From 254b9b0b55475a7d281f1ec535af5d62629d679e Mon Sep 17 00:00:00 2001
From: csh <458761603@qq.com>
Date: Fri, 13 Jun 2025 17:31:09 +0800
Subject: [PATCH] =?UTF-8?q?fix(export=5Ftorch=5Fscript):=20=E5=AF=B9?=
 =?UTF-8?q?=E9=BD=90=20T2S=20=E5=AE=9E=E7=8E=B0=E5=92=8C=E9=BB=98=E8=AE=A4?=
 =?UTF-8?q?=E5=8F=82=E6=95=B0?=
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---
 GPT_SoVITS/export_torch_script.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/GPT_SoVITS/export_torch_script.py b/GPT_SoVITS/export_torch_script.py
index 6a13c2d4..66999c0a 100644
--- a/GPT_SoVITS/export_torch_script.py
+++ b/GPT_SoVITS/export_torch_script.py
@@ -103,7 +103,7 @@ def logits_to_probs(
 @torch.jit.script
 def multinomial_sample_one_no_sync(probs_sort):
     # Does multinomial sampling without a cuda synchronization
-    q = torch.randn_like(probs_sort)
+    q = torch.empty_like(probs_sort).exponential_(1.0)
     return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int)
 
 
@@ -114,7 +114,7 @@ def sample(
     temperature: float = 1.0,
     top_k: Optional[int] = None,
     top_p: Optional[int] = None,
-    repetition_penalty: float = 1.0,
+    repetition_penalty: float = 1.35,
 ):
     probs = logits_to_probs(
         logits=logits,
@@ -309,8 +309,9 @@ class T2SBlock:
 
         attn = F.scaled_dot_product_attention(q, k, v)
 
-        attn = attn.permute(2, 0, 1, 3).reshape(batch_size * q_len, self.hidden_dim)
-        attn = attn.view(q_len, batch_size, self.hidden_dim).transpose(1, 0)
+        # attn = attn.permute(2, 0, 1, 3).reshape(batch_size * q_len, self.hidden_dim)
+        # attn = attn.view(q_len, batch_size, self.hidden_dim).transpose(1, 0)
+        attn = attn.transpose(1, 2).reshape(batch_size, q_len, -1)
         attn = F.linear(attn, self.out_w, self.out_b)
 
         x = x + attn