优化了代码逻辑，提升健壮性

api_v2.py

@@ -1,5 +1,5 @@
 """
-# api.py usage
+# web api 接口文档
 
 ` python api_v2.py -a 127.0.0.1 -p 9880 -c GPT_SoVITS/configs/tts_infer.yaml `
 
@@ -14,7 +14,7 @@
 
 endpoint: `/tts`
 GET:
-    `http://127.0.0.1:9880/tts?ref_audio_path=123.wav&prompt_text=一二三。&prompt_language=zh&text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_language=zh`
+    `http://127.0.0.1:9880/tts?text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_lang=zh&ref_audio_path=archive_jingyuan_1.wav&prompt_lang=zh&prompt_text=我是「罗浮」云骑将军景元。不必拘谨，「将军」只是一时的身份，你称呼我景元便可&text_split_method=cut5&batch_size=1&media_type=wav&streaming_mode=true`
 
 POST:
 ```json
@@ -27,7 +27,7 @@ POST:
     "top_k": 5,                   # int.(optional) top k sampling
     "top_p": 1,                   # float.(optional) top p sampling
     "temperature": 1,             # float.(optional) temperature for sampling
-    "text_split_method": "cut0",  # str.(optional) text split method, see text_segmentation_method.py for details.
+    "text_split_method": "cut5",  # str.(optional) text split method, see text_segmentation_method.py for details.
     "batch_size": 1,              # int.(optional) batch size for inference
     "batch_threshold": 0.75,      # float.(optional) threshold for batch splitting.
     "split_bucket: True,          # bool.(optional) whether to split the batch into multiple buckets.
@@ -90,26 +90,21 @@ RESP:
 ### 
     
 """
-from asyncio import sleep
 import os
 import sys
+from typing import Generator
 
 now_dir = os.getcwd()
 sys.path.append(now_dir)
 sys.path.append("%s/GPT_SoVITS" % (now_dir))
 
 import argparse
-import io
-# import os
 import subprocess
-# import sys
 import wave
-
 import signal
 import numpy as np
-import torch
 import soundfile as sf
-from fastapi import FastAPI, Request, HTTPException
+from fastapi import FastAPI, Request, HTTPException, Response
 from fastapi.responses import StreamingResponse, JSONResponse
 from fastapi import FastAPI, UploadFile, File
 import uvicorn
@@ -117,14 +112,13 @@ from io import BytesIO
 from tools.i18n.i18n import I18nAuto
 from GPT_SoVITS.TTS_infer_pack.TTS import TTS, TTS_Config
 from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
 # print(sys.path)
 i18n = I18nAuto()
 
 
 parser = argparse.ArgumentParser(description="GPT-SoVITS api")
-# parser.add_argument("-api_c", "--api_config", type=str, default="GPT_SoVITS/configs/api_config.yaml", help="api_config路径")
+parser.add_argument("-c", "--tts_config", type=str, default="GPT_SoVITS/configs/tts_infer.yaml", help="tts_infer路径")
-parser.add_argument("-tts_c", "--tts_config", type=str, default="GPT_SoVITS/configs/tts_infer.yaml", help="tts_infer路径")
-# parser.add_argument("-d", "--device", type=str, default="cpu", help="cuda / cpu / mps")
 parser.add_argument("-a", "--bind_addr", type=str, default="127.0.0.1", help="default: 127.0.0.1")
 parser.add_argument("-p", "--port", type=int, default="9880", help="default: 9880")
 args = parser.parse_args()
@@ -141,7 +135,24 @@ tts_config=TTS_Config(config_path)
 tts_pipeline = TTS(tts_config)
 
 APP = FastAPI()
-
+class TTS_Request(BaseModel):
+    text: str = None
+    text_lang: str = None
+    ref_audio_path: str = None
+    prompt_lang: str = None
+    prompt_text: str = ""
+    top_k:int = 5
+    top_p:float = 1
+    temperature:float = 1
+    text_split_method:str = "cut5"
+    batch_size:int = 1
+    batch_threshold:float = 0.75
+    split_bucket:bool = True
+    speed_factor:float = 1.0
+    fragment_interval:float = 0.3
+    seed:int = -1
+    media_type:str = "wav"
+    streaming_mode:bool = False
 
 ### modify from https://github.com/RVC-Boss/GPT-SoVITS/pull/894/files
 def pack_ogg(io_buffer:BytesIO, data:np.ndarray, rate:int):
@@ -196,7 +207,7 @@ def wave_header_chunk(frame_input=b"", channels=1, sample_width=2, sample_rate=3
     # This will create a wave header then append the frame input
     # It should be first on a streaming wav file
     # Other frames better should not have it (else you will hear some artifacts each chunk start)
-    wav_buf = io.BytesIO()
+    wav_buf = BytesIO()
     with wave.open(wav_buf, "wb") as vfout:
         vfout.setnchannels(channels)
         vfout.setsampwidth(sample_width)
@@ -215,7 +226,7 @@ def handle_control(command:str):
         exit(0)
 
 
-def tts_handle(req:dict):
+async def tts_handle(req:dict):
     """
     Text to speech handler.
     
@@ -257,38 +268,32 @@ def tts_handle(req:dict):
         return JSONResponse(status_code=400, content={"message": "ref_audio_path is required"})
     if text in [None, ""]:
         return JSONResponse(status_code=400, content={"message": "text is required"})
-    if (text_lang in [None, ""]) or (text_lang.lower() not in ["auto", "en", "zh", "ja",  "all_zh", "all_ja"]):
+    if (text_lang in [None, ""]) :
         return JSONResponse(status_code=400, content={"message": "text_language is required"})
+    elif text_lang.lower() not in ["auto", "en", "zh", "ja",  "all_zh", "all_ja"]:
+        return JSONResponse(status_code=400, content={"message": "text_language is not supported"})
     if media_type not in ["wav", "raw", "ogg", "aac"]:
         return JSONResponse(status_code=400, content={"message": "media_type is not supported"})
-    
+    elif media_type == "ogg" and  not streaming_mode:
+        return JSONResponse(status_code=400, content={"message": "ogg format is not supported in non-streaming mode"})
     
     try:
         tts_generator=tts_pipeline.run(req)
         
         if streaming_mode:
+            def streaming_generator(tts_generator:Generator, media_type:str):
                 if media_type == "wav":
-                def streaming_generator(tts_generator):
                     yield wave_header_chunk()
-                    for sr, chunk in tts_generator:
+                    media_type = "raw"
-                        yield pack_audio(BytesIO(), chunk, sr, "raw").getvalue()
-            else:
-                def streaming_generator(tts_generator):
                 for sr, chunk in tts_generator:
                     yield pack_audio(BytesIO(), chunk, sr, media_type).getvalue()
-
+            # _media_type = f"audio/{media_type}" if not (streaming_mode and media_type in ["wav", "raw"]) else f"audio/x-{media_type}"
-            _media_type = ""
+            return StreamingResponse(streaming_generator(tts_generator, media_type, ), media_type=f"audio/{media_type}")
-            if media_type in ["wav", "raw"]:
-                _media_type = f"audio/x-{media_type}"
-            else:
-                _media_type = f"audio/{media_type}"
-            return StreamingResponse(streaming_generator(tts_generator), media_type=_media_type)
     
         else:
-            audio_buffer = BytesIO()
             sr, audio_data = next(tts_generator)
-            audio_buffer = pack_audio(audio_buffer, audio_data, sr, media_type)
+            audio_data = pack_audio(BytesIO(), audio_data, sr, media_type).getvalue()
-            return StreamingResponse(audio_buffer, media_type=f"audio/{media_type}")
+            return Response(audio_data, media_type=f"audio/{media_type}")
     except Exception as e:
         return JSONResponse(status_code=400, content={"message": f"tts failed", "Exception": str(e)})
     
@@ -344,13 +349,13 @@ async def tts_get_endpoint(
         "media_type":media_type,
         "streaming_mode":streaming_mode,
     }
-    return tts_handle(req)
+    return await tts_handle(req)
                 
 
 @APP.post("/tts")
-async def tts_post_endpoint(request: Request):
+async def tts_post_endpoint(request: TTS_Request):
-    req = await request.json()
+    req = request.dict()
-    return tts_handle(req)
+    return await tts_handle(req)
 
 
 @APP.get("/set_refer_audio")
@@ -404,10 +409,6 @@ async def set_sovits_weights(weights_path: str = None):
 
 
 
-        
 if __name__ == "__main__":
-    try:
     uvicorn.run(APP, host=host, port=port, workers=1)
-    except KeyboardInterrupt:
+
-        os.kill(os.getpid(), signal.SIGTERM)
-        exit(0)