mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2025-01-30 18:22:51 +08:00
Rewrite syntax of infer-web.py (#536)
* Fix import location * use any * Correction of if Syntax * Class definitions to the front * format * fix if Syntax
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Ftps
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parent
c5758a89db
commit
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244
infer-web.py
244
infer-web.py
@ -1,11 +1,42 @@
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import torch, os, traceback, sys, warnings, shutil, numpy as np
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import os
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import shutil
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import sys
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import traceback
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import warnings
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import numpy as np
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import torch
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os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
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import logging
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import threading
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from time import sleep
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from subprocess import Popen
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import faiss
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from random import shuffle
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from subprocess import Popen
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from time import sleep
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import faiss
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import ffmpeg
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import gradio as gr
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import soundfile as sf
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from config import Config
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from fairseq import checkpoint_utils
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from i18n import I18nAuto
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from infer_pack.models import (
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SynthesizerTrnMs256NSFsid,
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SynthesizerTrnMs256NSFsid_nono,
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SynthesizerTrnMs768NSFsid,
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SynthesizerTrnMs768NSFsid_nono,
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)
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from infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
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from infer_uvr5 import _audio_pre_, _audio_pre_new
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from MDXNet import MDXNetDereverb
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from my_utils import load_audio
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from train.process_ckpt import change_info, extract_small_model, merge, show_info
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from vc_infer_pipeline import VC
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# from trainset_preprocess_pipeline import PreProcess
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logging.getLogger("numba").setLevel(logging.WARNING)
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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@ -19,41 +50,43 @@ os.makedirs(os.path.join(now_dir, "weights"), exist_ok=True)
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os.environ["TEMP"] = tmp
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warnings.filterwarnings("ignore")
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torch.manual_seed(114514)
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from i18n import I18nAuto
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import ffmpeg
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from MDXNet import MDXNetDereverb
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config = Config()
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i18n = I18nAuto()
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i18n.print()
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# 判断是否有能用来训练和加速推理的N卡
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ngpu = torch.cuda.device_count()
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gpu_infos = []
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mem = []
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if (not torch.cuda.is_available()) or ngpu == 0:
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if_gpu_ok = False
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else:
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if_gpu_ok = False
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if_gpu_ok = False
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if torch.cuda.is_available() or ngpu != 0:
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for i in range(ngpu):
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gpu_name = torch.cuda.get_device_name(i)
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if (
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"10" in gpu_name
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or "16" in gpu_name
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or "20" in gpu_name
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or "30" in gpu_name
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or "40" in gpu_name
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or "A2" in gpu_name.upper()
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or "A3" in gpu_name.upper()
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or "A4" in gpu_name.upper()
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or "P4" in gpu_name.upper()
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or "A50" in gpu_name.upper()
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or "A60" in gpu_name.upper()
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or "70" in gpu_name
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or "80" in gpu_name
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or "90" in gpu_name
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or "M4" in gpu_name.upper()
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or "T4" in gpu_name.upper()
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or "TITAN" in gpu_name.upper()
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): # A10#A100#V100#A40#P40#M40#K80#A4500
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if any(
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value in gpu_name.upper()
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for value in [
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"10",
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"16",
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"20",
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"30",
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"40",
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"A2",
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"A3",
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"A4",
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"P4",
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"A50",
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"A60",
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"70",
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"80",
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"90",
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"M4",
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"T4",
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"TITAN",
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]
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):
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# A10#A100#V100#A40#P40#M40#K80#A4500
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if_gpu_ok = True # 至少有一张能用的N卡
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gpu_infos.append("%s\t%s" % (i, gpu_name))
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mem.append(
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@ -65,32 +98,13 @@ else:
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+ 0.4
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)
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)
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if if_gpu_ok == True and len(gpu_infos) > 0:
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if if_gpu_ok and len(gpu_infos) > 0:
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gpu_info = "\n".join(gpu_infos)
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default_batch_size = min(mem) // 2
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else:
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gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练")
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default_batch_size = 1
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gpus = "-".join([i[0] for i in gpu_infos])
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from infer_pack.models import (
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SynthesizerTrnMs256NSFsid,
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SynthesizerTrnMs256NSFsid_nono,
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SynthesizerTrnMs768NSFsid,
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SynthesizerTrnMs768NSFsid_nono,
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)
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import soundfile as sf
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from fairseq import checkpoint_utils
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import gradio as gr
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import logging
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from vc_infer_pipeline import VC
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from config import Config
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from infer_uvr5 import _audio_pre_, _audio_pre_new
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from my_utils import load_audio
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from train.process_ckpt import show_info, change_info, merge, extract_small_model
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config = Config()
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# from trainset_preprocess_pipeline import PreProcess
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logging.getLogger("numba").setLevel(logging.WARNING)
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class ToolButton(gr.Button, gr.components.FormComponent):
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@ -164,7 +178,7 @@ def vc_single(
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if audio_max > 1:
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audio /= audio_max
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times = [0, 0, 0]
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if hubert_model == None:
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if not hubert_model:
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load_hubert()
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if_f0 = cpt.get("f0", 1)
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file_index = (
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@ -203,7 +217,7 @@ def vc_single(
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protect,
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f0_file=f0_file,
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)
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if resample_sr >= 16000 and tgt_sr != resample_sr:
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if tgt_sr != resample_sr >= 16000:
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tgt_sr = resample_sr
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index_info = (
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"Using index:%s." % file_index
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@ -385,7 +399,7 @@ def get_vc(sid):
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global n_spk, tgt_sr, net_g, vc, cpt, version
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if sid == "" or sid == []:
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global hubert_model
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if hubert_model != None: # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的
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if hubert_model is not None: # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的
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print("clean_empty_cache")
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del net_g, n_spk, vc, hubert_model, tgt_sr # ,cpt
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hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None
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@ -471,7 +485,7 @@ sr_dict = {
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def if_done(done, p):
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while 1:
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if p.poll() == None:
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if p.poll() is None:
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sleep(0.5)
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else:
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break
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@ -484,7 +498,7 @@ def if_done_multi(done, ps):
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# 只要有一个进程未结束都不停
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flag = 1
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for p in ps:
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if p.poll() == None:
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if p.poll() is None:
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flag = 0
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sleep(0.5)
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break
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@ -519,7 +533,7 @@ def preprocess_dataset(trainset_dir, exp_dir, sr, n_p):
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with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
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yield (f.read())
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sleep(1)
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if done[0] == True:
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if done[0]:
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break
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with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
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log = f.read()
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@ -557,7 +571,7 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19):
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) as f:
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yield (f.read())
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sleep(1)
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if done[0] == True:
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if done[0]:
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break
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with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
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log = f.read()
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@ -605,7 +619,7 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19):
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with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
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yield (f.read())
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sleep(1)
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if done[0] == True:
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if done[0]:
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break
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with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
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log = f.read()
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@ -616,51 +630,98 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19):
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def change_sr2(sr2, if_f0_3, version19):
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path_str = "" if version19 == "v1" else "_v2"
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f0_str = "f0" if if_f0_3 else ""
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if_pretrained_generator_exist = os.access("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK)
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if_pretrained_discriminator_exist = os.access("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK)
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if (if_pretrained_generator_exist == False):
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print("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
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if (if_pretrained_discriminator_exist == False):
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print("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
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return (
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("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_generator_exist else "",
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("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_discriminator_exist else "",
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{"visible": True, "__type__": "update"}
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if_pretrained_generator_exist = os.access(
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"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK
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)
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if_pretrained_discriminator_exist = os.access(
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"pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK
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)
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if if_pretrained_generator_exist is not False:
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print(
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"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2),
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"not exist, will not use pretrained model",
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)
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if if_pretrained_discriminator_exist is not False:
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print(
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"pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2),
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"not exist, will not use pretrained model",
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)
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return (
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"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)
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if if_pretrained_generator_exist
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else "",
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"pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)
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if if_pretrained_discriminator_exist
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else "",
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{"visible": True, "__type__": "update"},
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)
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def change_version19(sr2, if_f0_3, version19):
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path_str = "" if version19 == "v1" else "_v2"
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f0_str = "f0" if if_f0_3 else ""
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if_pretrained_generator_exist = os.access("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK)
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if_pretrained_discriminator_exist = os.access("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK)
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if (if_pretrained_generator_exist == False):
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print("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
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if (if_pretrained_discriminator_exist == False):
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print("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
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if_pretrained_generator_exist = os.access(
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"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK
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)
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if_pretrained_discriminator_exist = os.access(
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"pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK
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)
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if not if_pretrained_generator_exist:
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print(
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"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2),
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"not exist, will not use pretrained model",
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)
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if not if_pretrained_discriminator_exist:
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print(
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"pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2),
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"not exist, will not use pretrained model",
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)
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return (
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("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_generator_exist else "",
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("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_discriminator_exist else "",
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"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)
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if if_pretrained_generator_exist
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else "",
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"pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)
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if if_pretrained_discriminator_exist
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else "",
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)
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def change_f0(if_f0_3, sr2, version19): # f0method8,pretrained_G14,pretrained_D15
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path_str = "" if version19 == "v1" else "_v2"
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if_pretrained_generator_exist = os.access("pretrained%s/f0G%s.pth" % (path_str, sr2), os.F_OK)
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if_pretrained_discriminator_exist = os.access("pretrained%s/f0D%s.pth" % (path_str, sr2), os.F_OK)
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if (if_pretrained_generator_exist == False):
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print("pretrained%s/f0G%s.pth" % (path_str, sr2), "not exist, will not use pretrained model")
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if (if_pretrained_discriminator_exist == False):
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print("pretrained%s/f0D%s.pth" % (path_str, sr2), "not exist, will not use pretrained model")
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if_pretrained_generator_exist = os.access(
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"pretrained%s/f0G%s.pth" % (path_str, sr2), os.F_OK
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)
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if_pretrained_discriminator_exist = os.access(
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"pretrained%s/f0D%s.pth" % (path_str, sr2), os.F_OK
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)
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if not if_pretrained_generator_exist:
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print(
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"pretrained%s/f0G%s.pth" % (path_str, sr2),
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"not exist, will not use pretrained model",
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)
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if not if_pretrained_discriminator_exist:
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print(
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"pretrained%s/f0D%s.pth" % (path_str, sr2),
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"not exist, will not use pretrained model",
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)
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if if_f0_3:
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return (
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{"visible": True, "__type__": "update"},
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"pretrained%s/f0G%s.pth" % (path_str, sr2) if if_pretrained_generator_exist else "",
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"pretrained%s/f0D%s.pth" % (path_str, sr2) if if_pretrained_discriminator_exist else "",
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"pretrained%s/f0G%s.pth" % (path_str, sr2)
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if if_pretrained_generator_exist
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else "",
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"pretrained%s/f0D%s.pth" % (path_str, sr2)
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if if_pretrained_discriminator_exist
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else "",
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)
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return (
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{"visible": False, "__type__": "update"},
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("pretrained%s/G%s.pth" % (path_str, sr2)) if if_pretrained_generator_exist else "",
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("pretrained%s/D%s.pth" % (path_str, sr2)) if if_pretrained_discriminator_exist else "",
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("pretrained%s/G%s.pth" % (path_str, sr2))
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if if_pretrained_generator_exist
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else "",
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("pretrained%s/D%s.pth" % (path_str, sr2))
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if if_pretrained_discriminator_exist
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else "",
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)
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@ -809,7 +870,7 @@ def train_index(exp_dir1, version19):
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if version19 == "v1"
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else "%s/3_feature768" % (exp_dir)
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)
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if os.path.exists(feature_dir) == False:
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if not os.path.exists(feature_dir):
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return "请先进行特征提取!"
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listdir_res = list(os.listdir(feature_dir))
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if len(listdir_res) == 0:
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@ -1014,7 +1075,7 @@ def train1key(
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if gpus16:
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cmd = (
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config.python_cmd
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+" train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s"
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+ " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s"
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% (
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exp_dir1,
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sr2,
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@ -1098,10 +1159,7 @@ def train1key(
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# ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__])
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def change_info_(ckpt_path):
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if (
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os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log"))
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== False
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):
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if not os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log")):
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return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
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try:
|
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with open(
|
||||
@ -1116,8 +1174,6 @@ def change_info_(ckpt_path):
|
||||
return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
|
||||
|
||||
|
||||
from infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
|
||||
|
||||
|
||||
def export_onnx(ModelPath, ExportedPath):
|
||||
cpt = torch.load(ModelPath, map_location="cpu")
|
||||
|
||||
Loading…
Reference in New Issue
Block a user