Low memory inference fix (#536)

* For low_mem, use spawned workers instead of forked workers (resolves #36)
Used implementation from @lilydjwg: https://github.com/CorentinJ/Real-Time-Voice-Cloning/issues/36#issuecomment-529380190

* Different method of passing the seed for low_mem inference
Resolves #491, #529, #535

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)