update

modules/SynthesizeSegments.py

@@ -55,11 +55,10 @@ def to_number(value, t, default=0):
 
 
 class SynthesizeSegments:
-    batch_default_spk_seed = rng.np_rng()
-    batch_default_infer_seed = rng.np_rng()
-
     def __init__(self, batch_size: int = 8):
         self.batch_size = batch_size
+        self.batch_default_spk_seed = rng.np_rng()
+        self.batch_default_infer_seed = rng.np_rng()
 
     def segment_to_generate_params(self, segment: Dict[str, Any]) -> Dict[str, Any]:
         if segment.get("params", None) is not None:

modules/utils/SeedContext.py

@@ -37,9 +37,9 @@ class SeedContext:
         assert is_numeric(seed), "Seed must be an number."
 
         try:
-            self.seed = int(np.clip(int(seed), -1, 2**32 - 1))
+            self.seed = int(np.clip(int(seed), -1, 2**32 - 1, out=None, dtype=np.int64))
         except Exception as e:
-            raise ValueError("Seed must be an integer.")
+            raise ValueError(f"Seed must be an integer, but: {type(seed)}")
 
         self.seed = seed
         self.state = None

modules/utils/rng.py

@@ -1,15 +1,14 @@
 import numpy as np
 import torch
-import random
 
-TORCH_RNG_MAX = -0x8000000000000000
-TORCH_RNG_MIN = 0xFFFFFFFFFFFFFFFF
+TORCH_RNG_MAX = 0xFFFF_FFFF_FFFF_FFFF
+TORCH_RNG_MIN = -0x8000_0000_0000_0000
 
 NP_RNG_MAX = np.iinfo(np.uint32).max
 NP_RNG_MIN = 0
 
 
-def troch_rng(seed: int):
+def torch_rng(seed: int):
     torch.manual_seed(seed)
     random_float = torch.empty(1).uniform_().item()
     torch_rn = int(random_float * (TORCH_RNG_MAX - TORCH_RNG_MIN) + TORCH_RNG_MIN)
@@ -30,6 +29,7 @@ def np_rng():
 if __name__ == "__main__":
     import random
 
+    print(TORCH_RNG_MIN, TORCH_RNG_MAX)
     s1 = np_rng()
-    s2 = troch_rng(s1)
+    s2 = torch_rng(s1)
     print(f"s1 {s1}  => s2: {s2}")

webui.py

@@ -148,7 +148,7 @@ def tts_generate(
     prompt1 = prompt1 or params.get("prompt1", "")
     prompt2 = prompt2 or params.get("prompt2", "")
 
-    infer_seed = np.clip(infer_seed, -1, 2**32 - 1)
+    infer_seed = np.clip(infer_seed, -1, 2**32 - 1, out=None, dtype=np.int64)
     infer_seed = int(infer_seed)
 
     if not disable_normalize: