Update app.py

app.py

@@ -5,6 +5,14 @@ from transformers import AutoConfig
 from transformers import GPT2Tokenizer, GPT2LMHeadModel
 from itertools import chain
 
+import tempfile
+from typing import Optional
+from TTS.config import load_config
+import gradio as gr
+import numpy as np
+from TTS.utils.manage import ModelManager
+from TTS.utils.synthesizer import Synthesizer
+
 
 config = AutoConfig.from_pretrained('gorkemgoknar/gpt2chatbotenglish')
 model = GPT2LMHeadModel.from_pretrained('gorkemgoknar/gpt2chatbotenglish', config=config)
@@ -89,7 +97,33 @@ def get_chat_response(name,history=[], input_txt = "Hello , what is your name?")
 | Brother | Allnut | Rose | Qui-Gon | Jar Jar
 '''
 
-
+MODEL_NAME= "tts_models/multilingual/multi-dataset/your_tts"
+
+def tts(text: str, speaker_idx: str=None):
+    if len(text) > MAX_TXT_LEN:
+        text = text[:MAX_TXT_LEN]
+        print(f"Input text was cutoff since it went over the {MAX_TXT_LEN} character limit.")
+    print(text, model_name)
+    # download model
+    model_path, config_path, model_item = manager.download_model(f"tts_models/{MODEL_NAME}")
+    vocoder_name: Optional[str] = model_item["default_vocoder"]
+    # download vocoder
+    vocoder_path = None
+    vocoder_config_path = None
+    if vocoder_name is not None:
+        vocoder_path, vocoder_config_path, _ = manager.download_model(vocoder_name)
+    # init synthesizer
+    synthesizer = Synthesizer(
+        model_path, config_path, None, None, vocoder_path, vocoder_config_path,
+    )
+    # synthesize
+    if synthesizer is None:
+        raise NameError("model not found")
+    wavs = synthesizer.tts(text, speaker_idx)
+    # return output
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
+        synthesizer.save_wav(wavs, fp)
+        return fp.name
 
 
 def greet(character,message,history):
@@ -105,6 +139,7 @@ def greet(character,message,history):
 
 
   response = get_chat_response(character,history=history["message_history"],input_txt=message)
+  tts(response)
   
   history["message_history"].append((message, response))