新增VITS批量推理 GPT_SoVITS/TTS_infer_pack/TTS.py

fix some bugs   GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py
	fix some bugs   GPT_SoVITS/TTS_infer_pack/text_segmentation_method.py
	fix some bugs   GPT_SoVITS/inference_webui.py
	fix some bugs   GPT_SoVITS/module/models.py

GPT_SoVITS/module/models.py

@@ -1,5 +1,6 @@
 import copy
 import math
+from typing import List
 import torch
 from torch import nn
 from torch.nn import functional as F
@@ -985,6 +986,55 @@ class SynthesizerTrn(nn.Module):
 
         o = self.dec((z * y_mask)[:, :, :], g=ge)
         return o
+    
+    
+    @torch.no_grad()
+    def batched_decode(self, codes, y_lengths, text, text_lengths, refer, noise_scale=0.5):
+        ge = None
+        if refer is not None:
+            refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
+            refer_mask = torch.unsqueeze(
+                commons.sequence_mask(refer_lengths, refer.size(2)), 1
+            ).to(refer.dtype)
+            ge = self.ref_enc(refer * refer_mask, refer_mask)
+
+        # y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, codes.size(2)), 1).to(
+        #     codes.dtype
+        # )
+        y_lengths = (y_lengths * 2).long().to(codes.device)
+        text_lengths = text_lengths.long().to(text.device)
+        # y_lengths = torch.LongTensor([codes.size(2) * 2]).to(codes.device)
+        # text_lengths = torch.LongTensor([text.size(-1)]).to(text.device)
+
+        # 假设padding之后再decode没有问题, 影响未知，但听起来好像没问题？
+        quantized = self.quantizer.decode(codes)
+        if self.semantic_frame_rate == "25hz":
+            quantized = F.interpolate(
+                quantized, size=int(quantized.shape[-1] * 2), mode="nearest"
+            )
+
+        x, m_p, logs_p, y_mask = self.enc_p(
+            quantized, y_lengths, text, text_lengths, ge
+        )
+        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
+
+        z = self.flow(z_p, y_mask, g=ge, reverse=True)
+        z_masked = (z * y_mask)[:, :, :]
+        
+        # 串行。把padding部分去掉再decode
+        o_list:List[torch.Tensor] = []
+        for i in range(z_masked.shape[0]):
+            z_slice = z_masked[i, :, :y_lengths[i]].unsqueeze(0)
+            o = self.dec(z_slice, g=ge)[0, 0, :].detach()
+            o_list.append(o)
+        
+        # 并行（会有问题）。先decode，再把padding的部分去掉
+        # o = self.dec(z_masked, g=ge)
+        # upsample_rate = int(math.prod(self.upsample_rates))
+        # o_lengths = y_lengths*upsample_rate
+        # o_list = [o[i, 0, :idx].detach() for i, idx in enumerate(o_lengths)]
+
+        return o_list
 
     def extract_latent(self, x):
         ssl = self.ssl_proj(x)