Fix train_tts.py and uncomment code (coqui-ai#1051)

* Fix SE loading and language embedding logic

* remove trailing white space

* Uncomment resmapling code for SCL

TTS/bin/train_tts.py

@@ -1,4 +1,5 @@
 import os
+import torch
 
 from TTS.config import check_config_and_model_args, get_from_config_or_model_args, load_config, register_config
 from TTS.trainer import Trainer, TrainingArgs
@@ -53,15 +54,22 @@ def main():
         else:
             config.num_speakers = speaker_manager.num_speakers
     elif check_config_and_model_args(config, "use_d_vector_file", True):
-        speaker_manager = SpeakerManager(d_vectors_file_path=get_from_config_or_model_args(config, "d_vector_file"))
+        if check_config_and_model_args(config, "use_speaker_encoder_as_loss", True):
+            speaker_manager = SpeakerManager(
+                d_vectors_file_path=config.model_args.d_vector_file,
+                encoder_model_path=config.model_args.speaker_encoder_model_path,
+                encoder_config_path=config.model_args.speaker_encoder_config_path,
+                use_cuda=torch.cuda.is_available(),
+            )
+        else:
+            speaker_manager = SpeakerManager(d_vectors_file_path=get_from_config_or_model_args(config, "d_vector_file"))
+        config.num_speakers = speaker_manager.num_speakers
         if hasattr(config, "model_args"):
             config.model_args.num_speakers = speaker_manager.num_speakers
-        else:
-            config.num_speakers = speaker_manager.num_speakers
     else:
         speaker_manager = None
 
-    if hasattr(config, "use_language_embedding") and config.use_language_embedding:
+    if check_config_and_model_args(config, "use_language_embedding", True):
         language_manager = LanguageManager(config=config)
         if hasattr(config, "model_args"):
             config.model_args.num_languages = language_manager.num_languages

TTS/tts/models/vits.py

@@ -5,7 +5,7 @@
 
 import torch
 
-# import torchaudio
+import torchaudio
 from coqpit import Coqpit
 from torch import nn
 from torch.cuda.amp.autocast_mode import autocast
@@ -419,21 +419,12 @@ def init_multispeaker(self, config: Coqpit):
                 hasattr(self.speaker_manager.speaker_encoder, "audio_config")
                 and self.config.audio["sample_rate"] != self.speaker_manager.speaker_encoder.audio_config["sample_rate"]
             ):
-                # TODO: change this with torchaudio Resample
-                raise RuntimeError(
-                    " [!] To use the speaker consistency loss (SCL) you need to have matching sample rates between the TTS model ({}) and the speaker encoder ({})!".format(
-                        self.config.audio["sample_rate"],
-                        self.speaker_manager.speaker_encoder.audio_config["sample_rate"],
-                    )
-                )
-                # pylint: disable=W0101,W0105
-                """ self.audio_transform = torchaudio.transforms.Resample(
+                self.audio_transform = torchaudio.transforms.Resample(
                         orig_freq=self.audio_config["sample_rate"],
                         new_freq=self.speaker_manager.speaker_encoder.audio_config["sample_rate"],
                         )
-                else:
-                    self.audio_transform = None
-                """
+            else:
+                self.audio_transform = None
 
     def _init_speaker_embedding(self):
         # pylint: disable=attribute-defined-outside-init
@@ -458,6 +449,7 @@ def init_multilingual(self, config: Coqpit):
             self.language_manager = LanguageManager(language_ids_file_path=config.language_ids_file)
 
         if self.args.use_language_embedding and self.language_manager:
+            print(" > initialization of language-embedding layers.")
             self.num_languages = self.language_manager.num_languages
             self.embedded_language_dim = self.args.embedded_language_dim
             self.emb_l = nn.Embedding(self.num_languages, self.embedded_language_dim)
@@ -643,8 +635,8 @@ def forward(
 
             # resample audio to speaker encoder sample_rate
             # pylint: disable=W0105
-            """if self.audio_transform is not None:
-                wavs_batch = self.audio_transform(wavs_batch)"""
+            if self.audio_transform is not None:
+                wavs_batch = self.audio_transform(wavs_batch)
 
             pred_embs = self.speaker_manager.speaker_encoder.forward(wavs_batch, l2_norm=True)