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add bert

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Stardust·减
2023-09-05 10:00:54 +08:00
committed by GitHub
parent 316e0ba345
commit 6d4d123162
1 changed files with 11 additions and 7 deletions
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18
models.py
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@@ -267,7 +267,8 @@ class TextEncoder(nn.Module):
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(768, hidden_channels, 1)
self.encoder = attentions.Encoder(
hidden_channels,
filter_channels,
@@ -278,8 +279,11 @@ class TextEncoder(nn.Module):
gin_channels=self.gin_channels)
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
def forward(self, x, x_lengths, tone, language, bert, g=None):
x = (self.emb(x)+ self.tone_emb(tone)+ self.language_emb(language)+self.bert_proj(bert).transpose(1,2)) * math.sqrt(self.hidden_channels) # [b, t, h]
def forward(self, x, x_lengths, tone, language, bert, ja_bert, g=None):
bert_emb = self.zh_bert_proj(bert).transpose(1,2)
ja_bert_emb = = self.ja_bert_proj(ja_bert).transpose(1,2)
x = (self.emb(x)+ self.tone_emb(tone)+ self.language_emb(language)
+ bert_emb +ja_bert_emb) * math.sqrt(self.hidden_channels) # [b, t, h]
x = torch.transpose(x, 1, -1) # [b, h, t]
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
@@ -637,12 +641,12 @@ class SynthesizerTrn(nn.Module):
else:
self.ref_enc = ReferenceEncoder(spec_channels, gin_channels)
def forward(self, x, x_lengths, y, y_lengths, sid, tone, language, bert):
def forward(self, x, x_lengths, y, y_lengths, sid, tone, language, bert, ja_bert):
if self.n_speakers > 0:
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
else:
g = self.ref_enc(y.transpose(1,2)).unsqueeze(-1)
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert,g=g)
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert, ja_bert, g=g)
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
z_p = self.flow(z, y_mask, g=g)
@@ -681,14 +685,14 @@ class SynthesizerTrn(nn.Module):
o = self.dec(z_slice, g=g)
return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q), (x, logw, logw_)
def infer(self, x, x_lengths, sid, tone, language, bert, noise_scale=.667, length_scale=1, noise_scale_w=0.8, max_len=None, sdp_ratio=0,y=None):
def infer(self, x, x_lengths, sid, tone, language, bert, ja_bert, noise_scale=.667, length_scale=1, noise_scale_w=0.8, max_len=None, sdp_ratio=0,y=None):
#x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert)
# g = self.gst(y)
if self.n_speakers > 0:
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
else:
g = self.ref_enc(y.transpose(1,2)).unsqueeze(-1)
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert,g=g)
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert, ja_bert, g=g)
logw = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) * (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)