File size: 6,396 Bytes
7d51a93 8876cf1 7d51a93 8876cf1 7d51a93 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 | import torch
import torch.nn as nn
from .text_encoder import (
AttnEncoder,
TextEmbedderWrapper,
ConvNeXtWrapper,
)
class DPReferenceEncoder(nn.Module):
def __init__(
self,
in_channels: int = 144,
d_model: int = 64,
hidden_dim: int = 256,
num_blocks: int = 4,
num_queries: int = 8,
query_dim: int = 16,
num_heads: int = 2,
kernel_size: int = 5,
dilation_lst: list = None,
):
super().__init__()
self.d_model = d_model
self.num_queries = num_queries
self.query_dim = query_dim
mlp_ratio = hidden_dim // d_model
self.input_proj = nn.Conv1d(in_channels, d_model, kernel_size=1)
self.convnext = ConvNeXtWrapper(
d_model,
n_layers=num_blocks,
expansion_factor=mlp_ratio,
kernel_size=kernel_size,
dilation_lst=dilation_lst,
)
self.ref_keys = nn.Parameter(torch.randn(num_queries, query_dim) * 0.02)
self.attn1 = nn.MultiheadAttention(
embed_dim=query_dim, num_heads=num_heads, kdim=d_model, vdim=d_model, batch_first=True
)
self.attn2 = nn.MultiheadAttention(
embed_dim=query_dim, num_heads=num_heads, kdim=d_model, vdim=d_model, batch_first=True
)
def forward(self, z_ref: torch.Tensor, mask: torch.Tensor = None):
B = z_ref.shape[0]
x = self.input_proj(z_ref)
x = self.convnext(x, mask=mask)
kv = x.transpose(1, 2)
key_padding_mask = None
if mask is not None:
key_padding_mask = (mask.squeeze(1) == 0)
q0 = self.ref_keys.unsqueeze(0).expand(B, -1, -1)
q1, _ = self.attn1(query=q0, key=kv, value=kv, key_padding_mask=key_padding_mask, need_weights=False)
q2 = q0 + q1
out, _ = self.attn2(query=q2, key=kv, value=kv, key_padding_mask=key_padding_mask, need_weights=False)
return out.reshape(B, -1)
class DPTextEncoder(nn.Module):
def __init__(self, vocab_size=37, d_model=64):
super().__init__()
self.d_model = d_model
self.text_embedder = TextEmbedderWrapper(vocab_size, d_model)
self.convnext = ConvNeXtWrapper(d_model, n_layers=6, expansion_factor=4)
self.sentence_token = nn.Parameter(torch.randn(1, d_model, 1) * 0.02)
self.attn_encoder = AttnEncoder(
channels=d_model,
n_heads=2,
filter_channels=d_model * 4,
n_layers=2,
)
self.proj_out = nn.Sequential()
self.proj_out.add_module("net", nn.Conv1d(d_model, d_model, 1, bias=False))
def forward(self, text_ids, mask=None):
B, T = text_ids.shape
x = self.text_embedder(text_ids)
x = x.transpose(1, 2)
if mask is not None:
x = x * mask
u_token = self.sentence_token.expand(B, -1, -1)
x = torch.cat([u_token, x], dim=2)
if mask is not None:
mask_u = torch.ones(B, 1, 1, device=mask.device)
mask = torch.cat([mask_u, mask], dim=2)
x = self.convnext(x, mask=mask)
conv_out = x
x = self.attn_encoder(x, mask=mask)
x = x + conv_out
first_token = x[:, :, :1]
out = self.proj_out(first_token)
if mask is not None:
out = out * mask[:, :, :1]
return out.squeeze(2)
class DurationEstimator(nn.Module):
def __init__(self, text_dim=64, style_dim=128):
super().__init__()
self.layers = nn.ModuleList([
nn.Linear(text_dim + style_dim, 128),
nn.Linear(128, 1),
])
self.activation = nn.PReLU()
def forward(self, text_emb, style_emb, text_mask=None, return_log=False):
if style_emb.dim() > 2:
style_emb = style_emb.reshape(style_emb.shape[0], -1)
x = torch.cat([text_emb, style_emb], dim=1)
x = self.layers[0](x)
x = self.activation(x)
x = self.layers[1](x)
if return_log:
return x.squeeze(1)
return torch.exp(x).squeeze(1)
class TTSDurationModel(nn.Module):
def __init__(
self,
vocab_size=37,
style_dp=8,
style_dim=16,
ref_in_channels=144,
sentence_encoder_cfg=None,
style_encoder_cfg=None,
predictor_cfg=None,
):
super().__init__()
self.vocab_size = vocab_size
se_cfg = sentence_encoder_cfg or {}
st_cfg = style_encoder_cfg or {}
pr_cfg = predictor_cfg or {}
se_d_model = se_cfg.get("char_emb_dim", 64)
st_proj = st_cfg.get("proj_in", {})
st_d_model = st_proj.get("odim", 64)
st_convnext = st_cfg.get("convnext", {})
st_hidden_dim = st_convnext.get("intermediate_dim", 256)
st_num_blocks = st_convnext.get("num_layers", 4)
st_dilation = st_convnext.get("dilation_lst", None)
st_token_layer = st_cfg.get("style_token_layer", {})
st_num_queries = st_token_layer.get("n_style", style_dp)
st_query_dim = st_token_layer.get("style_value_dim", style_dim)
st_num_heads = st_token_layer.get("n_heads", 2)
pr_text_dim = pr_cfg.get("sentence_dim", 64)
pr_style_dim = pr_cfg.get("n_style", st_num_queries) * pr_cfg.get("style_dim", st_query_dim)
self.sentence_encoder = DPTextEncoder(vocab_size=vocab_size, d_model=se_d_model)
self.ref_encoder = DPReferenceEncoder(
in_channels=ref_in_channels,
d_model=st_d_model,
hidden_dim=st_hidden_dim,
num_blocks=st_num_blocks,
num_queries=st_num_queries,
query_dim=st_query_dim,
num_heads=st_num_heads,
dilation_lst=st_dilation,
)
self.predictor = DurationEstimator(text_dim=pr_text_dim, style_dim=pr_style_dim)
def forward(self, text_ids, z_ref=None, text_mask=None, ref_mask=None, style_dp=None, return_log=False):
text_emb = self.sentence_encoder(text_ids, mask=text_mask)
if style_dp is not None:
style_emb = style_dp
elif z_ref is not None:
style_emb = self.ref_encoder(z_ref, mask=ref_mask)
else:
raise ValueError("Either z_ref or style_dp must be provided")
return self.predictor(text_emb, style_emb, text_mask=text_mask, return_log=return_log)
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