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删除无用的Onnx文件 (#230)

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* Delete attentions_onnx.py

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Ναρουσέ·μ·γιουμεμί·Χινακάννα
2023-12-12 19:54:00 +08:00
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378
attentions_onnx.py
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import math
import torch
from torch import nn
from torch.nn import functional as F
import commons
import logging
logger = logging.getLogger(__name__)
class LayerNorm(nn.Module):
def __init__(self, channels, eps=1e-5):
super().__init__()
self.channels = channels
self.eps = eps
self.gamma = nn.Parameter(torch.ones(channels))
self.beta = nn.Parameter(torch.zeros(channels))
def forward(self, x):
x = x.transpose(1, -1)
x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
return x.transpose(1, -1)
@torch.jit.script
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
n_channels_int = n_channels[0]
in_act = input_a + input_b
t_act = torch.tanh(in_act[:, :n_channels_int, :])
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
acts = t_act * s_act
return acts
class Encoder(nn.Module):
def __init__(
self,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size=1,
p_dropout=0.0,
window_size=4,
isflow=True,
**kwargs
):
super().__init__()
self.hidden_channels = hidden_channels
self.filter_channels = filter_channels
self.n_heads = n_heads
self.n_layers = n_layers
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.window_size = window_size
# if isflow:
# cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1)
# self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
# self.cond_layer = weight_norm(cond_layer, name='weight')
# self.gin_channels = 256
self.cond_layer_idx = self.n_layers
if "gin_channels" in kwargs:
self.gin_channels = kwargs["gin_channels"]
if self.gin_channels != 0:
self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
# vits2 says 3rd block, so idx is 2 by default
self.cond_layer_idx = (
kwargs["cond_layer_idx"] if "cond_layer_idx" in kwargs else 2
)
logging.debug(self.gin_channels, self.cond_layer_idx)
assert (
self.cond_layer_idx < self.n_layers
), "cond_layer_idx should be less than n_layers"
self.drop = nn.Dropout(p_dropout)
self.attn_layers = nn.ModuleList()
self.norm_layers_1 = nn.ModuleList()
self.ffn_layers = nn.ModuleList()
self.norm_layers_2 = nn.ModuleList()
for i in range(self.n_layers):
self.attn_layers.append(
MultiHeadAttention(
hidden_channels,
hidden_channels,
n_heads,
p_dropout=p_dropout,
window_size=window_size,
)
)
self.norm_layers_1.append(LayerNorm(hidden_channels))
self.ffn_layers.append(
FFN(
hidden_channels,
hidden_channels,
filter_channels,
kernel_size,
p_dropout=p_dropout,
)
)
self.norm_layers_2.append(LayerNorm(hidden_channels))
def forward(self, x, x_mask, g=None):
attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
x = x * x_mask
for i in range(self.n_layers):
if i == self.cond_layer_idx and g is not None:
g = self.spk_emb_linear(g.transpose(1, 2))
g = g.transpose(1, 2)
x = x + g
x = x * x_mask
y = self.attn_layers[i](x, x, attn_mask)
y = self.drop(y)
x = self.norm_layers_1[i](x + y)
y = self.ffn_layers[i](x, x_mask)
y = self.drop(y)
x = self.norm_layers_2[i](x + y)
x = x * x_mask
return x
class MultiHeadAttention(nn.Module):
def __init__(
self,
channels,
out_channels,
n_heads,
p_dropout=0.0,
window_size=None,
heads_share=True,
block_length=None,
proximal_bias=False,
proximal_init=False,
):
super().__init__()
assert channels % n_heads == 0
self.channels = channels
self.out_channels = out_channels
self.n_heads = n_heads
self.p_dropout = p_dropout
self.window_size = window_size
self.heads_share = heads_share
self.block_length = block_length
self.proximal_bias = proximal_bias
self.proximal_init = proximal_init
self.attn = None
self.k_channels = channels // n_heads
self.conv_q = nn.Conv1d(channels, channels, 1)
self.conv_k = nn.Conv1d(channels, channels, 1)
self.conv_v = nn.Conv1d(channels, channels, 1)
self.conv_o = nn.Conv1d(channels, out_channels, 1)
self.drop = nn.Dropout(p_dropout)
if window_size is not None:
n_heads_rel = 1 if heads_share else n_heads
rel_stddev = self.k_channels**-0.5
self.emb_rel_k = nn.Parameter(
torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
* rel_stddev
)
self.emb_rel_v = nn.Parameter(
torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
* rel_stddev
)
nn.init.xavier_uniform_(self.conv_q.weight)
nn.init.xavier_uniform_(self.conv_k.weight)
nn.init.xavier_uniform_(self.conv_v.weight)
if proximal_init:
with torch.no_grad():
self.conv_k.weight.copy_(self.conv_q.weight)
self.conv_k.bias.copy_(self.conv_q.bias)
def forward(self, x, c, attn_mask=None):
q = self.conv_q(x)
k = self.conv_k(c)
v = self.conv_v(c)
x, self.attn = self.attention(q, k, v, mask=attn_mask)
x = self.conv_o(x)
return x
def attention(self, query, key, value, mask=None):
# reshape [b, d, t] -> [b, n_h, t, d_k]
b, d, t_s, t_t = (*key.size(), query.size(2))
query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
if self.window_size is not None:
assert (
t_s == t_t
), "Relative attention is only available for self-attention."
key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
rel_logits = self._matmul_with_relative_keys(
query / math.sqrt(self.k_channels), key_relative_embeddings
)
scores_local = self._relative_position_to_absolute_position(rel_logits)
scores = scores + scores_local
if self.proximal_bias:
assert t_s == t_t, "Proximal bias is only available for self-attention."
scores = scores + self._attention_bias_proximal(t_s).to(
device=scores.device, dtype=scores.dtype
)
if mask is not None:
scores = scores.masked_fill(mask == 0, -1e4)
if self.block_length is not None:
assert (
t_s == t_t
), "Local attention is only available for self-attention."
block_mask = (
torch.ones_like(scores)
.triu(-self.block_length)
.tril(self.block_length)
)
scores = scores.masked_fill(block_mask == 0, -1e4)
p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
p_attn = self.drop(p_attn)
output = torch.matmul(p_attn, value)
if self.window_size is not None:
relative_weights = self._absolute_position_to_relative_position(p_attn)
value_relative_embeddings = self._get_relative_embeddings(
self.emb_rel_v, t_s
)
output = output + self._matmul_with_relative_values(
relative_weights, value_relative_embeddings
)
output = (
output.transpose(2, 3).contiguous().view(b, d, t_t)
) # [b, n_h, t_t, d_k] -> [b, d, t_t]
return output, p_attn
def _matmul_with_relative_values(self, x, y):
"""
x: [b, h, l, m]
y: [h or 1, m, d]
ret: [b, h, l, d]
"""
ret = torch.matmul(x, y.unsqueeze(0))
return ret
def _matmul_with_relative_keys(self, x, y):
"""
x: [b, h, l, d]
y: [h or 1, m, d]
ret: [b, h, l, m]
"""
ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
return ret
def _get_relative_embeddings(self, relative_embeddings, length):
max_relative_position = 2 * self.window_size + 1
# Pad first before slice to avoid using cond ops.
pad_length = max(length - (self.window_size + 1), 0)
slice_start_position = max((self.window_size + 1) - length, 0)
slice_end_position = slice_start_position + 2 * length - 1
if pad_length > 0:
padded_relative_embeddings = F.pad(
relative_embeddings,
commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
)
else:
padded_relative_embeddings = relative_embeddings
used_relative_embeddings = padded_relative_embeddings[
:, slice_start_position:slice_end_position
]
return used_relative_embeddings
def _relative_position_to_absolute_position(self, x):
"""
x: [b, h, l, 2*l-1]
ret: [b, h, l, l]
"""
batch, heads, length, _ = x.size()
# Concat columns of pad to shift from relative to absolute indexing.
x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
# Concat extra elements so to add up to shape (len+1, 2*len-1).
x_flat = x.view([batch, heads, length * 2 * length])
x_flat = F.pad(
x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
)
# Reshape and slice out the padded elements.
x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
:, :, :length, length - 1 :
]
return x_final
def _absolute_position_to_relative_position(self, x):
"""
x: [b, h, l, l]
ret: [b, h, l, 2*l-1]
"""
batch, heads, length, _ = x.size()
# padd along column
x = F.pad(
x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
)
x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
# add 0's in the beginning that will skew the elements after reshape
x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
return x_final
def _attention_bias_proximal(self, length):
"""Bias for self-attention to encourage attention to close positions.
Args:
length: an integer scalar.
Returns:
a Tensor with shape [1, 1, length, length]
"""
r = torch.arange(length, dtype=torch.float32)
diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
class FFN(nn.Module):
def __init__(
self,
in_channels,
out_channels,
filter_channels,
kernel_size,
p_dropout=0.0,
activation=None,
causal=False,
):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.filter_channels = filter_channels
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.activation = activation
self.causal = causal
if causal:
self.padding = self._causal_padding
else:
self.padding = self._same_padding
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
self.drop = nn.Dropout(p_dropout)
def forward(self, x, x_mask):
x = self.conv_1(self.padding(x * x_mask))
if self.activation == "gelu":
x = x * torch.sigmoid(1.702 * x)
else:
x = torch.relu(x)
x = self.drop(x)
x = self.conv_2(self.padding(x * x_mask))
return x * x_mask
def _causal_padding(self, x):
if self.kernel_size == 1:
return x
pad_l = self.kernel_size - 1
pad_r = 0
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
x = F.pad(x, commons.convert_pad_shape(padding))
return x
def _same_padding(self, x):
if self.kernel_size == 1:
return x
pad_l = (self.kernel_size - 1) // 2
pad_r = self.kernel_size // 2
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
x = F.pad(x, commons.convert_pad_shape(padding))
return x

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models_onnx.py
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import math
import torch
from torch import nn
from torch.nn import functional as F
import commons
import modules
import attentions_onnx
from torch.nn import Conv1d, ConvTranspose1d, Conv2d
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
from commons import init_weights, get_padding
from text import symbols, num_tones, num_languages
class DurationDiscriminator(nn.Module): # vits2
def __init__(
self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
):
super().__init__()
self.in_channels = in_channels
self.filter_channels = filter_channels
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.gin_channels = gin_channels
self.drop = nn.Dropout(p_dropout)
self.conv_1 = nn.Conv1d(
in_channels, filter_channels, kernel_size, padding=kernel_size // 2
)
self.norm_1 = modules.LayerNorm(filter_channels)
self.conv_2 = nn.Conv1d(
filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
)
self.norm_2 = modules.LayerNorm(filter_channels)
self.dur_proj = nn.Conv1d(1, filter_channels, 1)
self.pre_out_conv_1 = nn.Conv1d(
2 * filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
)
self.pre_out_norm_1 = modules.LayerNorm(filter_channels)
self.pre_out_conv_2 = nn.Conv1d(
filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
)
self.pre_out_norm_2 = modules.LayerNorm(filter_channels)
if gin_channels != 0:
self.cond = nn.Conv1d(gin_channels, in_channels, 1)
self.output_layer = nn.Sequential(nn.Linear(filter_channels, 1), nn.Sigmoid())
def forward_probability(self, x, x_mask, dur, g=None):
dur = self.dur_proj(dur)
x = torch.cat([x, dur], dim=1)
x = self.pre_out_conv_1(x * x_mask)
x = torch.relu(x)
x = self.pre_out_norm_1(x)
x = self.drop(x)
x = self.pre_out_conv_2(x * x_mask)
x = torch.relu(x)
x = self.pre_out_norm_2(x)
x = self.drop(x)
x = x * x_mask
x = x.transpose(1, 2)
output_prob = self.output_layer(x)
return output_prob
def forward(self, x, x_mask, dur_r, dur_hat, g=None):
x = torch.detach(x)
if g is not None:
g = torch.detach(g)
x = x + self.cond(g)
x = self.conv_1(x * x_mask)
x = torch.relu(x)
x = self.norm_1(x)
x = self.drop(x)
x = self.conv_2(x * x_mask)
x = torch.relu(x)
x = self.norm_2(x)
x = self.drop(x)
output_probs = []
for dur in [dur_r, dur_hat]:
output_prob = self.forward_probability(x, x_mask, dur, g)
output_probs.append(output_prob)
return output_probs
class TransformerCouplingBlock(nn.Module):
def __init__(
self,
channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
n_flows=4,
gin_channels=0,
share_parameter=False,
):
super().__init__()
self.channels = channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.n_layers = n_layers
self.n_flows = n_flows
self.gin_channels = gin_channels
self.flows = nn.ModuleList()
self.wn = (
attentions_onnx.FFT(
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
isflow=True,
gin_channels=self.gin_channels,
)
if share_parameter
else None
)
for i in range(n_flows):
self.flows.append(
modules.TransformerCouplingLayer(
channels,
hidden_channels,
kernel_size,
n_layers,
n_heads,
p_dropout,
filter_channels,
mean_only=True,
wn_sharing_parameter=self.wn,
gin_channels=self.gin_channels,
)
)
self.flows.append(modules.Flip())
def forward(self, x, x_mask, g=None, reverse=True):
if not reverse:
for flow in self.flows:
x, _ = flow(x, x_mask, g=g, reverse=reverse)
else:
for flow in reversed(self.flows):
x = flow(x, x_mask, g=g, reverse=reverse)
return x
class StochasticDurationPredictor(nn.Module):
def __init__(
self,
in_channels,
filter_channels,
kernel_size,
p_dropout,
n_flows=4,
gin_channels=0,
):
super().__init__()
filter_channels = in_channels # it needs to be removed from future version.
self.in_channels = in_channels
self.filter_channels = filter_channels
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.n_flows = n_flows
self.gin_channels = gin_channels
self.log_flow = modules.Log()
self.flows = nn.ModuleList()
self.flows.append(modules.ElementwiseAffine(2))
for i in range(n_flows):
self.flows.append(
modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
)
self.flows.append(modules.Flip())
self.post_pre = nn.Conv1d(1, filter_channels, 1)
self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
self.post_convs = modules.DDSConv(
filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
)
self.post_flows = nn.ModuleList()
self.post_flows.append(modules.ElementwiseAffine(2))
for i in range(4):
self.post_flows.append(
modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
)
self.post_flows.append(modules.Flip())
self.pre = nn.Conv1d(in_channels, filter_channels, 1)
self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
self.convs = modules.DDSConv(
filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
)
if gin_channels != 0:
self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
def forward(self, x, x_mask, z, g=None):
x = torch.detach(x)
x = self.pre(x)
if g is not None:
g = torch.detach(g)
x = x + self.cond(g)
x = self.convs(x, x_mask)
x = self.proj(x) * x_mask
flows = list(reversed(self.flows))
flows = flows[:-2] + [flows[-1]] # remove a useless vflow
for flow in flows:
z = flow(z, x_mask, g=x, reverse=True)
z0, z1 = torch.split(z, [1, 1], 1)
logw = z0
return logw
class DurationPredictor(nn.Module):
def __init__(
self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
):
super().__init__()
self.in_channels = in_channels
self.filter_channels = filter_channels
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.gin_channels = gin_channels
self.drop = nn.Dropout(p_dropout)
self.conv_1 = nn.Conv1d(
in_channels, filter_channels, kernel_size, padding=kernel_size // 2
)
self.norm_1 = modules.LayerNorm(filter_channels)
self.conv_2 = nn.Conv1d(
filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
)
self.norm_2 = modules.LayerNorm(filter_channels)
self.proj = nn.Conv1d(filter_channels, 1, 1)
if gin_channels != 0:
self.cond = nn.Conv1d(gin_channels, in_channels, 1)
def forward(self, x, x_mask, g=None):
x = torch.detach(x)
if g is not None:
g = torch.detach(g)
x = x + self.cond(g)
x = self.conv_1(x * x_mask)
x = torch.relu(x)
x = self.norm_1(x)
x = self.drop(x)
x = self.conv_2(x * x_mask)
x = torch.relu(x)
x = self.norm_2(x)
x = self.drop(x)
x = self.proj(x * x_mask)
return x * x_mask
class TextEncoder(nn.Module):
def __init__(
self,
n_vocab,
out_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
gin_channels=0,
):
super().__init__()
self.n_vocab = n_vocab
self.out_channels = out_channels
self.hidden_channels = hidden_channels
self.filter_channels = filter_channels
self.n_heads = n_heads
self.n_layers = n_layers
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.gin_channels = gin_channels
self.emb = nn.Embedding(len(symbols), hidden_channels)
nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
self.tone_emb = nn.Embedding(num_tones, hidden_channels)
nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels**-0.5)
self.language_emb = nn.Embedding(num_languages, hidden_channels)
nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels**-0.5)
self.bert_proj = nn.Conv1d(1024, hidden_channels, 1)
self.ja_bert_proj = nn.Conv1d(1024, hidden_channels, 1)
self.en_bert_proj = nn.Conv1d(1024, hidden_channels, 1)
self.encoder = attentions_onnx.Encoder(
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
gin_channels=self.gin_channels,
)
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
def forward(self, x, x_lengths, tone, language, bert, ja_bert, en_bert, g=None):
x_mask = torch.ones_like(x).unsqueeze(0)
bert_emb = self.bert_proj(bert.transpose(0, 1).unsqueeze(0)).transpose(1, 2)
ja_bert_emb = self.ja_bert_proj(ja_bert.transpose(0, 1).unsqueeze(0)).transpose(
1, 2
)
en_bert_emb = self.en_bert_proj(en_bert.transpose(0, 1).unsqueeze(0)).transpose(
1, 2
)
x = (
self.emb(x)
+ self.tone_emb(tone)
+ self.language_emb(language)
+ bert_emb
+ ja_bert_emb
+ en_bert_emb
) * math.sqrt(
self.hidden_channels
) # [b, t, h]
x = torch.transpose(x, 1, -1) # [b, h, t]
x_mask = x_mask.to(x.dtype)
x = self.encoder(x * x_mask, x_mask, g=g)
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
return x, m, logs, x_mask
class ResidualCouplingBlock(nn.Module):
def __init__(
self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
n_flows=4,
gin_channels=0,
):
super().__init__()
self.channels = channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.dilation_rate = dilation_rate
self.n_layers = n_layers
self.n_flows = n_flows
self.gin_channels = gin_channels
self.flows = nn.ModuleList()
for i in range(n_flows):
self.flows.append(
modules.ResidualCouplingLayer(
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=gin_channels,
mean_only=True,
)
)
self.flows.append(modules.Flip())
def forward(self, x, x_mask, g=None, reverse=True):
if not reverse:
for flow in self.flows:
x, _ = flow(x, x_mask, g=g, reverse=reverse)
else:
for flow in reversed(self.flows):
x = flow(x, x_mask, g=g, reverse=reverse)
return x
class PosteriorEncoder(nn.Module):
def __init__(
self,
in_channels,
out_channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=0,
):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.dilation_rate = dilation_rate
self.n_layers = n_layers
self.gin_channels = gin_channels
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
self.enc = modules.WN(
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=gin_channels,
)
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
def forward(self, x, x_lengths, g=None):
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
x.dtype
)
x = self.pre(x) * x_mask
x = self.enc(x, x_mask, g=g)
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
return z, m, logs, x_mask
class Generator(torch.nn.Module):
def __init__(
self,
initial_channel,
resblock,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
gin_channels=0,
):
super(Generator, self).__init__()
self.num_kernels = len(resblock_kernel_sizes)
self.num_upsamples = len(upsample_rates)
self.conv_pre = Conv1d(
initial_channel, upsample_initial_channel, 7, 1, padding=3
)
resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
self.ups = nn.ModuleList()
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
self.ups.append(
weight_norm(
ConvTranspose1d(
upsample_initial_channel // (2**i),
upsample_initial_channel // (2 ** (i + 1)),
k,
u,
padding=(k - u) // 2,
)
)
)
self.resblocks = nn.ModuleList()
for i in range(len(self.ups)):
ch = upsample_initial_channel // (2 ** (i + 1))
for j, (k, d) in enumerate(
zip(resblock_kernel_sizes, resblock_dilation_sizes)
):
self.resblocks.append(resblock(ch, k, d))
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
self.ups.apply(init_weights)
if gin_channels != 0:
self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
def forward(self, x, g=None):
x = self.conv_pre(x)
if g is not None:
x = x + self.cond(g)
for i in range(self.num_upsamples):
x = F.leaky_relu(x, modules.LRELU_SLOPE)
x = self.ups[i](x)
xs = None
for j in range(self.num_kernels):
if xs is None:
xs = self.resblocks[i * self.num_kernels + j](x)
else:
xs += self.resblocks[i * self.num_kernels + j](x)
x = xs / self.num_kernels
x = F.leaky_relu(x)
x = self.conv_post(x)
x = torch.tanh(x)
return x
def remove_weight_norm(self):
print("Removing weight norm...")
for layer in self.ups:
remove_weight_norm(layer)
for layer in self.resblocks:
layer.remove_weight_norm()
class DiscriminatorP(torch.nn.Module):
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
super(DiscriminatorP, self).__init__()
self.period = period
self.use_spectral_norm = use_spectral_norm
norm_f = weight_norm if use_spectral_norm is False else spectral_norm
self.convs = nn.ModuleList(
[
norm_f(
Conv2d(
1,
32,
(kernel_size, 1),
(stride, 1),
padding=(get_padding(kernel_size, 1), 0),
)
),
norm_f(
Conv2d(
32,
128,
(kernel_size, 1),
(stride, 1),
padding=(get_padding(kernel_size, 1), 0),
)
),
norm_f(
Conv2d(
128,
512,
(kernel_size, 1),
(stride, 1),
padding=(get_padding(kernel_size, 1), 0),
)
),
norm_f(
Conv2d(
512,
1024,
(kernel_size, 1),
(stride, 1),
padding=(get_padding(kernel_size, 1), 0),
)
),
norm_f(
Conv2d(
1024,
1024,
(kernel_size, 1),
1,
padding=(get_padding(kernel_size, 1), 0),
)
),
]
)
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
def forward(self, x):
fmap = []
# 1d to 2d
b, c, t = x.shape
if t % self.period != 0: # pad first
n_pad = self.period - (t % self.period)
x = F.pad(x, (0, n_pad), "reflect")
t = t + n_pad
x = x.view(b, c, t // self.period, self.period)
for layer in self.convs:
x = layer(x)
x = F.leaky_relu(x, modules.LRELU_SLOPE)
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
x = torch.flatten(x, 1, -1)
return x, fmap
class DiscriminatorS(torch.nn.Module):
def __init__(self, use_spectral_norm=False):
super(DiscriminatorS, self).__init__()
norm_f = weight_norm if use_spectral_norm is False else spectral_norm
self.convs = nn.ModuleList(
[
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
]
)
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
def forward(self, x):
fmap = []
for layer in self.convs:
x = layer(x)
x = F.leaky_relu(x, modules.LRELU_SLOPE)
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
x = torch.flatten(x, 1, -1)
return x, fmap
class MultiPeriodDiscriminator(torch.nn.Module):
def __init__(self, use_spectral_norm=False):
super(MultiPeriodDiscriminator, self).__init__()
periods = [2, 3, 5, 7, 11]
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
discs = discs + [
DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
]
self.discriminators = nn.ModuleList(discs)
def forward(self, y, y_hat):
y_d_rs = []
y_d_gs = []
fmap_rs = []
fmap_gs = []
for i, d in enumerate(self.discriminators):
y_d_r, fmap_r = d(y)
y_d_g, fmap_g = d(y_hat)
y_d_rs.append(y_d_r)
y_d_gs.append(y_d_g)
fmap_rs.append(fmap_r)
fmap_gs.append(fmap_g)
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
class ReferenceEncoder(nn.Module):
"""
inputs --- [N, Ty/r, n_mels*r] mels
outputs --- [N, ref_enc_gru_size]
"""
def __init__(self, spec_channels, gin_channels=0):
super().__init__()
self.spec_channels = spec_channels
ref_enc_filters = [32, 32, 64, 64, 128, 128]
K = len(ref_enc_filters)
filters = [1] + ref_enc_filters
convs = [
weight_norm(
nn.Conv2d(
in_channels=filters[i],
out_channels=filters[i + 1],
kernel_size=(3, 3),
stride=(2, 2),
padding=(1, 1),
)
)
for i in range(K)
]
self.convs = nn.ModuleList(convs)
# self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)]) # noqa: E501
out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
self.gru = nn.GRU(
input_size=ref_enc_filters[-1] * out_channels,
hidden_size=256 // 2,
batch_first=True,
)
self.proj = nn.Linear(128, gin_channels)
def forward(self, inputs, mask=None):
N = inputs.size(0)
out = inputs.view(N, 1, -1, self.spec_channels) # [N, 1, Ty, n_freqs]
for conv in self.convs:
out = conv(out)
# out = wn(out)
out = F.relu(out) # [N, 128, Ty//2^K, n_mels//2^K]
out = out.transpose(1, 2) # [N, Ty//2^K, 128, n_mels//2^K]
T = out.size(1)
N = out.size(0)
out = out.contiguous().view(N, T, -1) # [N, Ty//2^K, 128*n_mels//2^K]
self.gru.flatten_parameters()
memory, out = self.gru(out) # out --- [1, N, 128]
return self.proj(out.squeeze(0))
def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
for i in range(n_convs):
L = (L - kernel_size + 2 * pad) // stride + 1
return L
class SynthesizerTrn(nn.Module):
"""
Synthesizer for Training
"""
def __init__(
self,
n_vocab,
spec_channels,
segment_size,
inter_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
resblock,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
n_speakers=256,
gin_channels=256,
use_sdp=True,
n_flow_layer=4,
n_layers_trans_flow=4,
flow_share_parameter=False,
use_transformer_flow=True,
**kwargs,
):
super().__init__()
self.n_vocab = n_vocab
self.spec_channels = spec_channels
self.inter_channels = inter_channels
self.hidden_channels = hidden_channels
self.filter_channels = filter_channels
self.n_heads = n_heads
self.n_layers = n_layers
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.resblock = resblock
self.resblock_kernel_sizes = resblock_kernel_sizes
self.resblock_dilation_sizes = resblock_dilation_sizes
self.upsample_rates = upsample_rates
self.upsample_initial_channel = upsample_initial_channel
self.upsample_kernel_sizes = upsample_kernel_sizes
self.segment_size = segment_size
self.n_speakers = n_speakers
self.gin_channels = gin_channels
self.n_layers_trans_flow = n_layers_trans_flow
self.use_spk_conditioned_encoder = kwargs.get(
"use_spk_conditioned_encoder", True
)
self.use_sdp = use_sdp
self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False)
self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01)
self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6)
self.current_mas_noise_scale = self.mas_noise_scale_initial
if self.use_spk_conditioned_encoder and gin_channels > 0:
self.enc_gin_channels = gin_channels
self.enc_p = TextEncoder(
n_vocab,
inter_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
gin_channels=self.enc_gin_channels,
)
self.dec = Generator(
inter_channels,
resblock,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
gin_channels=gin_channels,
)
self.enc_q = PosteriorEncoder(
spec_channels,
inter_channels,
hidden_channels,
5,
1,
16,
gin_channels=gin_channels,
)
if use_transformer_flow:
self.flow = TransformerCouplingBlock(
inter_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers_trans_flow,
5,
p_dropout,
n_flow_layer,
gin_channels=gin_channels,
share_parameter=flow_share_parameter,
)
else:
self.flow = ResidualCouplingBlock(
inter_channels,
hidden_channels,
5,
1,
n_flow_layer,
gin_channels=gin_channels,
)
self.sdp = StochasticDurationPredictor(
hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels
)
self.dp = DurationPredictor(
hidden_channels, 256, 3, 0.5, gin_channels=gin_channels
)
if n_speakers >= 1:
self.emb_g = nn.Embedding(n_speakers, gin_channels)
else:
self.ref_enc = ReferenceEncoder(spec_channels, gin_channels)
def export_onnx(
self,
path,
max_len=None,
sdp_ratio=0,
y=None,
):
noise_scale = 0.667
length_scale = 1
noise_scale_w = 0.8
x = torch.LongTensor(
[
0,
97,
0,
8,
0,
78,
0,
8,
0,
76,
0,
37,
0,
40,
0,
97,
0,
8,
0,
23,
0,
8,
0,
74,
0,
26,
0,
104,
0,
]
).unsqueeze(0)
tone = torch.zeros_like(x)
language = torch.zeros_like(x)
x_lengths = torch.LongTensor([x.shape[1]])
sid = torch.LongTensor([0])
bert = torch.randn(size=(x.shape[1], 1024))
ja_bert = torch.randn(size=(x.shape[1], 1024))
en_bert = torch.randn(size=(x.shape[1], 1024))
if self.n_speakers > 0:
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
torch.onnx.export(
self.emb_g,
(sid),
f"onnx/{path}/{path}_emb.onnx",
input_names=["sid"],
output_names=["g"],
verbose=True,
)
else:
g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)
torch.onnx.export(
self.enc_p,
(x, x_lengths, tone, language, bert, ja_bert, en_bert, g),
f"onnx/{path}/{path}_enc_p.onnx",
input_names=[
"x",
"x_lengths",
"t",
"language",
"bert_0",
"bert_1",
"bert_2",
"g",
],
output_names=["xout", "m_p", "logs_p", "x_mask"],
dynamic_axes={
"x": [0, 1],
"t": [0, 1],
"language": [0, 1],
"bert_0": [0],
"bert_1": [0],
"bert_2": [0],
"xout": [0, 2],
"m_p": [0, 2],
"logs_p": [0, 2],
"x_mask": [0, 2],
},
verbose=True,
opset_version=16,
)
x, m_p, logs_p, x_mask = self.enc_p(
x, x_lengths, tone, language, bert, ja_bert, en_bert, g=g
)
zinput = (
torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype)
* noise_scale_w
)
torch.onnx.export(
self.sdp,
(x, x_mask, zinput, g),
f"onnx/{path}/{path}_sdp.onnx",
input_names=["x", "x_mask", "zin", "g"],
output_names=["logw"],
dynamic_axes={"x": [0, 2], "x_mask": [0, 2], "zin": [0, 2], "logw": [0, 2]},
verbose=True,
)
torch.onnx.export(
self.dp,
(x, x_mask, g),
f"onnx/{path}/{path}_dp.onnx",
input_names=["x", "x_mask", "g"],
output_names=["logw"],
dynamic_axes={"x": [0, 2], "x_mask": [0, 2], "logw": [0, 2]},
verbose=True,
)
logw = self.sdp(x, x_mask, zinput, g=g) * (sdp_ratio) + self.dp(
x, x_mask, g=g
) * (1 - sdp_ratio)
w = torch.exp(logw) * x_mask * length_scale
w_ceil = torch.ceil(w)
y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(
x_mask.dtype
)
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
attn = commons.generate_path(w_ceil, attn_mask)
m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(
1, 2
) # [b, t', t], [b, t, d] -> [b, d, t']
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(
1, 2
) # [b, t', t], [b, t, d] -> [b, d, t']
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
torch.onnx.export(
self.flow,
(z_p, y_mask, g),
f"onnx/{path}/{path}_flow.onnx",
input_names=["z_p", "y_mask", "g"],
output_names=["z"],
dynamic_axes={"z_p": [0, 2], "y_mask": [0, 2], "z": [0, 2]},
verbose=True,
)
z = self.flow(z_p, y_mask, g=g, reverse=True)
z_in = (z * y_mask)[:, :, :max_len]
torch.onnx.export(
self.dec,
(z_in, g),
f"onnx/{path}/{path}_dec.onnx",
input_names=["z_in", "g"],
output_names=["o"],
dynamic_axes={"z_in": [0, 2], "o": [0, 2]},
verbose=True,
)
o = self.dec((z * y_mask)[:, :, :max_len], g=g)