
config_save_path = os.path.join(model_dir, "config.json")
|
177 |
+
with open(config_save_path, "r") as f:
|
178 |
+
data = f.read()
|
179 |
+
config = json.loads(data)
|
180 |
+
|
181 |
+
hparams =HParams(**config)
|
182 |
+
hparams.model_dir = model_dir
|
183 |
+
return hparams
|
184 |
+
|
185 |
+
|
186 |
+
def get_hparams_from_file(config_path):
|
187 |
+
with open(config_path, "r") as f:
|
188 |
+
data = f.read()
|
189 |
+
config = json.loads(data)
|
190 |
+
|
191 |
+
hparams =HParams(**config)
|
192 |
+
return hparams
|
193 |
+
|
194 |
+
|
195 |
+
def check_git_hash(model_dir):
|
196 |
+
source_dir = os.path.dirname(os.path.realpath(__file__))
|
197 |
+
if not os.path.exists(os.path.join(source_dir, ".git")):
|
198 |
+
logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
|
199 |
+
source_dir
|
200 |
+
))
|
201 |
+
return
|
202 |
+
|
203 |
+
cur_hash = subprocess.getoutput("git rev-parse HEAD")
|
204 |
+
|
205 |
+
path = os.path.join(model_dir, "githash")
|
206 |
+
if os.path.exists(path):
|
207 |
+
saved_hash = open(path).read()
|
208 |
+
if saved_hash != cur_hash:
|
209 |
+
logger.warn("git hash values are different. {}(saved) != {}(current)".format(
|
210 |
+
saved_hash[:8], cur_hash[:8]))
|
211 |
+
else:
|
212 |
+
open(path, "w").write(cur_hash)
|
213 |
+
|
214 |
+
|
215 |
+
def get_logger(model_dir, filename="train.log"):
|
216 |
+
global logger
|
217 |
+
logger = logging.getLogger(os.path.basename(model_dir))
|
218 |
+
logger.setLevel(logging.DEBUG)
|
219 |
+
|
220 |
+
formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
|
221 |
+
if not os.path.exists(model_dir):
|
222 |
+
os.makedirs(model_dir)
|
223 |
+
h = logging.FileHandler(os.path.join(model_dir, filename))
|
224 |
+
h.setLevel(logging.DEBUG)
|
225 |
+
h.setFormatter(formatter)
|
226 |
+
logger.addHandler(h)
|
227 |
+
return logger
|
228 |
+
|
229 |
+
|
230 |
+
class HParams():
|
231 |
+
def __init__(self, **kwargs):
|
232 |
+
for k, v in kwargs.items():
|
233 |
+
if type(v) == dict:
|
234 |
+
v = HParams(**v)
|
235 |
+
self[k] = v
|
236 |
+
|
237 |
+
def keys(self):
|
238 |
+
return self.__dict__.keys()
|
239 |
+
|
240 |
+
def items(self):
|
241 |
+
return self.__dict__.items()
|
242 |
+
|
243 |
+
def values(self):
|
244 |
+
return self.__dict__.values()
|
245 |
+
|
246 |
+
def __len__(self):
|
247 |
+
return len(self.__dict__)
|
248 |
+
|
249 |
+
def __getitem__(self, key):
|
250 |
+
return getattr(self, key)
|
251 |
+
|
252 |
+
def __setitem__(self, key, value):
|
253 |
+
return setattr(self, key, value)
|
254 |
+
|
255 |
+
def __contains__(self, key):
|
256 |
+
return key in self.__dict__
|
257 |
+
|
258 |
+
def __repr__(self):
|
259 |
+
return self.__dict__.__repr__()
|