Make CLI work

TTS/tts/models/xtts.py

@@ -11,7 +11,7 @@ from TTS.tts.layers.xtts.gpt import GPT
 from TTS.tts.layers.xtts.hifigan_decoder import HifiDecoder
 from TTS.tts.layers.xtts.stream_generator import init_stream_support
 from TTS.tts.layers.xtts.tokenizer import VoiceBpeTokenizer, split_sentence
-from TTS.tts.layers.xtts.speaker_manager import SpeakerManager
+from TTS.tts.layers.xtts.xtts_manager import SpeakerManager, LanguageManager
 from TTS.tts.models.base_tts import BaseTTS
 from TTS.utils.io import load_fsspec
 
@@ -379,7 +379,7 @@ class Xtts(BaseTTS):
 
         return gpt_cond_latents, speaker_embedding
 
-    def synthesize(self, text, config, speaker_wav, language, **kwargs):
+    def synthesize(self, text, config, speaker_wav, language, speaker_id, **kwargs):
         """Synthesize speech with the given input text.
 
         Args:
@@ -394,12 +394,6 @@ class Xtts(BaseTTS):
             `text_input` as text token IDs after tokenizer, `voice_samples` as samples used for cloning, `conditioning_latents`
             as latents used at inference.
 
-        """
-        return self.inference_with_config(text, config, ref_audio_path=speaker_wav, language=language, **kwargs)
-
-    def inference_with_config(self, text, config, ref_audio_path, language, **kwargs):
-        """
-        inference with config
         """
         assert (
             "zh-cn" if language == "zh" else language in self.config.languages
@@ -411,13 +405,18 @@ class Xtts(BaseTTS):
             "repetition_penalty": config.repetition_penalty,
             "top_k": config.top_k,
             "top_p": config.top_p,
+        }
+        settings.update(kwargs)  # allow overriding of preset settings with kwargs
+        if speaker_id is not None:
+            gpt_cond_latent, speaker_embedding = self.speaker_manager.speakers[speaker_id].values()
+            return self.inference(text, language, gpt_cond_latent, speaker_embedding, **settings)
+        settings.update({
             "gpt_cond_len": config.gpt_cond_len,
             "gpt_cond_chunk_len": config.gpt_cond_chunk_len,
             "max_ref_len": config.max_ref_len,
             "sound_norm_refs": config.sound_norm_refs,
-        }
+        })
-        settings.update(kwargs)  # allow overriding of preset settings with kwargs
+        return self.full_inference(text, speaker_wav, language, **settings)
-        return self.full_inference(text, ref_audio_path, language, **settings)
 
     @torch.inference_mode()
     def full_inference(
@@ -753,8 +752,9 @@ class Xtts(BaseTTS):
 
         model_path = checkpoint_path or os.path.join(checkpoint_dir, "model.pth")
         vocab_path = vocab_path or os.path.join(checkpoint_dir, "vocab.json")
-        speaker_file_path = speaker_file_path or os.path.join(checkpoint_dir, "speakers.json")
+        speaker_file_path = speaker_file_path or os.path.join(checkpoint_dir, "speakers_xtts.pth")
 
+        self.language_manager = LanguageManager(config)
         self.speaker_manager = None
         if os.path.exists(speaker_file_path):
             self.speaker_manager = SpeakerManager(speaker_file_path)

TTS/utils/synthesizer.py

@@ -305,7 +305,7 @@ class Synthesizer(nn.Module):
         speaker_embedding = None
         speaker_id = None
         if self.tts_speakers_file or hasattr(self.tts_model.speaker_manager, "name_to_id"):
-            if speaker_name and isinstance(speaker_name, str):
+            if speaker_name and isinstance(speaker_name, str) and not self.tts_config.model == "xtts":
                 if self.tts_config.use_d_vector_file:
                     # get the average speaker embedding from the saved d_vectors.
                     speaker_embedding = self.tts_model.speaker_manager.get_mean_embedding(
@@ -335,7 +335,9 @@ class Synthesizer(nn.Module):
         # handle multi-lingual
         language_id = None
         if self.tts_languages_file or (
-            hasattr(self.tts_model, "language_manager") and self.tts_model.language_manager is not None
+            hasattr(self.tts_model, "language_manager") 
+            and self.tts_model.language_manager is not None
+            and not self.tts_config.model == "xtts"
         ):
             if len(self.tts_model.language_manager.name_to_id) == 1:
                 language_id = list(self.tts_model.language_manager.name_to_id.values())[0]
@@ -366,6 +368,7 @@ class Synthesizer(nn.Module):
         if (
             speaker_wav is not None
             and self.tts_model.speaker_manager is not None
+            and hasattr(self.tts_model.speaker_manager, "encoder_ap")
             and self.tts_model.speaker_manager.encoder_ap is not None
         ):
             speaker_embedding = self.tts_model.speaker_manager.compute_embedding_from_clip(speaker_wav)