修复gpt的loss计算问题 (#2537)

* 修复gpt的loss计算问题

* fallback tts config

GPT_SoVITS/AR/models/t2s_model.py

@@ -356,7 +356,7 @@ class Text2SemanticDecoder(nn.Module):
         x = self.ar_text_embedding(x)
         x = x + self.bert_proj(bert_feature.transpose(1, 2))
         x = self.ar_text_position(x)
-        x_mask = make_pad_mask(x_lens)
+        x_mask = make_pad_mask_left(x_lens)
 
         y_mask = make_pad_mask(y_lens)
         y_mask_int = y_mask.type(torch.int64)
@@ -420,7 +420,7 @@ class Text2SemanticDecoder(nn.Module):
             mask=xy_attn_mask,
         )
         x_len = x_lens.max()
-        logits = self.ar_predict_layer(xy_dec[:, x_len:])
+        logits = self.ar_predict_layer(xy_dec[:, x_len-1:])
 
         ###### DPO #############
         reject_xy_pos, reject_xy_attn_mask, reject_targets = self.make_input_data(
@@ -432,7 +432,7 @@ class Text2SemanticDecoder(nn.Module):
             mask=reject_xy_attn_mask,
         )
         x_len = x_lens.max()
-        reject_logits = self.ar_predict_layer(reject_xy_dec[:, x_len:])
+        reject_logits = self.ar_predict_layer(reject_xy_dec[:, x_len-1:])
 
         # loss
         # from feiteng: 每次 duration 越多, 梯度更新也应该更多, 所以用 sum
@@ -455,7 +455,7 @@ class Text2SemanticDecoder(nn.Module):
         x = self.ar_text_embedding(x)
         x = x + self.bert_proj(bert_feature.transpose(1, 2))
         x = self.ar_text_position(x)
-        x_mask = make_pad_mask(x_lens)
+        x_mask = make_pad_mask_left(x_lens)
 
         y_mask = make_pad_mask(y_lens)
         y_mask_int = y_mask.type(torch.int64)
@@ -502,7 +502,7 @@ class Text2SemanticDecoder(nn.Module):
             (xy_pos, None),
             mask=xy_attn_mask,
         )
-        logits = self.ar_predict_layer(xy_dec[:, x_len:]).permute(0, 2, 1)
+        logits = self.ar_predict_layer(xy_dec[:, x_len-1:]).permute(0, 2, 1)
         # loss
         # from feiteng: 每次 duration 越多, 梯度更新也应该更多, 所以用 sum
         loss = F.cross_entropy(logits, targets, reduction="sum")
@@ -578,7 +578,7 @@ class Text2SemanticDecoder(nn.Module):
     def pad_y_eos(self, y, y_mask_int, eos_id):
         targets = F.pad(y, (0, 1), value=0) + eos_id * F.pad(y_mask_int, (0, 1), value=1)
         # 错位
-        return targets[:, :-1], targets[:, 1:]
+        return targets[:, :-1], targets
 
     def infer_panel_batch_infer(
         self,