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unified_voice fixes

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James Betker 2022-03-21 14:44:00 -06:00
parent 1ad18d29a8
commit e735d8e1fa
1 changed files with 6 additions and 0 deletions
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6
codes/models/audio/tts/unified_voice2.py
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@ -434,6 +434,8 @@ class UnifiedVoice(nn.Module):
for j in range(speech_conditioning_input.shape[1]): for j in range(speech_conditioning_input.shape[1]):
conds.append(self.conditioning_encoder(speech_conditioning_input[:, j])) conds.append(self.conditioning_encoder(speech_conditioning_input[:, j]))
conds = torch.stack(conds, dim=1) conds = torch.stack(conds, dim=1)
if self.average_conditioning_embeddings:
conds = conds.mean(dim=1).unsqueeze(1)
text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token) text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token)
text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(text_inputs) + self.text_solo_embedding text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(text_inputs) + self.text_solo_embedding
@ -460,6 +462,8 @@ class UnifiedVoice(nn.Module):
for j in range(speech_conditioning_input.shape[1]): for j in range(speech_conditioning_input.shape[1]):
conds.append(self.conditioning_encoder(speech_conditioning_input[:, j])) conds.append(self.conditioning_encoder(speech_conditioning_input[:, j]))
conds = torch.stack(conds, dim=1) conds = torch.stack(conds, dim=1)
if self.average_conditioning_embeddings:
conds = conds.mean(dim=1).unsqueeze(1)
mel_codes, mel_targets = self.build_aligned_inputs_and_targets(mel_codes, self.start_mel_token, self.stop_mel_token) mel_codes, mel_targets = self.build_aligned_inputs_and_targets(mel_codes, self.start_mel_token, self.stop_mel_token)
if raw_mels is not None: if raw_mels is not None:
@ -496,6 +500,8 @@ class UnifiedVoice(nn.Module):
for j in range(speech_conditioning_input.shape[1]): for j in range(speech_conditioning_input.shape[1]):
conds.append(self.conditioning_encoder(speech_conditioning_input[:, j])) conds.append(self.conditioning_encoder(speech_conditioning_input[:, j]))
conds = torch.stack(conds, dim=1) conds = torch.stack(conds, dim=1)
if self.average_conditioning_embeddings:
conds = conds.mean(dim=1).unsqueeze(1)
emb = torch.cat([conds, text_emb], dim=1) emb = torch.cat([conds, text_emb], dim=1)
self.inference_model.store_mel_emb(emb) self.inference_model.store_mel_emb(emb)