Delete web.py

web.py

@@ -1,802 +0,0 @@
-import os
-import sys
-from dotenv import load_dotenv
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-load_dotenv()
-load_dotenv("sha256.env")
-
-if sys.platform == "darwin":
-    os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
-
-from infer.modules.vc import VC, show_info, hash_similarity
-from infer.modules.uvr5.modules import uvr
-from infer.lib.train.process_ckpt import (
-    change_info,
-    extract_small_model,
-    merge,
-)
-from i18n.i18n import I18nAuto
-from configs import Config
-from sklearn.cluster import MiniBatchKMeans
-import torch, platform
-import numpy as np
-import gradio as gr
-import faiss
-import pathlib
-import json
-from time import sleep
-from subprocess import Popen
-from random import shuffle
-import warnings
-import traceback
-import threading
-import shutil
-import logging
-
-
-logging.getLogger("numba").setLevel(logging.WARNING)
-logging.getLogger("httpx").setLevel(logging.WARNING)
-
-logger = logging.getLogger(__name__)
-
-tmp = os.path.join(now_dir, "TEMP")
-shutil.rmtree(tmp, ignore_errors=True)
-os.makedirs(tmp, exist_ok=True)
-os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True)
-os.makedirs(os.path.join(now_dir, "assets/weights"), exist_ok=True)
-os.environ["TEMP"] = tmp
-warnings.filterwarnings("ignore")
-torch.manual_seed(114514)
-
-
-config = Config()
-vc = VC(config)
-
-if not config.nocheck:
-    from infer.lib.rvcmd import check_all_assets, download_all_assets
-
-    if not check_all_assets(update=config.update):
-        if config.update:
-            download_all_assets(tmpdir=tmp)
-            if not check_all_assets(update=config.update):
-                logging.error("counld not satisfy all assets needed.")
-                exit(1)
-
-if config.dml == True:
-
-    def forward_dml(ctx, x, scale):
-        ctx.scale = scale
-        res = x.clone().detach()
-        return res
-
-    import fairseq
-
-    fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml
-
-i18n = I18nAuto()
-logger.info(i18n)
-# 判断是否有能用来训练和加速推理的N卡
-ngpu = torch.cuda.device_count()
-gpu_infos = []
-mem = []
-if_gpu_ok = False
-
-if torch.cuda.is_available() or ngpu != 0:
-    for i in range(ngpu):
-        gpu_name = torch.cuda.get_device_name(i)
-        if any(
-            value in gpu_name.upper()
-            for value in [
-                "10",
-                "16",
-                "20",
-                "30",
-                "40",
-                "A2",
-                "A3",
-                "A4",
-                "P4",
-                "A50",
-                "500",
-                "A60",
-                "70",
-                "80",
-                "90",
-                "M4",
-                "T4",
-                "TITAN",
-                "4060",
-                "L",
-                "6000",
-            ]
-        ):
-            # A10#A100#V100#A40#P40#M40#K80#A4500
-            if_gpu_ok = True  # 至少有一张能用的N卡
-            gpu_infos.append("%s\t%s" % (i, gpu_name))
-            mem.append(
-                int(
-                    torch.cuda.get_device_properties(i).total_memory
-                    / 1024
-                    / 1024
-                    / 1024
-                    + 0.4
-                )
-            )
-if if_gpu_ok and len(gpu_infos) > 0:
-    gpu_info = "\n".join(gpu_infos)
-    default_batch_size = min(mem) // 2
-else:
-    gpu_info = i18n(
-        "Unfortunately, there is no compatible GPU available to support your training."
-    )
-    default_batch_size = 1
-gpus = "-".join([i[0] for i in gpu_infos])
-
-
-weight_root = os.getenv("weight_root")
-weight_uvr5_root = os.getenv("weight_uvr5_root")
-index_root = os.getenv("index_root")
-outside_index_root = os.getenv("outside_index_root")
-
-names = []
-for name in os.listdir(weight_root):
-    if name.endswith(".pth"):
-        names.append(name)
-index_paths = []
-
-
-def lookup_indices(index_root):
-    global index_paths
-    for root, dirs, files in os.walk(index_root, topdown=False):
-        for name in files:
-            if name.endswith(".index") and "trained" not in name:
-                index_paths.append("%s/%s" % (root, name))
-
-
-lookup_indices(index_root)
-lookup_indices(outside_index_root)
-uvr5_names = []
-for name in os.listdir(weight_uvr5_root):
-    if name.endswith(".pth") or "onnx" in name:
-        uvr5_names.append(name.replace(".pth", ""))
-
-
-def change_choices():
-    names = []
-    for name in os.listdir(weight_root):
-        if name.endswith(".pth"):
-            names.append(name)
-    index_paths = []
-    for root, dirs, files in os.walk(index_root, topdown=False):
-        for name in files:
-            if name.endswith(".index") and "trained" not in name:
-                index_paths.append("%s/%s" % (root, name))
-    return {"choices": sorted(names), "__type__": "update"}, {
-        "choices": sorted(index_paths),
-        "__type__": "update",
-    }
-
-
-def clean():
-    return {"value": "", "__type__": "update"}
-
-
-def export_onnx(ModelPath, ExportedPath):
-    from rvc.onnx import export_onnx as eo
-
-    eo(ModelPath, ExportedPath)
-
-
-sr_dict = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-def if_done(done, p):
-    while 1:
-        if p.poll() is None:
-            sleep(0.5)
-        else:
-            break
-    done[0] = True
-
-
-def if_done_multi(done, ps):
-    while 1:
-        # poll==None代表进程未结束
-        # 只要有一个进程未结束都不停
-        flag = 1
-        for p in ps:
-            if p.poll() is None:
-                flag = 0
-                sleep(0.5)
-                break
-        if flag == 1:
-            break
-    done[0] = True
-
-
-def preprocess_dataset(trainset_dir, exp_dir, sr, n_p):
-    sr = sr_dict[sr]
-    os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
-    f = open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "w")
-    f.close()
-    cmd = '"%s" infer/modules/train/preprocess.py "%s" %s %s "%s/logs/%s" %s %.1f' % (
-        config.python_cmd,
-        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:
-            yield (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
-
-
-# but2.click(extract_f0,[gpus6,np7,f0method8,if_f0_3,trainset_dir4],[info2])
-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" infer/modules/train/extract/extract_f0_print.py "%s/logs/%s" %s %s'
-                % (
-                    config.python_cmd,
-                    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" infer/modules/train/extract/extract_f0_rmvpe.py %s %s %s "%s/logs/%s" %s '
-                        % (
-                            config.python_cmd,
-                            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
-                    + ' infer/modules/train/extract/extract_f0_rmvpe_dml.py "%s/logs/%s" '
-                    % (
-                        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:
-                yield (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" infer/modules/train/extract_feature_print.py %s %s %s %s "%s/logs/%s" %s %s'
-            % (
-                config.python_cmd,
-                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:
-            yield (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 get_pretrained_models(path_str, f0_str, sr2):
-    if_pretrained_generator_exist = os.access(
-        "assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK
-    )
-    if_pretrained_discriminator_exist = os.access(
-        "assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK
-    )
-    if not if_pretrained_generator_exist:
-        logger.warning(
-            "assets/pretrained%s/%sG%s.pth not exist, will not use pretrained model",
-            path_str,
-            f0_str,
-            sr2,
-        )
-    if not if_pretrained_discriminator_exist:
-        logger.warning(
-            "assets/pretrained%s/%sD%s.pth not exist, will not use pretrained model",
-            path_str,
-            f0_str,
-            sr2,
-        )
-    return (
-        (
-            "assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)
-            if if_pretrained_generator_exist
-            else ""
-        ),
-        (
-            "assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)
-            if if_pretrained_discriminator_exist
-            else ""
-        ),
-    )
-
-
-def change_sr2(sr2, if_f0_3, version19):
-    path_str = "" if version19 == "v1" else "_v2"
-    f0_str = "f0" if if_f0_3 else ""
-    return get_pretrained_models(path_str, f0_str, sr2)
-
-
-def change_version19(sr2, if_f0_3, version19):
-    path_str = "" if version19 == "v1" else "_v2"
-    if sr2 == "32k" and version19 == "v1":
-        sr2 = "40k"
-    to_return_sr2 = (
-        {"choices": ["40k", "48k"], "__type__": "update", "value": sr2}
-        if version19 == "v1"
-        else {"choices": ["40k", "48k", "32k"], "__type__": "update", "value": sr2}
-    )
-    f0_str = "f0" if if_f0_3 else ""
-    return (
-        *get_pretrained_models(path_str, f0_str, sr2),
-        to_return_sr2,
-    )
-
-
-def change_f0(if_f0_3, sr2, version19):  # f0method8,pretrained_G14,pretrained_D15
-    path_str = "" if version19 == "v1" else "_v2"
-    return (
-        {"visible": if_f0_3, "__type__": "update"},
-        {"visible": if_f0_3, "__type__": "update"},
-        *get_pretrained_models(path_str, "f0" if if_f0_3 == True else "", sr2),
-    )
-
-
-# but3.click(click_train,[exp_dir1,sr2,if_f0_3,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16])
-def 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,
-    author,
-):
-    # 生成filelist
-    exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
-    os.makedirs(exp_dir, exist_ok=True)
-    gt_wavs_dir = "%s/0_gt_wavs" % (exp_dir)
-    feature_dir = (
-        "%s/3_feature256" % (exp_dir)
-        if version19 == "v1"
-        else "%s/3_feature768" % (exp_dir)
-    )
-    if if_f0_3:
-        f0_dir = "%s/2a_f0" % (exp_dir)
-        f0nsf_dir = "%s/2b-f0nsf" % (exp_dir)
-        names = (
-            set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)])
-            & set([name.split(".")[0] for name in os.listdir(feature_dir)])
-            & set([name.split(".")[0] for name in os.listdir(f0_dir)])
-            & set([name.split(".")[0] for name in os.listdir(f0nsf_dir)])
-        )
-    else:
-        names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set(
-            [name.split(".")[0] for name in os.listdir(feature_dir)]
-        )
-    opt = []
-    for name in names:
-        if if_f0_3:
-            opt.append(
-                "%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"
-                % (
-                    gt_wavs_dir.replace("\\", "\\\\"),
-                    name,
-                    feature_dir.replace("\\", "\\\\"),
-                    name,
-                    f0_dir.replace("\\", "\\\\"),
-                    name,
-                    f0nsf_dir.replace("\\", "\\\\"),
-                    name,
-                    spk_id5,
-                )
-            )
-        else:
-            opt.append(
-                "%s/%s.wav|%s/%s.npy|%s"
-                % (
-                    gt_wavs_dir.replace("\\", "\\\\"),
-                    name,
-                    feature_dir.replace("\\", "\\\\"),
-                    name,
-                    spk_id5,
-                )
-            )
-    fea_dim = 256 if version19 == "v1" else 768
-    if if_f0_3:
-        for _ in range(2):
-            opt.append(
-                "%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"
-                % (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5)
-            )
-    else:
-        for _ in range(2):
-            opt.append(
-                "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s"
-                % (now_dir, sr2, now_dir, fea_dim, spk_id5)
-            )
-    shuffle(opt)
-    with open("%s/filelist.txt" % exp_dir, "w") as f:
-        f.write("\n".join(opt))
-    logger.debug("Write filelist done")
-    logger.info("Use gpus: %s", str(gpus16))
-    if pretrained_G14 == "":
-        logger.info("No pretrained Generator")
-    if pretrained_D15 == "":
-        logger.info("No pretrained Discriminator")
-    if version19 == "v1" or sr2 == "40k":
-        config_path = "v1/%s.json" % sr2
-    else:
-        config_path = "v2/%s.json" % sr2
-    config_save_path = os.path.join(exp_dir, "config.json")
-    if not pathlib.Path(config_save_path).exists():
-        with open(config_save_path, "w", encoding="utf-8") as f:
-            json.dump(
-                config.json_config[config_path],
-                f,
-                ensure_ascii=False,
-                indent=4,
-                sort_keys=True,
-            )
-            f.write("\n")
-    cmd = (
-        '"%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 -a "%s"'
-        % (
-            config.python_cmd,
-            exp_dir1,
-            sr2,
-            1 if if_f0_3 else 0,
-            batch_size12,
-            total_epoch11,
-            save_epoch10,
-            '-pg "%s"' % pretrained_G14 if pretrained_G14 != "" else "",
-            '-pd "%s"' % pretrained_D15 if pretrained_D15 != "" else "",
-            1 if if_save_latest13 == i18n("Yes") else 0,
-            1 if if_cache_gpu17 == i18n("Yes") else 0,
-            1 if if_save_every_weights18 == i18n("Yes") else 0,
-            version19,
-            author,
-        )
-    )
-    if gpus16:
-        cmd += ' -g "%s"' % (gpus16)
-
-    logger.info("Execute: " + cmd)
-    p = Popen(cmd, shell=True, cwd=now_dir)
-    p.wait()
-    return "Training complete. You can check the training logs in the console or the 'train.log' file under the experiment folder."
-
-
-# but4.click(train_index, [exp_dir1], info3)
-def train_index(exp_dir1, version19):
-    # exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
-    exp_dir = "logs/%s" % (exp_dir1)
-    os.makedirs(exp_dir, exist_ok=True)
-    feature_dir = (
-        "%s/3_feature256" % (exp_dir)
-        if version19 == "v1"
-        else "%s/3_feature768" % (exp_dir)
-    )
-    if not os.path.exists(feature_dir):
-        return "请先进行特征提取!"
-    listdir_res = list(os.listdir(feature_dir))
-    if len(listdir_res) == 0:
-        return "请先进行特征提取！"
-    infos = []
-    npys = []
-    for name in sorted(listdir_res):
-        phone = np.load("%s/%s" % (feature_dir, name))
-        npys.append(phone)
-    big_npy = np.concatenate(npys, 0)
-    big_npy_idx = np.arange(big_npy.shape[0])
-    np.random.shuffle(big_npy_idx)
-    big_npy = big_npy[big_npy_idx]
-    if big_npy.shape[0] > 2e5:
-        infos.append("Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0])
-        yield "\n".join(infos)
-        try:
-            big_npy = (
-                MiniBatchKMeans(
-                    n_clusters=10000,
-                    verbose=True,
-                    batch_size=256 * config.n_cpu,
-                    compute_labels=False,
-                    init="random",
-                )
-                .fit(big_npy)
-                .cluster_centers_
-            )
-        except:
-            info = traceback.format_exc()
-            logger.info(info)
-            infos.append(info)
-            yield "\n".join(infos)
-
-    np.save("%s/total_fea.npy" % exp_dir, big_npy)
-    n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
-    infos.append("%s,%s" % (big_npy.shape, n_ivf))
-    yield "\n".join(infos)
-    index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf)
-    # index = faiss.index_factory(256if version19=="v1"else 768, "IVF%s,PQ128x4fs,RFlat"%n_ivf)
-    infos.append("training")
-    yield "\n".join(infos)
-    index_ivf = faiss.extract_index_ivf(index)  #
-    index_ivf.nprobe = 1
-    index.train(big_npy)
-    faiss.write_index(
-        index,
-        "%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index"
-        % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
-    )
-    infos.append("adding")
-    yield "\n".join(infos)
-    batch_size_add = 8192
-    for i in range(0, big_npy.shape[0], batch_size_add):
-        index.add(big_npy[i : i + batch_size_add])
-    index_save_path = "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index" % (
-        exp_dir,
-        n_ivf,
-        index_ivf.nprobe,
-        exp_dir1,
-        version19,
-    )
-    faiss.write_index(index, index_save_path)
-    infos.append(i18n("Successfully built index into") + " " + index_save_path)
-    link_target = "%s/%s_IVF%s_Flat_nprobe_%s_%s_%s.index" % (
-        outside_index_root,
-        exp_dir1,
-        n_ivf,
-        index_ivf.nprobe,
-        exp_dir1,
-        version19,
-    )
-    try:
-        link = os.link if platform.system() == "Windows" else os.symlink
-        link(index_save_path, link_target)
-        infos.append(i18n("Link index to outside folder") + " " + link_target)
-    except:
-        infos.append(
-            i18n("Link index to outside folder")
-            + " "
-            + link_target
-            + " "
-            + i18n("Fail")
-        )
-
-    # faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
-    # infos.append("成功构建索引，added_IVF%s_Flat_FastScan_%s.index"%(n_ivf,version19))
-    yield "\n".join(infos)
-
-
-# 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(
-    exp_dir1,
-    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,
-    author,
-):
-    infos = []
-
-    def get_info_str(strr):
-        infos.append(strr)
-        return "\n".join(infos)
-
-    # step1:Process data
-    yield get_info_str(i18n("Step 1: Processing data"))
-    [get_info_str(_) for _ in preprocess_dataset(trainset_dir4, exp_dir1, sr2, np7)]
-
-    # step2a:提取音高
-    yield get_info_str(i18n("step2:Pitch extraction & feature extraction"))
-    [
-        get_info_str(_)
-        for _ in extract_f0_feature(
-            gpus16, np7, f0method8, if_f0_3, exp_dir1, version19, gpus_rmvpe
-        )
-    ]
-
-    # step3a:Train model
-    yield get_info_str(i18n("Step 3a: Model training started"))
-    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,
-        author,
-    )
-    yield get_info_str(
-        i18n(
-            "Training complete. You can check the training logs in the console or the 'train.log' file under the experiment folder."
-        )
-    )
-
-    # step3b:训练索引
-    [get_info_str(_) for _ in train_index(exp_dir1, version19)]
-    yield get_info_str(i18n("All processes have been completed!"))
-
-
-#                    ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__])
-def change_info_(ckpt_path):
-    if not os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log")):
-        return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
-    try:
-        with open(
-            ckpt_path.replace(os.path.basename(ckpt_path), "train.log"), "r"
-        ) as f:
-            info = eval(f.read().strip("\n").split("\n")[0].split("\t")[-1])
-            sr, f0 = info["sample_rate"], info["if_f0"]
-            version = "v2" if ("version" in info and info["version"] == "v2") else "v1"
-            return sr, str(f0), version
-    except:
-        traceback.print_exc()
-        return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
-
-
-F0GPUVisible = config.dml == False
-
-
-def change_f0_method(f0method8):
-    if f0method8 == "rmvpe_gpu":
-        visible = F0GPUVisible
-    else:
-        visible = False
-    return {"visible": visible, "__type__": "update"}
-
-
-