let synthesizer to pass speaker encoder file paths to speaker manager

TTS/utils/synthesizer.py

@@ -25,6 +25,8 @@ class Synthesizer(object):
         tts_speakers_file: str = "",
         vocoder_checkpoint: str = "",
         vocoder_config: str = "",
+        encoder_checkpoint: str = "",
+        encoder_config: str = "",
         use_cuda: bool = False,
     ) -> None:
         """General 🐸 TTS interface for inference. It takes a tts and a vocoder
@@ -41,6 +43,8 @@ class Synthesizer(object):
             tts_config_path (str): path to the tts config file.
             vocoder_checkpoint (str, optional): path to the vocoder model file. Defaults to None.
             vocoder_config (str, optional): path to the vocoder config file. Defaults to None.
+            encoder_checkpoint (str, optional): path to the speaker encoder model file. Defaults to `""`,
+            encoder_config (str, optional): path to the speaker encoder config file. Defaults to `""`,
             use_cuda (bool, optional): enable/disable cuda. Defaults to False.
         """
         self.tts_checkpoint = tts_checkpoint
@@ -48,6 +52,8 @@ class Synthesizer(object):
         self.tts_speakers_file = tts_speakers_file
         self.vocoder_checkpoint = vocoder_checkpoint
         self.vocoder_config = vocoder_config
+        self.encoder_checkpoint = encoder_checkpoint
+        self.encoder_config = encoder_config
         self.use_cuda = use_cuda
 
         self.tts_model = None
@@ -69,16 +75,37 @@ class Synthesizer(object):
 
     @staticmethod
     def _get_segmenter(lang: str):
+        """get the sentence segmenter for the given language.
+
+        Args:
+            lang (str): target language code.
+
+        Returns:
+            [type]: [description]
+        """
         return pysbd.Segmenter(language=lang, clean=True)
 
     def _load_speakers(self, speaker_file: str) -> None:
+        """Load the SpeakerManager to organize multi-speaker TTS. It loads the speakers meta-data and the speaker
+        encoder if it is defined.
+
+        Args:
+            speaker_file (str): path to the speakers meta-data file.
+        """
         print("Loading speakers ...")
-        self.speaker_manager = SpeakerManager()
+        self.speaker_manager = SpeakerManager(encoder_model_path=self.encoder_checkpoint, encoder_config_path=self.encoder_config)
         self.speaker_manager.load_x_vectors_file(self.tts_config.get("external_speaker_embedding_file", speaker_file))
         self.num_speakers = self.speaker_manager.num_speakers
         self.speaker_embedding_dim = self.speaker_manager.x_vector_dim
 
     def _load_tts(self, tts_checkpoint: str, tts_config_path: str, use_cuda: bool) -> None:
+        """Load the TTS model.
+
+        Args:
+            tts_checkpoint (str): path to the model checkpoint.
+            tts_config_path (str): path to the model config file.
+            use_cuda (bool): enable/disable CUDA use.
+        """
         # pylint: disable=global-statement
 
         global symbols, phonemes
@@ -109,6 +136,13 @@ class Synthesizer(object):
             self.tts_model.cuda()
 
     def _load_vocoder(self, model_file: str, model_config: str, use_cuda: bool) -> None:
+        """Load the vocoder model.
+
+        Args:
+            model_file (str): path to the model checkpoint.
+            model_config (str): path to the model config file.
+            use_cuda (bool): enable/disable CUDA use.
+        """
         self.vocoder_config = load_config(model_config)
         self.vocoder_ap = AudioProcessor(verbose=False, **self.vocoder_config["audio"])
         self.vocoder_model = setup_generator(self.vocoder_config)
@@ -117,24 +151,54 @@ class Synthesizer(object):
             self.vocoder_model.cuda()
 
     def _split_into_sentences(self, text) -> List[str]:
+        """Split give text into sentences.
+
+        Args:
+            text (str): input text in string format.
+
+        Returns:
+            List[str]: list of sentences.
+        """
         return self.seg.segment(text)
 
     def save_wav(self, wav: List[int], path: str) -> None:
+        """Save the waveform as a file.
+
+        Args:
+            wav (List[int]): waveform as a list of values.
+            path (str): output path to save the waveform.
+        """
         wav = np.array(wav)
         self.ap.save_wav(wav, path, self.output_sample_rate)
 
-    def tts(self, text: str, speaker_idx: str = "", style_wav=None) -> List[int]:
+    def tts(self, text: str, speaker_idx: str = "", speaker_wav=None, style_wav=None) -> List[int]:
+        """🐸 TTS magic. Run all the models and generate speech.
+
+        Args:
+            text (str): input text.
+            speaker_idx (str, optional): spekaer id for multi-speaker models. Defaults to "".
+            speaker_wav ():
+            style_wav ([type], optional): style waveform for GST. Defaults to None.
+
+        Returns:
+            List[int]: [description]
+        """
         start_time = time.time()
         wavs = []
         sens = self._split_into_sentences(text)
         print(" > Text splitted to sentences.")
         print(sens)
 
+        # get the speaker embedding from the saved x_vectors.
         if speaker_idx and isinstance(speaker_idx, str):
             speaker_embedding = self.speaker_manager.get_x_vectors_by_speaker(speaker_idx)[0]
         else:
             speaker_embedding = None
 
+        # compute a new x_vector from the given clip.
+        if speaker_wav is not None:
+            speaker_embedding = self.speaker_manager.compute_x_vector_from_clip(speaker_wav)
+
         use_gl = self.vocoder_model is None
 
         for sen in sens: