diff --git a/backend/pyi_rth_numpy_compat.py b/backend/pyi_rth_numpy_compat.py
index 2e3b5468..683f4dea 100644
--- a/backend/pyi_rth_numpy_compat.py
+++ b/backend/pyi_rth_numpy_compat.py
@@ -48,25 +48,40 @@ def _patch_torch_from_numpy():
 
             _orig = torch.from_numpy
 
-            def _safe_from_numpy(arr, _orig=_orig, _c=ctypes, _np=np, _t=torch):
+            # Explicit numpy → torch dtype map. Silent fallback to float32 on
+            # unknown dtypes would reinterpret the memcpy'd bytes as fp32 and
+            # silently corrupt data (e.g. fp16 tensors from some TTS engines),
+            # so we raise instead.
+            dtype_map = {
+                "float16": _t.float16,
+                "float32": _t.float32,
+                "float64": _t.float64,
+                "int8": _t.int8,
+                "int16": _t.int16,
+                "int32": _t.int32,
+                "int64": _t.int64,
+                "uint8": _t.uint8,
+                "bool": _t.bool,
+                "complex64": _t.complex64,
+                "complex128": _t.complex128,
+            }
+
+            def _safe_from_numpy(
+                arr, _orig=_orig, _c=ctypes, _np=np, _t=torch, _map=dtype_map
+            ):
                 try:
                     return _orig(arr)
                 except RuntimeError:
                     a = _np.ascontiguousarray(arr)
-                    dtype_map = {
-                        "float32": _t.float32,
-                        "float64": _t.float64,
-                        "int32": _t.int32,
-                        "int64": _t.int64,
-                        "int16": _t.int16,
-                        "int8": _t.int8,
-                        "uint8": _t.uint8,
-                        "bool": _t.bool,
-                    }
-                    out = _t.empty(
-                        list(a.shape),
-                        dtype=dtype_map.get(str(a.dtype), _t.float32),
-                    )
+                    key = str(a.dtype)
+                    if key not in _map:
+                        raise TypeError(
+                            f"pyi_rth_numpy_compat: unsupported numpy dtype "
+                            f"{key!r} in torch.from_numpy fallback; add an "
+                            f"explicit mapping rather than silently copying "
+                            f"bytes into the wrong dtype."
+                        )
+                    out = _t.empty(list(a.shape), dtype=_map[key])
                     _c.memmove(out.data_ptr(), a.ctypes.data, a.nbytes)
                     return out