mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-10-07 23:48:48 +08:00
Merge 2f85395675d35fabcea00131a70f040a899d0dcc into d8bcc732d747e3e52ea20d6340e57dac18bef06d
This commit is contained in:
Tong Li
GitHub
commit
40f1e4e81c
3
.gitignore
vendored
3
.gitignore
vendored
@ -10,3 +10,6 @@ reference
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GPT_weights
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SoVITS_weights
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TEMP
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app.log
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gweight.txt
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sweight.txt
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@ -8,6 +8,8 @@
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'''
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import os, re, logging
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import LangSegment
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from pyutils.logs import llog
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logging.getLogger("markdown_it").setLevel(logging.ERROR)
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logging.getLogger("urllib3").setLevel(logging.ERROR)
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logging.getLogger("httpcore").setLevel(logging.ERROR)
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@ -15,7 +17,6 @@ logging.getLogger("httpx").setLevel(logging.ERROR)
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logging.getLogger("asyncio").setLevel(logging.ERROR)
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logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
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logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
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import pdb
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import torch
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if os.path.exists("./gweight.txt"):
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@ -65,7 +66,7 @@ from text import cleaned_text_to_sequence
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from text.cleaner import clean_text
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from time import time as ttime
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from module.mel_processing import spectrogram_torch
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from my_utils import load_audio
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from pyutils.np_utils import load_audio
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from tools.i18n.i18n import I18nAuto
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i18n = I18nAuto()
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@ -556,19 +557,47 @@ def change_choices():
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pretrained_sovits_name = "GPT_SoVITS/pretrained_models/s2G488k.pth"
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pretrained_gpt_name = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
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SoVITS_weight_root = "SoVITS_weights"
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GPT_weight_root = "GPT_weights"
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os.makedirs(SoVITS_weight_root, exist_ok=True)
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os.makedirs(GPT_weight_root, exist_ok=True)
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SoVITS_weight_root = ["SoVITS_weights","trained"]
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GPT_weight_root = ["GPT_weights","trained"]
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for path in SoVITS_weight_root:
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os.makedirs(path, exist_ok=True)
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for path in GPT_weight_root:
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os.makedirs(path, exist_ok=True)
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def get_weights_names():
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SoVITS_names = [pretrained_sovits_name]
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for name in os.listdir(SoVITS_weight_root):
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if name.endswith(".pth"): SoVITS_names.append("%s/%s" % (SoVITS_weight_root, name))
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for path in SoVITS_weight_root:
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llog.info(f"scan model path:{path}")
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for name in os.listdir(path):
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llog.info(f"scan sub model path:{name}")
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#if os.path.isdir(name): no working
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if os.path.isfile(name):
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if name.endswith(".pth"): SoVITS_names.append("%s/%s" % (path, name))
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else:
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subPath = os.path.join(path, name)
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for modelName in os.listdir(subPath):
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if modelName.endswith(".pth"):
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modelPath = os.path.join(subPath,modelName)
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llog.info(f"add model path:{modelPath}")
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SoVITS_names.append(modelPath)
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GPT_names = [pretrained_gpt_name]
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for name in os.listdir(GPT_weight_root):
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if name.endswith(".ckpt"): GPT_names.append("%s/%s" % (GPT_weight_root, name))
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for path in GPT_weight_root:
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for name in os.listdir(path):
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if os.path.isfile(name):
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if name.endswith(".ckpt"): GPT_names.append("%s/%s" % (path, name))
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else:
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subPath = os.path.join(path, name)
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for modelName in os.listdir(subPath):
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if modelName.endswith(".pth"):
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modelPath = os.path.join(subPath, modelName)
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llog.info(f"add model path:{modelPath}")
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GPT_names.append(modelPath)
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return SoVITS_names, GPT_names
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@ -1,23 +1,14 @@
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import time
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import logging
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import os
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import random
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import traceback
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import numpy as np
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import torch
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import torch.utils.data
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from tqdm import tqdm
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from module import commons
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from module.mel_processing import spectrogram_torch
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from text import cleaned_text_to_sequence
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from utils import load_wav_to_torch, load_filepaths_and_text
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import torch.nn.functional as F
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from functools import lru_cache
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import requests
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from scipy.io import wavfile
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from io import BytesIO
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from my_utils import load_audio
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from pyutils.np_utils import load_audio
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# ZeroDivisionError fixed by Tybost (https://github.com/RVC-Boss/GPT-SoVITS/issues/79)
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class TextAudioSpeakerLoader(torch.utils.data.Dataset):
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@ -9,7 +9,6 @@ cnhubert.cnhubert_base_path=cnhubert_base_path
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ssl_model = cnhubert.get_model()
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from text import cleaned_text_to_sequence
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import soundfile
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from my_utils import load_audio
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import os
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import json
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@ -12,12 +12,12 @@ opt_dir= os.environ.get("opt_dir")
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cnhubert.cnhubert_base_path= os.environ.get("cnhubert_base_dir")
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is_half=eval(os.environ.get("is_half","True"))
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import pdb,traceback,numpy as np,logging
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import traceback,numpy as np
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from scipy.io import wavfile
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import librosa,torch
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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from my_utils import load_audio
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from pyutils.np_utils import load_audio
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# from config import cnhubert_base_path
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# cnhubert.cnhubert_base_path=cnhubert_base_path
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0
GPT_SoVITS/pyutils/__init__.py
Normal file
0
GPT_SoVITS/pyutils/__init__.py
Normal file
24
GPT_SoVITS/pyutils/logs.py
Normal file
24
GPT_SoVITS/pyutils/logs.py
Normal file
@ -0,0 +1,24 @@
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import logging
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from logging.handlers import RotatingFileHandler
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# 设置日志记录器
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llog = logging.getLogger(__name__)
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llog.setLevel(logging.INFO)
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llog.propagate = False # 防止日志事件传递给根记录器
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# 创建控制台日志处理器
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console_handler = logging.StreamHandler()
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console_handler.setLevel(logging.INFO)
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# 创建文件日志处理器
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file_handler = RotatingFileHandler('app.log', maxBytes=1024 * 1024 * 10, backupCount=5)
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file_handler.setLevel(logging.INFO)
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# 设置日志格式，包括文件名和行号
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formatter = logging.Formatter('%(asctime)s - %(filename)s:%(lineno)d - %(levelname)s - %(message)s')
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console_handler.setFormatter(formatter)
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file_handler.setFormatter(formatter)
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# 将处理器添加到日志记录器
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llog.addHandler(console_handler)
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#llog.addHandler(file_handler)
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@ -1,17 +1,14 @@
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import os
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import glob
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import sys
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import argparse
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import logging
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import glob
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import json
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import logging
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import os
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import subprocess
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import sys
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import traceback
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import librosa
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import numpy as np
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from scipy.io.wavfile import read
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import torch
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import logging
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logging.getLogger("numba").setLevel(logging.ERROR)
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logging.getLogger("matplotlib").setLevel(logging.ERROR)
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736
api.py
736
api.py
@ -1,736 +0,0 @@
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"""
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# api.py usage
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` python api.py -dr "123.wav" -dt "一二三。" -dl "zh" `
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## 执行参数:
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`-s` - `SoVITS模型路径, 可在 config.py 中指定`
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`-g` - `GPT模型路径, 可在 config.py 中指定`
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调用请求缺少参考音频时使用
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`-dr` - `默认参考音频路径`
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`-dt` - `默认参考音频文本`
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`-dl` - `默认参考音频语种, "中文","英文","日文","zh","en","ja"`
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`-d` - `推理设备, "cuda","cpu"`
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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, 可在 config.py 中指定`
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`-fp` - `覆盖 config.py 使用全精度`
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`-hp` - `覆盖 config.py 使用半精度`
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`-sm` - `流式返回模式, 默认不启用, "close","c", "normal","n", "keepalive","k"`
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·-mt` - `返回的音频编码格式, 流式默认ogg, 非流式默认wav, "wav", "ogg", "aac"`
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·-cp` - `文本切分符号设定, 默认为空, 以",.，。"字符串的方式传入`
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`-hb` - `cnhubert路径`
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`-b` - `bert路径`
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## 调用:
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### 推理
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endpoint: `/`
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使用执行参数指定的参考音频:
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GET:
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`http://127.0.0.1:9880?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`
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POST:
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```json
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{
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"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。",
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"text_language": "zh"
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}
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```
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使用执行参数指定的参考音频并设定分割符号:
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GET:
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`http://127.0.0.1:9880?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&cut_punc=，。`
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POST:
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```json
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{
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"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。",
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"text_language": "zh",
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"cut_punc": "，。",
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}
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```
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手动指定当次推理所使用的参考音频:
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GET:
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`http://127.0.0.1:9880?refer_wav_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`
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POST:
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```json
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{
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"refer_wav_path": "123.wav",
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"prompt_text": "一二三。",
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"prompt_language": "zh",
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"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。",
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"text_language": "zh"
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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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### 更换默认参考音频
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endpoint: `/change_refer`
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key与推理端一样
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GET:
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`http://127.0.0.1:9880/change_refer?refer_wav_path=123.wav&prompt_text=一二三。&prompt_language=zh`
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POST:
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```json
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{
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"refer_wav_path": "123.wav",
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"prompt_text": "一二三。",
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"prompt_language": "zh"
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}
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```
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RESP:
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成功: json, http code 200
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失败: json, 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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`http://127.0.0.1:9880/control?command=restart`
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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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"""
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import argparse
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import os,re
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import sys
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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 signal
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import LangSegment
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from time import time as ttime
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import torch
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import librosa
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import soundfile as sf
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from fastapi import FastAPI, Request, HTTPException
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from fastapi.responses import StreamingResponse, JSONResponse
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import uvicorn
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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import numpy as np
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from feature_extractor import cnhubert
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from io import BytesIO
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from module.models import SynthesizerTrn
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from AR.models.t2s_lightning_module import Text2SemanticLightningModule
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from text import cleaned_text_to_sequence
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from text.cleaner import clean_text
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from module.mel_processing import spectrogram_torch
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from my_utils import load_audio
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import config as global_config
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import logging
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import subprocess
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class DefaultRefer:
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def __init__(self, path, text, language):
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self.path = args.default_refer_path
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self.text = args.default_refer_text
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self.language = args.default_refer_language
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def is_ready(self) -> bool:
|
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return is_full(self.path, self.text, self.language)
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|
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def is_empty(*items): # 任意一项不为空返回False
|
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for item in items:
|
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if item is not None and item != "":
|
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return False
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return True
|
||||
|
||||
|
||||
def is_full(*items): # 任意一项为空返回False
|
||||
for item in items:
|
||||
if item is None or item == "":
|
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return False
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||||
return True
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||||
|
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|
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def change_sovits_weights(sovits_path):
|
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global vq_model, hps
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dict_s2 = torch.load(sovits_path, map_location="cpu")
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hps = dict_s2["config"]
|
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hps = DictToAttrRecursive(hps)
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hps.model.semantic_frame_rate = "25hz"
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model_params_dict = vars(hps.model)
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vq_model = SynthesizerTrn(
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hps.data.filter_length // 2 + 1,
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hps.train.segment_size // hps.data.hop_length,
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n_speakers=hps.data.n_speakers,
|
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**model_params_dict
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)
|
||||
if ("pretrained" not in sovits_path):
|
||||
del vq_model.enc_q
|
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if is_half == True:
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vq_model = vq_model.half().to(device)
|
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else:
|
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vq_model = vq_model.to(device)
|
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vq_model.eval()
|
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vq_model.load_state_dict(dict_s2["weight"], strict=False)
|
||||
|
||||
|
||||
def change_gpt_weights(gpt_path):
|
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global hz, max_sec, t2s_model, config
|
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hz = 50
|
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dict_s1 = torch.load(gpt_path, map_location="cpu")
|
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config = dict_s1["config"]
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max_sec = config["data"]["max_sec"]
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t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
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||||
t2s_model.load_state_dict(dict_s1["weight"])
|
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if is_half == True:
|
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t2s_model = t2s_model.half()
|
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t2s_model = t2s_model.to(device)
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||||
t2s_model.eval()
|
||||
total = sum([param.nelement() for param in t2s_model.parameters()])
|
||||
logger.info("Number of parameter: %.2fM" % (total / 1e6))
|
||||
|
||||
|
||||
def get_bert_feature(text, word2ph):
|
||||
with torch.no_grad():
|
||||
inputs = tokenizer(text, return_tensors="pt")
|
||||
for i in inputs:
|
||||
inputs[i] = inputs[i].to(device) #####输入是long不用管精度问题，精度随bert_model
|
||||
res = bert_model(**inputs, output_hidden_states=True)
|
||||
res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
|
||||
assert len(word2ph) == len(text)
|
||||
phone_level_feature = []
|
||||
for i in range(len(word2ph)):
|
||||
repeat_feature = res[i].repeat(word2ph[i], 1)
|
||||
phone_level_feature.append(repeat_feature)
|
||||
phone_level_feature = torch.cat(phone_level_feature, dim=0)
|
||||
# if(is_half==True):phone_level_feature=phone_level_feature.half()
|
||||
return phone_level_feature.T
|
||||
|
||||
|
||||
def clean_text_inf(text, language):
|
||||
phones, word2ph, norm_text = clean_text(text, language)
|
||||
phones = cleaned_text_to_sequence(phones)
|
||||
return phones, word2ph, norm_text
|
||||
|
||||
|
||||
def get_bert_inf(phones, word2ph, norm_text, language):
|
||||
language=language.replace("all_","")
|
||||
if language == "zh":
|
||||
bert = get_bert_feature(norm_text, word2ph).to(device)#.to(dtype)
|
||||
else:
|
||||
bert = torch.zeros(
|
||||
(1024, len(phones)),
|
||||
dtype=torch.float16 if is_half == True else torch.float32,
|
||||
).to(device)
|
||||
|
||||
return bert
|
||||
|
||||
|
||||
def get_phones_and_bert(text,language):
|
||||
if language in {"en","all_zh","all_ja"}:
|
||||
language = language.replace("all_","")
|
||||
if language == "en":
|
||||
LangSegment.setfilters(["en"])
|
||||
formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
|
||||
else:
|
||||
# 因无法区别中日文汉字,以用户输入为准
|
||||
formattext = text
|
||||
while " " in formattext:
|
||||
formattext = formattext.replace(" ", " ")
|
||||
phones, word2ph, norm_text = clean_text_inf(formattext, language)
|
||||
if language == "zh":
|
||||
bert = get_bert_feature(norm_text, word2ph).to(device)
|
||||
else:
|
||||
bert = torch.zeros(
|
||||
(1024, len(phones)),
|
||||
dtype=torch.float16 if is_half == True else torch.float32,
|
||||
).to(device)
|
||||
elif language in {"zh", "ja","auto"}:
|
||||
textlist=[]
|
||||
langlist=[]
|
||||
LangSegment.setfilters(["zh","ja","en","ko"])
|
||||
if language == "auto":
|
||||
for tmp in LangSegment.getTexts(text):
|
||||
if tmp["lang"] == "ko":
|
||||
langlist.append("zh")
|
||||
textlist.append(tmp["text"])
|
||||
else:
|
||||
langlist.append(tmp["lang"])
|
||||
textlist.append(tmp["text"])
|
||||
else:
|
||||
for tmp in LangSegment.getTexts(text):
|
||||
if tmp["lang"] == "en":
|
||||
langlist.append(tmp["lang"])
|
||||
else:
|
||||
# 因无法区别中日文汉字,以用户输入为准
|
||||
langlist.append(language)
|
||||
textlist.append(tmp["text"])
|
||||
# logger.info(textlist)
|
||||
# logger.info(langlist)
|
||||
phones_list = []
|
||||
bert_list = []
|
||||
norm_text_list = []
|
||||
for i in range(len(textlist)):
|
||||
lang = langlist[i]
|
||||
phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
|
||||
bert = get_bert_inf(phones, word2ph, norm_text, lang)
|
||||
phones_list.append(phones)
|
||||
norm_text_list.append(norm_text)
|
||||
bert_list.append(bert)
|
||||
bert = torch.cat(bert_list, dim=1)
|
||||
phones = sum(phones_list, [])
|
||||
norm_text = ''.join(norm_text_list)
|
||||
|
||||
return phones,bert.to(torch.float16 if is_half == True else torch.float32),norm_text
|
||||
|
||||
|
||||
class DictToAttrRecursive:
|
||||
def __init__(self, input_dict):
|
||||
for key, value in input_dict.items():
|
||||
if isinstance(value, dict):
|
||||
# 如果值是字典，递归调用构造函数
|
||||
setattr(self, key, DictToAttrRecursive(value))
|
||||
else:
|
||||
setattr(self, key, value)
|
||||
|
||||
|
||||
def get_spepc(hps, filename):
|
||||
audio = load_audio(filename, int(hps.data.sampling_rate))
|
||||
audio = torch.FloatTensor(audio)
|
||||
audio_norm = audio
|
||||
audio_norm = audio_norm.unsqueeze(0)
|
||||
spec = spectrogram_torch(audio_norm, hps.data.filter_length, hps.data.sampling_rate, hps.data.hop_length,
|
||||
hps.data.win_length, center=False)
|
||||
return spec
|
||||
|
||||
|
||||
def pack_audio(audio_bytes, data, rate):
|
||||
if media_type == "ogg":
|
||||
audio_bytes = pack_ogg(audio_bytes, data, rate)
|
||||
elif media_type == "aac":
|
||||
audio_bytes = pack_aac(audio_bytes, data, rate)
|
||||
else:
|
||||
# wav无法流式, 先暂存raw
|
||||
audio_bytes = pack_raw(audio_bytes, data, rate)
|
||||
|
||||
return audio_bytes
|
||||
|
||||
|
||||
def pack_ogg(audio_bytes, data, rate):
|
||||
with sf.SoundFile(audio_bytes, mode='w', samplerate=rate, channels=1, format='ogg') as audio_file:
|
||||
audio_file.write(data)
|
||||
|
||||
return audio_bytes
|
||||
|
||||
|
||||
def pack_raw(audio_bytes, data, rate):
|
||||
audio_bytes.write(data.tobytes())
|
||||
|
||||
return audio_bytes
|
||||
|
||||
|
||||
def pack_wav(audio_bytes, rate):
|
||||
data = np.frombuffer(audio_bytes.getvalue(),dtype=np.int16)
|
||||
wav_bytes = BytesIO()
|
||||
sf.write(wav_bytes, data, rate, format='wav')
|
||||
|
||||
return wav_bytes
|
||||
|
||||
|
||||
def pack_aac(audio_bytes, data, rate):
|
||||
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())
|
||||
audio_bytes.write(out)
|
||||
|
||||
return audio_bytes
|
||||
|
||||
|
||||
def read_clean_buffer(audio_bytes):
|
||||
audio_chunk = audio_bytes.getvalue()
|
||||
audio_bytes.truncate(0)
|
||||
audio_bytes.seek(0)
|
||||
|
||||
return audio_bytes, audio_chunk
|
||||
|
||||
|
||||
def cut_text(text, punc):
|
||||
punc_list = [p for p in punc if p in {",", ".", ";", "?", "!", "、", "，", "。", "？", "！", "；", "：", "…"}]
|
||||
if len(punc_list) > 0:
|
||||
punds = r"[" + "".join(punc_list) + r"]"
|
||||
text = text.strip("\n")
|
||||
items = re.split(f"({punds})", text)
|
||||
mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])]
|
||||
# 在句子不存在符号或句尾无符号的时候保证文本完整
|
||||
if len(items)%2 == 1:
|
||||
mergeitems.append(items[-1])
|
||||
text = "\n".join(mergeitems)
|
||||
|
||||
while "\n\n" in text:
|
||||
text = text.replace("\n\n", "\n")
|
||||
|
||||
return text
|
||||
|
||||
|
||||
def only_punc(text):
|
||||
return not any(t.isalnum() or t.isalpha() for t in text)
|
||||
|
||||
|
||||
def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language):
|
||||
t0 = ttime()
|
||||
prompt_text = prompt_text.strip("\n")
|
||||
prompt_language, text = prompt_language, text.strip("\n")
|
||||
zero_wav = np.zeros(int(hps.data.sampling_rate * 0.3), dtype=np.float16 if is_half == True else np.float32)
|
||||
with torch.no_grad():
|
||||
wav16k, sr = librosa.load(ref_wav_path, sr=16000)
|
||||
wav16k = torch.from_numpy(wav16k)
|
||||
zero_wav_torch = torch.from_numpy(zero_wav)
|
||||
if (is_half == True):
|
||||
wav16k = wav16k.half().to(device)
|
||||
zero_wav_torch = zero_wav_torch.half().to(device)
|
||||
else:
|
||||
wav16k = wav16k.to(device)
|
||||
zero_wav_torch = zero_wav_torch.to(device)
|
||||
wav16k = torch.cat([wav16k, zero_wav_torch])
|
||||
ssl_content = ssl_model.model(wav16k.unsqueeze(0))["last_hidden_state"].transpose(1, 2) # .float()
|
||||
codes = vq_model.extract_latent(ssl_content)
|
||||
prompt_semantic = codes[0, 0]
|
||||
t1 = ttime()
|
||||
prompt_language = dict_language[prompt_language.lower()]
|
||||
text_language = dict_language[text_language.lower()]
|
||||
phones1, bert1, norm_text1 = get_phones_and_bert(prompt_text, prompt_language)
|
||||
texts = text.split("\n")
|
||||
audio_bytes = BytesIO()
|
||||
|
||||
for text in texts:
|
||||
# 简单防止纯符号引发参考音频泄露
|
||||
if only_punc(text):
|
||||
continue
|
||||
|
||||
audio_opt = []
|
||||
phones2, bert2, norm_text2 = get_phones_and_bert(text, text_language)
|
||||
bert = torch.cat([bert1, bert2], 1)
|
||||
|
||||
all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
|
||||
bert = bert.to(device).unsqueeze(0)
|
||||
all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
|
||||
prompt = prompt_semantic.unsqueeze(0).to(device)
|
||||
t2 = ttime()
|
||||
with torch.no_grad():
|
||||
# pred_semantic = t2s_model.model.infer(
|
||||
pred_semantic, idx = t2s_model.model.infer_panel(
|
||||
all_phoneme_ids,
|
||||
all_phoneme_len,
|
||||
prompt,
|
||||
bert,
|
||||
# prompt_phone_len=ph_offset,
|
||||
top_k=config['inference']['top_k'],
|
||||
early_stop_num=hz * max_sec)
|
||||
t3 = ttime()
|
||||
# print(pred_semantic.shape,idx)
|
||||
pred_semantic = pred_semantic[:, -idx:].unsqueeze(0) # .unsqueeze(0)#mq要多unsqueeze一次
|
||||
refer = get_spepc(hps, ref_wav_path) # .to(device)
|
||||
if (is_half == True):
|
||||
refer = refer.half().to(device)
|
||||
else:
|
||||
refer = refer.to(device)
|
||||
# audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
|
||||
audio = \
|
||||
vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0),
|
||||
refer).detach().cpu().numpy()[
|
||||
0, 0] ###试试重建不带上prompt部分
|
||||
audio_opt.append(audio)
|
||||
audio_opt.append(zero_wav)
|
||||
t4 = ttime()
|
||||
audio_bytes = pack_audio(audio_bytes,(np.concatenate(audio_opt, 0) * 32768).astype(np.int16),hps.data.sampling_rate)
|
||||
# logger.info("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
|
||||
if stream_mode == "normal":
|
||||
audio_bytes, audio_chunk = read_clean_buffer(audio_bytes)
|
||||
yield audio_chunk
|
||||
|
||||
if not stream_mode == "normal":
|
||||
if media_type == "wav":
|
||||
audio_bytes = pack_wav(audio_bytes,hps.data.sampling_rate)
|
||||
yield audio_bytes.getvalue()
|
||||
|
||||
|
||||
|
||||
def handle_control(command):
|
||||
if command == "restart":
|
||||
os.execl(g_config.python_exec, g_config.python_exec, *sys.argv)
|
||||
elif command == "exit":
|
||||
os.kill(os.getpid(), signal.SIGTERM)
|
||||
exit(0)
|
||||
|
||||
|
||||
def handle_change(path, text, language):
|
||||
if is_empty(path, text, language):
|
||||
return JSONResponse({"code": 400, "message": '缺少任意一项以下参数: "path", "text", "language"'}, status_code=400)
|
||||
|
||||
if path != "" or path is not None:
|
||||
default_refer.path = path
|
||||
if text != "" or text is not None:
|
||||
default_refer.text = text
|
||||
if language != "" or language is not None:
|
||||
default_refer.language = language
|
||||
|
||||
logger.info(f"当前默认参考音频路径: {default_refer.path}")
|
||||
logger.info(f"当前默认参考音频文本: {default_refer.text}")
|
||||
logger.info(f"当前默认参考音频语种: {default_refer.language}")
|
||||
logger.info(f"is_ready: {default_refer.is_ready()}")
|
||||
|
||||
|
||||
return JSONResponse({"code": 0, "message": "Success"}, status_code=200)
|
||||
|
||||
|
||||
def handle(refer_wav_path, prompt_text, prompt_language, text, text_language, cut_punc):
|
||||
if (
|
||||
refer_wav_path == "" or refer_wav_path is None
|
||||
or prompt_text == "" or prompt_text is None
|
||||
or prompt_language == "" or prompt_language is None
|
||||
):
|
||||
refer_wav_path, prompt_text, prompt_language = (
|
||||
default_refer.path,
|
||||
default_refer.text,
|
||||
default_refer.language,
|
||||
)
|
||||
if not default_refer.is_ready():
|
||||
return JSONResponse({"code": 400, "message": "未指定参考音频且接口无预设"}, status_code=400)
|
||||
|
||||
if cut_punc == None:
|
||||
text = cut_text(text,default_cut_punc)
|
||||
else:
|
||||
text = cut_text(text,cut_punc)
|
||||
|
||||
return StreamingResponse(get_tts_wav(refer_wav_path, prompt_text, prompt_language, text, text_language), media_type="audio/"+media_type)
|
||||
|
||||
|
||||
|
||||
|
||||
# --------------------------------
|
||||
# 初始化部分
|
||||
# --------------------------------
|
||||
dict_language = {
|
||||
"中文": "all_zh",
|
||||
"英文": "en",
|
||||
"日文": "all_ja",
|
||||
"中英混合": "zh",
|
||||
"日英混合": "ja",
|
||||
"多语种混合": "auto", #多语种启动切分识别语种
|
||||
"all_zh": "all_zh",
|
||||
"en": "en",
|
||||
"all_ja": "all_ja",
|
||||
"zh": "zh",
|
||||
"ja": "ja",
|
||||
"auto": "auto",
|
||||
}
|
||||
|
||||
# logger
|
||||
logging.config.dictConfig(uvicorn.config.LOGGING_CONFIG)
|
||||
logger = logging.getLogger('uvicorn')
|
||||
|
||||
# 获取配置
|
||||
g_config = global_config.Config()
|
||||
|
||||
# 获取参数
|
||||
parser = argparse.ArgumentParser(description="GPT-SoVITS api")
|
||||
|
||||
parser.add_argument("-s", "--sovits_path", type=str, default=g_config.sovits_path, help="SoVITS模型路径")
|
||||
parser.add_argument("-g", "--gpt_path", type=str, default=g_config.gpt_path, help="GPT模型路径")
|
||||
parser.add_argument("-dr", "--default_refer_path", type=str, default="", help="默认参考音频路径")
|
||||
parser.add_argument("-dt", "--default_refer_text", type=str, default="", help="默认参考音频文本")
|
||||
parser.add_argument("-dl", "--default_refer_language", type=str, default="", help="默认参考音频语种")
|
||||
parser.add_argument("-d", "--device", type=str, default=g_config.infer_device, help="cuda / cpu")
|
||||
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=g_config.api_port, help="default: 9880")
|
||||
parser.add_argument("-fp", "--full_precision", action="store_true", default=False, help="覆盖config.is_half为False, 使用全精度")
|
||||
parser.add_argument("-hp", "--half_precision", action="store_true", default=False, help="覆盖config.is_half为True, 使用半精度")
|
||||
# bool值的用法为 `python ./api.py -fp ...`
|
||||
# 此时 full_precision==True, half_precision==False
|
||||
parser.add_argument("-sm", "--stream_mode", type=str, default="close", help="流式返回模式, close / normal / keepalive")
|
||||
parser.add_argument("-mt", "--media_type", type=str, default="wav", help="音频编码格式, wav / ogg / aac")
|
||||
parser.add_argument("-cp", "--cut_punc", type=str, default="", help="文本切分符号设定, 符号范围,.;?!、，。？！；：…")
|
||||
# 切割常用分句符为 `python ./api.py -cp ".?!。？！"`
|
||||
parser.add_argument("-hb", "--hubert_path", type=str, default=g_config.cnhubert_path, help="覆盖config.cnhubert_path")
|
||||
parser.add_argument("-b", "--bert_path", type=str, default=g_config.bert_path, help="覆盖config.bert_path")
|
||||
|
||||
args = parser.parse_args()
|
||||
sovits_path = args.sovits_path
|
||||
gpt_path = args.gpt_path
|
||||
device = args.device
|
||||
port = args.port
|
||||
host = args.bind_addr
|
||||
cnhubert_base_path = args.hubert_path
|
||||
bert_path = args.bert_path
|
||||
default_cut_punc = args.cut_punc
|
||||
|
||||
# 应用参数配置
|
||||
default_refer = DefaultRefer(args.default_refer_path, args.default_refer_text, args.default_refer_language)
|
||||
|
||||
# 模型路径检查
|
||||
if sovits_path == "":
|
||||
sovits_path = g_config.pretrained_sovits_path
|
||||
logger.warn(f"未指定SoVITS模型路径, fallback后当前值: {sovits_path}")
|
||||
if gpt_path == "":
|
||||
gpt_path = g_config.pretrained_gpt_path
|
||||
logger.warn(f"未指定GPT模型路径, fallback后当前值: {gpt_path}")
|
||||
|
||||
# 指定默认参考音频, 调用方 未提供/未给全 参考音频参数时使用
|
||||
if default_refer.path == "" or default_refer.text == "" or default_refer.language == "":
|
||||
default_refer.path, default_refer.text, default_refer.language = "", "", ""
|
||||
logger.info("未指定默认参考音频")
|
||||
else:
|
||||
logger.info(f"默认参考音频路径: {default_refer.path}")
|
||||
logger.info(f"默认参考音频文本: {default_refer.text}")
|
||||
logger.info(f"默认参考音频语种: {default_refer.language}")
|
||||
|
||||
# 获取半精度
|
||||
is_half = g_config.is_half
|
||||
if args.full_precision:
|
||||
is_half = False
|
||||
if args.half_precision:
|
||||
is_half = True
|
||||
if args.full_precision and args.half_precision:
|
||||
is_half = g_config.is_half # 炒饭fallback
|
||||
logger.info(f"半精: {is_half}")
|
||||
|
||||
# 流式返回模式
|
||||
if args.stream_mode.lower() in ["normal","n"]:
|
||||
stream_mode = "normal"
|
||||
logger.info("流式返回已开启")
|
||||
else:
|
||||
stream_mode = "close"
|
||||
|
||||
# 音频编码格式
|
||||
if args.media_type.lower() in ["aac","ogg"]:
|
||||
media_type = args.media_type.lower()
|
||||
elif stream_mode == "close":
|
||||
media_type = "wav"
|
||||
else:
|
||||
media_type = "ogg"
|
||||
logger.info(f"编码格式: {media_type}")
|
||||
|
||||
# 初始化模型
|
||||
cnhubert.cnhubert_base_path = cnhubert_base_path
|
||||
tokenizer = AutoTokenizer.from_pretrained(bert_path)
|
||||
bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
|
||||
ssl_model = cnhubert.get_model()
|
||||
if is_half:
|
||||
bert_model = bert_model.half().to(device)
|
||||
ssl_model = ssl_model.half().to(device)
|
||||
else:
|
||||
bert_model = bert_model.to(device)
|
||||
ssl_model = ssl_model.to(device)
|
||||
change_sovits_weights(sovits_path)
|
||||
change_gpt_weights(gpt_path)
|
||||
|
||||
|
||||
|
||||
|
||||
# --------------------------------
|
||||
# 接口部分
|
||||
# --------------------------------
|
||||
app = FastAPI()
|
||||
|
||||
@app.post("/set_model")
|
||||
async def set_model(request: Request):
|
||||
json_post_raw = await request.json()
|
||||
global gpt_path
|
||||
gpt_path=json_post_raw.get("gpt_model_path")
|
||||
global sovits_path
|
||||
sovits_path=json_post_raw.get("sovits_model_path")
|
||||
logger.info("gptpath"+gpt_path+";vitspath"+sovits_path)
|
||||
change_sovits_weights(sovits_path)
|
||||
change_gpt_weights(gpt_path)
|
||||
return "ok"
|
||||
|
||||
|
||||
@app.post("/control")
|
||||
async def control(request: Request):
|
||||
json_post_raw = await request.json()
|
||||
return handle_control(json_post_raw.get("command"))
|
||||
|
||||
|
||||
@app.get("/control")
|
||||
async def control(command: str = None):
|
||||
return handle_control(command)
|
||||
|
||||
|
||||
@app.post("/change_refer")
|
||||
async def change_refer(request: Request):
|
||||
json_post_raw = await request.json()
|
||||
return handle_change(
|
||||
json_post_raw.get("refer_wav_path"),
|
||||
json_post_raw.get("prompt_text"),
|
||||
json_post_raw.get("prompt_language")
|
||||
)
|
||||
|
||||
|
||||
@app.get("/change_refer")
|
||||
async def change_refer(
|
||||
refer_wav_path: str = None,
|
||||
prompt_text: str = None,
|
||||
prompt_language: str = None
|
||||
):
|
||||
return handle_change(refer_wav_path, prompt_text, prompt_language)
|
||||
|
||||
|
||||
@app.post("/")
|
||||
async def tts_endpoint(request: Request):
|
||||
json_post_raw = await request.json()
|
||||
return handle(
|
||||
json_post_raw.get("refer_wav_path"),
|
||||
json_post_raw.get("prompt_text"),
|
||||
json_post_raw.get("prompt_language"),
|
||||
json_post_raw.get("text"),
|
||||
json_post_raw.get("text_language"),
|
||||
json_post_raw.get("cut_punc"),
|
||||
)
|
||||
|
||||
|
||||
@app.get("/")
|
||||
async def tts_endpoint(
|
||||
refer_wav_path: str = None,
|
||||
prompt_text: str = None,
|
||||
prompt_language: str = None,
|
||||
text: str = None,
|
||||
text_language: str = None,
|
||||
cut_punc: str = None,
|
||||
):
|
||||
return handle(refer_wav_path, prompt_text, prompt_language, text, text_language, cut_punc)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
uvicorn.run(app, host=host, port=port, workers=1)
|
||||
65
config.py
65
config.py
@ -1,4 +1,4 @@
|
||||
import sys,os
|
||||
import sys, os
|
||||
|
||||
import torch
|
||||
|
||||
@ -7,8 +7,8 @@ sovits_path = ""
|
||||
gpt_path = ""
|
||||
is_half_str = os.environ.get("is_half", "True")
|
||||
is_half = True if is_half_str.lower() == 'true' else False
|
||||
is_share_str = os.environ.get("is_share","False")
|
||||
is_share= True if is_share_str.lower() == 'true' else False
|
||||
is_share_str = os.environ.get("is_share", "False")
|
||||
is_share = True if is_share_str.lower() == 'true' else False
|
||||
|
||||
cnhubert_path = "GPT_SoVITS/pretrained_models/chinese-hubert-base"
|
||||
bert_path = "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
|
||||
@ -18,9 +18,9 @@ pretrained_gpt_path = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=
|
||||
exp_root = "logs"
|
||||
python_exec = sys.executable or "python"
|
||||
if torch.cuda.is_available():
|
||||
infer_device = "cuda"
|
||||
infer_device = "cuda"
|
||||
else:
|
||||
infer_device = "cpu"
|
||||
infer_device = "cpu"
|
||||
|
||||
webui_port_main = 9874
|
||||
webui_port_uvr5 = 9873
|
||||
@ -30,37 +30,38 @@ webui_port_subfix = 9871
|
||||
api_port = 9880
|
||||
|
||||
if infer_device == "cuda":
|
||||
gpu_name = torch.cuda.get_device_name(0)
|
||||
if (
|
||||
("16" in gpu_name and "V100" not in gpu_name.upper())
|
||||
or "P40" in gpu_name.upper()
|
||||
or "P10" in gpu_name.upper()
|
||||
or "1060" in gpu_name
|
||||
or "1070" in gpu_name
|
||||
or "1080" in gpu_name
|
||||
):
|
||||
is_half=False
|
||||
gpu_name = torch.cuda.get_device_name(0)
|
||||
if (
|
||||
("16" in gpu_name and "V100" not in gpu_name.upper())
|
||||
or "P40" in gpu_name.upper()
|
||||
or "P10" in gpu_name.upper()
|
||||
or "1060" in gpu_name
|
||||
or "1070" in gpu_name
|
||||
or "1080" in gpu_name
|
||||
):
|
||||
is_half = False
|
||||
|
||||
if (infer_device == "cpu"): is_half = False
|
||||
|
||||
if(infer_device=="cpu"):is_half=False
|
||||
|
||||
class Config:
|
||||
def __init__(self):
|
||||
self.sovits_path = sovits_path
|
||||
self.gpt_path = gpt_path
|
||||
self.is_half = is_half
|
||||
def __init__(self):
|
||||
self.sovits_path = sovits_path
|
||||
self.gpt_path = gpt_path
|
||||
self.is_half = is_half
|
||||
|
||||
self.cnhubert_path = cnhubert_path
|
||||
self.bert_path = bert_path
|
||||
self.pretrained_sovits_path = pretrained_sovits_path
|
||||
self.pretrained_gpt_path = pretrained_gpt_path
|
||||
self.cnhubert_path = cnhubert_path
|
||||
self.bert_path = bert_path
|
||||
self.pretrained_sovits_path = pretrained_sovits_path
|
||||
self.pretrained_gpt_path = pretrained_gpt_path
|
||||
|
||||
self.exp_root = exp_root
|
||||
self.python_exec = python_exec
|
||||
self.infer_device = infer_device
|
||||
self.exp_root = exp_root
|
||||
self.python_exec = python_exec
|
||||
self.infer_device = infer_device
|
||||
|
||||
self.webui_port_main = webui_port_main
|
||||
self.webui_port_uvr5 = webui_port_uvr5
|
||||
self.webui_port_infer_tts = webui_port_infer_tts
|
||||
self.webui_port_subfix = webui_port_subfix
|
||||
self.webui_port_main = webui_port_main
|
||||
self.webui_port_uvr5 = webui_port_uvr5
|
||||
self.webui_port_infer_tts = webui_port_infer_tts
|
||||
self.webui_port_subfix = webui_port_subfix
|
||||
|
||||
self.api_port = api_port
|
||||
self.api_port = api_port
|
||||
|
||||
68
docs/cn/inference.md
Normal file
68
docs/cn/inference.md
Normal file
@ -0,0 +1,68 @@
|
||||
# 推理
|
||||
|
||||
## Windows
|
||||
|
||||
### 使用cpu推理
|
||||
本文档介绍如何使用cpu进行推理,使用cpu的推理速度有点慢,但不是很慢
|
||||
|
||||
#### 安装依赖
|
||||
```
|
||||
# 拉取项目代码
|
||||
git clone --depth=1 https://github.com/RVC-Boss/GPT-SoVITS
|
||||
cd GPT-SoVITS
|
||||
|
||||
# 安装好 Miniconda 之后，先创建一个虚拟环境：
|
||||
conda create -n GPTSoVits python=3.9
|
||||
conda activate GPTSoVits
|
||||
|
||||
# 安装依赖：
|
||||
pip install -r requirements.txt
|
||||
|
||||
# (可选)如果网络环境不好，可以考虑换源(比如清华源)：
|
||||
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -r requirements.txt
|
||||
```
|
||||
|
||||
#### 添加预训练模型
|
||||
```
|
||||
# 安装 huggingface-cli 用于和 huggingface hub 交互
|
||||
pip install huggingface_hub
|
||||
# 登录 huggingface-cli
|
||||
huggingface-cli login
|
||||
|
||||
# 下载模型, 由于模型文件较大，可能需要一段时间
|
||||
# --local-dir-use-symlinks False 用于解决 macOS alias 文件的问题
|
||||
# 会下载到 GPT_SoVITS/pretrained_models 文件夹下
|
||||
huggingface-cli download --resume-download lj1995/GPT-SoVITS --local-dir GPT_SoVITS/pretrained_models --local-dir-use-symlinks False
|
||||
```
|
||||
|
||||
#### 添加微调模型(可选)
|
||||
笔者是将微调添加到了GPT-SoVITS/trained 内容如下,正常情况下包含 openai_alloy-e15.ckpt 和openai_alloy_e8_s112.pth 即可
|
||||
```
|
||||
├── .gitignore
|
||||
├── openai_alloy
|
||||
│ ├── infer_config.json
|
||||
│ ├── openai_alloy-e15.ckpt
|
||||
│ ├── openai_alloy_e8_s112.pth
|
||||
│ ├── output-2.txt
|
||||
│ ├── output-2.wav
|
||||
```
|
||||
|
||||
#### 启动推理webtui
|
||||
```
|
||||
python.exe GPT_SoVITS/inference_webui.py
|
||||
```
|
||||
配置如下
|
||||
|
||||
|
||||
### 使用gpu推理
|
||||
请根据你的操作系统选择合适的cuda版本
|
||||
```
|
||||
pip uninstall torch torchaudio torchvision -y
|
||||
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
|
||||
```
|
||||
检查 torch和cuda是否可用
|
||||
```
|
||||
>>> import torch
|
||||
>>> torch.cuda.is_available()
|
||||
True
|
||||
```
|
||||
BIN
docs/cn/inference_cpu_files/1.jpg
Normal file
BIN
docs/cn/inference_cpu_files/1.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 134 KiB |
18
docs/cn/inference_cpu_files/memory.md
Normal file
18
docs/cn/inference_cpu_files/memory.md
Normal file
@ -0,0 +1,18 @@
|
||||
加载模型后需要占用的内存容量如下,单位是MB
|
||||
```
|
||||
{
|
||||
"memory_usage": {
|
||||
"rss": 1029.30078125,
|
||||
"vms": 4505.546875,
|
||||
"percent": 6.5181980027997115
|
||||
}
|
||||
}
|
||||
|
||||
{
|
||||
"gpu_memory_usage": {
|
||||
"used_memory": 2640,
|
||||
"total_memory": 4096,
|
||||
"percent": 64.453125
|
||||
}
|
||||
}
|
||||
```
|
||||
49
main.py
Normal file
49
main.py
Normal file
@ -0,0 +1,49 @@
|
||||
import argparse
|
||||
|
||||
import uvicorn
|
||||
import config as global_config
|
||||
|
||||
from app import app
|
||||
from cmd_args import CmdArgs
|
||||
|
||||
g_config = global_config.Config()
|
||||
# 获取参数
|
||||
parser = argparse.ArgumentParser(description="GPT-SoVITS api")
|
||||
|
||||
parser.add_argument("-s", "--sovits_path", type=str, default=g_config.sovits_path, help="SoVITS模型路径")
|
||||
parser.add_argument("-g", "--gpt_path", type=str, default=g_config.gpt_path, help="GPT模型路径")
|
||||
parser.add_argument("-dr", "--default_refer_path", type=str, default="", help="默认参考音频路径")
|
||||
parser.add_argument("-dt", "--default_refer_text", type=str, default="", help="默认参考音频文本")
|
||||
parser.add_argument("-dl", "--default_refer_language", type=str, default="", help="默认参考音频语种")
|
||||
parser.add_argument("-d", "--device", type=str, default=g_config.infer_device, help="cuda / cpu")
|
||||
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=g_config.api_port, help="default: 9880")
|
||||
parser.add_argument("-fp", "--full_precision", action="store_true", default=False,
|
||||
help="覆盖config.is_half为False, 使用全精度")
|
||||
parser.add_argument("-hp", "--half_precision", action="store_true", default=False,
|
||||
help="覆盖config.is_half为True, 使用半精度")
|
||||
# bool值的用法为 `python ./tts_service.py -fp ...`
|
||||
# 此时 full_precision==True, half_precision==False
|
||||
parser.add_argument("-sm", "--stream_mode", type=str, default="close", help="流式返回模式, close / normal / keepalive")
|
||||
parser.add_argument("-mt", "--media_type", type=str, default="wav", help="音频编码格式, wav / ogg / aac")
|
||||
parser.add_argument("-cp", "--cut_punc", type=str, default="", help="文本切分符号设定, 符号范围,.;?!、，。？！；：…")
|
||||
# 切割常用分句符为 `python ./tts_service.py -cp ".?!。？！"`
|
||||
parser.add_argument("-hb", "--hubert_path", type=str, default=g_config.cnhubert_path, help="覆盖config.cnhubert_path")
|
||||
parser.add_argument("-b", "--bert_path", type=str, default=g_config.bert_path, help="覆盖config.bert_path")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# 保存参数到单例对象中
|
||||
cmd_args = CmdArgs()
|
||||
cmd_args.set_args(args)
|
||||
import server
|
||||
server.register_Hanlder(app)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
port = args.port
|
||||
host = args.bind_addr
|
||||
uvicorn.run(app, host=host, port=port, workers=1)
|
||||
|
||||
|
||||
@ -25,4 +25,4 @@ jieba_fast
|
||||
jieba
|
||||
LangSegment>=0.2.0
|
||||
Faster_Whisper
|
||||
wordsegment
|
||||
wordsegmentpsutil
|
||||
|
||||
115
server/api.md
Normal file
115
server/api.md
Normal file
@ -0,0 +1,115 @@
|
||||
# api
|
||||
|
||||
` python api.py -dr "123.wav" -dt "一二三。" -dl "zh" `
|
||||
|
||||
## 执行参数:
|
||||
|
||||
`-s` - `SoVITS模型路径, 可在 config.py 中指定`
|
||||
`-g` - `GPT模型路径, 可在 config.py 中指定`
|
||||
|
||||
调用请求缺少参考音频时使用
|
||||
`-dr` - `默认参考音频路径`
|
||||
`-dt` - `默认参考音频文本`
|
||||
`-dl` - `默认参考音频语种, "中文","英文","日文","zh","en","ja"`
|
||||
|
||||
`-d` - `推理设备, "cuda","cpu"`
|
||||
`-a` - `绑定地址, 默认"127.0.0.1"`
|
||||
`-p` - `绑定端口, 默认9880, 可在 config.py 中指定`
|
||||
`-fp` - `覆盖 config.py 使用全精度`
|
||||
`-hp` - `覆盖 config.py 使用半精度`
|
||||
`-sm` - `流式返回模式, 默认不启用, "close","c", "normal","n", "keepalive","k"`
|
||||
·-mt` - `返回的音频编码格式, 流式默认ogg, 非流式默认wav, "wav", "ogg", "aac"`
|
||||
·-cp` - `文本切分符号设定, 默认为空, 以",.，。"字符串的方式传入`
|
||||
|
||||
`-hb` - `cnhubert路径`
|
||||
`-b` - `bert路径`
|
||||
|
||||
## 调用:
|
||||
|
||||
### 推理
|
||||
|
||||
endpoint: `/`
|
||||
|
||||
使用执行参数指定的参考音频:
|
||||
- GET:
|
||||
`http://127.0.0.1:9880?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`
|
||||
|
||||
- POST:
|
||||
```json
|
||||
{
|
||||
"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"
|
||||
}
|
||||
```
|
||||
|
||||
使用执行参数指定的参考音频并设定分割符号:
|
||||
- GET:
|
||||
`http://127.0.0.1:9880?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&cut_punc=，。`
|
||||
- POST:
|
||||
```json
|
||||
{
|
||||
"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",
|
||||
"cut_punc": "，。"
|
||||
}
|
||||
```
|
||||
|
||||
手动指定当次推理所使用的参考音频:
|
||||
- GET:
|
||||
`http://127.0.0.1:9880?refer_wav_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`
|
||||
- POST:
|
||||
```json
|
||||
{
|
||||
"refer_wav_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"
|
||||
}
|
||||
```
|
||||
|
||||
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
|
||||
|
||||
|
||||
### 更换默认参考音频
|
||||
|
||||
endpoint: `/change_refer`
|
||||
|
||||
key与推理端一样
|
||||
|
||||
- GET:
|
||||
`http://127.0.0.1:9880/change_refer?refer_wav_path=123.wav&prompt_text=一二三。&prompt_language=zh`
|
||||
- POST:
|
||||
```json
|
||||
{
|
||||
"refer_wav_path": "123.wav",
|
||||
"prompt_text": "一二三。",
|
||||
"prompt_language": "zh"
|
||||
}
|
||||
```
|
||||
|
||||
RESP:
|
||||
成功: json, http code 200
|
||||
失败: json, 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
|
||||
6
server/app.py
Normal file
6
server/app.py
Normal file
@ -0,0 +1,6 @@
|
||||
from fastapi import FastAPI
|
||||
|
||||
from pyutils.logs import llog
|
||||
|
||||
llog.info("start server")
|
||||
app = FastAPI()
|
||||
13
server/cmd_args.py
Normal file
13
server/cmd_args.py
Normal file
@ -0,0 +1,13 @@
|
||||
class CmdArgs:
|
||||
_instance = None
|
||||
|
||||
def __new__(cls, *args, **kwargs):
|
||||
if cls._instance is None:
|
||||
cls._instance = super(CmdArgs, cls).__new__(cls)
|
||||
return cls._instance
|
||||
|
||||
def set_args(self, args):
|
||||
self.args = args
|
||||
|
||||
def get_args(self):
|
||||
return self.args
|
||||
84
server/handlers.py
Normal file
84
server/handlers.py
Normal file
@ -0,0 +1,84 @@
|
||||
from fastapi import APIRouter, Request
|
||||
|
||||
from memory_service import get_memory_usage, get_gpu_memory_usage
|
||||
from pyutils.logs import llog
|
||||
from tts_service import change_sovits_weights, change_gpt_weights, handle_control, handle_change, handle
|
||||
|
||||
index_router = APIRouter()
|
||||
|
||||
@index_router.post("/set_model")
|
||||
async def set_model(request: Request):
|
||||
json_post_raw = await request.json()
|
||||
global gpt_path
|
||||
gpt_path = json_post_raw.get("gpt_model_path")
|
||||
global sovits_path
|
||||
sovits_path = json_post_raw.get("sovits_model_path")
|
||||
llog.info("gptpath" + gpt_path + ";vitspath" + sovits_path)
|
||||
change_sovits_weights(sovits_path)
|
||||
change_gpt_weights(gpt_path)
|
||||
return "ok"
|
||||
|
||||
|
||||
@index_router.post("/control")
|
||||
async def control(request: Request):
|
||||
json_post_raw = await request.json()
|
||||
return handle_control(json_post_raw.get("command"))
|
||||
|
||||
|
||||
@index_router.get("/control")
|
||||
async def control(command: str = None):
|
||||
return handle_control(command)
|
||||
|
||||
|
||||
@index_router.post("/change_refer")
|
||||
async def change_refer(request: Request):
|
||||
json_post_raw = await request.json()
|
||||
return handle_change(
|
||||
json_post_raw.get("refer_wav_path"),
|
||||
json_post_raw.get("prompt_text"),
|
||||
json_post_raw.get("prompt_language")
|
||||
)
|
||||
|
||||
|
||||
@index_router.get("/change_refer")
|
||||
async def change_refer(
|
||||
refer_wav_path: str = None,
|
||||
prompt_text: str = None,
|
||||
prompt_language: str = None
|
||||
):
|
||||
return handle_change(refer_wav_path, prompt_text, prompt_language)
|
||||
|
||||
|
||||
@index_router.post("/")
|
||||
async def tts_endpoint(request: Request):
|
||||
json_post_raw = await request.json()
|
||||
return handle(
|
||||
json_post_raw.get("refer_wav_path"),
|
||||
json_post_raw.get("prompt_text"),
|
||||
json_post_raw.get("prompt_language"),
|
||||
json_post_raw.get("text"),
|
||||
json_post_raw.get("text_language"),
|
||||
json_post_raw.get("cut_punc"),
|
||||
)
|
||||
|
||||
|
||||
@index_router.get("/")
|
||||
async def tts_endpoint(
|
||||
refer_wav_path: str = None,
|
||||
prompt_text: str = None,
|
||||
prompt_language: str = None,
|
||||
text: str = None,
|
||||
text_language: str = None,
|
||||
cut_punc: str = None,
|
||||
):
|
||||
return handle(refer_wav_path, prompt_text, prompt_language, text, text_language, cut_punc)
|
||||
|
||||
@index_router.get("/memory-usage")
|
||||
def read_memory_usage():
|
||||
memory_usage = get_memory_usage()
|
||||
return {"memory_usage": memory_usage}
|
||||
|
||||
@index_router.get("/gpu-memory-usage")
|
||||
def read_gpu_memory_usage():
|
||||
gpu_memory_usage = get_gpu_memory_usage()
|
||||
return {"gpu_memory_usage": gpu_memory_usage}
|
||||
37
server/memory_service.py
Normal file
37
server/memory_service.py
Normal file
@ -0,0 +1,37 @@
|
||||
import psutil
|
||||
import subprocess
|
||||
|
||||
|
||||
def get_memory_usage():
|
||||
process = psutil.Process()
|
||||
mem_info = process.memory_info()
|
||||
memory_usage = {
|
||||
"rss": mem_info.rss / (1024 ** 2), # Resident Set Size
|
||||
"vms": mem_info.vms / (1024 ** 2), # Virtual Memory Size
|
||||
"percent": process.memory_percent() # Percentage of memory usage
|
||||
}
|
||||
return memory_usage
|
||||
|
||||
|
||||
def get_gpu_memory_usage():
|
||||
try:
|
||||
result = subprocess.run(
|
||||
["nvidia-smi", "--query-gpu=memory.used,memory.total", "--format=csv,nounits,noheader"],
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.PIPE,
|
||||
check=True,
|
||||
text=True
|
||||
)
|
||||
output = result.stdout.strip()
|
||||
if output:
|
||||
used_memory, total_memory = map(int, output.split(', '))
|
||||
gpu_memory_usage = {
|
||||
"used_memory": used_memory, # in MiB
|
||||
"total_memory": total_memory, # in MiB
|
||||
"percent": (used_memory / total_memory) * 100 # Percentage of GPU memory usage
|
||||
}
|
||||
return gpu_memory_usage
|
||||
else:
|
||||
return {"error": "No GPU found or unable to query GPU memory usage."}
|
||||
except Exception as e:
|
||||
return {"error": str(e)}
|
||||
3
server/server.py
Normal file
3
server/server.py
Normal file
@ -0,0 +1,3 @@
|
||||
def register_Hanlder(app):
|
||||
from handlers import index_router
|
||||
app.include_router(index_router)
|
||||
510
server/tts_service.py
Normal file
510
server/tts_service.py
Normal file
@ -0,0 +1,510 @@
|
||||
import os
|
||||
import re
|
||||
import sys
|
||||
|
||||
from cmd_args import CmdArgs
|
||||
from pyutils.logs import llog
|
||||
|
||||
current_project_dir = os.getcwd()
|
||||
sys.path.append(current_project_dir)
|
||||
sys.path.append("%s/GPT_SoVITS" % (current_project_dir))
|
||||
|
||||
import signal
|
||||
import LangSegment
|
||||
from time import time as ttime
|
||||
import torch
|
||||
import librosa
|
||||
import soundfile as sf
|
||||
from fastapi.responses import StreamingResponse, JSONResponse
|
||||
from transformers import AutoModelForMaskedLM, AutoTokenizer
|
||||
import numpy as np
|
||||
from feature_extractor import cnhubert
|
||||
from io import BytesIO
|
||||
from module.models import SynthesizerTrn
|
||||
from AR.models.t2s_lightning_module import Text2SemanticLightningModule
|
||||
from text import cleaned_text_to_sequence
|
||||
from text.cleaner import clean_text
|
||||
from module.mel_processing import spectrogram_torch
|
||||
from pyutils.np_utils import load_audio
|
||||
import subprocess
|
||||
import config as global_config
|
||||
|
||||
g_config = global_config.Config()
|
||||
|
||||
|
||||
class DefaultRefer:
|
||||
def __init__(self, path, text, language):
|
||||
self.path = args.default_refer_path
|
||||
self.text = args.default_refer_text
|
||||
self.language = args.default_refer_language
|
||||
|
||||
def is_ready(self) -> bool:
|
||||
return is_not_empty(self.path, self.text, self.language)
|
||||
|
||||
|
||||
def is_empty(*items): # 任意一项不为空返回False
|
||||
for item in items:
|
||||
if item is not None and item != "":
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def is_not_empty(*items): # 任意一项为空返回False
|
||||
for item in items:
|
||||
if item is None or item == "":
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def change_sovits_weights(sovits_path):
|
||||
global vq_model, hps
|
||||
dict_s2 = torch.load(sovits_path, map_location="cpu")
|
||||
hps = dict_s2["config"]
|
||||
hps = DictToAttrRecursive(hps)
|
||||
hps.model.semantic_frame_rate = "25hz"
|
||||
model_params_dict = vars(hps.model)
|
||||
vq_model = SynthesizerTrn(
|
||||
hps.data.filter_length // 2 + 1,
|
||||
hps.train.segment_size // hps.data.hop_length,
|
||||
n_speakers=hps.data.n_speakers,
|
||||
**model_params_dict
|
||||
)
|
||||
if ("pretrained" not in sovits_path):
|
||||
del vq_model.enc_q
|
||||
if is_half == True:
|
||||
vq_model = vq_model.half().to(device)
|
||||
else:
|
||||
vq_model = vq_model.to(device)
|
||||
vq_model.eval()
|
||||
vq_model.load_state_dict(dict_s2["weight"], strict=False)
|
||||
|
||||
|
||||
def change_gpt_weights(gpt_path):
|
||||
global hz, max_sec, t2s_model, config
|
||||
hz = 50
|
||||
dict_s1 = torch.load(gpt_path, map_location="cpu")
|
||||
config = dict_s1["config"]
|
||||
max_sec = config["data"]["max_sec"]
|
||||
t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
|
||||
t2s_model.load_state_dict(dict_s1["weight"])
|
||||
if is_half == True:
|
||||
t2s_model = t2s_model.half()
|
||||
t2s_model = t2s_model.to(device)
|
||||
t2s_model.eval()
|
||||
total = sum([param.nelement() for param in t2s_model.parameters()])
|
||||
llog.info("Number of parameter: %.2fM" % (total / 1e6))
|
||||
|
||||
|
||||
def get_bert_feature(text, word2ph):
|
||||
with torch.no_grad():
|
||||
inputs = tokenizer(text, return_tensors="pt")
|
||||
for i in inputs:
|
||||
inputs[i] = inputs[i].to(device) #####输入是long不用管精度问题，精度随bert_model
|
||||
res = bert_model(**inputs, output_hidden_states=True)
|
||||
res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
|
||||
assert len(word2ph) == len(text)
|
||||
phone_level_feature = []
|
||||
for i in range(len(word2ph)):
|
||||
repeat_feature = res[i].repeat(word2ph[i], 1)
|
||||
phone_level_feature.append(repeat_feature)
|
||||
phone_level_feature = torch.cat(phone_level_feature, dim=0)
|
||||
# if(is_half==True):phone_level_feature=phone_level_feature.half()
|
||||
return phone_level_feature.T
|
||||
|
||||
|
||||
def clean_text_inf(text, language):
|
||||
phones, word2ph, norm_text = clean_text(text, language)
|
||||
phones = cleaned_text_to_sequence(phones)
|
||||
return phones, word2ph, norm_text
|
||||
|
||||
|
||||
def get_bert_inf(phones, word2ph, norm_text, language):
|
||||
language = language.replace("all_", "")
|
||||
if language == "zh":
|
||||
bert = get_bert_feature(norm_text, word2ph).to(device) # .to(dtype)
|
||||
else:
|
||||
bert = torch.zeros(
|
||||
(1024, len(phones)),
|
||||
dtype=torch.float16 if is_half == True else torch.float32,
|
||||
).to(device)
|
||||
|
||||
return bert
|
||||
|
||||
|
||||
def get_phones_and_bert(text, language):
|
||||
if language in {"en", "all_zh", "all_ja"}:
|
||||
language = language.replace("all_", "")
|
||||
if language == "en":
|
||||
LangSegment.setfilters(["en"])
|
||||
formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
|
||||
else:
|
||||
# 因无法区别中日文汉字,以用户输入为准
|
||||
formattext = text
|
||||
while " " in formattext:
|
||||
formattext = formattext.replace(" ", " ")
|
||||
phones, word2ph, norm_text = clean_text_inf(formattext, language)
|
||||
if language == "zh":
|
||||
bert = get_bert_feature(norm_text, word2ph).to(device)
|
||||
else:
|
||||
bert = torch.zeros(
|
||||
(1024, len(phones)),
|
||||
dtype=torch.float16 if is_half == True else torch.float32,
|
||||
).to(device)
|
||||
elif language in {"zh", "ja", "auto"}:
|
||||
textlist = []
|
||||
langlist = []
|
||||
LangSegment.setfilters(["zh", "ja", "en", "ko"])
|
||||
if language == "auto":
|
||||
for tmp in LangSegment.getTexts(text):
|
||||
if tmp["lang"] == "ko":
|
||||
langlist.append("zh")
|
||||
textlist.append(tmp["text"])
|
||||
else:
|
||||
langlist.append(tmp["lang"])
|
||||
textlist.append(tmp["text"])
|
||||
else:
|
||||
for tmp in LangSegment.getTexts(text):
|
||||
if tmp["lang"] == "en":
|
||||
langlist.append(tmp["lang"])
|
||||
else:
|
||||
# 因无法区别中日文汉字,以用户输入为准
|
||||
langlist.append(language)
|
||||
textlist.append(tmp["text"])
|
||||
# llog.info(textlist)
|
||||
# llog.info(langlist)
|
||||
phones_list = []
|
||||
bert_list = []
|
||||
norm_text_list = []
|
||||
for i in range(len(textlist)):
|
||||
lang = langlist[i]
|
||||
phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
|
||||
bert = get_bert_inf(phones, word2ph, norm_text, lang)
|
||||
phones_list.append(phones)
|
||||
norm_text_list.append(norm_text)
|
||||
bert_list.append(bert)
|
||||
bert = torch.cat(bert_list, dim=1)
|
||||
phones = sum(phones_list, [])
|
||||
norm_text = ''.join(norm_text_list)
|
||||
|
||||
return phones, bert.to(torch.float16 if is_half == True else torch.float32), norm_text
|
||||
|
||||
|
||||
class DictToAttrRecursive:
|
||||
def __init__(self, input_dict):
|
||||
for key, value in input_dict.items():
|
||||
if isinstance(value, dict):
|
||||
# 如果值是字典，递归调用构造函数
|
||||
setattr(self, key, DictToAttrRecursive(value))
|
||||
else:
|
||||
setattr(self, key, value)
|
||||
|
||||
|
||||
def get_spepc(hps, filename):
|
||||
audio = load_audio(filename, int(hps.data.sampling_rate))
|
||||
audio = torch.FloatTensor(audio)
|
||||
audio_norm = audio
|
||||
audio_norm = audio_norm.unsqueeze(0)
|
||||
spec = spectrogram_torch(audio_norm, hps.data.filter_length, hps.data.sampling_rate, hps.data.hop_length,
|
||||
hps.data.win_length, center=False)
|
||||
return spec
|
||||
|
||||
|
||||
def pack_audio(audio_bytes, data, rate):
|
||||
if media_type == "ogg":
|
||||
audio_bytes = pack_ogg(audio_bytes, data, rate)
|
||||
elif media_type == "aac":
|
||||
audio_bytes = pack_aac(audio_bytes, data, rate)
|
||||
else:
|
||||
# wav无法流式, 先暂存raw
|
||||
audio_bytes = pack_raw(audio_bytes, data, rate)
|
||||
|
||||
return audio_bytes
|
||||
|
||||
|
||||
def pack_ogg(audio_bytes, data, rate):
|
||||
with sf.SoundFile(audio_bytes, mode='w', samplerate=rate, channels=1, format='ogg') as audio_file:
|
||||
audio_file.write(data)
|
||||
|
||||
return audio_bytes
|
||||
|
||||
|
||||
def pack_raw(audio_bytes, data, rate):
|
||||
audio_bytes.write(data.tobytes())
|
||||
|
||||
return audio_bytes
|
||||
|
||||
|
||||
def pack_wav(audio_bytes, rate):
|
||||
data = np.frombuffer(audio_bytes.getvalue(), dtype=np.int16)
|
||||
wav_bytes = BytesIO()
|
||||
sf.write(wav_bytes, data, rate, format='wav')
|
||||
|
||||
return wav_bytes
|
||||
|
||||
|
||||
def pack_aac(audio_bytes, data, rate):
|
||||
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())
|
||||
audio_bytes.write(out)
|
||||
|
||||
return audio_bytes
|
||||
|
||||
|
||||
def read_clean_buffer(audio_bytes):
|
||||
audio_chunk = audio_bytes.getvalue()
|
||||
audio_bytes.truncate(0)
|
||||
audio_bytes.seek(0)
|
||||
|
||||
return audio_bytes, audio_chunk
|
||||
|
||||
|
||||
def cut_text(text, punc):
|
||||
punc_list = [p for p in punc if p in {",", ".", ";", "?", "!", "、", "，", "。", "？", "！", "；", "：", "…"}]
|
||||
if len(punc_list) > 0:
|
||||
punds = r"[" + "".join(punc_list) + r"]"
|
||||
text = text.strip("\n")
|
||||
items = re.split(f"({punds})", text)
|
||||
mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])]
|
||||
# 在句子不存在符号或句尾无符号的时候保证文本完整
|
||||
if len(items) % 2 == 1:
|
||||
mergeitems.append(items[-1])
|
||||
text = "\n".join(mergeitems)
|
||||
|
||||
while "\n\n" in text:
|
||||
text = text.replace("\n\n", "\n")
|
||||
|
||||
return text
|
||||
|
||||
|
||||
def only_punc(text):
|
||||
return not any(t.isalnum() or t.isalpha() for t in text)
|
||||
|
||||
|
||||
def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language):
|
||||
t0 = ttime()
|
||||
prompt_text = prompt_text.strip("\n")
|
||||
prompt_language, text = prompt_language, text.strip("\n")
|
||||
zero_wav = np.zeros(int(hps.data.sampling_rate * 0.3), dtype=np.float16 if is_half == True else np.float32)
|
||||
with torch.no_grad():
|
||||
wav16k, sr = librosa.load(ref_wav_path, sr=16000)
|
||||
wav16k = torch.from_numpy(wav16k)
|
||||
zero_wav_torch = torch.from_numpy(zero_wav)
|
||||
if (is_half == True):
|
||||
wav16k = wav16k.half().to(device)
|
||||
zero_wav_torch = zero_wav_torch.half().to(device)
|
||||
else:
|
||||
wav16k = wav16k.to(device)
|
||||
zero_wav_torch = zero_wav_torch.to(device)
|
||||
wav16k = torch.cat([wav16k, zero_wav_torch])
|
||||
ssl_content = ssl_model.model(wav16k.unsqueeze(0))["last_hidden_state"].transpose(1, 2) # .float()
|
||||
codes = vq_model.extract_latent(ssl_content)
|
||||
prompt_semantic = codes[0, 0]
|
||||
t1 = ttime()
|
||||
prompt_language = dict_language[prompt_language.lower()]
|
||||
text_language = dict_language[text_language.lower()]
|
||||
phones1, bert1, norm_text1 = get_phones_and_bert(prompt_text, prompt_language)
|
||||
texts = text.split("\n")
|
||||
audio_bytes = BytesIO()
|
||||
|
||||
for text in texts:
|
||||
# 简单防止纯符号引发参考音频泄露
|
||||
if only_punc(text):
|
||||
continue
|
||||
|
||||
audio_opt = []
|
||||
phones2, bert2, norm_text2 = get_phones_and_bert(text, text_language)
|
||||
bert = torch.cat([bert1, bert2], 1)
|
||||
|
||||
all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
|
||||
bert = bert.to(device).unsqueeze(0)
|
||||
all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
|
||||
prompt = prompt_semantic.unsqueeze(0).to(device)
|
||||
t2 = ttime()
|
||||
with torch.no_grad():
|
||||
# pred_semantic = t2s_model.model.infer(
|
||||
pred_semantic, idx = t2s_model.model.infer_panel(
|
||||
all_phoneme_ids,
|
||||
all_phoneme_len,
|
||||
prompt,
|
||||
bert,
|
||||
# prompt_phone_len=ph_offset,
|
||||
top_k=config['inference']['top_k'],
|
||||
early_stop_num=hz * max_sec)
|
||||
t3 = ttime()
|
||||
# print(pred_semantic.shape,idx)
|
||||
pred_semantic = pred_semantic[:, -idx:].unsqueeze(0) # .unsqueeze(0)#mq要多unsqueeze一次
|
||||
refer = get_spepc(hps, ref_wav_path) # .to(device)
|
||||
if (is_half == True):
|
||||
refer = refer.half().to(device)
|
||||
else:
|
||||
refer = refer.to(device)
|
||||
# audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
|
||||
audio = \
|
||||
vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0),
|
||||
refer).detach().cpu().numpy()[
|
||||
0, 0] ###试试重建不带上prompt部分
|
||||
audio_opt.append(audio)
|
||||
audio_opt.append(zero_wav)
|
||||
t4 = ttime()
|
||||
audio_bytes = pack_audio(audio_bytes, (np.concatenate(audio_opt, 0) * 32768).astype(np.int16),
|
||||
hps.data.sampling_rate)
|
||||
# llog.info("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
|
||||
if stream_mode == "normal":
|
||||
audio_bytes, audio_chunk = read_clean_buffer(audio_bytes)
|
||||
yield audio_chunk
|
||||
|
||||
if not stream_mode == "normal":
|
||||
if media_type == "wav":
|
||||
audio_bytes = pack_wav(audio_bytes, hps.data.sampling_rate)
|
||||
yield audio_bytes.getvalue()
|
||||
|
||||
|
||||
def handle_control(command):
|
||||
if command == "restart":
|
||||
os.execl(g_config.python_exec, g_config.python_exec, *sys.argv)
|
||||
elif command == "exit":
|
||||
os.kill(os.getpid(), signal.SIGTERM)
|
||||
exit(0)
|
||||
|
||||
|
||||
def handle_change(path, text, language):
|
||||
if is_empty(path, text, language):
|
||||
return JSONResponse({"code": 400, "message": '缺少任意一项以下参数: "path", "text", "language"'}, status_code=400)
|
||||
|
||||
if path != "" or path is not None:
|
||||
default_refer.path = path
|
||||
if text != "" or text is not None:
|
||||
default_refer.text = text
|
||||
if language != "" or language is not None:
|
||||
default_refer.language = language
|
||||
|
||||
llog.info(f"当前默认参考音频路径: {default_refer.path}")
|
||||
llog.info(f"当前默认参考音频文本: {default_refer.text}")
|
||||
llog.info(f"当前默认参考音频语种: {default_refer.language}")
|
||||
llog.info(f"is_ready: {default_refer.is_ready()}")
|
||||
|
||||
return JSONResponse({"code": 0, "message": "Success"}, status_code=200)
|
||||
|
||||
|
||||
def handle(refer_wav_path, prompt_text, prompt_language, text, text_language, cut_punc):
|
||||
if (
|
||||
refer_wav_path == "" or refer_wav_path is None
|
||||
or prompt_text == "" or prompt_text is None
|
||||
or prompt_language == "" or prompt_language is None
|
||||
):
|
||||
refer_wav_path, prompt_text, prompt_language = (
|
||||
default_refer.path,
|
||||
default_refer.text,
|
||||
default_refer.language,
|
||||
)
|
||||
if not default_refer.is_ready():
|
||||
return JSONResponse({"code": 400, "message": "未指定参考音频且接口无预设"}, status_code=400)
|
||||
|
||||
if cut_punc == None:
|
||||
text = cut_text(text, default_cut_punc)
|
||||
else:
|
||||
text = cut_text(text, cut_punc)
|
||||
|
||||
return StreamingResponse(get_tts_wav(refer_wav_path, prompt_text, prompt_language, text, text_language),
|
||||
media_type="audio/" + media_type)
|
||||
|
||||
|
||||
# --------------------------------
|
||||
# 初始化部分
|
||||
# --------------------------------
|
||||
dict_language = {
|
||||
"中文": "all_zh",
|
||||
"英文": "en",
|
||||
"日文": "all_ja",
|
||||
"中英混合": "zh",
|
||||
"日英混合": "ja",
|
||||
"多语种混合": "auto", # 多语种启动切分识别语种
|
||||
"all_zh": "all_zh",
|
||||
"en": "en",
|
||||
"all_ja": "all_ja",
|
||||
"zh": "zh",
|
||||
"ja": "ja",
|
||||
"auto": "auto",
|
||||
}
|
||||
|
||||
# 获取配置
|
||||
cmd_args = CmdArgs()
|
||||
args = cmd_args.get_args()
|
||||
sovits_path = args.sovits_path
|
||||
gpt_path = args.gpt_path
|
||||
device = args.device
|
||||
|
||||
cnhubert_base_path = args.hubert_path
|
||||
bert_path = args.bert_path
|
||||
default_cut_punc = args.cut_punc
|
||||
|
||||
# 应用参数配置
|
||||
default_refer = DefaultRefer(args.default_refer_path, args.default_refer_text, args.default_refer_language)
|
||||
|
||||
# 模型路径检查
|
||||
if sovits_path == "":
|
||||
sovits_path = g_config.pretrained_sovits_path
|
||||
llog.warn(f"未指定SoVITS模型路径, fallback后当前值: {sovits_path}")
|
||||
if gpt_path == "":
|
||||
gpt_path = g_config.pretrained_gpt_path
|
||||
llog.warn(f"未指定GPT模型路径, fallback后当前值: {gpt_path}")
|
||||
|
||||
# 指定默认参考音频, 调用方 未提供/未给全 参考音频参数时使用
|
||||
if default_refer.path == "" or default_refer.text == "" or default_refer.language == "":
|
||||
default_refer.path, default_refer.text, default_refer.language = "", "", ""
|
||||
llog.info("未指定默认参考音频")
|
||||
else:
|
||||
llog.info(f"默认参考音频路径: {default_refer.path}")
|
||||
llog.info(f"默认参考音频文本: {default_refer.text}")
|
||||
llog.info(f"默认参考音频语种: {default_refer.language}")
|
||||
|
||||
# 获取半精度
|
||||
is_half = g_config.is_half
|
||||
if args.full_precision:
|
||||
is_half = False
|
||||
if args.half_precision:
|
||||
is_half = True
|
||||
if args.full_precision and args.half_precision:
|
||||
is_half = g_config.is_half # 炒饭fallback
|
||||
llog.info(f"半精: {is_half}")
|
||||
|
||||
# 流式返回模式
|
||||
if args.stream_mode.lower() in ["normal", "n"]:
|
||||
stream_mode = "normal"
|
||||
llog.info("流式返回已开启")
|
||||
else:
|
||||
stream_mode = "close"
|
||||
|
||||
# 音频编码格式
|
||||
if args.media_type.lower() in ["aac", "ogg"]:
|
||||
media_type = args.media_type.lower()
|
||||
elif stream_mode == "close":
|
||||
media_type = "wav"
|
||||
else:
|
||||
media_type = "ogg"
|
||||
llog.info(f"编码格式: {media_type}")
|
||||
|
||||
# 初始化模型
|
||||
cnhubert.cnhubert_base_path = cnhubert_base_path
|
||||
tokenizer = AutoTokenizer.from_pretrained(bert_path)
|
||||
bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
|
||||
ssl_model = cnhubert.get_model()
|
||||
if is_half:
|
||||
bert_model = bert_model.half().to(device)
|
||||
ssl_model = ssl_model.half().to(device)
|
||||
else:
|
||||
bert_model = bert_model.to(device)
|
||||
ssl_model = ssl_model.to(device)
|
||||
change_sovits_weights(sovits_path)
|
||||
change_gpt_weights(gpt_path)
|
||||
3
trained/.gitignore
vendored
Normal file
3
trained/.gitignore
vendored
Normal file
@ -0,0 +1,3 @@
|
||||
*
|
||||
!.gitignore
|
||||
!character_info.json
|
||||
Loading…
x
Reference in New Issue
Block a user