MCP relegated to proxy Pendrokar/xVASynth-MCP

gr_client.py

@@ -540,25 +540,6 @@ class BlocksDemo:
 		surprise,
 		deepmoji_checked
 	):
-		"""
-		Convert the text to speech using xVASynth xVAPitch models.
-		
-		Args:
-			input_text:	string; from which to create the audio
-			voice:	Literal['ccby_nvidia_hifi_6670_M', 'ccby_nv_hifi_11614_F', 'ccby_nvidia_hifi_11697_F', 'ccby_nvidia_hifi_12787_F', 'ccby_nvidia_hifi_6097_M', 'ccby_nvidia_hifi_6671_M', 'ccby_nvidia_hifi_8051_F', 'ccby_nvidia_hifi_9017_M', 'ccby_nvidia_hifi_9136_F', 'ccby_nvidia_hifi_92_F']; the only viable Voice model filenames
-			lang:	Literal['en', 'de', 'es', 'it', 'fr', 'ru', 'tr', 'la', 'ro', 'da', 'vi', 'ha', 'nl', 'zh', 'ar', 'uk', 'hi', 'ko', 'pl', 'sw', 'fi', 'hu', 'pt', 'yo', 'sv', 'el', 'wo', 'jp']; the language of input_text
-			pacing:	float (numeric value between 0.5 and 2.0); Duration
-			pitch:	float (numeric value between 0 and 1.0); Pitch
-			energy:	float (numeric value between 0.1 and 1.0); Energy
-			anger:	float (numeric value between 0 and 1.0); 😠 Anger
-			happy:	float (numeric value between 0 and 1.0); 😃 Happiness
-			sad:	float (numeric value between 0 and 1.0); 😭 Sadness
-			surprise:	float (numeric value between 0 and 1.0); 😮 Surprise
-			deepmoji_checked: bool; use DeepMoji to parse English text and fill the emotional values
-			
-		Returns:
-			Tuple of (output_audio_path, arpabet_html, final_anger_ratio, final_happiness_ratio, final_sadness_ratio, final_surprise_ratio, response) where output_audio_path is the filepath of output audio
-		"""
 		wav_path, arpabet_html, angry, happy, sad, surprise, response = client.predict(
 			input_text,	# str  in 'Input Text' Textbox component
 			voice,	# Literal['ccby_nvidia_hifi_6670_M', 'ccby_nv_hifi_11614_F', 'ccby_nvidia_hifi_11697_F', 'ccby_nvidia_hifi_12787_F', 'ccby_nvidia_hifi_6097_M', 'ccby_nvidia_hifi_6671_M', 'ccby_nvidia_hifi_8051_F', 'ccby_nvidia_hifi_9017_M', 'ccby_nvidia_hifi_9136_F', 'ccby_nvidia_hifi_92_F']  in 'Voice' Radio component