fix: torch.from_numpy crash with numpy 2.x in frozen binary (#361)

torch is compiled against numpy 1.x. numpy 2.x changed the ABI version
returned by PyArray_GetNDArrayCVersion() (0x01000009 → 0x02000000), so
torch's is_numpy_available() always returns False and torch.from_numpy()
raises RuntimeError. This causes TTS generation to fail with:

  ValueError: Unable to create tensor, you should probably activate
  padding with 'padding=True'

Two fixes:

1. Pin numpy<2.0 in requirements.txt so new builds bundle a compatible
   numpy version. (The existing comment already flagged this intention
   but the upper bound was never added.)

2. Add a PyInstaller runtime hook (pyi_rth_numpy_compat.py) that installs
   a ctypes memmove fallback for torch.from_numpy() at startup. Runtime
   hooks run after FrozenImporter is registered so frozen torch is
   importable. The fallback catches RuntimeError from the C-level ABI
   check and copies the numpy array into a new tensor via raw memory copy,
   bypassing the check entirely. This is a belt-and-suspenders fix that
   works regardless of the bundled numpy version.

Co-authored-by: aimaaaimaa <[email protected]>
Co-authored-by: Claude Sonnet 4.6 <[email protected]>

backend/build_binary.py

@@ -52,6 +52,16 @@ def build_server(cuda=False):
     if platform.system() == "Windows":
         args.append("--noconsole")
 
+    # numpy 2.x / torch ABI mismatch fix: install memmove fallback for
+    # torch.from_numpy() before the app starts. Runtime hooks run after
+    # FrozenImporter is registered so frozen torch/numpy are importable.
+    args.extend(
+        [
+            "--runtime-hook",
+            str(backend_dir / "pyi_rth_numpy_compat.py"),
+        ]
+    )
+
     # Add local qwen_tts path if specified (for editable installs)
     qwen_tts_path = os.getenv("QWEN_TTS_PATH")
     if qwen_tts_path and Path(qwen_tts_path).exists():