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import torch
from torch import nn
from torch.nn.utils import remove_weight_norm, weight_norm
from fish_speech.models.vits_decoder.modules.attentions import MultiHeadAttention
class MRTE(nn.Module):
def __init__(
self,
content_enc_channels=192,
hidden_size=512,
out_channels=192,
n_heads=4,
):
super(MRTE, self).__init__()
self.cross_attention = MultiHeadAttention(hidden_size, hidden_size, n_heads)
self.c_pre = nn.Conv1d(content_enc_channels, hidden_size, 1)
self.text_pre = nn.Conv1d(content_enc_channels, hidden_size, 1)
self.c_post = nn.Conv1d(hidden_size, out_channels, 1)
def forward(self, ssl_enc, ssl_mask, text, text_mask, ge, test=None):
if ge == None:
ge = 0
attn_mask = text_mask.unsqueeze(2) * ssl_mask.unsqueeze(-1)
ssl_enc = self.c_pre(ssl_enc * ssl_mask)
text_enc = self.text_pre(text * text_mask)
if test != None:
if test == 0:
x = (
self.cross_attention(
ssl_enc * ssl_mask, text_enc * text_mask, attn_mask
)
+ ssl_enc
+ ge
)
elif test == 1:
x = ssl_enc + ge
elif test == 2:
x = (
self.cross_attention(
ssl_enc * 0 * ssl_mask, text_enc * text_mask, attn_mask
)
+ ge
)
else:
raise ValueError("test should be 0,1,2")
else:
x = (
self.cross_attention(
ssl_enc * ssl_mask, text_enc * text_mask, attn_mask
)
+ ssl_enc
+ ge
)
x = self.c_post(x * ssl_mask)
return x
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