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git clone https://github.com/svc-develop-team/so-vits-svc.gitpip install -r requirements.txt1# vec256l9/vec768l12
2wget https://ibm.box.com/s/z1wgl1stco8ffooyatzdwsqn2psd9lrr
3# hubert-soft
4wget https://github.com/bshall/hubert/releases/download/v0.1/hubert-soft-0d54a1f4.pt
5# nsf_hifigan
6wget -P pretrain/ https://github.com/openvpi/vocoders/releases/download/nsf-hifigan-v1/nsf_hifigan_20221211.zip
7unzip -od pretrain/nsf_hifigan pretrain/nsf_hifigan_20221211.zipdataset_raw
├───speaker0
│ ├───xxx1-xxx1.wav
│ ├───...
│ └───Lxx-0xx8.wav
└───speaker1
├───xx2-0xxx2.wav
├───...
└───xxx7-xxx007.wavdataset_raw
├─ABeiDuo
├─AiErHaiSen
├─AiLiSi
├─AiLuoYi
├─AnBo
├─BaBaLa
├─BaChongShenZi
├─BaiShu
├─BanNiTe
├─BeiDou
├─ChongYun
├─DaDaLiYa
├─DaiInSiLeiBu
├─DiAoNa
├─DiLuKe
├─DiXiYa
├─DuoLi
├─DuoTuoLei
├─FaLuShan
├─FeiXieEr
├─FengYuanWanYe
├─GanYu
├─HuangYanYiDou
├─HuTao
├─JiuQiRen
├─JiuTiaoShaLou
├─KaiYa
├─KanDiSi
├─KaWei
├─KeLai
├─KeLi
├─KeQing
├─Kong
├─LaiLa
├─LeiDianJiangJun
├─LeiZe
├─LiSha
├─LiuLangZhe
├─LuoShaLiYa
├─LuYeYuanPingZang
├─MiKa
├─MoNa
├─NaXiDa
├─NiLu
├─NingGuang
├─NuoAiEr
├─OuLa
├─PaiMeng
├─Qin
├─QiQi
├─SaiNo
├─ShanHuGongXingHai
├─ShaTang
├─ShenHe
├─ShenLiLingHua
├─ShenLiLingRen
├─SiKaLaMuQi
├─TiNaLi
├─TuoMa
├─WeiBing
├─WenDi
├─WuLang
├─XiangLing
├─Xiao
├─XiaoGong
├─XingQiu
├─XiNuoLa
├─XinYan
├─YanFei
├─YaoYao
├─YeLan
├─Ying
├─YunJing
├─ZaoYou
└─ZhongLi1#重采样
2python resample.py --skip_loudnorm
3#选择编码器vec768l12,生成config.json、train.txt和val.txt
4python preprocess_flist_config.py --speech_encoder vec768l12 --vol_aug
5#选择f0预测器crepe,生成预训练文件
6python preprocess_hubert_f0.py --f0_predictor crepe --num_processes 8{
"train": {
"log_interval": 2000,
"eval_interval": 2000,
"seed": 1234,
"epochs": 10000,
"learning_rate": 0.0001,
"betas": [
0.8,
0.99
],
"eps": 1e-09,
"batch_size": 1,
"fp16_run": false,
"lr_decay": 0.999875,
"segment_size": 10240,
"init_lr_ratio": 1,
"warmup_epochs": 0,
"c_mel": 45,
"c_kl": 1.0,
"use_sr": true,
"max_speclen": 512,
"port": "8001",
"keep_ckpts": 0,
"all_in_mem": false
},
"data": {
"training_files": "filelists/train.txt",
"validation_files": "filelists/val.txt",
"max_wav_value": 32768.0,
"sampling_rate": 44100,
"filter_length": 2048,
"hop_length": 512,
"win_length": 2048,
"n_mel_channels": 80,
"mel_fmin": 0.0,
"mel_fmax": 22050
},
"model": {
"inter_channels": 192,
"hidden_channels": 192,
"filter_channels": 768,
"n_heads": 2,
"n_layers": 6,
"kernel_size": 3,
"p_dropout": 0.1,
"resblock": "1",
"resblock_kernel_sizes": [
3,
7,
11
],
"resblock_dilation_sizes": [
[
1,
3,
5
],
[
1,
3,
5
],
[
1,
3,
5
]
],
"upsample_rates": [
8,
8,
2,
2,
2
],
"upsample_initial_channel": 512,
"upsample_kernel_sizes": [
16,
16,
4,
4,
4
],
"n_layers_q": 3,
"use_spectral_norm": false,
"gin_channels": 768,
"ssl_dim": 768,
"n_speakers": 75,
"speech_encoder": "vec768l12",
"speaker_embedding": false
},
"spk": {
"ABeiDuo": 0,
"AiErHaiSen": 1,
"AiLiSi": 2,
"AiLuoYi": 3,
"AnBo": 4,
"BaBaLa": 5,
"BaChongShenZi": 6,
"BaiShu": 7,
"BanNiTe": 8,
"BeiDou": 9,
"ChongYun": 10,
"DaDaLiYa": 11,
"DaiInSiLeiBu": 12,
"DiAoNa": 13,
"DiLuKe": 14,
"DiXiYa": 15,
"DuoLi": 16,
"DuoTuoLei": 17,
"FaLuShan": 18,
"FeiXieEr": 19,
"FengYuanWanYe": 20,
"GanYu": 21,
"HuangYanYiDou": 22,
"HuTao": 23,
"JiuQiRen": 24,
"JiuTiaoShaLou": 25,
"KaiYa": 26,
"KanDiSi": 27,
"KaWei": 28,
"KeLai": 29,
"KeLi": 30,
"KeQing": 31,
"Kong": 32,
"LaiLa": 33,
"LeiDianJiangJun": 34,
"LeiZe": 35,
"LiSha": 36,
"LiuLangZhe": 37,
"LuoShaLiYa": 38,
"LuYeYuanPingZang": 39,
"MiKa": 40,
"MoNa": 41,
"NaXiDa": 42,
"NiLu": 43,
"NingGuang": 44,
"NuoAiEr": 45,
"OuLa": 46,
"PaiMeng": 47,
"Qin": 48,
"QiQi": 49,
"SaiNo": 50,
"ShanHuGongXingHai": 51,
"ShaTang": 52,
"ShenHe": 53,
"ShenLiLingHua": 54,
"ShenLiLingRen": 55,
"SiKaLaMuQi": 56,
"TiNaLi": 57,
"TuoMa": 58,
"WeiBing": 59,
"WenDi": 60,
"WuLang": 61,
"XiangLing": 62,
"Xiao": 63,
"XiaoGong": 64,
"XingQiu": 65,
"XiNuoLa": 66,
"XinYan": 67,
"YanFei": 68,
"YaoYao": 69,
"YeLan": 70,
"Ying": 71,
"YunJing": 72,
"ZaoYou": 73,
"ZhongLi": 74
}
}1"log_interval": 2000, //每2000steps验证一次模型
2"eval_interval": 2000, //每2000steps保存一次模型
3"learning_rate": 0.0001, //学习率
4"batch_size": 1, //每次取1条语音进行训练(显存6GB推荐)
5"keep_ckpts": 0, //保存最新几个模型,0为不删除模型
6"speech_encoder": "vec768l12" //编码器python train.py -c configs/config.json -m 44kpython compress_model.py -c="configs/config.json" -i="logs/44k/G_200000.pth" -o="logs/44k/GeshinImpact.pth"1# Example
2python inference_main.py -m "logs/44k/GeshinImpact.pth" -c "configs/config.json" -n "干声(不含伴奏).wav" -t 0 -s "NaXiDa" -f0p crepe -lea 1