""" # WebAPI文档 ` python api_v2.py -a 127.0.0.1 -p 9880 -c GPT_SoVITS/configs/tts_infer.yaml ` ## 执行参数: `-a` - `绑定地址, 默认"127.0.0.1"` `-p` - `绑定端口, 默认9880` `-c` - `TTS配置文件路径, 默认"GPT_SoVITS/configs/tts_infer.yaml"` ## 调用: ### 推理 endpoint: `/tts` GET: ``` 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 { "text": "", # str.(required) text to be synthesized "text_lang": "", # str.(required) language of the text to be synthesized "ref_audio_path": "", # str.(required) reference audio path. "prompt_text": "", # str.(optional) prompt text for the reference audio "prompt_lang": "", # str.(required) language of the prompt text for the reference audio "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": "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. "speed_factor":1.0, # float.(optional) control the speed of the synthesized audio. "fragment_interval":0.3, # float.(optional) to control the interval of the audio fragment. "seed": -1, # int.(optional) random seed for reproducibility. "media_type": "wav", # str.(optional) media type of the output audio, support "wav", "raw", "ogg", "aac". "streaming_mode": false, # bool.(optional) whether to return a streaming response. "parallel_infer": True, # bool.(optional) whether to use parallel inference. "repetition_penalty": 1.35 # float.(optional) repetition penalty for T2S model. } ``` RESP: 成功: 直接返回 wav 音频流， http code 200 失败: 返回包含错误信息的 json, http code 400 ### 命令控制 endpoint: `/control` command: "restart": 重新运行 "exit": 结束运行 GET: ``` http://127.0.0.1:9880/control?command=restart ``` POST: ```json { "command": "restart" } ``` RESP: 无 ### 切换GPT模型 endpoint: `/set_gpt_weights` GET: ``` http://127.0.0.1:9880/set_gpt_weights?weights_path=GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt ``` RESP: 成功: 返回"success", http code 200 失败: 返回包含错误信息的 json, http code 400 ### 切换Sovits模型 endpoint: `/set_sovits_weights` GET: ``` http://127.0.0.1:9880/set_sovits_weights?weights_path=GPT_SoVITS/pretrained_models/s2G488k.pth ``` RESP: 成功: 返回"success", http code 200 失败: 返回包含错误信息的 json, http code 400 """ import os import sys import traceback from typing import Generator now_dir = os.getcwd() sys.path.append(now_dir) sys.path.append("%s/GPT_SoVITS" % (now_dir)) import argparse import subprocess import wave import signal import numpy as np import soundfile as sf from fastapi import FastAPI, Request, HTTPException, Response from fastapi.responses import StreamingResponse, JSONResponse from fastapi import FastAPI, UploadFile, File import uvicorn from io import BytesIO from tools.i18n.i18n import I18nAuto from GPT_SoVITS.TTS_infer_pack.TTS import TTS, TTS_Config from GPT_SoVITS.TTS_infer_pack.text_segmentation_method import get_method_names as get_cut_method_names from fastapi.responses import StreamingResponse from pydantic import BaseModel # print(sys.path) i18n = I18nAuto() cut_method_names = get_cut_method_names() parser = argparse.ArgumentParser(description="GPT-SoVITS api") parser.add_argument("-c", "--tts_config", type=str, default="GPT_SoVITS/configs/tts_infer.yaml", help="tts_infer路径") 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() config_path = args.tts_config # device = args.device port = args.port host = args.bind_addr argv = sys.argv if config_path in [None, ""]: config_path = "GPT-SoVITS/configs/tts_infer.yaml" 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 parallel_infer:bool = True repetition_penalty:float = 1.35 ### modify from https://github.com/RVC-Boss/GPT-SoVITS/pull/894/files def pack_ogg(io_buffer:BytesIO, data:np.ndarray, rate:int): with sf.SoundFile(io_buffer, mode='w', samplerate=rate, channels=1, format='ogg') as audio_file: audio_file.write(data) return io_buffer def pack_raw(io_buffer:BytesIO, data:np.ndarray, rate:int): io_buffer.write(data.tobytes()) return io_buffer def pack_wav(io_buffer:BytesIO, data:np.ndarray, rate:int): io_buffer = BytesIO() sf.write(io_buffer, data, rate, format='wav') return io_buffer def pack_aac(io_buffer:BytesIO, data:np.ndarray, rate:int): process = subprocess.Popen([ 'ffmpeg', '-f', 's16le', # 输入16位有符号小端整数PCM '-ar', str(rate), # 设置采样率 '-ac', '1', # 单声道 '-i', 'pipe:0', # 从管道读取输入 '-c:a', 'aac', # 音频编码器为AAC '-b:a', '192k', # 比特率 '-vn', # 不包含视频 '-f', 'adts', # 输出AAC数据流格式 'pipe:1' # 将输出写入管道 ], stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE) out, _ = process.communicate(input=data.tobytes()) io_buffer.write(out) return io_buffer def pack_audio(io_buffer:BytesIO, data:np.ndarray, rate:int, media_type:str): if media_type == "ogg": io_buffer = pack_ogg(io_buffer, data, rate) elif media_type == "aac": io_buffer = pack_aac(io_buffer, data, rate) elif media_type == "wav": io_buffer = pack_wav(io_buffer, data, rate) else: io_buffer = pack_raw(io_buffer, data, rate) io_buffer.seek(0) return io_buffer # from https://huggingface.co/spaces/coqui/voice-chat-with-mistral/blob/main/app.py def wave_header_chunk(frame_input=b"", channels=1, sample_width=2, sample_rate=32000): # 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 = BytesIO() with wave.open(wav_buf, "wb") as vfout: vfout.setnchannels(channels) vfout.setsampwidth(sample_width) vfout.setframerate(sample_rate) vfout.writeframes(frame_input) wav_buf.seek(0) return wav_buf.read() def handle_control(command:str): if command == "restart": os.execl(sys.executable, sys.executable, *argv) elif command == "exit": os.kill(os.getpid(), signal.SIGTERM) exit(0) def check_params(req:dict): text:str = req.get("text", "") text_lang:str = req.get("text_lang", "") ref_audio_path:str = req.get("ref_audio_path", "") streaming_mode:bool = req.get("streaming_mode", False) media_type:str = req.get("media_type", "wav") prompt_lang:str = req.get("prompt_lang", "") text_split_method:str = req.get("text_split_method", "cut5") if ref_audio_path in [None, ""]: 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, ""]) : return JSONResponse(status_code=400, content={"message": "text_lang is required"}) elif text_lang.lower() not in tts_config.languages: return JSONResponse(status_code=400, content={"message": "text_lang is not supported"}) if (prompt_lang in [None, ""]) : return JSONResponse(status_code=400, content={"message": "prompt_lang is required"}) elif prompt_lang.lower() not in tts_config.languages: return JSONResponse(status_code=400, content={"message": "prompt_lang 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"}) if text_split_method not in cut_method_names: return JSONResponse(status_code=400, content={"message": f"text_split_method:{text_split_method} is not supported"}) return None async def tts_handle(req:dict): """ Text to speech handler. Args: req (dict): { "text": "", # str.(required) text to be synthesized "text_lang: "", # str.(required) language of the text to be synthesized "ref_audio_path": "", # str.(required) reference audio path "prompt_text": "", # str.(optional) prompt text for the reference audio "prompt_lang": "", # str.(required) language of the prompt text for the reference audio "top_k": 5, # int. top k sampling "top_p": 1, # float. top p sampling "temperature": 1, # float. temperature for sampling "text_split_method": "cut5", # str. text split method, see text_segmentation_method.py for details. "batch_size": 1, # int. batch size for inference "batch_threshold": 0.75, # float. threshold for batch splitting. "split_bucket: True, # bool. whether to split the batch into multiple buckets. "speed_factor":1.0, # float. control the speed of the synthesized audio. "fragment_interval":0.3, # float. to control the interval of the audio fragment. "seed": -1, # int. random seed for reproducibility. "media_type": "wav", # str. media type of the output audio, support "wav", "raw", "ogg", "aac". "streaming_mode": False, # bool. whether to return a streaming response. "parallel_infer": True, # bool.(optional) whether to use parallel inference. "repetition_penalty": 1.35 # float.(optional) repetition penalty for T2S model. } returns: StreamingResponse: audio stream response. """ streaming_mode = req.get("streaming_mode", False) media_type = req.get("media_type", "wav") check_res = check_params(req) if check_res is not None: return check_res if streaming_mode: req["return_fragment"] = True try: tts_generator=tts_pipeline.run(req) if streaming_mode: def streaming_generator(tts_generator:Generator, media_type:str): if media_type == "wav": yield wave_header_chunk() media_type = "raw" 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}" return StreamingResponse(streaming_generator(tts_generator, media_type, ), media_type=f"audio/{media_type}") else: sr, audio_data = next(tts_generator) audio_data = pack_audio(BytesIO(), audio_data, sr, media_type).getvalue() 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)}) @APP.get("/control") async def control(command: str = None): if command is None: return JSONResponse(status_code=400, content={"message": "command is required"}) handle_control(command) @APP.get("/tts") async def tts_get_endpoint( 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 = "cut0", 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, parallel_infer:bool = True, repetition_penalty:float = 1.35 ): req = { "text": text, "text_lang": text_lang.lower(), "ref_audio_path": ref_audio_path, "prompt_text": prompt_text, "prompt_lang": prompt_lang.lower(), "top_k": top_k, "top_p": top_p, "temperature": temperature, "text_split_method": text_split_method, "batch_size":int(batch_size), "batch_threshold":float(batch_threshold), "speed_factor":float(speed_factor), "split_bucket":split_bucket, "fragment_interval":fragment_interval, "seed":seed, "media_type":media_type, "streaming_mode":streaming_mode, "parallel_infer":parallel_infer, "repetition_penalty":float(repetition_penalty) } return await tts_handle(req) @APP.post("/tts") async def tts_post_endpoint(request: TTS_Request): req = request.dict() return await tts_handle(req) @APP.get("/set_refer_audio") async def set_refer_aduio(refer_audio_path: str = None): try: tts_pipeline.set_ref_audio(refer_audio_path) except Exception as e: return JSONResponse(status_code=400, content={"message": f"set refer audio failed", "Exception": str(e)}) return JSONResponse(status_code=200, content={"message": "success"}) # @APP.post("/set_refer_audio") # async def set_refer_aduio_post(audio_file: UploadFile = File(...)): # try: # # 检查文件类型，确保是音频文件 # if not audio_file.content_type.startswith("audio/"): # return JSONResponse(status_code=400, content={"message": "file type is not supported"}) # os.makedirs("uploaded_audio", exist_ok=True) # save_path = os.path.join("uploaded_audio", audio_file.filename) # # 保存音频文件到服务器上的一个目录 # with open(save_path , "wb") as buffer: # buffer.write(await audio_file.read()) # tts_pipeline.set_ref_audio(save_path) # except Exception as e: # return JSONResponse(status_code=400, content={"message": f"set refer audio failed", "Exception": str(e)}) # return JSONResponse(status_code=200, content={"message": "success"}) @APP.get("/set_gpt_weights") async def set_gpt_weights(weights_path: str = None): try: if weights_path in ["", None]: return JSONResponse(status_code=400, content={"message": "gpt weight path is required"}) tts_pipeline.init_t2s_weights(weights_path) except Exception as e: return JSONResponse(status_code=400, content={"message": f"change gpt weight failed", "Exception": str(e)}) return JSONResponse(status_code=200, content={"message": "success"}) @APP.get("/set_sovits_weights") async def set_sovits_weights(weights_path: str = None): try: if weights_path in ["", None]: return JSONResponse(status_code=400, content={"message": "sovits weight path is required"}) tts_pipeline.init_vits_weights(weights_path) except Exception as e: return JSONResponse(status_code=400, content={"message": f"change sovits weight failed", "Exception": str(e)}) return JSONResponse(status_code=200, content={"message": "success"}) if __name__ == "__main__": try: uvicorn.run(APP, host=host, port=port, workers=1) except Exception as e: traceback.print_exc() os.kill(os.getpid(), signal.SIGTERM) exit(0)