Sync with main branch (#1134)

* api接口，修复文本切分符号设定中，中文分号错写为英文分号的问题 (#1001)

* 一些小问题修复 (#1021)

* fix import error. It may happen when calling
 api.py

* Update README.md

* Update gpt-sovits_kaggle.ipynb

* Update gpt-sovits_kaggle.ipynb

* fix path error
delete useless line wraps

* 删除重复的 COPY 指令 (#1073)

* [优化] 1Aa-文本获取 (#1102)

* Filter unsupported languages

* add feedback

* simplify modification

* fix detail

* Update english.py (#1106)

copy but not ref the phones list becoz it will be extend later, if not do so,it will affect the self.cmu dict values.

* Update models.py

* modify freeze_quantizer mode, avoid quantizer's codebook updating (#953)

---------

Co-authored-by: FengQingYunDan <pingdengjia0liu@163.com>
Co-authored-by: Kenn Zhang <breakstring@hotmail.com>
Co-authored-by: 蓝梦实 <36986837+SapphireLab@users.noreply.github.com>
Co-authored-by: lyris <lyris@users.noreply.github.com>
Co-authored-by: hcwu1993 <15855138469@163.com>

GPT_SoVITS/module/models.py

@@ -16,6 +16,7 @@ from module.mrte_model import MRTE
 from module.quantize import ResidualVectorQuantizer
 from text import symbols
 from torch.cuda.amp import autocast
+import contextlib
 
 
 class StochasticDurationPredictor(nn.Module):
@@ -891,9 +892,10 @@ class SynthesizerTrn(nn.Module):
             self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 1, stride=1)
 
         self.quantizer = ResidualVectorQuantizer(dimension=ssl_dim, n_q=1, bins=1024)
-        if freeze_quantizer:
-            self.ssl_proj.requires_grad_(False)
-            self.quantizer.requires_grad_(False)
+        self.freeze_quantizer = freeze_quantizer
+        # if freeze_quantizer:
+        #     self.ssl_proj.requires_grad_(False)
+        #     self.quantizer.requires_grad_(False)
             #self.quantizer.eval()
             # self.enc_p.text_embedding.requires_grad_(False)
             # self.enc_p.encoder_text.requires_grad_(False)
@@ -906,6 +908,11 @@ class SynthesizerTrn(nn.Module):
         ge = self.ref_enc(y * y_mask, y_mask)
 
         with autocast(enabled=False):
+            maybe_no_grad = torch.no_grad() if self.freeze_quantizer else contextlib.nullcontext()
+            with maybe_no_grad:
+                if self.freeze_quantizer:
+                    self.ssl_proj.eval()
+                    self.quantizer.eval()
             ssl = self.ssl_proj(ssl)
             quantized, codes, commit_loss, quantized_list = self.quantizer(
                 ssl, layers=[0]