619 lines
20 KiB
Python
619 lines
20 KiB
Python
import os
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import torch
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import json
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import faiss
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import platform
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import traceback
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import pathlib
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import logging
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import fairseq
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import numpy as np
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import threading
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from random import shuffle
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from time import sleep
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from subprocess import Popen
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from sklearn.cluster import MiniBatchKMeans
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from .i18n.i18n import I18nAuto
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from .configs.config import Config
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from .infer.modules.vc.modules import VC
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logging.getLogger("numba").setLevel(logging.WARNING)
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logging.getLogger("httpx").setLevel(logging.WARNING)
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logger = logging.getLogger(__name__)
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i18n = I18nAuto()
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logger.info(i18n)
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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_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 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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"500",
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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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"4060",
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"L",
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"6000",
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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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int(
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torch.cuda.get_device_properties(i).total_memory
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/ 1024
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/ 1024
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/ 1024
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+ 0.4
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)
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)
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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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config = Config()
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vc = VC(config)
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if config.dml == True:
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def forward_dml(ctx, x, scale):
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ctx.scale = scale
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res = x.clone().detach()
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return res
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fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml
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import folder_paths
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now_dir = os.path.dirname(os.path.abspath(__file__))
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out_path = folder_paths.get_output_directory()
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weight_root = os.path.join(out_path,"RVC")
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os.makedirs(weight_root, exist_ok=True)
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outside_index_root = weight_root
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sr_dict = {
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"32k": 32000,
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"40k": 40000,
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"48k": 48000,
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}
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def if_done(done, p):
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while 1:
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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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done[0] = True
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def if_done_multi(done, ps):
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while 1:
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# poll==None代表进程未结束
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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() is None:
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flag = 0
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sleep(0.5)
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break
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if flag == 1:
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break
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done[0] = True
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# but3.click(click_train,[exp_dir1,sr2,if_f0_3,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16])
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def click_train(
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exp_dir1,
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sr2,
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if_f0_3,
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spk_id5,
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save_epoch10,
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total_epoch11,
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batch_size12,
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if_save_latest13,
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pretrained_G14,
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pretrained_D15,
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gpus16,
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if_cache_gpu17,
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if_save_every_weights18,
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version19,
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):
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# 生成filelist
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exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
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os.makedirs(exp_dir, exist_ok=True)
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gt_wavs_dir = "%s/0_gt_wavs" % (exp_dir)
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feature_dir = (
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"%s/3_feature256" % (exp_dir)
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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 if_f0_3:
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f0_dir = "%s/2a_f0" % (exp_dir)
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f0nsf_dir = "%s/2b-f0nsf" % (exp_dir)
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names = (
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set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)])
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& set([name.split(".")[0] for name in os.listdir(feature_dir)])
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& set([name.split(".")[0] for name in os.listdir(f0_dir)])
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& set([name.split(".")[0] for name in os.listdir(f0nsf_dir)])
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)
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else:
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names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set(
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[name.split(".")[0] for name in os.listdir(feature_dir)]
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)
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opt = []
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for name in names:
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if if_f0_3:
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opt.append(
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"%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"
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% (
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gt_wavs_dir.replace("\\", "\\\\"),
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name,
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feature_dir.replace("\\", "\\\\"),
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name,
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f0_dir.replace("\\", "\\\\"),
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name,
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f0nsf_dir.replace("\\", "\\\\"),
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name,
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spk_id5,
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)
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)
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else:
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opt.append(
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"%s/%s.wav|%s/%s.npy|%s"
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% (
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gt_wavs_dir.replace("\\", "\\\\"),
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name,
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feature_dir.replace("\\", "\\\\"),
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name,
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spk_id5,
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)
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)
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fea_dim = 256 if version19 == "v1" else 768
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if if_f0_3:
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for _ in range(2):
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opt.append(
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"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
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% (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5)
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)
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else:
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for _ in range(2):
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opt.append(
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"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s"
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% (now_dir, sr2, now_dir, fea_dim, spk_id5)
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)
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shuffle(opt)
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with open("%s/filelist.txt" % exp_dir, "w") as f:
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f.write("\n".join(opt))
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logger.debug("Write filelist done")
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# 生成config#无需生成config
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# cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e mi-test -sr 40k -f0 1 -bs 4 -g 0 -te 10 -se 5 -pg pretrained/f0G40k.pth -pd pretrained/f0D40k.pth -l 1 -c 0"
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logger.info("Use gpus: %s", str(gpus16))
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if pretrained_G14 == "":
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logger.info("No pretrained Generator")
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if pretrained_D15 == "":
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logger.info("No pretrained Discriminator")
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if version19 == "v1" or sr2 == "40k":
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config_path = "v1/%s.json" % sr2
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else:
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config_path = "v2/%s.json" % sr2
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config_save_path = os.path.join(exp_dir, "config.json")
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if not pathlib.Path(config_save_path).exists():
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with open(config_save_path, "w", encoding="utf-8") as f:
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json.dump(
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config.json_config[config_path],
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f,
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ensure_ascii=False,
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indent=4,
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sort_keys=True,
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)
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f.write("\n")
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if gpus16:
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cmd = (
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'"%s" %s/infer/modules/train/train.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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config.python_cmd,
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now_dir,
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exp_dir1,
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sr2,
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1 if if_f0_3 else 0,
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batch_size12,
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gpus16,
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total_epoch11,
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save_epoch10,
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"-pg %s" % pretrained_G14 if pretrained_G14 != "" else "",
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"-pd %s" % pretrained_D15 if pretrained_D15 != "" else "",
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1 if if_save_latest13 == i18n("是") else 0,
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1 if if_cache_gpu17 == i18n("是") else 0,
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1 if if_save_every_weights18 == i18n("是") else 0,
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version19,
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)
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)
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else:
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cmd = (
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'"%s" %s/infer/modules/train/train.py -e "%s" -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s'
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% (
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config.python_cmd,
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now_dir,
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exp_dir1,
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sr2,
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1 if if_f0_3 else 0,
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batch_size12,
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total_epoch11,
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save_epoch10,
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"-pg %s" % pretrained_G14 if pretrained_G14 != "" else "",
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"-pd %s" % pretrained_D15 if pretrained_D15 != "" else "",
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1 if if_save_latest13 == i18n("是") else 0,
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1 if if_cache_gpu17 == i18n("是") else 0,
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1 if if_save_every_weights18 == i18n("是") else 0,
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version19,
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)
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)
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logger.info("Execute: " + cmd)
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p = Popen(cmd, shell=True, cwd=now_dir)
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p.wait()
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return "训练结束, 您可查看控制台训练日志或实验文件夹下的train.log"
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# but4.click(train_index, [exp_dir1], info3)
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def train_index(exp_dir1, version19):
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# exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
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exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
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os.makedirs(exp_dir, exist_ok=True)
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feature_dir = (
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"%s/3_feature256" % (exp_dir)
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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 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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return "请先进行特征提取！"
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infos = []
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npys = []
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for name in sorted(listdir_res):
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phone = np.load("%s/%s" % (feature_dir, name))
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npys.append(phone)
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big_npy = np.concatenate(npys, 0)
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big_npy_idx = np.arange(big_npy.shape[0])
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np.random.shuffle(big_npy_idx)
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big_npy = big_npy[big_npy_idx]
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if big_npy.shape[0] > 2e5:
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infos.append("Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0])
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logger.info("\n".join(infos))
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try:
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big_npy = (
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MiniBatchKMeans(
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n_clusters=10000,
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verbose=True,
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batch_size=256 * config.n_cpu,
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compute_labels=False,
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init="random",
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)
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.fit(big_npy)
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.cluster_centers_
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)
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except:
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info = traceback.format_exc()
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logger.info(info)
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infos.append(info)
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logger.info("\n".join(infos))
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np.save("%s/total_fea.npy" % exp_dir, big_npy)
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n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
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infos.append("%s,%s" % (big_npy.shape, n_ivf))
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logger.info("\n".join(infos))
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index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf)
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# index = faiss.index_factory(256if version19=="v1"else 768, "IVF%s,PQ128x4fs,RFlat"%n_ivf)
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infos.append("training")
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logger.info("\n".join(infos))
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index_ivf = faiss.extract_index_ivf(index) #
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index_ivf.nprobe = 1
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index.train(big_npy)
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faiss.write_index(
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index,
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"%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index"
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% (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
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)
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infos.append("adding")
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logger.info("\n".join(infos))
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batch_size_add = 8192
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for i in range(0, big_npy.shape[0], batch_size_add):
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index.add(big_npy[i : i + batch_size_add])
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faiss.write_index(
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index,
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"%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index"
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% (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
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)
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infos.append(
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"成功构建索引 added_IVF%s_Flat_nprobe_%s_%s_%s.index"
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% (n_ivf, index_ivf.nprobe, exp_dir1, version19)
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)
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try:
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import shutil
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shutil.copy(
|
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"%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index"
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% (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
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"%s/%s_IVF%s_Flat_nprobe_%s_%s_%s.index"
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% (
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outside_index_root,
|
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exp_dir1,
|
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n_ivf,
|
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index_ivf.nprobe,
|
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exp_dir1,
|
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version19,
|
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),
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)
|
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infos.append("链接索引到外部-%s" % (outside_index_root))
|
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except:
|
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infos.append("链接索引到外部-%s失败" % (outside_index_root))
|
|
|
|
# faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
|
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# infos.append("成功构建索引，added_IVF%s_Flat_FastScan_%s.index"%(n_ivf,version19))
|
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logger.info("\n".join(infos))
|
|
|
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# but5.click(train1key, [exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0method8, save_epoch10, total_epoch11, batch_size12, if_save_latest13, pretrained_G14, pretrained_D15, gpus16, if_cache_gpu17], info3)
|
|
def train1key(
|
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exp_dir1,
|
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sr2,
|
|
if_f0_3,
|
|
trainset_dir4,
|
|
spk_id5,
|
|
np7,
|
|
f0method8,
|
|
save_epoch10,
|
|
total_epoch11,
|
|
batch_size12,
|
|
if_save_latest13,
|
|
pretrained_G14,
|
|
pretrained_D15,
|
|
gpus16,
|
|
if_cache_gpu17,
|
|
if_save_every_weights18,
|
|
version19,
|
|
gpus_rmvpe,
|
|
):
|
|
infos = []
|
|
|
|
def get_info_str(strr):
|
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infos.append(strr)
|
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return "\n".join(infos)
|
|
|
|
# step3a:训练模型
|
|
logger.info(get_info_str(i18n("step3a:正在训练模型")))
|
|
click_train(
|
|
exp_dir1,
|
|
sr2,
|
|
if_f0_3,
|
|
spk_id5,
|
|
save_epoch10,
|
|
total_epoch11,
|
|
batch_size12,
|
|
if_save_latest13,
|
|
pretrained_G14,
|
|
pretrained_D15,
|
|
gpus16,
|
|
if_cache_gpu17,
|
|
if_save_every_weights18,
|
|
version19,
|
|
)
|
|
logger.info(get_info_str(
|
|
i18n("训练结束, 您可查看控制台训练日志或实验文件夹下的train.log")
|
|
))
|
|
|
|
# step3b:训练索引
|
|
train_index(exp_dir1, version19)
|
|
logger.info(get_info_str(i18n("全流程结束！")))
|
|
|
|
|
|
def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, gpus_rmvpe):
|
|
gpus = gpus.split("-")
|
|
os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
|
|
f = open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "w")
|
|
f.close()
|
|
if if_f0:
|
|
if f0method != "rmvpe_gpu":
|
|
cmd = (
|
|
'"%s" %s/infer/modules/train/extract/extract_f0_print.py "%s/logs/%s" %s %s'
|
|
% (
|
|
config.python_cmd,
|
|
now_dir,
|
|
now_dir,
|
|
exp_dir,
|
|
n_p,
|
|
f0method,
|
|
)
|
|
)
|
|
logger.info("Execute: " + cmd)
|
|
p = Popen(
|
|
cmd, shell=True, cwd=now_dir
|
|
) # , stdin=PIPE, stdout=PIPE,stderr=PIPE
|
|
# 煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
|
|
done = [False]
|
|
threading.Thread(
|
|
target=if_done,
|
|
args=(
|
|
done,
|
|
p,
|
|
),
|
|
).start()
|
|
else:
|
|
if gpus_rmvpe != "-":
|
|
gpus_rmvpe = gpus_rmvpe.split("-")
|
|
leng = len(gpus_rmvpe)
|
|
ps = []
|
|
for idx, n_g in enumerate(gpus_rmvpe):
|
|
cmd = (
|
|
'"%s" %s/infer/modules/train/extract/extract_f0_rmvpe.py %s %s %s "%s/logs/%s" %s '
|
|
% (
|
|
config.python_cmd,
|
|
now_dir,
|
|
leng,
|
|
idx,
|
|
n_g,
|
|
now_dir,
|
|
exp_dir,
|
|
config.is_half,
|
|
)
|
|
)
|
|
logger.info("Execute: " + cmd)
|
|
p = Popen(
|
|
cmd, shell=True, cwd=now_dir
|
|
) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
|
|
ps.append(p)
|
|
# 煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
|
|
done = [False]
|
|
threading.Thread(
|
|
target=if_done_multi, #
|
|
args=(
|
|
done,
|
|
ps,
|
|
),
|
|
).start()
|
|
else:
|
|
cmd = (
|
|
config.python_cmd
|
|
+ ' %s/infer/modules/train/extract/extract_f0_rmvpe_dml.py "%s/logs/%s" '
|
|
% (
|
|
now_dir,
|
|
now_dir,
|
|
exp_dir,
|
|
)
|
|
)
|
|
logger.info("Execute: " + cmd)
|
|
p = Popen(
|
|
cmd, shell=True, cwd=now_dir
|
|
) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
|
|
p.wait()
|
|
done = [True]
|
|
while 1:
|
|
with open(
|
|
"%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r"
|
|
) as f:
|
|
logger.info(f.read())
|
|
sleep(1)
|
|
if done[0]:
|
|
break
|
|
with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
|
|
log = f.read()
|
|
logger.info(log)
|
|
# yield log
|
|
# 对不同part分别开多进程
|
|
"""
|
|
n_part=int(sys.argv[1])
|
|
i_part=int(sys.argv[2])
|
|
i_gpu=sys.argv[3]
|
|
exp_dir=sys.argv[4]
|
|
os.environ["CUDA_VISIBLE_DEVICES"]=str(i_gpu)
|
|
"""
|
|
leng = len(gpus)
|
|
ps = []
|
|
for idx, n_g in enumerate(gpus):
|
|
cmd = (
|
|
'"%s" %s/infer/modules/train/extract_feature_print.py %s %s %s %s "%s/logs/%s" %s %s'
|
|
% (
|
|
config.python_cmd,
|
|
now_dir,
|
|
config.device,
|
|
leng,
|
|
idx,
|
|
n_g,
|
|
now_dir,
|
|
exp_dir,
|
|
version19,
|
|
config.is_half,
|
|
)
|
|
)
|
|
logger.info("Execute: " + cmd)
|
|
p = Popen(
|
|
cmd, shell=True, cwd=now_dir
|
|
) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
|
|
ps.append(p)
|
|
# 煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
|
|
done = [False]
|
|
threading.Thread(
|
|
target=if_done_multi,
|
|
args=(
|
|
done,
|
|
ps,
|
|
),
|
|
).start()
|
|
while 1:
|
|
with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
|
|
logger.info(f.read())
|
|
sleep(1)
|
|
if done[0]:
|
|
break
|
|
with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
|
|
log = f.read()
|
|
logger.info(log)
|
|
#yield log
|
|
|
|
|
|
|
|
|
|
def preprocess_dataset(trainset_dir, exp_dir, sr, n_p):
|
|
sr = sr_dict[sr]
|
|
os.makedirs(os.path.join(now_dir,"logs",exp_dir), exist_ok=True)
|
|
f = open(os.path.join(now_dir,"logs",exp_dir,"preprocess.log"), "w")
|
|
f.close()
|
|
cmd = '"%s" %s/infer/modules/train/preprocess.py "%s" %s %s "%s/logs/%s" %s %.1f' % (
|
|
config.python_cmd,
|
|
now_dir,
|
|
trainset_dir,
|
|
sr,
|
|
n_p,
|
|
now_dir,
|
|
exp_dir,
|
|
config.noparallel,
|
|
config.preprocess_per,
|
|
)
|
|
logger.info("Execute: " + cmd)
|
|
# , stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir
|
|
p = Popen(cmd, shell=True)
|
|
# 煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
|
|
done = [False]
|
|
threading.Thread(
|
|
target=if_done,
|
|
args=(
|
|
done,
|
|
p,
|
|
),
|
|
).start()
|
|
|
|
while 1:
|
|
with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
|
|
logger.info(f.read())
|
|
sleep(1)
|
|
if done[0]:
|
|
break
|
|
with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
|
|
log = f.read()
|
|
logger.info(log)
|
|
# yield log |