feat: 通过依赖注入来获取asr和tts对象

app/controllers/chat_controller.py

@@ -1,6 +1,8 @@
 from ..schemas.chat_schema import *
 from ..dependencies.logger import get_logger
 from ..dependencies.summarizer import get_summarizer
+from ..dependencies.asr import get_asr
+from ..dependencies.tts import get_tts
 from .controller_enum import *
 from ..models import UserCharacter, Session, Character, User
 from utils.audio_utils import VAD
@@ -19,21 +21,14 @@ logger = get_logger()
 # 依赖注入获取context总结服务
 summarizer = get_summarizer()
 
-# --------------------初始化本地ASR-----------------------
-from utils.stt.modified_funasr import ModifiedRecognizer
-
-asr = ModifiedRecognizer()
-logger.info("本地ASR初始化成功")
+# -----------------------获取ASR-------------------------
+asr = get_asr()
 # -------------------------------------------------------
 
-# --------------------初始化本地VITS----------------------
-from utils.tts.vits_utils import TextToSpeech
-
-tts = TextToSpeech(device='cpu')
-logger.info("本地TTS初始化成功")
+# -------------------------TTS--------------------------
+tts = get_tts()
 # -------------------------------------------------------
 
-
 # 依赖注入获取Config
 Config = get_config()
 
@@ -488,7 +483,7 @@ async def scl_llm_handler(ws,session_id,response_type,llm_info,tts_info,db,redis
                             redis.set(session_id,json.dumps(session_content,ensure_ascii=False)) #更新session
                             is_first = True
                             llm_response = ""
-                        if token_count >  summarizer.max_token * 0.7:  #如果llm返回的token数大于60%的最大token数，则进行文本摘要
+                        if token_count >  summarizer.max_token * 0.7:  #如果llm返回的token数大于70%的最大token数，则进行文本摘要
                             system_prompt = messages[0]['content']
                             summary = await summarizer.summarize(messages)
                             events = user_info['events']

app/dependencies/asr.py

@@ -0,0 +1,11 @@
+from utils.stt.modified_funasr import ModifiedRecognizer
+from app.dependencies.logger import get_logger
+
+logger = get_logger()
+
+#初始化全局asr对象
+asr = ModifiedRecognizer()
+logger.info("ASR初始化成功")
+
+def get_asr():
+    return asr

app/dependencies/tts.py

@@ -0,0 +1,11 @@
+from utils.tts.openvoice_utils import TextToSpeech
+from app.dependencies.logger import get_logger
+
+logger = get_logger()
+
+#初始化全局tts对象
+tts = TextToSpeech(use_tone_convert=True,device='cuda')
+logger.info("TTS初始化成功")
+
+def get_tts():
+    return tts

requirements.txt

@@ -21,4 +21,9 @@ apscheduler
 aiohttp
 faster_whisper
 whisper_timestamped
+<<<<<<< Updated upstream
 modelscope
+=======
+modelscope
+wavmark
+>>>>>>> Stashed changes