added speed as argument

TTS/api.py

@@ -168,9 +168,7 @@ class TTS(nn.Module):
         self.synthesizer = None
         self.model_name = model_name
 
-        model_path, config_path, vocoder_path, vocoder_config_path, model_dir = self.download_model_by_name(
-            model_name
-        )
+        model_path, config_path, vocoder_path, vocoder_config_path, model_dir = self.download_model_by_name(model_name)
 
         # init synthesizer
         # None values are fetch from the model
@@ -283,6 +281,7 @@ class TTS(nn.Module):
             style_text=None,
             reference_speaker_name=None,
             split_sentences=split_sentences,
+            speed=1.0,
             **kwargs,
         )
         return wav
@@ -337,6 +336,7 @@ class TTS(nn.Module):
             language=language,
             speaker_wav=speaker_wav,
             split_sentences=split_sentences,
+            speed=1.0,
             **kwargs,
         )
         self.synthesizer.save_wav(wav=wav, path=file_path, pipe_out=pipe_out)

TTS/tts/models/xtts.py

@@ -379,7 +379,7 @@ class Xtts(BaseTTS):
 
         return gpt_cond_latents, speaker_embedding
 
-    def synthesize(self, text, config, speaker_wav, language, speaker_id=None, **kwargs):
+    def synthesize(self, text, config, speaker_wav, language, speaker_id=None, speed=1.0, **kwargs):
         """Synthesize speech with the given input text.
 
         Args:
@@ -410,13 +410,15 @@ class Xtts(BaseTTS):
         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,
-        })
-        return self.full_inference(text, speaker_wav, language, **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,
+            }
+        )
+        return self.full_inference(text, speaker_wav, language, speed, **settings)
 
     @torch.inference_mode()
     def full_inference(
@@ -424,6 +426,7 @@ class Xtts(BaseTTS):
         text,
         ref_audio_path,
         language,
+        speed,
         # GPT inference
         temperature=0.75,
         length_penalty=1.0,
@@ -484,6 +487,7 @@ class Xtts(BaseTTS):
             max_ref_length=max_ref_len,
             sound_norm_refs=sound_norm_refs,
         )
+        self.speed = speed
 
         return self.inference(
             text,
@@ -518,6 +522,7 @@ class Xtts(BaseTTS):
         enable_text_splitting=False,
         **hf_generate_kwargs,
     ):
+        speed = self.speed
         language = language.split("-")[0]  # remove the country code
         length_scale = 1.0 / max(speed, 0.05)
         gpt_cond_latent = gpt_cond_latent.to(self.device)
@@ -756,13 +761,11 @@ 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")
-
-        if speaker_file_path is None and checkpoint_dir is not None:
-            speaker_file_path = os.path.join(checkpoint_dir, "speakers_xtts.pth")
+        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 speaker_file_path is not None and os.path.exists(speaker_file_path):
+        if os.path.exists(speaker_file_path):
             self.speaker_manager = SpeakerManager(speaker_file_path)
 
         if os.path.exists(vocab_path):

TTS/utils/synthesizer.py

@@ -265,6 +265,7 @@ class Synthesizer(nn.Module):
         reference_wav=None,
         reference_speaker_name=None,
         split_sentences: bool = True,
+        speed=1.0,
         **kwargs,
     ) -> List[int]:
         """🐸 TTS magic. Run all the models and generate speech.
@@ -391,6 +392,7 @@ class Synthesizer(nn.Module):
                         d_vector=speaker_embedding,
                         speaker_wav=speaker_wav,
                         language=language_name,
+                        speed=1.0,
                         **kwargs,
                     )
                 else: