diff --git a/synthesizer/inference.py b/synthesizer/inference.py
index cbade5f589e1513d0e3d3788c0814cc69aef7c8a..5a51a03e302ef006864b700fe0eb3c0f698810f5 100644
--- a/synthesizer/inference.py
+++ b/synthesizer/inference.py
@@ -2,6 +2,7 @@ from synthesizer.tacotron2 import Tacotron2
 from synthesizer.hparams import hparams
 from multiprocess.pool import Pool  # You're free to use either one
 #from multiprocessing import Pool   # 
+from multiprocess.context import SpawnContext
 from synthesizer import audio
 from pathlib import Path
 from typing import Union, List
@@ -97,16 +98,16 @@ class Synthesizer:
             # Low memory inference mode: load the model upon every request. The model has to be 
             # loaded in a separate process to be able to release GPU memory (a simple workaround 
             # to tensorflow's intricacies)
-            specs, alignments = Pool(1).starmap(Synthesizer._one_shot_synthesize_spectrograms, 
-                                                [(self.checkpoint_fpath, embeddings, texts)])[0]
+            specs, alignments = Pool(1, context=SpawnContext()).starmap(Synthesizer._one_shot_synthesize_spectrograms,
+                                                [(self.checkpoint_fpath, embeddings, texts, self._seed)])[0]
     
         return (specs, alignments) if return_alignments else specs
 
     @staticmethod
-    def _one_shot_synthesize_spectrograms(checkpoint_fpath, embeddings, texts):
+    def _one_shot_synthesize_spectrograms(checkpoint_fpath, embeddings, texts, seed):
         # Load the model and forward the inputs
         tf.compat.v1.reset_default_graph()
-        model = Tacotron2(checkpoint_fpath, hparams, seed=self._seed)
+        model = Tacotron2(checkpoint_fpath, hparams, seed=seed)
         specs, alignments = model.my_synthesize(embeddings, texts)
         
         # Detach the outputs (not doing so will cause the process to hang)