diff --git a/TTS/api.py b/TTS/api.py
index 7abc188e..0900db05 100644
--- a/TTS/api.py
+++ b/TTS/api.py
@@ -88,7 +88,7 @@ class TTS(nn.Module):
 
     @property
     def is_multi_speaker(self):
-        if hasattr(self.synthesizer.tts_model, "speaker_manager") and self.synthesizer.tts_model.speaker_manager:
+        if hasattr(self.synthesizer.tts_model, "speaker_Smanager") and self.synthesizer.tts_model.speaker_manager:
             return self.synthesizer.tts_model.speaker_manager.num_speakers > 1
         return False
 
@@ -243,6 +243,7 @@ class TTS(nn.Module):
         emotion: str = None,
         speed: float = None,
         split_sentences: bool = True,
+        SuppresPrintStatements: bool = False,
         **kwargs,
     ):
         """Convert text to speech.
@@ -267,6 +268,9 @@ class TTS(nn.Module):
                 Split text into sentences, synthesize them separately and concatenate the file audio.
                 Setting it False uses more VRAM and possibly hit model specific text length or VRAM limits. Only
                 applicable to the 🐸TTS models. Defaults to True.
+            SuppresPrintStatements (bool, optional):
+                Suppress All the Print statements so that when runnging the function in thread the print statement will not apears in the terminal.
+                Setting it to True will suppress the print statements
             kwargs (dict, optional):
                 Additional arguments for the model.
         """
@@ -283,6 +287,7 @@ class TTS(nn.Module):
             style_text=None,
             reference_speaker_name=None,
             split_sentences=split_sentences,
+            SuppresPrintStatements=SuppresPrintStatements,
             **kwargs,
         )
         return wav
diff --git a/TTS/utils/synthesizer.py b/TTS/utils/synthesizer.py
index b98647c3..2666f868 100644
--- a/TTS/utils/synthesizer.py
+++ b/TTS/utils/synthesizer.py
@@ -265,6 +265,7 @@ class Synthesizer(nn.Module):
         reference_wav=None,
         reference_speaker_name=None,
         split_sentences: bool = True,
+        SuppresPrintStatements: bool= False,
         **kwargs,
     ) -> List[int]:
         """🐸 TTS magic. Run all the models and generate speech.
@@ -279,6 +280,7 @@ class Synthesizer(nn.Module):
             reference_wav ([type], optional): reference waveform for voice conversion. Defaults to None.
             reference_speaker_name ([type], optional): speaker id of reference waveform. Defaults to None.
             split_sentences (bool, optional): split the input text into sentences. Defaults to True.
+            SuppresPrintStatements (bool, optional): Suppress the Print statements.
             **kwargs: additional arguments to pass to the TTS model.
         Returns:
             List[int]: [description]
@@ -294,9 +296,11 @@ class Synthesizer(nn.Module):
         if text:
             sens = [text]
             if split_sentences:
-                print(" > Text splitted to sentences.")
+                if not SuppresPrintStatements:
+                    print(" > Text splitted to sentences.")
                 sens = self.split_into_sentences(text)
-            print(sens)
+            if not SuppresPrintStatements:
+                print(sens)
 
         # handle multi-speaker
         if "voice_dir" in kwargs:
@@ -420,7 +424,8 @@ class Synthesizer(nn.Module):
                         self.vocoder_config["audio"]["sample_rate"] / self.tts_model.ap.sample_rate,
                     ]
                     if scale_factor[1] != 1:
-                        print(" > interpolating tts model output.")
+                        if not SuppresPrintStatements:
+                            print(" > interpolating tts model output.")
                         vocoder_input = interpolate_vocoder_input(scale_factor, vocoder_input)
                     else:
                         vocoder_input = torch.tensor(vocoder_input).unsqueeze(0)  # pylint: disable=not-callable
@@ -484,7 +489,8 @@ class Synthesizer(nn.Module):
                     self.vocoder_config["audio"]["sample_rate"] / self.tts_model.ap.sample_rate,
                 ]
                 if scale_factor[1] != 1:
-                    print(" > interpolating tts model output.")
+                    if not SuppresPrintStatements:
+                        print(" > interpolating tts model output.")
                     vocoder_input = interpolate_vocoder_input(scale_factor, vocoder_input)
                 else:
                     vocoder_input = torch.tensor(vocoder_input).unsqueeze(0)  # pylint: disable=not-callable
@@ -500,6 +506,7 @@ class Synthesizer(nn.Module):
         # compute stats
         process_time = time.time() - start_time
         audio_time = len(wavs) / self.tts_config.audio["sample_rate"]
-        print(f" > Processing time: {process_time}")
-        print(f" > Real-time factor: {process_time / audio_time}")
+        if not SuppresPrintStatements:
+            print(f" > Processing time: {process_time}")
+            print(f" > Real-time factor: {process_time / audio_time}")
         return wavs