| import os, sys |
| import datetime, subprocess |
| from mega import Mega |
| now_dir = os.getcwd() |
| sys.path.append(now_dir) |
| import logging |
| import shutil |
| import threading |
| import traceback |
| import warnings |
| from random import shuffle |
| from subprocess import Popen |
| from time import sleep |
| import json |
| import pathlib |
|
|
| import fairseq |
| import faiss |
| import gradio as gr |
| import numpy as np |
| import torch |
| from dotenv import load_dotenv |
| from sklearn.cluster import MiniBatchKMeans |
|
|
| from configs.config import Config |
| from i18n.i18n import I18nAuto |
| from infer.lib.train.process_ckpt import ( |
| change_info, |
| extract_small_model, |
| merge, |
| show_info, |
| ) |
| from infer.modules.uvr5.modules import uvr |
| from infer.modules.vc.modules import VC |
| logging.getLogger("numba").setLevel(logging.WARNING) |
|
|
| logger = logging.getLogger(__name__) |
|
|
| tmp = os.path.join(now_dir, "TEMP") |
| shutil.rmtree(tmp, ignore_errors=True) |
| shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True) |
| shutil.rmtree("%s/runtime/Lib/site-packages/uvr5_pack" % (now_dir), 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.makedirs(os.path.join(now_dir, "audios"), exist_ok=True) |
| os.environ["TEMP"] = tmp |
| warnings.filterwarnings("ignore") |
| torch.manual_seed(114514) |
|
|
|
|
| load_dotenv() |
| config = Config() |
| vc = VC(config) |
|
|
| if config.dml == True: |
|
|
| def forward_dml(ctx, x, scale): |
| ctx.scale = scale |
| res = x.clone().detach() |
| return res |
|
|
| fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml |
| i18n = I18nAuto() |
| logger.info(i18n) |
| |
| 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", |
| ] |
| ): |
| |
| if_gpu_ok = True |
| 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("很遗憾您这没有能用的显卡来支持您训练") |
| default_batch_size = 1 |
| gpus = "-".join([i[0] for i in gpu_infos]) |
|
|
|
|
| class ToolButton(gr.Button, gr.components.FormComponent): |
| """Small button with single emoji as text, fits inside gradio forms""" |
|
|
| def __init__(self, **kwargs): |
| super().__init__(variant="tool", **kwargs) |
|
|
| def get_block_name(self): |
| return "button" |
|
|
|
|
| weight_root = os.getenv("weight_root") |
| weight_uvr5_root = os.getenv("weight_uvr5_root") |
| index_root = os.getenv("index_root") |
|
|
| 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)) |
| 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)) |
| audio_files=[] |
| for filename in os.listdir("./audios"): |
| if filename.endswith(('.wav','.mp3','.ogg')): |
| audio_files.append('./audios/'+filename) |
| return {"choices": sorted(names), "__type__": "update"}, { |
| "choices": sorted(index_paths), |
| "__type__": "update", |
| }, {"choices": sorted(audio_files), "__type__": "update"} |
|
|
| def clean(): |
| return {"value": "", "__type__": "update"} |
|
|
|
|
| def export_onnx(): |
| from infer.modules.onnx.export import export_onnx as eo |
|
|
| eo() |
|
|
|
|
| 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: |
| |
| |
| 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() |
| per = 3.0 if config.is_half else 3.7 |
| 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, |
| per, |
| ) |
| logger.info(cmd) |
| p = Popen(cmd, shell=True) |
| |
| 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 |
|
|
|
|
| |
| 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(cmd) |
| p = Popen( |
| cmd, shell=True, cwd=now_dir |
| ) |
| |
| 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(cmd) |
| p = Popen( |
| cmd, shell=True, cwd=now_dir |
| ) |
| ps.append(p) |
| |
| 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(cmd) |
| p = Popen( |
| cmd, shell=True, 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 |
| |
| """ |
| 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 True' |
| % ( |
| config.python_cmd, |
| config.device, |
| leng, |
| idx, |
| n_g, |
| now_dir, |
| exp_dir, |
| version19, |
| ) |
| ) |
| logger.info(cmd) |
| p = Popen( |
| cmd, shell=True, cwd=now_dir |
| ) |
| ps.append(p) |
| |
| 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.warn( |
| "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.warn( |
| "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): |
| path_str = "" if version19 == "v1" else "_v2" |
| return ( |
| {"visible": if_f0_3, "__type__": "update"}, |
| *get_pretrained_models(path_str, "f0", sr2), |
| ) |
|
|
|
|
| |
| 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, |
| ): |
| |
| 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") |
| if gpus16: |
| cmd = ( |
| '"%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' |
| % ( |
| config.python_cmd, |
| exp_dir1, |
| sr2, |
| 1 if if_f0_3 else 0, |
| batch_size12, |
| gpus16, |
| 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("是") else 0, |
| 1 if if_cache_gpu17 == i18n("是") else 0, |
| 1 if if_save_every_weights18 == i18n("是") else 0, |
| version19, |
| ) |
| ) |
| else: |
| 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' |
| % ( |
| 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("是") else 0, |
| 1 if if_cache_gpu17 == i18n("是") else 0, |
| 1 if if_save_every_weights18 == i18n("是") else 0, |
| version19, |
| ) |
| ) |
| logger.info(cmd) |
| p = Popen(cmd, shell=True, cwd=now_dir) |
| p.wait() |
| 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" |
|
|
|
|
| |
| def train_index(exp_dir1, version19): |
| |
| 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) |
| |
| 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]) |
| faiss.write_index( |
| index, |
| "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index" |
| % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), |
| ) |
| infos.append( |
| "成功构建索引，added_IVF%s_Flat_nprobe_%s_%s_%s.index" |
| % (n_ivf, index_ivf.nprobe, exp_dir1, version19) |
| ) |
| |
| |
| yield "\n".join(infos) |
|
|
|
|
| |
| 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, |
| ): |
| infos = [] |
|
|
| def get_info_str(strr): |
| infos.append(strr) |
| return "\n".join(infos) |
|
|
| |
| yield get_info_str(i18n("step1:正在处理数据")) |
| [get_info_str(_) for _ in preprocess_dataset(trainset_dir4, exp_dir1, sr2, np7)] |
|
|
| |
| yield get_info_str(i18n("step2:正在提取音高&正在提取特征")) |
| [ |
| get_info_str(_) |
| for _ in extract_f0_feature( |
| gpus16, np7, f0method8, if_f0_3, exp_dir1, version19, gpus_rmvpe |
| ) |
| ] |
|
|
| |
| yield 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, |
| ) |
| yield 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")) |
|
|
| |
| [get_info_str(_) for _ in train_index(exp_dir1, version19)] |
| yield get_info_str(i18n("全流程结束！")) |
|
|
|
|
| |
| 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"} |
|
|
| def find_model(): |
| if len(names) > 0: |
| vc.get_vc(sorted(names)[0],None,None) |
| return sorted(names)[0] |
| else: |
| try: |
| gr.Info("Do not forget to choose a model.") |
| except: |
| pass |
| return '' |
| |
| def find_audios(index=False): |
| audio_files=[] |
| if not os.path.exists('./audios'): os.mkdir("./audios") |
| for filename in os.listdir("./audios"): |
| if filename.endswith(('.wav','.mp3','.ogg')): |
| audio_files.append("./audios/"+filename) |
| if index: |
| if len(audio_files) > 0: return sorted(audio_files)[0] |
| else: return "" |
| elif len(audio_files) > 0: return sorted(audio_files) |
| else: return [] |
|
|
| def get_index(): |
| if find_model() != '': |
| chosen_model=sorted(names)[0].split(".")[0] |
| logs_path="./logs/"+chosen_model |
| if os.path.exists(logs_path): |
| for file in os.listdir(logs_path): |
| if file.endswith(".index"): |
| return os.path.join(logs_path, file) |
| return '' |
| else: |
| return '' |
| |
| def get_indexes(): |
| indexes_list=[] |
| for dirpath, dirnames, filenames in os.walk("./logs/"): |
| for filename in filenames: |
| if filename.endswith(".index"): |
| indexes_list.append(os.path.join(dirpath,filename)) |
| if len(indexes_list) > 0: |
| return indexes_list |
| else: |
| return '' |
| |
| def save_wav(file): |
| try: |
| file_path=file.name |
| shutil.move(file_path,'./audios') |
| return './audios/'+os.path.basename(file_path) |
| except AttributeError: |
| try: |
| new_name = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")+'.wav' |
| new_path='./audios/'+new_name |
| shutil.move(file,new_path) |
| return new_path |
| except TypeError: |
| return None |
|
|
| def download_from_url(url, model): |
| if model =='': |
| try: |
| model = url.split('/')[-1].split('?')[0] |
| except: |
| return "You need to name your model. For example: My-Model" |
| url=url.replace('/blob/main/','/resolve/main/') |
| model=model.replace('.pth','').replace('.index','').replace('.zip','') |
| if url == '': |
| return "URL cannot be left empty." |
| url = url.strip() |
| zip_dirs = ["zips", "unzips"] |
| for directory in zip_dirs: |
| if os.path.exists(directory): |
| shutil.rmtree(directory) |
| os.makedirs("zips", exist_ok=True) |
| os.makedirs("unzips", exist_ok=True) |
| zipfile = model + '.zip' |
| zipfile_path = './zips/' + zipfile |
| try: |
| if url.endswith('.pth'): |
| subprocess.run(["wget", url, "-O", f'./assets/weights/{model}.pth']) |
| return f"Sucessfully downloaded as {model}.pth" |
| if url.endswith('.index'): |
| if not os.path.exists(f'./logs/{model}'): os.makedirs(f'./logs/{model}') |
| subprocess.run(["wget", url, "-O", f'./logs/{model}/added_{model}.index']) |
| return f"Successfully downloaded as added_{model}.index" |
| if "drive.google.com" in url: |
| subprocess.run(["gdown", url, "--fuzzy", "-O", zipfile_path]) |
| elif "mega.nz" in url: |
| m = Mega() |
| m.download_url(url, './zips') |
| else: |
| subprocess.run(["wget", url, "-O", zipfile_path]) |
| for filename in os.listdir("./zips"): |
| if filename.endswith(".zip"): |
| zipfile_path = os.path.join("./zips/",filename) |
| shutil.unpack_archive(zipfile_path, "./unzips", 'zip') |
| else: |
| return "No zipfile found." |
| for root, dirs, files in os.walk('./unzips'): |
| for file in files: |
| file_path = os.path.join(root, file) |
| if file.endswith(".index"): |
| os.mkdir(f'./logs/{model}') |
| shutil.copy2(file_path,f'./logs/{model}') |
| elif "G_" not in file and "D_" not in file and file.endswith(".pth"): |
| shutil.copy(file_path,f'./assets/weights/{model}.pth') |
| shutil.rmtree("zips") |
| shutil.rmtree("unzips") |
| return "Success." |
| except: |
| return "There's been an error." |
|
|
| def upload_to_dataset(files, dir): |
| if dir == '': |
| dir = './dataset/'+datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S") |
| if not os.path.exists(dir): |
| os.makedirs(dir) |
| for file in files: |
| path=file.name |
| shutil.copy2(path,dir) |
| try: |
| gr.Info(i18n("处理数据")) |
| except: |
| pass |
| return i18n("处理数据"), {"value":dir,"__type__":"update"} |
|
|
| def download_model_files(model): |
| model_found = False |
| index_found = False |
| if os.path.exists(f'./assets/weights/{model}.pth'): model_found = True |
| if os.path.exists(f'./logs/{model}'): |
| for file in os.listdir(f'./logs/{model}'): |
| if file.endswith('.index') and 'added' in file: |
| log_file = file |
| index_found = True |
| if model_found and index_found: |
| return [f'./assets/weights/{model}.pth', f'./logs/{model}/{log_file}'], "Done" |
| elif model_found and not index_found: |
| return f'./assets/weights/{model}.pth', "Could not find Index file." |
| elif index_found and not model_found: |
| return f'./logs/{model}/{log_file}', f'Make sure the Voice Name is correct. I could not find {model}.pth' |
| else: |
| return None, f'Could not find {model}.pth or corresponding Index file.' |
|
|
| def update_visibility(visible): |
| if visible: |
| return {"visible":True,"__type__":"update"},{"visible":True,"__type__":"update"} |
| else: |
| return {"visible":False,"__type__":"update"},{"visible":False,"__type__":"update"} |
|
|
| def get_pretrains(string): |
| pretrains = [] |
| for file in os.listdir('assets/pretrained_v2'): |
| if string in file: |
| pretrains.append(os.path.join('assets/pretrained_v2',file)) |
| return pretrains |
|
|
| with gr.Blocks(title="🔊",theme=gr.themes.Base(primary_hue="rose",neutral_hue="zinc")) as app: |
| with gr.Row(): |
| gr.HTML("<img src='file/a.png' alt='image'>") |
| with gr.Tabs(): |
| with gr.TabItem(i18n("模型推理")): |
| with gr.Row(): |
| sid0 = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names), value=find_model()) |
| refresh_button = gr.Button(i18n("刷新音色列表和索引路径"), variant="primary") |
| |
| spk_item = gr.Slider( |
| minimum=0, |
| maximum=2333, |
| step=1, |
| label=i18n("请选择说话人id"), |
| value=0, |
| visible=False, |
| interactive=True, |
| ) |
| |
| |
| |
| vc_transform0 = gr.Number( |
| label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 |
| ) |
| but0 = gr.Button(i18n("转换"), variant="primary") |
| with gr.Row(): |
| with gr.Column(): |
| with gr.Row(): |
| dropbox = gr.File(label="Drop your audio here & hit the Reload button.") |
| with gr.Row(): |
| record_button=gr.Audio(source="microphone", label="OR Record audio.", type="filepath") |
| with gr.Row(): |
| input_audio0 = gr.Dropdown( |
| label=i18n("输入待处理音频文件路径(默认是正确格式示例)"), |
| value=find_audios(True), |
| choices=find_audios() |
| ) |
| record_button.change(fn=save_wav, inputs=[record_button], outputs=[input_audio0]) |
| dropbox.upload(fn=save_wav, inputs=[dropbox], outputs=[input_audio0]) |
| with gr.Column(): |
| with gr.Accordion(label=i18n("自动检测index路径,下拉式选择(dropdown)"), open=False): |
| file_index2 = gr.Dropdown( |
| label=i18n("自动检测index路径,下拉式选择(dropdown)"), |
| choices=get_indexes(), |
| interactive=True, |
| value=get_index() |
| ) |
| index_rate1 = gr.Slider( |
| minimum=0, |
| maximum=1, |
| label=i18n("检索特征占比"), |
| value=0.66, |
| interactive=True, |
| ) |
| vc_output2 = gr.Audio(label=i18n("输出音频(右下角三个点,点了可以下载)")) |
| with gr.Accordion(label=i18n("常规设置"), open=False): |
| f0method0 = gr.Radio( |
| label=i18n( |
| "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU" |
| ), |
| choices=["pm", "harvest", "crepe", "rmvpe"] |
| if config.dml == False |
| else ["pm", "harvest", "rmvpe"], |
| value="rmvpe", |
| interactive=True, |
| ) |
| filter_radius0 = gr.Slider( |
| minimum=0, |
| maximum=7, |
| label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"), |
| value=3, |
| step=1, |
| interactive=True, |
| ) |
| resample_sr0 = gr.Slider( |
| minimum=0, |
| maximum=48000, |
| label=i18n("后处理重采样至最终采样率，0为不进行重采样"), |
| value=0, |
| step=1, |
| interactive=True, |
| visible=False |
| ) |
| rms_mix_rate0 = gr.Slider( |
| minimum=0, |
| maximum=1, |
| label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"), |
| value=0.21, |
| interactive=True, |
| ) |
| protect0 = gr.Slider( |
| minimum=0, |
| maximum=0.5, |
| label=i18n( |
| "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果" |
| ), |
| value=0.33, |
| step=0.01, |
| interactive=True, |
| ) |
| file_index1 = gr.Textbox( |
| label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"), |
| value="", |
| interactive=True, |
| visible=False |
| ) |
| refresh_button.click( |
| fn=change_choices, |
| inputs=[], |
| outputs=[sid0, file_index2, input_audio0], |
| api_name="infer_refresh", |
| ) |
| |
| |
| |
| |
| |
| with gr.Row(): |
| f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调"), visible=False) |
| with gr.Row(): |
| vc_output1 = gr.Textbox(label=i18n("输出信息")) |
| but0.click( |
| vc.vc_single, |
| [ |
| spk_item, |
| input_audio0, |
| vc_transform0, |
| f0_file, |
| f0method0, |
| file_index1, |
| file_index2, |
| |
| index_rate1, |
| filter_radius0, |
| resample_sr0, |
| rms_mix_rate0, |
| protect0, |
| ], |
| [vc_output1, vc_output2], |
| api_name="infer_convert", |
| ) |
| with gr.Row(): |
| with gr.Accordion(open=False, label=i18n("批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. ")): |
| with gr.Row(): |
| opt_input = gr.Textbox(label=i18n("指定输出文件夹"), value="opt") |
| vc_transform1 = gr.Number( |
| label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 |
| ) |
| f0method1 = gr.Radio( |
| label=i18n( |
| "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU" |
| ), |
| choices=["pm", "harvest", "crepe", "rmvpe"] |
| if config.dml == False |
| else ["pm", "harvest", "rmvpe"], |
| value="pm", |
| interactive=True, |
| ) |
| with gr.Row(): |
| filter_radius1 = gr.Slider( |
| minimum=0, |
| maximum=7, |
| label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"), |
| value=3, |
| step=1, |
| interactive=True, |
| visible=False |
| ) |
| with gr.Row(): |
| file_index3 = gr.Textbox( |
| label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"), |
| value="", |
| interactive=True, |
| visible=False |
| ) |
| file_index4 = gr.Dropdown( |
| label=i18n("自动检测index路径,下拉式选择(dropdown)"), |
| choices=sorted(index_paths), |
| interactive=True, |
| visible=False |
| ) |
| refresh_button.click( |
| fn=lambda: change_choices()[1], |
| inputs=[], |
| outputs=file_index4, |
| api_name="infer_refresh_batch", |
| ) |
| |
| |
| |
| |
| |
| index_rate2 = gr.Slider( |
| minimum=0, |
| maximum=1, |
| label=i18n("检索特征占比"), |
| value=1, |
| interactive=True, |
| visible=False |
| ) |
| with gr.Row(): |
| resample_sr1 = gr.Slider( |
| minimum=0, |
| maximum=48000, |
| label=i18n("后处理重采样至最终采样率，0为不进行重采样"), |
| value=0, |
| step=1, |
| interactive=True, |
| visible=False |
| ) |
| rms_mix_rate1 = gr.Slider( |
| minimum=0, |
| maximum=1, |
| label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"), |
| value=0.21, |
| interactive=True, |
| ) |
| protect1 = gr.Slider( |
| minimum=0, |
| maximum=0.5, |
| label=i18n( |
| "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果" |
| ), |
| value=0.33, |
| step=0.01, |
| interactive=True, |
| ) |
| with gr.Row(): |
| dir_input = gr.Textbox( |
| label=i18n("输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)"), |
| value="./audios", |
| ) |
| inputs = gr.File( |
| file_count="multiple", label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹") |
| ) |
| with gr.Row(): |
| format1 = gr.Radio( |
| label=i18n("导出文件格式"), |
| choices=["wav", "flac", "mp3", "m4a"], |
| value="wav", |
| interactive=True, |
| ) |
| but1 = gr.Button(i18n("转换"), variant="primary") |
| vc_output3 = gr.Textbox(label=i18n("输出信息")) |
| but1.click( |
| vc.vc_multi, |
| [ |
| spk_item, |
| dir_input, |
| opt_input, |
| inputs, |
| vc_transform1, |
| f0method1, |
| file_index1, |
| file_index2, |
| |
| index_rate1, |
| filter_radius1, |
| resample_sr1, |
| rms_mix_rate1, |
| protect1, |
| format1, |
| ], |
| [vc_output3], |
| api_name="infer_convert_batch", |
| ) |
| sid0.change( |
| fn=vc.get_vc, |
| inputs=[sid0, protect0, protect1], |
| outputs=[spk_item, protect0, protect1, file_index2, file_index4], |
| ) |
| with gr.TabItem("Download Model"): |
| with gr.Row(): |
| url=gr.Textbox(label="Enter the URL to the Model:") |
| with gr.Row(): |
| model = gr.Textbox(label="Name your model:") |
| download_button=gr.Button("Download") |
| with gr.Row(): |
| status_bar=gr.Textbox(label="") |
| download_button.click(fn=download_from_url, inputs=[url, model], outputs=[status_bar]) |
| with gr.Row(): |
| gr.Markdown( |
| """ |
| ❤️ If you use this and like it, help me keep it.❤️ |
| https://paypal.me/lesantillan |
| """ |
| ) |
| with gr.TabItem(i18n("训练")): |
| with gr.Row(): |
| with gr.Column(): |
| exp_dir1 = gr.Textbox(label=i18n("输入实验名"), value="My-Voice") |
| np7 = gr.Slider( |
| minimum=0, |
| maximum=config.n_cpu, |
| step=1, |
| label=i18n("提取音高和处理数据使用的CPU进程数"), |
| value=int(np.ceil(config.n_cpu / 1.5)), |
| interactive=True, |
| ) |
| sr2 = gr.Radio( |
| label=i18n("目标采样率"), |
| choices=["40k", "32k"], |
| value="40k", |
| interactive=True, |
| visible=True |
| ) |
| if_f0_3 = gr.Radio( |
| label=i18n("模型是否带音高指导(唱歌一定要, 语音可以不要)"), |
| choices=[True, False], |
| value=True, |
| interactive=True, |
| visible=False |
| ) |
| version19 = gr.Radio( |
| label=i18n("版本"), |
| choices=["v1", "v2"], |
| value="v2", |
| interactive=True, |
| visible=False, |
| ) |
| trainset_dir4 = gr.Textbox( |
| label=i18n("输入训练文件夹路径"), value='./dataset/'+datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S") |
| ) |
| easy_uploader = gr.Files(label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹"),file_types=['audio']) |
| but1 = gr.Button(i18n("处理数据"), variant="primary") |
| info1 = gr.Textbox(label=i18n("输出信息"), value="") |
| easy_uploader.upload(fn=upload_to_dataset, inputs=[easy_uploader, trainset_dir4], outputs=[info1, trainset_dir4]) |
| gpus6 = gr.Textbox( |
| label=i18n("以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"), |
| value=gpus, |
| interactive=True, |
| visible=F0GPUVisible, |
| ) |
| gpu_info9 = gr.Textbox( |
| label=i18n("显卡信息"), value=gpu_info, visible=F0GPUVisible |
| ) |
| spk_id5 = gr.Slider( |
| minimum=0, |
| maximum=4, |
| step=1, |
| label=i18n("请指定说话人id"), |
| value=0, |
| interactive=True, |
| visible=False |
| ) |
| but1.click( |
| preprocess_dataset, |
| [trainset_dir4, exp_dir1, sr2, np7], |
| [info1], |
| api_name="train_preprocess", |
| ) |
| with gr.Column(): |
| f0method8 = gr.Radio( |
| label=i18n( |
| "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU" |
| ), |
| choices=["pm", "harvest", "dio", "rmvpe", "rmvpe_gpu"], |
| value="rmvpe_gpu", |
| interactive=True, |
| ) |
| gpus_rmvpe = gr.Textbox( |
| label=i18n( |
| "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程" |
| ), |
| value="%s-%s" % (gpus, gpus), |
| interactive=True, |
| visible=F0GPUVisible, |
| ) |
| but2 = gr.Button(i18n("特征提取"), variant="primary") |
| info2 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) |
| f0method8.change( |
| fn=change_f0_method, |
| inputs=[f0method8], |
| outputs=[gpus_rmvpe], |
| ) |
| but2.click( |
| extract_f0_feature, |
| [ |
| gpus6, |
| np7, |
| f0method8, |
| if_f0_3, |
| exp_dir1, |
| version19, |
| gpus_rmvpe, |
| ], |
| [info2], |
| api_name="train_extract_f0_feature", |
| ) |
| with gr.Column(): |
| total_epoch11 = gr.Slider( |
| minimum=2, |
| maximum=1000, |
| step=1, |
| label=i18n("总训练轮数total_epoch"), |
| value=150, |
| interactive=True, |
| ) |
| gpus16 = gr.Textbox( |
| label=i18n("以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"), |
| value="0", |
| interactive=True, |
| visible=True |
| ) |
| but3 = gr.Button(i18n("训练模型"), variant="primary") |
| but4 = gr.Button(i18n("训练特征索引"), variant="primary") |
| info3 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=10) |
| with gr.Accordion(label=i18n("常规设置"), open=False): |
| save_epoch10 = gr.Slider( |
| minimum=1, |
| maximum=50, |
| step=1, |
| label=i18n("保存频率save_every_epoch"), |
| value=25, |
| interactive=True, |
| ) |
| batch_size12 = gr.Slider( |
| minimum=1, |
| maximum=40, |
| step=1, |
| label=i18n("每张显卡的batch_size"), |
| value=default_batch_size, |
| interactive=True, |
| ) |
| if_save_latest13 = gr.Radio( |
| label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"), |
| choices=[i18n("是"), i18n("否")], |
| value=i18n("是"), |
| interactive=True, |
| visible=False |
| ) |
| if_cache_gpu17 = gr.Radio( |
| label=i18n( |
| "是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速" |
| ), |
| choices=[i18n("是"), i18n("否")], |
| value=i18n("否"), |
| interactive=True, |
| ) |
| if_save_every_weights18 = gr.Radio( |
| label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"), |
| choices=[i18n("是"), i18n("否")], |
| value=i18n("是"), |
| interactive=True, |
| ) |
| display_advanced_settings = gr.Checkbox( |
| value=False, |
| label="Advanced Settings" |
| ) |
| pretrained_G14 = gr.Dropdown( |
| label=i18n("加载预训练底模G路径"), |
| value="assets/pretrained_v2/f0G40k.pth", |
| choices=get_pretrains('G'), |
| interactive=True, |
| visible=False |
| ) |
| pretrained_D15 = gr.Dropdown( |
| label=i18n("加载预训练底模D路径"), |
| value="assets/pretrained_v2/f0D40k.pth", |
| interactive=True, |
| choices=get_pretrains('D'), |
| visible=False |
| ) |
| display_advanced_settings.change(fn=update_visibility,inputs=[display_advanced_settings],outputs=[pretrained_G14,pretrained_D15]) |
| with gr.Row(): |
| download_model = gr.Button('5.Download Model') |
| with gr.Row(): |
| model_files = gr.Files(label='Your Model and Index file can be downloaded here:') |
| download_model.click(fn=download_model_files, inputs=[exp_dir1], outputs=[model_files, info3]) |
| with gr.Row(): |
| sr2.change( |
| change_sr2, |
| [sr2, if_f0_3, version19], |
| [pretrained_G14, pretrained_D15], |
| ) |
| version19.change( |
| change_version19, |
| [sr2, if_f0_3, version19], |
| [pretrained_G14, pretrained_D15, sr2], |
| ) |
| if_f0_3.change( |
| change_f0, |
| [if_f0_3, sr2, version19], |
| [f0method8, pretrained_G14, pretrained_D15], |
| ) |
| with gr.Row(): |
| but5 = gr.Button(i18n("一键训练"), variant="primary", visible=False) |
| but3.click( |
| 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, |
| ], |
| info3, |
| api_name="train_start", |
| ) |
| but4.click(train_index, [exp_dir1, version19], info3) |
| but5.click( |
| 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, |
| ], |
| info3, |
| api_name="train_start_all", |
| ) |
|
|
| if config.iscolab: |
| app.queue().launch(share=True,debug=True) |
| else: |
| app.queue().launch( |
| server_name="0.0.0.0", |
| inbrowser=not config.noautoopen, |
| server_port=config.listen_port, |
| quiet=True, |
| ) |
