replace the namedtupple for an object to avoid pickling issues

sparktts/modules/speaker/perceiver_encoder.py

@@ -15,7 +15,6 @@
 
 # Adapted from https://github.com/lucidrains/naturalspeech2-pytorch/blob/659bec7f7543e7747e809e950cc2f84242fbeec7/naturalspeech2_pytorch/naturalspeech2_pytorch.py#L532
 
-from collections import namedtuple
 from functools import wraps
 
 import torch
@@ -63,12 +62,21 @@ class Attend(nn.Module):
             use_flash and version.parse(torch.__version__) < version.parse("2.0.0")
         ), "in order to use flash attention, you must be using pytorch 2.0 or above"
 
-        # determine efficient attention configs for cuda and cpu
-        self.config = namedtuple(
-            "EfficientAttentionConfig",
-            ["enable_flash", "enable_math", "enable_mem_efficient"],
-        )
-        self.cpu_config = self.config(True, True, True)
+        # Define config as a regular class instead of namedtuple
+        class EfficientAttentionConfig:
+            def __init__(self, enable_flash, enable_math, enable_mem_efficient):
+                self.enable_flash = enable_flash
+                self.enable_math = enable_math
+                self.enable_mem_efficient = enable_mem_efficient
+
+            def _asdict(self):
+                return {
+                    'enable_flash': self.enable_flash,
+                    'enable_math': self.enable_math,
+                    'enable_mem_efficient': self.enable_mem_efficient
+                }
+
+        self.cpu_config = EfficientAttentionConfig(True, True, True)
         self.cuda_config = None
 
         if not torch.cuda.is_available() or not use_flash:
@@ -85,7 +93,7 @@ class Attend(nn.Module):
             print_once(
                 "Non-A100 GPU detected, using math or mem efficient attention if input tensor is on cuda"
             )
-            self.cuda_config = self.config(False, True, True)
+            self.cuda_config = EfficientAttentionConfig(False, True, True)
 
     def get_mask(self, n, device):
         if exists(self.mask) and self.mask.shape[-1] >= n: