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chore: add the ability of lru cache for api v3 to improve the inference speed when exchange model weights (#1058)
* chore: add the ability of lru cache for api v3 to improve the inference speed when exchange model weights * chore: Dockerfile start api v3 * chore: api default port from 127.0.0.1 to 0.0.0.0 * chore: make gpu happy when do tts * chore: rollback Dockerfile * chore: fix * chore: fix --------- Co-authored-by: kevin.zhang <kevin.zhang@cardinfolink.com>
This commit is contained in:
Kevin Zhang
GitHub
parent
2cafde159c
commit
50c3664496
@ -182,6 +182,12 @@ class TTS_Config:
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def __repr__(self):
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return self.__str__()
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def __hash__(self):
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return hash(self.configs_path)
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def __eq__(self, other):
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return isinstance(other, TTS_Config) and self.configs_path == other.configs_path
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class TTS:
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def __init__(self, configs: Union[dict, str, TTS_Config]):
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467
api_v3.py
Normal file
467
api_v3.py
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@ -0,0 +1,467 @@
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"""
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# 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
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` python api_v2.py -a 127.0.0.1 -p 9880 -c GPT_SoVITS/configs/tts_infer.yaml `
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## 执行参数:
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`-a` - `绑定地址, 默认"127.0.0.1"`
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`-p` - `绑定端口, 默认9880`
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`-c` - `TTS配置文件路径, 默认"GPT_SoVITS/configs/tts_infer.yaml"`
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## 调用:
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### 推理
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endpoint: `/tts`
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GET:
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```
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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
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```
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POST:
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```json
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{
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"text": "", # str.(required) text to be synthesized
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"text_lang": "", # str.(required) language of the text to be synthesized
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"ref_audio_path": "", # str.(required) reference audio path.
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"prompt_text": "", # str.(optional) prompt text for the reference audio
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"prompt_lang": "", # str.(required) language of the prompt text for the reference audio
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"top_k": 5, # int.(optional) top k sampling
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"top_p": 1, # float.(optional) top p sampling
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"temperature": 1, # float.(optional) temperature for sampling
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"text_split_method": "cut5", # str.(optional) text split method, see text_segmentation_method.py for details.
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"batch_size": 1, # int.(optional) batch size for inference
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"batch_threshold": 0.75, # float.(optional) threshold for batch splitting.
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"split_bucket": true, # bool.(optional) whether to split the batch into multiple buckets.
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"speed_factor":1.0, # float.(optional) control the speed of the synthesized audio.
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"fragment_interval":0.3, # float.(optional) to control the interval of the audio fragment.
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"seed": -1, # int.(optional) random seed for reproducibility.
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"media_type": "wav", # str.(optional) media type of the output audio, support "wav", "raw", "ogg", "aac".
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"streaming_mode": false, # bool.(optional) whether to return a streaming response.
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"parallel_infer": True, # bool.(optional) whether to use parallel inference.
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"repetition_penalty": 1.35, # float.(optional) repetition penalty for T2S model.
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"tts_infer_yaml_path": “GPT_SoVITS/configs/tts_infer.yaml” # str.(optional) tts infer yaml path
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}
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```
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RESP:
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成功: 直接返回 wav 音频流， http code 200
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失败: 返回包含错误信息的 json, http code 400
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### 命令控制
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endpoint: `/control`
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command:
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"restart": 重新运行
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"exit": 结束运行
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GET:
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```
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http://127.0.0.1:9880/control?command=restart
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```
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POST:
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```json
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{
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"command": "restart"
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}
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```
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RESP: 无
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### 切换GPT模型
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endpoint: `/set_gpt_weights`
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GET:
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```
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http://127.0.0.1:9880/set_gpt_weights?weights_path=GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
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```
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RESP:
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成功: 返回"success", http code 200
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失败: 返回包含错误信息的 json, http code 400
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### 切换Sovits模型
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endpoint: `/set_sovits_weights`
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GET:
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```
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http://127.0.0.1:9880/set_sovits_weights?weights_path=GPT_SoVITS/pretrained_models/s2G488k.pth
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```
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RESP:
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成功: 返回"success", http code 200
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失败: 返回包含错误信息的 json, http code 400
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"""
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import os
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import sys
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import traceback
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from typing import Generator
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import torch
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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sys.path.append("%s/GPT_SoVITS" % (now_dir))
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import argparse
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import subprocess
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import wave
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import signal
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import numpy as np
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import soundfile as sf
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from fastapi import Response
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from fastapi.responses import JSONResponse
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from fastapi import FastAPI
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import uvicorn
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from io import BytesIO
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from GPT_SoVITS.TTS_infer_pack.TTS import TTS, TTS_Config
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from GPT_SoVITS.TTS_infer_pack.text_segmentation_method import get_method_names as get_cut_method_names
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from fastapi.responses import StreamingResponse
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from pydantic import BaseModel
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from functools import lru_cache
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cut_method_names = get_cut_method_names()
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parser = argparse.ArgumentParser(description="GPT-SoVITS api")
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parser.add_argument("-a", "--bind_addr", type=str, default="0.0.0.0", help="default: 0.0.0.0")
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parser.add_argument("-p", "--port", type=int, default="9880", help="default: 9880")
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args = parser.parse_args()
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port = args.port
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host = args.bind_addr
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argv = sys.argv
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APP = FastAPI()
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class TTS_Request(BaseModel):
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text: str = None
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text_lang: str = None
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ref_audio_path: str = None
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prompt_lang: str = None
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prompt_text: str = ""
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top_k: int = 5
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top_p: float = 1
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temperature: float = 1
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text_split_method: str = "cut5"
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batch_size: int = 1
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batch_threshold: float = 0.75
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split_bucket: bool = True
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speed_factor: float = 1.0
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fragment_interval: float = 0.3
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seed: int = -1
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media_type: str = "wav"
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streaming_mode: bool = False
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parallel_infer: bool = True
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repetition_penalty: float = 1.35
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tts_infer_yaml_path: str = None
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"""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"""
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@lru_cache(maxsize=10)
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def get_tts_instance(tts_config: TTS_Config) -> TTS:
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print(f"load tts config from {tts_config.configs_path}")
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return TTS(tts_config)
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def pack_ogg(io_buffer: BytesIO, data: np.ndarray, rate: int):
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"""modify from https://github.com/RVC-Boss/GPT-SoVITS/pull/894/files"""
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with sf.SoundFile(io_buffer, mode='w', samplerate=rate, channels=1, format='ogg') as audio_file:
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audio_file.write(data)
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return io_buffer
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def pack_raw(io_buffer: BytesIO, data: np.ndarray, rate: int):
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io_buffer.write(data.tobytes())
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return io_buffer
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def pack_wav(io_buffer: BytesIO, data: np.ndarray, rate: int):
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io_buffer = BytesIO()
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sf.write(io_buffer, data, rate, format='wav')
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return io_buffer
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def pack_aac(io_buffer: BytesIO, data: np.ndarray, rate: int):
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process = subprocess.Popen([
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'ffmpeg',
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'-f', 's16le', # 输入16位有符号小端整数PCM
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'-ar', str(rate), # 设置采样率
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'-ac', '1', # 单声道
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'-i', 'pipe:0', # 从管道读取输入
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'-c:a', 'aac', # 音频编码器为AAC
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'-b:a', '192k', # 比特率
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'-vn', # 不包含视频
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'-f', 'adts', # 输出AAC数据流格式
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'pipe:1' # 将输出写入管道
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], stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
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out, _ = process.communicate(input=data.tobytes())
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io_buffer.write(out)
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return io_buffer
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def pack_audio(io_buffer: BytesIO, data: np.ndarray, rate: int, media_type: str):
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if media_type == "ogg":
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io_buffer = pack_ogg(io_buffer, data, rate)
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elif media_type == "aac":
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io_buffer = pack_aac(io_buffer, data, rate)
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elif media_type == "wav":
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io_buffer = pack_wav(io_buffer, data, rate)
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else:
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io_buffer = pack_raw(io_buffer, data, rate)
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io_buffer.seek(0)
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return io_buffer
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# from https://huggingface.co/spaces/coqui/voice-chat-with-mistral/blob/main/app.py
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def wave_header_chunk(frame_input=b"", channels=1, sample_width=2, sample_rate=32000):
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# This will create a wave header then append the frame input
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# It should be first on a streaming wav file
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# Other frames better should not have it (else you will hear some artifacts each chunk start)
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wav_buf = BytesIO()
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with wave.open(wav_buf, "wb") as vfout:
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vfout.setnchannels(channels)
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vfout.setsampwidth(sample_width)
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vfout.setframerate(sample_rate)
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vfout.writeframes(frame_input)
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wav_buf.seek(0)
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return wav_buf.read()
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def handle_control(command: str):
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if command == "restart":
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os.execl(sys.executable, sys.executable, *argv)
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elif command == "exit":
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os.kill(os.getpid(), signal.SIGTERM)
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exit(0)
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def check_params(req: dict, tts_config: TTS_Config):
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text: str = req.get("text", "")
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text_lang: str = req.get("text_lang", "")
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ref_audio_path: str = req.get("ref_audio_path", "")
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streaming_mode: bool = req.get("streaming_mode", False)
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media_type: str = req.get("media_type", "wav")
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prompt_lang: str = req.get("prompt_lang", "")
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text_split_method: str = req.get("text_split_method", "cut5")
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if ref_audio_path in [None, ""]:
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return JSONResponse(status_code=400, content={"message": "ref_audio_path is required"})
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if text in [None, ""]:
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return JSONResponse(status_code=400, content={"message": "text is required"})
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if (text_lang in [None, ""]):
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return JSONResponse(status_code=400, content={"message": "text_lang is required"})
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elif text_lang.lower() not in tts_config.languages:
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return JSONResponse(status_code=400, content={"message": "text_lang is not supported"})
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if (prompt_lang in [None, ""]):
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return JSONResponse(status_code=400, content={"message": "prompt_lang is required"})
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elif prompt_lang.lower() not in tts_config.languages:
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return JSONResponse(status_code=400, content={"message": "prompt_lang is not supported"})
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if media_type not in ["wav", "raw", "ogg", "aac"]:
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return JSONResponse(status_code=400, content={"message": "media_type is not supported"})
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elif media_type == "ogg" and not streaming_mode:
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return JSONResponse(status_code=400, content={"message": "ogg format is not supported in non-streaming mode"})
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if text_split_method not in cut_method_names:
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return JSONResponse(status_code=400,
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content={"message": f"text_split_method:{text_split_method} is not supported"})
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return None
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async def tts_handle(req: dict):
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"""
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Text to speech handler.
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Args:
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req (dict):
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{
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"text": "", # str.(required) text to be synthesized
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"text_lang: "", # str.(required) language of the text to be synthesized
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"ref_audio_path": "", # str.(required) reference audio path
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"prompt_text": "", # str.(optional) prompt text for the reference audio
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"prompt_lang": "", # str.(required) language of the prompt text for the reference audio
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"top_k": 5, # int. top k sampling
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"top_p": 1, # float. top p sampling
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"temperature": 1, # float. temperature for sampling
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"text_split_method": "cut5", # str. text split method, see text_segmentation_method.py for details.
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"batch_size": 1, # int. batch size for inference
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"batch_threshold": 0.75, # float. threshold for batch splitting.
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"split_bucket: True, # bool. whether to split the batch into multiple buckets.
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"speed_factor":1.0, # float. control the speed of the synthesized audio.
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"fragment_interval":0.3, # float. to control the interval of the audio fragment.
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"seed": -1, # int. random seed for reproducibility.
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"media_type": "wav", # str. media type of the output audio, support "wav", "raw", "ogg", "aac".
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"streaming_mode": False, # bool. whether to return a streaming response.
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"parallel_infer": True, # bool.(optional) whether to use parallel inference.
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"repetition_penalty": 1.35 # float.(optional) repetition penalty for T2S model.
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}
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returns:
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StreamingResponse: audio stream response.
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"""
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streaming_mode = req.get("streaming_mode", False)
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media_type = req.get("media_type", "wav")
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tts_infer_yaml_path = req.get("tts_infer_yaml_path", "GPT_SoVITS/configs/tts_infer.yaml")
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tts_config = TTS_Config(tts_infer_yaml_path)
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check_res = check_params(req, tts_config)
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if check_res is not None:
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return check_res
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if streaming_mode:
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req["return_fragment"] = True
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try:
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tts_instance = get_tts_instance(tts_config)
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move_to_gpu(tts_instance, tts_config)
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tts_generator = tts_instance.run(req)
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if streaming_mode:
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def streaming_generator(tts_generator: Generator, media_type: str):
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if media_type == "wav":
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yield wave_header_chunk()
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media_type = "raw"
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for sr, chunk in tts_generator:
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yield pack_audio(BytesIO(), chunk, sr, media_type).getvalue()
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move_to_cpu(tts_instance)
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# _media_type = f"audio/{media_type}" if not (streaming_mode and media_type in ["wav", "raw"]) else f"audio/x-{media_type}"
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return StreamingResponse(streaming_generator(tts_generator, media_type, ), media_type=f"audio/{media_type}")
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else:
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sr, audio_data = next(tts_generator)
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audio_data = pack_audio(BytesIO(), audio_data, sr, media_type).getvalue()
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move_to_cpu(tts_instance)
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return Response(audio_data, media_type=f"audio/{media_type}")
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except Exception as e:
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return JSONResponse(status_code=400, content={"message": f"tts failed", "Exception": str(e)})
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def move_to_cpu(tts):
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cpu_device = torch.device('cpu')
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tts.set_device(cpu_device)
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print("Moved TTS models to CPU to save GPU memory.")
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def move_to_gpu(tts: TTS, tts_config: TTS_Config):
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tts.set_device(tts_config.device)
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print("Moved TTS models back to GPU for performance.")
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@APP.get("/control")
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async def control(command: str = None):
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if command is None:
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return JSONResponse(status_code=400, content={"message": "command is required"})
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handle_control(command)
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@APP.get("/tts")
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async def tts_get_endpoint(
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text: str = None,
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text_lang: str = None,
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ref_audio_path: str = None,
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prompt_lang: str = None,
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prompt_text: str = "",
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top_k: int = 5,
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top_p: float = 1,
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temperature: float = 1,
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text_split_method: str = "cut0",
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batch_size: int = 1,
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batch_threshold: float = 0.75,
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split_bucket: bool = True,
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speed_factor: float = 1.0,
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fragment_interval: float = 0.3,
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seed: int = -1,
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media_type: str = "wav",
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streaming_mode: bool = False,
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parallel_infer: bool = True,
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repetition_penalty: float = 1.35,
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tts_infer_yaml_path: str = "GPT_SoVITS/configs/tts_infer.yaml"
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):
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req = {
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"text": text,
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"text_lang": text_lang.lower(),
|
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"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)
|
||||
Loading…
x
Reference in New Issue
Block a user