diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index cd1df0b..eaacb52 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -182,6 +182,12 @@ class TTS_Config: def __repr__(self): return self.__str__() + def __hash__(self): + return hash(self.configs_path) + + def __eq__(self, other): + return isinstance(other, TTS_Config) and self.configs_path == other.configs_path + class TTS: def __init__(self, configs: Union[dict, str, TTS_Config]): diff --git a/api_v3.py b/api_v3.py new file mode 100644 index 0000000..a121dfc --- /dev/null +++ b/api_v3.py @@ -0,0 +1,467 @@ +""" +# WebAPI文档 (3.0) - 使用了缓存技术，初始化时使用LRU Cache TTS 实例，缓存加载模型的世界，达到减少切换不同语音时的推理时间 + +` 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. + "tts_infer_yaml_path": “GPT_SoVITS/configs/tts_infer.yaml” # str.(optional) tts infer yaml path +} +``` + +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 + +import torch + +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 Response +from fastapi.responses import JSONResponse +from fastapi import FastAPI +import uvicorn +from io import BytesIO +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 +from functools import lru_cache + +cut_method_names = get_cut_method_names() + +parser = argparse.ArgumentParser(description="GPT-SoVITS api") +parser.add_argument("-a", "--bind_addr", type=str, default="0.0.0.0", help="default: 0.0.0.0") +parser.add_argument("-p", "--port", type=int, default="9880", help="default: 9880") +args = parser.parse_args() +port = args.port +host = args.bind_addr +argv = sys.argv + +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 + tts_infer_yaml_path: str = None + """推理时需要加载的声音模型的yaml配置文件路径，如：GPT_SoVITS/configs/tts_infer.yaml""" + + +@lru_cache(maxsize=10) +def get_tts_instance(tts_config: TTS_Config) -> TTS: + print(f"load tts config from {tts_config.configs_path}") + return TTS(tts_config) + + +def pack_ogg(io_buffer: BytesIO, data: np.ndarray, rate: int): + """modify from https://github.com/RVC-Boss/GPT-SoVITS/pull/894/files""" + 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, tts_config: TTS_Config): + 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") + tts_infer_yaml_path = req.get("tts_infer_yaml_path", "GPT_SoVITS/configs/tts_infer.yaml") + + tts_config = TTS_Config(tts_infer_yaml_path) + check_res = check_params(req, tts_config) + if check_res is not None: + return check_res + + if streaming_mode: + req["return_fragment"] = True + + try: + tts_instance = get_tts_instance(tts_config) + + move_to_gpu(tts_instance, tts_config) + + tts_generator = tts_instance.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() + move_to_cpu(tts_instance) + + # _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() + move_to_cpu(tts_instance) + 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)}) + + +def move_to_cpu(tts): + cpu_device = torch.device('cpu') + tts.set_device(cpu_device) + print("Moved TTS models to CPU to save GPU memory.") + + +def move_to_gpu(tts: TTS, tts_config: TTS_Config): + tts.set_device(tts_config.device) + print("Moved TTS models back to GPU for performance.") + + +@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, + tts_infer_yaml_path: str = "GPT_SoVITS/configs/tts_infer.yaml" +): + 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), + "tts_infer_yaml_path": tts_infer_yaml_path + } + + 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_audio(refer_audio_path: str = None, tts_infer_yaml_path: str = "GPT_SoVITS/configs/tts_infer.yaml"): + try: + tts_config = TTS_Config(tts_infer_yaml_path) + tts_instance = get_tts_instance(tts_config) + tts_instance.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.get("/set_gpt_weights") +async def set_gpt_weights(weights_path: str = None, tts_infer_yaml_path: str = "GPT_SoVITS/configs/tts_infer.yaml"): + try: + if weights_path in ["", None]: + return JSONResponse(status_code=400, content={"message": "gpt weight path is required"}) + + tts_config = TTS_Config(tts_infer_yaml_path) + tts_instance = get_tts_instance(tts_config) + tts_instance.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, tts_infer_yaml_path: str = "GPT_SoVITS/configs/tts_infer.yaml"): + try: + if weights_path in ["", None]: + return JSONResponse(status_code=400, content={"message": "sovits weight path is required"}) + + tts_config = TTS_Config(tts_infer_yaml_path) + tts_instance = get_tts_instance(tts_config) + tts_instance.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)