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support gpt-sovits v2

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support gpt-sovits v2
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RVC-Boss 2024-08-02 15:48:22 +08:00 committed by GitHub
parent af2d119573
commit 0d037f8915
1 changed files with 19 additions and 14 deletions
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33
GPT_SoVITS/module/models.py
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@ -1,5 +1,7 @@
import copy import copy
import math import math
import os
import torch import torch
from torch import nn from torch import nn
from torch.nn import functional as F from torch.nn import functional as F
@ -879,9 +881,11 @@ class SynthesizerTrn(nn.Module):
inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels
) )
self.ref_enc = modules.MelStyleEncoder( self.version=os.environ.get("version","v1")
spec_channels, style_vector_dim=gin_channels if(self.version=="v1"):
) self.ref_enc = modules.MelStyleEncoder(spec_channels, style_vector_dim=gin_channels)
else:
self.ref_enc = modules.MelStyleEncoder(704, style_vector_dim=gin_channels)
ssl_dim = 768 ssl_dim = 768
assert semantic_frame_rate in ["25hz", "50hz"] assert semantic_frame_rate in ["25hz", "50hz"]
@ -893,20 +897,15 @@ class SynthesizerTrn(nn.Module):
self.quantizer = ResidualVectorQuantizer(dimension=ssl_dim, n_q=1, bins=1024) self.quantizer = ResidualVectorQuantizer(dimension=ssl_dim, n_q=1, bins=1024)
self.freeze_quantizer = freeze_quantizer self.freeze_quantizer = freeze_quantizer
# if freeze_quantizer:
# self.ssl_proj.requires_grad_(False)
# self.quantizer.requires_grad_(False)
#self.quantizer.eval()
# self.enc_p.text_embedding.requires_grad_(False)
# self.enc_p.encoder_text.requires_grad_(False)
# self.enc_p.mrte.requires_grad_(False)
def forward(self, ssl, y, y_lengths, text, text_lengths): def forward(self, ssl, y, y_lengths, text, text_lengths):
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to( y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(
y.dtype y.dtype
) )
ge = self.ref_enc(y * y_mask, y_mask) if(self.version=="v1"):
ge = self.ref_enc(y * y_mask, y_mask)
else:
ge = self.ref_enc(y[:,:704] * y_mask, y_mask)
with autocast(enabled=False): with autocast(enabled=False):
maybe_no_grad = torch.no_grad() if self.freeze_quantizer else contextlib.nullcontext() maybe_no_grad = torch.no_grad() if self.freeze_quantizer else contextlib.nullcontext()
with maybe_no_grad: with maybe_no_grad:
@ -947,7 +946,10 @@ class SynthesizerTrn(nn.Module):
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to( y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(
y.dtype y.dtype
) )
ge = self.ref_enc(y * y_mask, y_mask) if(self.version=="v1"):
ge = self.ref_enc(y * y_mask, y_mask)
else:
ge = self.ref_enc(y[:,:704] * y_mask, y_mask)
ssl = self.ssl_proj(ssl) ssl = self.ssl_proj(ssl)
quantized, codes, commit_loss, _ = self.quantizer(ssl, layers=[0]) quantized, codes, commit_loss, _ = self.quantizer(ssl, layers=[0])
@ -974,7 +976,10 @@ class SynthesizerTrn(nn.Module):
refer_mask = torch.unsqueeze( refer_mask = torch.unsqueeze(
commons.sequence_mask(refer_lengths, refer.size(2)), 1 commons.sequence_mask(refer_lengths, refer.size(2)), 1
).to(refer.dtype) ).to(refer.dtype)
ge = self.ref_enc(refer * refer_mask, refer_mask) if (self.version == "v1"):
ge = self.ref_enc(refer * refer_mask, refer_mask)
else:
ge = self.ref_enc(refer[:, :704] * refer_mask, refer_mask)
y_lengths = torch.LongTensor([codes.size(2) * 2]).to(codes.device) y_lengths = torch.LongTensor([codes.size(2) * 2]).to(codes.device)
text_lengths = torch.LongTensor([text.size(-1)]).to(text.device) text_lengths = torch.LongTensor([text.size(-1)]).to(text.device)