zhangshuai/MeloTTS
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training code done

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wl-zhao
2024-03-10 13:05:02 +00:00
parent c9c57a17f4
commit 7ade7b740e
16 changed files with 1533 additions and 47 deletions
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26
melo/models.py
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@@ -3,14 +3,15 @@ import torch
from torch import nn
from torch.nn import functional as F
from . import commons
from . import modules
from . import attentions
from melo import commons
from melo import modules
from melo import attentions
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 melo.commons import init_weights, get_padding
import melo.monotonic_align as monotonic_align
class DurationDiscriminator(nn.Module): # vits2
@@ -782,7 +783,6 @@ class SynthesizerTrn(nn.Module):
num_languages=None,
num_tones=None,
norm_refenc=False,
use_se=False,
**kwargs
):
super().__init__()
@@ -878,16 +878,12 @@ class SynthesizerTrn(nn.Module):
hidden_channels, 256, 3, 0.5, gin_channels=gin_channels
)
if n_speakers > 1:
if use_se:
emb_dim = 512
self.emb_g = nn.Linear(emb_dim, gin_channels)
else:
self.emb_g = nn.Embedding(n_speakers, gin_channels)
if n_speakers > 0:
self.emb_g = nn.Embedding(n_speakers, gin_channels)
else:
self.ref_enc = ReferenceEncoder(spec_channels, gin_channels, layernorm=norm_refenc)
self.use_vc = use_vc
self.use_se = use_se
def forward(self, x, x_lengths, y, y_lengths, sid, tone, language, bert, ja_bert):
if self.n_speakers > 0:
@@ -1024,11 +1020,7 @@ class SynthesizerTrn(nn.Module):
# print('max/min of o:', o.max(), o.min())
return o, attn, y_mask, (z, z_p, m_p, logs_p)
def voice_conversion(self, y, y_lengths, sid_src, sid_tgt, tau=1.0):
if self.use_se:
sid_src = self.emb_g(sid_src).unsqueeze(-1)
sid_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
def voice_conversion(self, y, y_lengths, sid_src, sid_tgt, tau=1.0):
g_src = sid_src
g_tgt = sid_tgt
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src, tau=tau)