添加导出成 TorchScript 的脚本用于支持python以外的语言 (#1640)

* Fix onnx_export to support v2

* delete some useless code & add some args type for export torch-script

* Add export_torch_script.py

* (export_torch_script.py) 整合 vits 和 t2s 成一个 model 导出

* 恢复 `t2s_model.py` 把改动移到 `export_torch_script.py`

GPT_SoVITS/module/models_onnx.py

@@ -1,5 +1,6 @@
 import copy
 import math
+from typing import Optional
 import torch
 from torch import nn
 from torch.nn import functional as F
@@ -11,7 +12,6 @@ from module import attentions_onnx as attentions
 from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
 from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
 from module.commons import init_weights, get_padding
-from module.mrte_model import MRTE
 from module.quantize import ResidualVectorQuantizer
 # from text import symbols
 from text import symbols as symbols_v1
@@ -218,7 +218,7 @@ class TextEncoder(nn.Module):
             symbols = symbols_v2.symbols
         self.text_embedding = nn.Embedding(len(symbols), hidden_channels)
 
-        self.mrte = MRTE()
+        self.mrte = attentions.MRTE()
 
         self.encoder2 = attentions.Encoder(
             hidden_channels,
@@ -249,25 +249,6 @@ class TextEncoder(nn.Module):
         m, logs = torch.split(stats, self.out_channels, dim=1)
         return y, m, logs, y_mask
 
-    def extract_latent(self, x):
-        x = self.ssl_proj(x)
-        quantized, codes, commit_loss, quantized_list = self.quantizer(x)
-        return codes.transpose(0, 1)
-
-    def decode_latent(self, codes, y_mask, refer, refer_mask, ge):
-        quantized = self.quantizer.decode(codes)
-
-        y = self.vq_proj(quantized) * y_mask
-        y = self.encoder_ssl(y * y_mask, y_mask)
-
-        y = self.mrte(y, y_mask, refer, refer_mask, ge)
-
-        y = self.encoder2(y * y_mask, y_mask)
-
-        stats = self.proj(y) * y_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return y, m, logs, y_mask, quantized
-
 
 class ResidualCouplingBlock(nn.Module):
     def __init__(
@@ -448,7 +429,7 @@ class Generator(torch.nn.Module):
         if gin_channels != 0:
             self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
 
-    def forward(self, x, g=None):
+    def forward(self, x, g:Optional[torch.Tensor]=None):
         x = self.conv_pre(x)
         if g is not None:
             x = x + self.cond(g)
@@ -870,15 +851,15 @@ class SynthesizerTrn(nn.Module):
             upsample_kernel_sizes,
             gin_channels=gin_channels,
         )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
+        # self.enc_q = PosteriorEncoder(
+        #     spec_channels,
+        #     inter_channels,
+        #     hidden_channels,
+        #     5,
+        #     1,
+        #     16,
+        #     gin_channels=gin_channels,
+        # )
         self.flow = ResidualCouplingBlock(
             inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels
         )