add check arguments for GlowTTS and multispeaker training bug fix

TTS/bin/train_glow_tts.py

@@ -15,7 +15,7 @@ from TTS.tts.datasets.TTSDataset import MyDataset
 from TTS.tts.layers.losses import GlowTTSLoss
 from TTS.tts.utils.distribute import (DistributedSampler, init_distributed,
                                       reduce_tensor)
-from TTS.tts.utils.generic_utils import setup_model
+from TTS.tts.utils.generic_utils import setup_model, check_config_tts
 from TTS.tts.utils.io import save_best_model, save_checkpoint
 from TTS.tts.utils.measures import alignment_diagonal_score
 from TTS.tts.utils.speakers import (get_speakers, load_speaker_mapping,
@@ -602,6 +602,7 @@ if __name__ == '__main__':
     # setup output paths and read configs
     c = load_config(args.config_path)
     # check_config(c)
+    check_config_tts(c)
     _ = os.path.dirname(os.path.realpath(__file__))
 
     if c.apex_amp_level:

TTS/tts/models/glow_tts.py

@@ -37,7 +37,8 @@ class GlowTts(nn.Module):
                  hidden_channels_enc=None,
                  hidden_channels_dec=None,
                  use_encoder_prenet=False,
-                 encoder_type="transformer"):
+                 encoder_type="transformer",
+                 external_speaker_embedding_dim=None):
 
         super().__init__()
         self.num_chars = num_chars
@@ -68,6 +69,13 @@ class GlowTts(nn.Module):
         self.noise_scale = 0.66
         self.length_scale = 1.
 
+        # if is a multispeaker and c_in_channels is 0, set to 256
+        if num_speakers > 1:
+            if self.c_in_channels == 0 and not external_speaker_embedding_dim:
+                self.c_in_channels = 256
+            elif external_speaker_embedding_dim:
+                self.c_in_channels = external_speaker_embedding_dim
+
         self.encoder = Encoder(num_chars,
                                out_channels=out_channels,
                                hidden_channels=hidden_channels,
@@ -94,7 +102,7 @@ class GlowTts(nn.Module):
                                sigmoid_scale=sigmoid_scale,
                                c_in_channels=c_in_channels)
 
-        if num_speakers > 1:
+        if num_speakers > 1 and not external_speaker_embedding_dim:
             self.emb_g = nn.Embedding(num_speakers, c_in_channels)
             nn.init.uniform_(self.emb_g.weight, -0.1, 0.1)
 

TTS/tts/utils/generic_utils.py

@@ -129,10 +129,11 @@ def setup_model(num_chars, num_speakers, c, speaker_embedding_dim=None):
                         use_encoder_prenet=True)
     return model
 
-
+def is_tacotron(c):
+    return False if c['model'] == 'glow_tts' else True
 
 def check_config_tts(c):
-    check_argument('model', c, enum_list=['tacotron', 'tacotron2'], restricted=True, val_type=str)
+    check_argument('model', c, enum_list=['tacotron', 'tacotron2', 'glow_tts'], restricted=True, val_type=str)
     check_argument('run_name', c, restricted=True, val_type=str)
     check_argument('run_description', c, val_type=str)
 
@@ -195,27 +196,30 @@ def check_config_tts(c):
     check_argument('seq_len_norm', c, restricted=True, val_type=bool)
 
     # tacotron prenet
-    check_argument('memory_size', c, restricted=True, val_type=int, min_val=-1)
-    check_argument('prenet_type', c, restricted=True, val_type=str, enum_list=['original', 'bn'])
-    check_argument('prenet_dropout', c, restricted=True, val_type=bool)
+    check_argument('memory_size', c, restricted=is_tacotron(c), val_type=int, min_val=-1)
+    check_argument('prenet_type', c, restricted=is_tacotron(c), val_type=str, enum_list=['original', 'bn'])
+    check_argument('prenet_dropout', c, restricted=is_tacotron(c), val_type=bool)
 
     # attention
-    check_argument('attention_type', c, restricted=True, val_type=str, enum_list=['graves', 'original'])
-    check_argument('attention_heads', c, restricted=True, val_type=int)
-    check_argument('attention_norm', c, restricted=True, val_type=str, enum_list=['sigmoid', 'softmax'])
-    check_argument('windowing', c, restricted=True, val_type=bool)
-    check_argument('use_forward_attn', c, restricted=True, val_type=bool)
-    check_argument('forward_attn_mask', c, restricted=True, val_type=bool)
-    check_argument('transition_agent', c, restricted=True, val_type=bool)
-    check_argument('transition_agent', c, restricted=True, val_type=bool)
-    check_argument('location_attn', c, restricted=True, val_type=bool)
-    check_argument('bidirectional_decoder', c, restricted=True, val_type=bool)
-    check_argument('double_decoder_consistency', c, restricted=True, val_type=bool)
+    check_argument('attention_type', c, restricted=is_tacotron(c), val_type=str, enum_list=['graves', 'original'])
+    check_argument('attention_heads', c, restricted=is_tacotron(c), val_type=int)
+    check_argument('attention_norm', c, restricted=is_tacotron(c), val_type=str, enum_list=['sigmoid', 'softmax'])
+    check_argument('windowing', c, restricted=is_tacotron(c), val_type=bool)
+    check_argument('use_forward_attn', c, restricted=is_tacotron(c), val_type=bool)
+    check_argument('forward_attn_mask', c, restricted=is_tacotron(c), val_type=bool)
+    check_argument('transition_agent', c, restricted=is_tacotron(c), val_type=bool)
+    check_argument('transition_agent', c, restricted=is_tacotron(c), val_type=bool)
+    check_argument('location_attn', c, restricted=is_tacotron(c), val_type=bool)
+    check_argument('bidirectional_decoder', c, restricted=is_tacotron(c), val_type=bool)
+    check_argument('double_decoder_consistency', c, restricted=is_tacotron(c), val_type=bool)
     check_argument('ddc_r', c, restricted='double_decoder_consistency' in c.keys(), min_val=1, max_val=7, val_type=int)
 
     # stopnet
-    check_argument('stopnet', c, restricted=True, val_type=bool)
-    check_argument('separate_stopnet', c, restricted=True, val_type=bool)
+    check_argument('stopnet', c, restricted=is_tacotron(c), val_type=bool)
+    check_argument('separate_stopnet', c, restricted=is_tacotron(c), val_type=bool)
+
+    # GlowTTS parameters
+    check_argument('encoder_type', c, restricted=not is_tacotron(c), val_type=str)
 
     # tensorboard
     check_argument('print_step', c, restricted=True, val_type=int, min_val=1)
@@ -240,15 +244,16 @@ def check_config_tts(c):
 
     # multi-speaker and gst
     check_argument('use_speaker_embedding', c, restricted=True, val_type=bool)
-    check_argument('use_external_speaker_embedding_file', c, restricted=True, val_type=bool)
-    check_argument('external_speaker_embedding_file', c, restricted=True, val_type=str)
-    check_argument('use_gst', c, restricted=True, val_type=bool)
-    check_argument('gst', c, restricted=True, val_type=dict)
-    check_argument('gst_style_input', c['gst'], restricted=True, val_type=[str, dict])
-    check_argument('gst_embedding_dim', c['gst'], restricted=True, val_type=int, min_val=0, max_val=1000)
-    check_argument('gst_use_speaker_embedding', c['gst'], restricted=True, val_type=bool)
-    check_argument('gst_num_heads', c['gst'], restricted=True, val_type=int, min_val=2, max_val=10)
-    check_argument('gst_style_tokens', c['gst'], restricted=True, val_type=int, min_val=1, max_val=1000)
+    check_argument('use_external_speaker_embedding_file', c, restricted=True if c['use_speaker_embedding'] else False, val_type=bool)
+    check_argument('external_speaker_embedding_file', c, restricted=True if c['use_external_speaker_embedding_file'] else False, val_type=str)
+    check_argument('use_gst', c, restricted=is_tacotron(c), val_type=bool)
+    if c['use_gst']:
+        check_argument('gst', c, restricted=is_tacotron(c), val_type=dict)
+        check_argument('gst_style_input', c['gst'], restricted=is_tacotron(c), val_type=[str, dict])
+        check_argument('gst_embedding_dim', c['gst'], restricted=is_tacotron(c), val_type=int, min_val=0, max_val=1000)
+        check_argument('gst_use_speaker_embedding', c['gst'], restricted=is_tacotron(c), val_type=bool)
+        check_argument('gst_num_heads', c['gst'], restricted=is_tacotron(c), val_type=int, min_val=2, max_val=10)
+        check_argument('gst_style_tokens', c['gst'], restricted=is_tacotron(c), val_type=int, min_val=1, max_val=1000)
 
     # datasets - checking only the first entry
     check_argument('datasets', c, restricted=True, val_type=list)