Fix batched generation in quick_start.py

quick_start.py

@@ -28,23 +28,17 @@
     parser.add_argument('--input_text', '-i', type=str, help='[string] Type in something here and TTS will generate it!')
     parser.add_argument('--batched', '-b', dest='batched', action='store_true', help='Fast Batched Generation (lower quality)')
     parser.add_argument('--unbatched', '-u', dest='batched', action='store_false', help='Slower Unbatched Generation (better quality)')
-    parser.add_argument('--target', '-t', type=int, help='[int] number of samples in each batch index')
-    parser.add_argument('--overlap', '-o', type=int, help='[int] number of crossover samples')
     parser.add_argument('--force_cpu', '-c', action='store_true', help='Forces CPU-only training, even when in CUDA capable environment')
     parser.add_argument('--hp_file', metavar='FILE', default='hparams.py',
                         help='The file to use for the hyperparameters')
     args = parser.parse_args()
 
     hp.configure(args.hp_file)  # Load hparams from file
 
-    parser.set_defaults(batched=hp.voc_gen_batched)
-    parser.set_defaults(target=hp.voc_target)
-    parser.set_defaults(overlap=hp.voc_overlap)
+    parser.set_defaults(batched=True)
     parser.set_defaults(input_text=None)
 
     batched = args.batched
-    target = args.target
-    overlap = args.overlap
     input_text = args.input_text
 
     if not args.force_cpu and torch.cuda.is_available():
@@ -105,8 +99,8 @@
     simple_table([('WaveRNN', str(voc_k) + 'k'),
                   (f'Tacotron(r={r})', str(tts_k) + 'k'),
                   ('Generation Mode', 'Batched' if batched else 'Unbatched'),
-                  ('Target Samples', target if batched else 'N/A'),
-                  ('Overlap Samples', overlap if batched else 'N/A')])
+                  ('Target Samples', 11_000 if batched else 'N/A'),
+                  ('Overlap Samples', 550 if batched else 'N/A')])
 
     for i, x in enumerate(inputs, 1):
 
@@ -123,6 +117,6 @@
         m = torch.tensor(m).unsqueeze(0)
         m = (m + 4) / 8
 
-        voc_model.generate(m, save_path, batched, target, overlap, hp.mu_law)
+        voc_model.generate(m, save_path, batched, 11_000, 550, hp.mu_law)
 
     print('\n\nDone.\n')