tuna2134/sbv2-v2
1
0
Fork 0
Code Issues Pull Requests 1 Actions Packages Projects Releases Wiki Activity

Support V210 train and fix some bugs and improve

Browse Source
This commit is contained in:
litagin02
2023-12-23 12:59:57 +09:00
parent 3090f5837d
commit 6c171fcea0
8 changed files with 1365 additions and 900 deletions
Download Patch File Download Diff File Expand all files Collapse all files

14
README.md
View File

@@ -1,3 +1,17 @@
# 変更点
- `keep_ckpts`のバグを修正
- 圧縮モデルを保存する設定を追加: `config.json`の`save_compressed_models`を`true`にすると、modelの保存時に圧縮モデルも保存される（これと`keep_ckpts`を`1`とかにしとけばかなり容量の節約に）
- Ver 2.1での学習をサポート（`train_ms_V210.py`）
# TODO
- [ ] Ver 2.2での学習をサポート
- [ ] Ver 2.1, 2.2での学習でのbf16対応
- [ ] 推論のWebUIでのバージョンに応じた感情指定のサポート
- [ ] より良い推論WebUI？
- [ ] 学習のWebUI?
以下本家のREADME.md
<div align="center">
<img alt="LOGO" src="https://cdn.jsdelivr.net/gh/fishaudio/fish-diffusion@main/images/logo_512x512.png" width="256" height="256" />

902
configs/config.json
View File

@@ -2,14 +2,12 @@
"train": {
"log_interval": 200,
"eval_interval": 1000,
"save_compressed_models": true,
"seed": 42,
"epochs": 1000,
"learning_rate": 0.0002,
"betas": [
0.8,
0.99
],
"eps": 1e-09,
"betas": [0.8, 0.99],
"eps": 1e-9,
"batch_size": 16,
"bf16_run": false,
"lr_decay": 0.99995,
@@ -38,859 +36,7 @@
"mel_fmax": null,
"add_blank": true,
"n_speakers": 850,
"cleaned_text": true,
"spk2id": {
"派蒙_ZH": 0,
"纳西妲_ZH": 1,
"凯亚_ZH": 2,
"阿贝多_ZH": 3,
"温迪_ZH": 4,
"枫原万叶_ZH": 5,
"钟离_ZH": 6,
"荒泷一斗_ZH": 7,
"八重神子_ZH": 8,
"艾尔海森_ZH": 9,
"提纳里_ZH": 10,
"迪希雅_ZH": 11,
"卡维_ZH": 12,
"宵宫_ZH": 13,
"那维莱特_ZH": 14,
"莱依拉_ZH": 15,
"赛诺_ZH": 16,
"莫娜_ZH": 17,
"诺艾尔_ZH": 18,
"托马_ZH": 19,
"凝光_ZH": 20,
"林尼_ZH": 21,
"北斗_ZH": 22,
"柯莱_ZH": 23,
"神里绫华_ZH": 24,
"可莉_ZH": 25,
"芭芭拉_ZH": 26,
"雷电将军_ZH": 27,
"娜维娅_ZH": 28,
"芙宁娜_ZH": 29,
"珊瑚宫心海_ZH": 30,
"鹿野院平藏_ZH": 31,
"迪奥娜_ZH": 32,
"琴_ZH": 33,
"五郎_ZH": 34,
"班尼特_ZH": 35,
"达达利亚_ZH": 36,
"安柏_ZH": 37,
"莱欧斯利_ZH": 38,
"夜兰_ZH": 39,
"妮露_ZH": 40,
"辛焱_ZH": 41,
"丽莎_ZH": 42,
"珐露珊_ZH": 43,
"魈_ZH": 44,
"香菱_ZH": 45,
"迪卢克_ZH": 46,
"砂糖_ZH": 47,
"烟绯_ZH": 48,
"早柚_ZH": 49,
"云堇_ZH": 50,
"刻晴_ZH": 51,
"重云_ZH": 52,
"优菈_ZH": 53,
"胡桃_ZH": 54,
"流浪者_ZH": 55,
"久岐忍_ZH": 56,
"神里绫人_ZH": 57,
"甘雨_ZH": 58,
"戴因斯雷布_ZH": 59,
"菲谢尔_ZH": 60,
"白术_ZH": 61,
"行秋_ZH": 62,
"九条裟罗_ZH": 63,
"夏洛蒂_ZH": 64,
"雷泽_ZH": 65,
"申鹤_ZH": 66,
"荧_ZH": 67,
"空_ZH": 68,
"迪娜泽黛_ZH": 69,
"凯瑟琳_ZH": 70,
"多莉_ZH": 71,
"坎蒂丝_ZH": 72,
"琳妮特_ZH": 73,
"萍姥姥_ZH": 74,
"罗莎莉亚_ZH": 75,
"埃德_ZH": 76,
"爱贝尔_ZH": 77,
"伊迪娅_ZH": 78,
"留云借风真君_ZH": 79,
"绮良良_ZH": 80,
"陌生人_ZH": 81,
"七七_ZH": 82,
"式大将_ZH": 83,
"瑶瑶_ZH": 84,
"奥兹_ZH": 85,
"菲米尼_ZH": 86,
"米卡_ZH": 87,
"哲平_ZH": 88,
"浮游水蕈兽·元素生命_ZH": 89,
"大肉丸_ZH": 90,
"托克_ZH": 91,
"蒂玛乌斯_ZH": 92,
"昆钧_ZH": 93,
"欧菲妮_ZH": 94,
"塞琉斯_ZH": 95,
"仆人_ZH": 96,
"迈勒斯_ZH": 97,
"希格雯_ZH": 98,
"阿守_ZH": 99,
"拉赫曼_ZH": 100,
"杜拉夫_ZH": 101,
"伊利亚斯_ZH": 102,
"阿晃_ZH": 103,
"旁白_ZH": 104,
"爱德琳_ZH": 105,
"埃洛伊_ZH": 106,
"德沃沙克_ZH": 107,
"玛乔丽_ZH": 108,
"塞塔蕾_ZH": 109,
"柊千里_ZH": 110,
"海芭夏_ZH": 111,
"九条镰治_ZH": 112,
"阿娜耶_ZH": 113,
"笼钓瓶一心_ZH": 114,
"回声海螺_ZH": 115,
"劳维克_ZH": 116,
"元太_ZH": 117,
"阿扎尔_ZH": 118,
"查尔斯_ZH": 119,
"阿洛瓦_ZH": 120,
"埃勒曼_ZH": 121,
"纳比尔_ZH": 122,
"莎拉_ZH": 123,
"康纳_ZH": 124,
"博来_ZH": 125,
"玛塞勒_ZH": 126,
"阿祇_ZH": 127,
"博士_ZH": 128,
"玛格丽特_ZH": 129,
"迪尔菲_ZH": 130,
"宛烟_ZH": 131,
"羽生田千鹤_ZH": 132,
"海妮耶_ZH": 133,
"旅行者_ZH": 134,
"霍夫曼_ZH": 135,
"佐西摩斯_ZH": 136,
"鹿野奈奈_ZH": 137,
"舒伯特_ZH": 138,
"天叔_ZH": 139,
"艾莉丝_ZH": 140,
"龙二_ZH": 141,
"莺儿_ZH": 142,
"嘉良_ZH": 143,
"一心传名刀_ZH": 144,
"珊瑚_ZH": 145,
"言笑_ZH": 146,
"久利须_ZH": 147,
"嘉玛_ZH": 148,
"艾文_ZH": 149,
"克洛琳德_ZH": 150,
"丹吉尔_ZH": 151,
"女士_ZH": 152,
"白老先生_ZH": 153,
"天目十五_ZH": 154,
"老孟_ZH": 155,
"巴达维_ZH": 156,
"长生_ZH": 157,
"吴船长_ZH": 158,
"拉齐_ZH": 159,
"艾伯特_ZH": 160,
"松浦_ZH": 161,
"埃泽_ZH": 162,
"阿圆_ZH": 163,
"莫塞伊思_ZH": 164,
"阿拉夫_ZH": 165,
"杜吉耶_ZH": 166,
"石头_ZH": 167,
"百闻_ZH": 168,
"波洛_ZH": 169,
"斯坦利_ZH": 170,
"博易_ZH": 171,
"迈蒙_ZH": 172,
"掇星攫辰天君_ZH": 173,
"毗伽尔_ZH": 174,
"芙卡洛斯_ZH": 175,
"恶龙_ZH": 176,
"恕筠_ZH": 177,
"知易_ZH": 178,
"克列门特_ZH": 179,
"大慈树王_ZH": 180,
"西拉杰_ZH": 181,
"上杉_ZH": 182,
"阿尔卡米_ZH": 183,
"纯水精灵_ZH": 184,
"常九爷_ZH": 185,
"沙扎曼_ZH": 186,
"田铁嘴_ZH": 187,
"克罗索_ZH": 188,
"阿巴图伊_ZH": 189,
"阿佩普_ZH": 190,
"埃尔欣根_ZH": 191,
"萨赫哈蒂_ZH": 192,
"塔杰·拉德卡尼_ZH": 193,
"安西_ZH": 194,
"陆行岩本真蕈·元素生命_ZH": 195,
"派蒙_JP": 196,
"纳西妲_JP": 197,
"凯亚_JP": 198,
"阿贝多_JP": 199,
"温迪_JP": 200,
"枫原万叶_JP": 201,
"钟离_JP": 202,
"荒泷一斗_JP": 203,
"八重神子_JP": 204,
"艾尔海森_JP": 205,
"提纳里_JP": 206,
"迪希雅_JP": 207,
"卡维_JP": 208,
"宵宫_JP": 209,
"那维莱特_JP": 210,
"莱依拉_JP": 211,
"赛诺_JP": 212,
"莫娜_JP": 213,
"诺艾尔_JP": 214,
"托马_JP": 215,
"凝光_JP": 216,
"林尼_JP": 217,
"北斗_JP": 218,
"柯莱_JP": 219,
"神里绫华_JP": 220,
"可莉_JP": 221,
"芭芭拉_JP": 222,
"雷电将军_JP": 223,
"娜维娅_JP": 224,
"芙宁娜_JP": 225,
"珊瑚宫心海_JP": 226,
"鹿野院平藏_JP": 227,
"迪奥娜_JP": 228,
"琴_JP": 229,
"五郎_JP": 230,
"班尼特_JP": 231,
"达达利亚_JP": 232,
"安柏_JP": 233,
"莱欧斯利_JP": 234,
"夜兰_JP": 235,
"妮露_JP": 236,
"辛焱_JP": 237,
"丽莎_JP": 238,
"珐露珊_JP": 239,
"魈_JP": 240,
"香菱_JP": 241,
"迪卢克_JP": 242,
"砂糖_JP": 243,
"烟绯_JP": 244,
"早柚_JP": 245,
"云堇_JP": 246,
"刻晴_JP": 247,
"重云_JP": 248,
"优菈_JP": 249,
"胡桃_JP": 250,
"流浪者_JP": 251,
"久岐忍_JP": 252,
"神里绫人_JP": 253,
"甘雨_JP": 254,
"戴因斯雷布_JP": 255,
"菲谢尔_JP": 256,
"白术_JP": 257,
"行秋_JP": 258,
"九条裟罗_JP": 259,
"夏洛蒂_JP": 260,
"雷泽_JP": 261,
"申鹤_JP": 262,
"空_JP": 263,
"荧_JP": 264,
"迪娜泽黛_JP": 265,
"凯瑟琳_JP": 266,
"多莉_JP": 267,
"坎蒂丝_JP": 268,
"琳妮特_JP": 269,
"萍姥姥_JP": 270,
"罗莎莉亚_JP": 271,
"埃德_JP": 272,
"爱贝尔_JP": 273,
"伊迪娅_JP": 274,
"留云借风真君_JP": 275,
"绮良良_JP": 276,
"陌生人_JP": 277,
"七七_JP": 278,
"式大将_JP": 279,
"瑶瑶_JP": 280,
"奥兹_JP": 281,
"菲米尼_JP": 282,
"米卡_JP": 283,
"哲平_JP": 284,
"浮游水蕈兽·元素生命_JP": 285,
"大肉丸_JP": 286,
"托克_JP": 287,
"蒂玛乌斯_JP": 288,
"昆钧_JP": 289,
"欧菲妮_JP": 290,
"塞琉斯_JP": 291,
"仆人_JP": 292,
"迈勒斯_JP": 293,
"希格雯_JP": 294,
"阿守_JP": 295,
"拉赫曼_JP": 296,
"杜拉夫_JP": 297,
"伊利亚斯_JP": 298,
"阿晃_JP": 299,
"旁白_JP": 300,
"爱德琳_JP": 301,
"埃洛伊_JP": 302,
"德沃沙克_JP": 303,
"玛乔丽_JP": 304,
"塞塔蕾_JP": 305,
"柊千里_JP": 306,
"海芭夏_JP": 307,
"九条镰治_JP": 308,
"阿娜耶_JP": 309,
"笼钓瓶一心_JP": 310,
"回声海螺_JP": 311,
"劳维克_JP": 312,
"元太_JP": 313,
"阿扎尔_JP": 314,
"查尔斯_JP": 315,
"阿洛瓦_JP": 316,
"埃勒曼_JP": 317,
"纳比尔_JP": 318,
"莎拉_JP": 319,
"康纳_JP": 320,
"博来_JP": 321,
"玛塞勒_JP": 322,
"阿祇_JP": 323,
"博士_JP": 324,
"迪尔菲_JP": 325,
"玛格丽特_JP": 326,
"宛烟_JP": 327,
"羽生田千鹤_JP": 328,
"海妮耶_JP": 329,
"霍夫曼_JP": 330,
"旅行者_JP": 331,
"佐西摩斯_JP": 332,
"舒伯特_JP": 333,
"鹿野奈奈_JP": 334,
"天叔_JP": 335,
"龙二_JP": 336,
"艾莉丝_JP": 337,
"莺儿_JP": 338,
"嘉良_JP": 339,
"珊瑚_JP": 340,
"言笑_JP": 341,
"一心传名刀_JP": 342,
"费迪南德_JP": 343,
"久利须_JP": 344,
"嘉玛_JP": 345,
"艾文_JP": 346,
"克洛琳德_JP": 347,
"丹吉尔_JP": 348,
"天目十五_JP": 349,
"女士_JP": 350,
"老孟_JP": 351,
"白老先生_JP": 352,
"舍利夫_JP": 353,
"巴达维_JP": 354,
"拉齐_JP": 355,
"长生_JP": 356,
"吴船长_JP": 357,
"艾伯特_JP": 358,
"松浦_JP": 359,
"埃泽_JP": 360,
"阿圆_JP": 361,
"阿拉夫_JP": 362,
"莫塞伊思_JP": 363,
"石头_JP": 364,
"百闻_JP": 365,
"杜吉耶_JP": 366,
"波洛_JP": 367,
"掇星攫辰天君_JP": 368,
"迈蒙_JP": 369,
"博易_JP": 370,
"诗筠_JP": 371,
"斯坦利_JP": 372,
"毗伽尔_JP": 373,
"芙卡洛斯_JP": 374,
"恶龙_JP": 375,
"小仓澪_JP": 376,
"恕筠_JP": 377,
"知易_JP": 378,
"克列门特_JP": 379,
"大慈树王_JP": 380,
"望雅_JP": 381,
"黑田_JP": 382,
"卡莉娜_JP": 383,
"马姆杜_JP": 384,
"科林斯_JP": 385,
"上杉_JP": 386,
"西拉杰_JP": 387,
"菲尔戈黛特_JP": 388,
"一平_JP": 389,
"纯水精灵_JP": 390,
"阿尔卡米_JP": 391,
"老戴_JP": 392,
"谢赫祖拜尔_JP": 393,
"沙扎曼_JP": 394,
"田铁嘴_JP": 395,
"小野寺_JP": 396,
"百识_JP": 397,
"克罗索_JP": 398,
"莱斯格_JP": 399,
"芷巧_JP": 400,
"加藤洋平_JP": 401,
"阿巴图伊_JP": 402,
"埃尔欣根_JP": 403,
"斯嘉莉_JP": 404,
"阿佩普_JP": 405,
"巫女_JP": 406,
"卡布斯_JP": 407,
"洛伦佐_JP": 408,
"萨赫哈蒂_JP": 409,
"娜德瓦_JP": 410,
"塞德娜_JP": 411,
"塔杰·拉德卡尼_JP": 412,
"绘星_JP": 413,
"泽田_JP": 414,
"安西_JP": 415,
"拉伊德_JP": 416,
"亚卡巴_JP": 417,
"有乐斋_JP": 418,
"莱昂_JP": 419,
"尤苏波夫_JP": 420,
"夏妮_JP": 421,
"埃舍尔_JP": 422,
"萨齐因_JP": 423,
"古山_JP": 424,
"自称渊上之物_JP": 425,
"丹羽_JP": 426,
"塞萨尔的日记_JP": 427,
"派蒙_EN": 428,
"纳西妲_EN": 429,
"凯亚_EN": 430,
"阿贝多_EN": 431,
"温迪_EN": 432,
"枫原万叶_EN": 433,
"钟离_EN": 434,
"荒泷一斗_EN": 435,
"八重神子_EN": 436,
"艾尔海森_EN": 437,
"提纳里_EN": 438,
"迪希雅_EN": 439,
"卡维_EN": 440,
"宵宫_EN": 441,
"莱依拉_EN": 442,
"那维莱特_EN": 443,
"赛诺_EN": 444,
"莫娜_EN": 445,
"诺艾尔_EN": 446,
"托马_EN": 447,
"凝光_EN": 448,
"林尼_EN": 449,
"北斗_EN": 450,
"柯莱_EN": 451,
"神里绫华_EN": 452,
"可莉_EN": 453,
"芭芭拉_EN": 454,
"雷电将军_EN": 455,
"娜维娅_EN": 456,
"芙宁娜_EN": 457,
"珊瑚宫心海_EN": 458,
"鹿野院平藏_EN": 459,
"迪奥娜_EN": 460,
"五郎_EN": 461,
"琴_EN": 462,
"班尼特_EN": 463,
"达达利亚_EN": 464,
"安柏_EN": 465,
"莱欧斯利_EN": 466,
"夜兰_EN": 467,
"妮露_EN": 468,
"辛焱_EN": 469,
"珐露珊_EN": 470,
"丽莎_EN": 471,
"魈_EN": 472,
"香菱_EN": 473,
"迪卢克_EN": 474,
"砂糖_EN": 475,
"烟绯_EN": 476,
"早柚_EN": 477,
"云堇_EN": 478,
"刻晴_EN": 479,
"重云_EN": 480,
"优菈_EN": 481,
"胡桃_EN": 482,
"流浪者_EN": 483,
"久岐忍_EN": 484,
"神里绫人_EN": 485,
"甘雨_EN": 486,
"戴因斯雷布_EN": 487,
"菲谢尔_EN": 488,
"白术_EN": 489,
"行秋_EN": 490,
"九条裟罗_EN": 491,
"夏洛蒂_EN": 492,
"雷泽_EN": 493,
"申鹤_EN": 494,
"荧_EN": 495,
"空_EN": 496,
"迪娜泽黛_EN": 497,
"凯瑟琳_EN": 498,
"多莉_EN": 499,
"坎蒂丝_EN": 500,
"琳妮特_EN": 501,
"萍姥姥_EN": 502,
"罗莎莉亚_EN": 503,
"埃德_EN": 504,
"爱贝尔_EN": 505,
"伊迪娅_EN": 506,
"留云借风真君_EN": 507,
"绮良良_EN": 508,
"陌生人_EN": 509,
"七七_EN": 510,
"式大将_EN": 511,
"瑶瑶_EN": 512,
"奥兹_EN": 513,
"菲米尼_EN": 514,
"米卡_EN": 515,
"哲平_EN": 516,
"浮游水蕈兽·元素生命_EN": 517,
"大肉丸_EN": 518,
"托克_EN": 519,
"蒂玛乌斯_EN": 520,
"昆钧_EN": 521,
"欧菲妮_EN": 522,
"塞琉斯_EN": 523,
"仆人_EN": 524,
"迈勒斯_EN": 525,
"希格雯_EN": 526,
"阿守_EN": 527,
"拉赫曼_EN": 528,
"杜拉夫_EN": 529,
"伊利亚斯_EN": 530,
"阿晃_EN": 531,
"旁白_EN": 532,
"爱德琳_EN": 533,
"埃洛伊_EN": 534,
"德沃沙克_EN": 535,
"玛乔丽_EN": 536,
"塞塔蕾_EN": 537,
"柊千里_EN": 538,
"海芭夏_EN": 539,
"九条镰治_EN": 540,
"阿娜耶_EN": 541,
"笼钓瓶一心_EN": 542,
"回声海螺_EN": 543,
"劳维克_EN": 544,
"元太_EN": 545,
"阿扎尔_EN": 546,
"查尔斯_EN": 547,
"阿洛瓦_EN": 548,
"埃勒曼_EN": 549,
"纳比尔_EN": 550,
"莎拉_EN": 551,
"康纳_EN": 552,
"博来_EN": 553,
"玛塞勒_EN": 554,
"阿祇_EN": 555,
"博士_EN": 556,
"迪尔菲_EN": 557,
"宛烟_EN": 558,
"玛格丽特_EN": 559,
"羽生田千鹤_EN": 560,
"海妮耶_EN": 561,
"霍夫曼_EN": 562,
"旅行者_EN": 563,
"佐西摩斯_EN": 564,
"鹿野奈奈_EN": 565,
"舒伯特_EN": 566,
"天叔_EN": 567,
"艾莉丝_EN": 568,
"龙二_EN": 569,
"莺儿_EN": 570,
"嘉良_EN": 571,
"珊瑚_EN": 572,
"费迪南德_EN": 573,
"言笑_EN": 574,
"一心传名刀_EN": 575,
"久利须_EN": 576,
"嘉玛_EN": 577,
"艾文_EN": 578,
"克洛琳德_EN": 579,
"丹吉尔_EN": 580,
"女士_EN": 581,
"天目十五_EN": 582,
"老孟_EN": 583,
"白老先生_EN": 584,
"舍利夫_EN": 585,
"巴达维_EN": 586,
"拉齐_EN": 587,
"长生_EN": 588,
"吴船长_EN": 589,
"艾伯特_EN": 590,
"松浦_EN": 591,
"埃泽_EN": 592,
"阿圆_EN": 593,
"阿拉夫_EN": 594,
"莫塞伊思_EN": 595,
"石头_EN": 596,
"百闻_EN": 597,
"杜吉耶_EN": 598,
"波洛_EN": 599,
"斯坦利_EN": 600,
"掇星攫辰天君_EN": 601,
"迈蒙_EN": 602,
"博易_EN": 603,
"诗筠_EN": 604,
"毗伽尔_EN": 605,
"慧心_EN": 606,
"芙卡洛斯_EN": 607,
"恶龙_EN": 608,
"小仓澪_EN": 609,
"恕筠_EN": 610,
"知易_EN": 611,
"克列门特_EN": 612,
"大慈树王_EN": 613,
"维多利亚_EN": 614,
"黑田_EN": 615,
"马姆杜_EN": 616,
"科林斯_EN": 617,
"上杉_EN": 618,
"西拉杰_EN": 619,
"宁禄_EN": 620,
"纯水精灵_EN": 621,
"常九爷_EN": 622,
"阿尔卡米_EN": 623,
"沙扎曼_EN": 624,
"田铁嘴_EN": 625,
"加萨尼_EN": 626,
"克罗索_EN": 627,
"星稀_EN": 628,
"莱斯格_EN": 629,
"阿巴图伊_EN": 630,
"埃尔欣根_EN": 631,
"阿佩普_EN": 632,
"萨赫哈蒂_EN": 633,
"洛伦佐_EN": 634,
"塔杰·拉德卡尼_EN": 635,
"泽田_EN": 636,
"安西_EN": 637,
"埃舍尔_EN": 638,
"三月七_ZH": 639,
"丹恒_ZH": 640,
"希儿_ZH": 641,
"娜塔莎_ZH": 642,
"希露瓦_ZH": 643,
"瓦尔特_ZH": 644,
"佩拉_ZH": 645,
"布洛妮娅_ZH": 646,
"虎克_ZH": 647,
"素裳_ZH": 648,
"克拉拉_ZH": 649,
"符玄_ZH": 650,
"白露_ZH": 651,
"杰帕德_ZH": 652,
"景元_ZH": 653,
"藿藿_ZH": 654,
"姬子_ZH": 655,
"穹_ZH": 656,
"星_ZH": 657,
"卡芙卡_ZH": 658,
"桂乃芬_ZH": 659,
"艾丝妲_ZH": 660,
"玲可_ZH": 661,
"彦卿_ZH": 662,
"托帕_ZH": 663,
"驭空_ZH": 664,
"浮烟_ZH": 665,
"停云_ZH": 666,
"镜流_ZH": 667,
"罗刹_ZH": 668,
"卢卡_ZH": 669,
"史瓦罗_ZH": 670,
"黑塔_ZH": 671,
"桑博_ZH": 672,
"伦纳德_ZH": 673,
"明曦_ZH": 674,
"银狼_ZH": 675,
"帕姆_ZH": 676,
"青雀_ZH": 677,
"乔瓦尼_ZH": 678,
"公输师傅_ZH": 679,
"晴霓_ZH": 680,
"螺丝咕姆_ZH": 681,
"阿兰_ZH": 682,
"奥列格_ZH": 683,
"丹枢_ZH": 684,
"尾巴_ZH": 685,
"寒鸦_ZH": 686,
"雪衣_ZH": 687,
"可可利亚_ZH": 688,
"青镞_ZH": 689,
"半夏_ZH": 690,
"银枝_ZH": 691,
"大毫_ZH": 692,
"霄翰_ZH": 693,
"信使_ZH": 694,
"费斯曼_ZH": 695,
"绿芙蓉_ZH": 696,
"金人会长_ZH": 697,
"维利特_ZH": 698,
"维尔德_ZH": 699,
"斯科特_ZH": 700,
"卡波特_ZH": 701,
"刃_ZH": 702,
"岩明_ZH": 703,
"浣溪_ZH": 704,
"三月七_JP": 705,
"丹恒_JP": 706,
"希儿_JP": 707,
"娜塔莎_JP": 708,
"希露瓦_JP": 709,
"瓦尔特_JP": 710,
"佩拉_JP": 711,
"布洛妮娅_JP": 712,
"虎克_JP": 713,
"素裳_JP": 714,
"克拉拉_JP": 715,
"符玄_JP": 716,
"白露_JP": 717,
"杰帕德_JP": 718,
"景元_JP": 719,
"藿藿_JP": 720,
"姬子_JP": 721,
"卡芙卡_JP": 722,
"穹_JP": 723,
"星_JP": 724,
"桂乃芬_JP": 725,
"艾丝妲_JP": 726,
"彦卿_JP": 727,
"玲可_JP": 728,
"托帕_JP": 729,
"驭空_JP": 730,
"浮烟_JP": 731,
"停云_JP": 732,
"镜流_JP": 733,
"罗刹_JP": 734,
"卢卡_JP": 735,
"史瓦罗_JP": 736,
"黑塔_JP": 737,
"桑博_JP": 738,
"伦纳德_JP": 739,
"明曦_JP": 740,
"银狼_JP": 741,
"帕姆_JP": 742,
"青雀_JP": 743,
"乔瓦尼_JP": 744,
"公输师傅_JP": 745,
"晴霓_JP": 746,
"螺丝咕姆_JP": 747,
"阿兰_JP": 748,
"奥列格_JP": 749,
"丹枢_JP": 750,
"尾巴_JP": 751,
"寒鸦_JP": 752,
"雪衣_JP": 753,
"可可利亚_JP": 754,
"青镞_JP": 755,
"半夏_JP": 756,
"银枝_JP": 757,
"大毫_JP": 758,
"霄翰_JP": 759,
"信使_JP": 760,
"费斯曼_JP": 761,
"绿芙蓉_JP": 762,
"金人会长_JP": 763,
"维利特_JP": 764,
"维尔德_JP": 765,
"斯科特_JP": 766,
"刃_JP": 767,
"卡波特_JP": 768,
"岩明_JP": 769,
"浣溪_JP": 770,
"净砚_JP": 771,
"紫月季_JP": 772,
"歌蒂_JP": 773,
"奇怪的云骑_JP": 774,
"幻胧_JP": 775,
"斯薇塔_JP": 776,
"隐书_JP": 777,
"三月七_EN": 778,
"丹恒_EN": 779,
"希儿_EN": 780,
"娜塔莎_EN": 781,
"希露瓦_EN": 782,
"瓦尔特_EN": 783,
"佩拉_EN": 784,
"布洛妮娅_EN": 785,
"虎克_EN": 786,
"素裳_EN": 787,
"克拉拉_EN": 788,
"符玄_EN": 789,
"白露_EN": 790,
"杰帕德_EN": 791,
"景元_EN": 792,
"藿藿_EN": 793,
"姬子_EN": 794,
"卡芙卡_EN": 795,
"穹_EN": 796,
"星_EN": 797,
"桂乃芬_EN": 798,
"艾丝妲_EN": 799,
"彦卿_EN": 800,
"玲可_EN": 801,
"托帕_EN": 802,
"驭空_EN": 803,
"浮烟_EN": 804,
"停云_EN": 805,
"镜流_EN": 806,
"罗刹_EN": 807,
"卢卡_EN": 808,
"史瓦罗_EN": 809,
"黑塔_EN": 810,
"桑博_EN": 811,
"伦纳德_EN": 812,
"明曦_EN": 813,
"银狼_EN": 814,
"帕姆_EN": 815,
"青雀_EN": 816,
"乔瓦尼_EN": 817,
"公输师傅_EN": 818,
"晴霓_EN": 819,
"螺丝咕姆_EN": 820,
"阿兰_EN": 821,
"奥列格_EN": 822,
"丹枢_EN": 823,
"尾巴_EN": 824,
"寒鸦_EN": 825,
"雪衣_EN": 826,
"可可利亚_EN": 827,
"青镞_EN": 828,
"半夏_EN": 829,
"银枝_EN": 830,
"大毫_EN": 831,
"霄翰_EN": 832,
"信使_EN": 833,
"费斯曼_EN": 834,
"绿芙蓉_EN": 835,
"金人会长_EN": 836,
"维利特_EN": 837,
"维尔德_EN": 838,
"刃_EN": 839,
"卡波特_EN": 840,
"岩明_EN": 841,
"浣溪_EN": 842,
"紫月季_EN": 843,
"幻胧_EN": 844,
"女声_EN": 845,
"陆景和": 846,
"莫弈": 847,
"左然": 848,
"夏彦": 849
}
"cleaned_text": true
},
"model": {
"use_spk_conditioned_encoder": true,
@@ -905,43 +51,15 @@
"kernel_size": 3,
"p_dropout": 0.1,
"resblock": "1",
"resblock_kernel_sizes": [
3,
7,
11
],
"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
[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,
8,
2,
2
],
"upsample_kernel_sizes": [16, 16, 8, 2, 2],
"n_layers_q": 3,
"use_spectral_norm": false,
"gin_channels": 512,

4
data_utils.py
View File

@@ -299,6 +299,10 @@ class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
self.boundaries = boundaries
self.buckets, self.num_samples_per_bucket = self._create_buckets()
logger.info(f"Bucket info: {self.num_samples_per_bucket}")
logger.info(
f"Unuseful samples: {len(self.lengths) - sum(self.num_samples_per_bucket)}"
)
self.total_size = sum(self.num_samples_per_bucket)
self.num_samples = self.total_size // self.num_replicas

155
emo_gen.py Normal file
View File

@@ -0,0 +1,155 @@
import argparse
import os
from pathlib import Path
import librosa
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import Dataset
from torch.utils.data import DataLoader, Dataset
from tqdm import tqdm
from transformers import Wav2Vec2Processor
from transformers.models.wav2vec2.modeling_wav2vec2 import (
Wav2Vec2Model,
Wav2Vec2PreTrainedModel,
)
import utils
from config import config
class RegressionHead(nn.Module):
r"""Classification head."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.final_dropout)
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
def forward(self, features, **kwargs):
x = features
x = self.dropout(x)
x = self.dense(x)
x = torch.tanh(x)
x = self.dropout(x)
x = self.out_proj(x)
return x
class EmotionModel(Wav2Vec2PreTrainedModel):
r"""Speech emotion classifier."""
def __init__(self, config):
super().__init__(config)
self.config = config
self.wav2vec2 = Wav2Vec2Model(config)
self.classifier = RegressionHead(config)
self.init_weights()
def forward(
self,
input_values,
):
outputs = self.wav2vec2(input_values)
hidden_states = outputs[0]
hidden_states = torch.mean(hidden_states, dim=1)
logits = self.classifier(hidden_states)
return hidden_states, logits
class AudioDataset(Dataset):
def __init__(self, list_of_wav_files, sr, processor):
self.list_of_wav_files = list_of_wav_files
self.processor = processor
self.sr = sr
def __len__(self):
return len(self.list_of_wav_files)
def __getitem__(self, idx):
wav_file = self.list_of_wav_files[idx]
audio_data, _ = librosa.load(wav_file, sr=self.sr)
processed_data = self.processor(audio_data, sampling_rate=self.sr)[
"input_values"
][0]
return torch.from_numpy(processed_data)
def process_func(
x: np.ndarray,
sampling_rate: int,
model: EmotionModel,
processor: Wav2Vec2Processor,
device: str,
embeddings: bool = False,
) -> np.ndarray:
r"""Predict emotions or extract embeddings from raw audio signal."""
model = model.to(device)
y = processor(x, sampling_rate=sampling_rate)
y = y["input_values"][0]
y = torch.from_numpy(y).unsqueeze(0).to(device)
# run through model
with torch.no_grad():
y = model(y)[0 if embeddings else 1]
# convert to numpy
y = y.detach().cpu().numpy()
return y
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"-c", "--config", type=str, default=config.bert_gen_config.config_path
)
parser.add_argument(
"--num_processes", type=int, default=config.bert_gen_config.num_processes
)
args, _ = parser.parse_known_args()
config_path = args.config
hps = utils.get_hparams_from_file(config_path)
device = config.bert_gen_config.device
model_name = "./emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim"
REPO_ID = "audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim"
if not Path(model_name).joinpath("pytorch_model.bin").exists():
utils.download_emo_models(config.mirror, REPO_ID, model_name)
processor = Wav2Vec2Processor.from_pretrained(model_name)
model = EmotionModel.from_pretrained(model_name).to(device)
lines = []
with open(hps.data.training_files, encoding="utf-8") as f:
lines.extend(f.readlines())
with open(hps.data.validation_files, encoding="utf-8") as f:
lines.extend(f.readlines())
wavnames = [line.split("|")[0] for line in lines]
dataset = AudioDataset(wavnames, 16000, processor)
data_loader = DataLoader(
dataset,
batch_size=1,
shuffle=False,
num_workers=min(args.num_processes, os.cpu_count() - 1),
)
with torch.no_grad():
for i, data in tqdm(enumerate(data_loader), total=len(data_loader)):
wavname = wavnames[i]
emo_path = wavname.replace(".wav", ".emo.npy")
if os.path.exists(emo_path):
continue
emb = model(data.to(device))[0].detach().cpu().numpy()
np.save(emo_path, emb)
print("Emo vec 生成完毕!")

414
oldVersion/V210/data_utils.py Normal file
View File

@@ -0,0 +1,414 @@
import os
import random
import torch
import torch.utils.data
from tqdm import tqdm
import numpy as np
from tools.log import logger
import commons
from mel_processing import spectrogram_torch, mel_spectrogram_torch
from utils import load_wav_to_torch, load_filepaths_and_text
from text import cleaned_text_to_sequence
from config import config
"""Multi speaker version"""
class TextAudioSpeakerLoader(torch.utils.data.Dataset):
"""
1) loads audio, speaker_id, text pairs
2) normalizes text and converts them to sequences of integers
3) computes spectrograms from audio files.
"""
def __init__(self, audiopaths_sid_text, hparams):
self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
self.max_wav_value = hparams.max_wav_value
self.sampling_rate = hparams.sampling_rate
self.filter_length = hparams.filter_length
self.hop_length = hparams.hop_length
self.win_length = hparams.win_length
self.sampling_rate = hparams.sampling_rate
self.spk_map = hparams.spk2id
self.hparams = hparams
self.use_mel_spec_posterior = getattr(
hparams, "use_mel_posterior_encoder", False
)
if self.use_mel_spec_posterior:
self.n_mel_channels = getattr(hparams, "n_mel_channels", 80)
self.cleaned_text = getattr(hparams, "cleaned_text", False)
self.add_blank = hparams.add_blank
self.min_text_len = getattr(hparams, "min_text_len", 1)
self.max_text_len = getattr(hparams, "max_text_len", 384)
random.seed(1234)
random.shuffle(self.audiopaths_sid_text)
self._filter()
def _filter(self):
"""
Filter text & store spec lengths
"""
# Store spectrogram lengths for Bucketing
# wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
# spec_length = wav_length // hop_length
audiopaths_sid_text_new = []
lengths = []
skipped = 0
logger.info("Init dataset...")
for _id, spk, language, text, phones, tone, word2ph in tqdm(
self.audiopaths_sid_text
):
audiopath = f"{_id}"
if self.min_text_len <= len(phones) and len(phones) <= self.max_text_len:
phones = phones.split(" ")
tone = [int(i) for i in tone.split(" ")]
word2ph = [int(i) for i in word2ph.split(" ")]
audiopaths_sid_text_new.append(
[audiopath, spk, language, text, phones, tone, word2ph]
)
lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
else:
skipped += 1
logger.info(
"skipped: "
+ str(skipped)
+ ", total: "
+ str(len(self.audiopaths_sid_text))
)
self.audiopaths_sid_text = audiopaths_sid_text_new
self.lengths = lengths
def get_audio_text_speaker_pair(self, audiopath_sid_text):
# separate filename, speaker_id and text
audiopath, sid, language, text, phones, tone, word2ph = audiopath_sid_text
bert, ja_bert, en_bert, phones, tone, language = self.get_text(
text, word2ph, phones, tone, language, audiopath
)
spec, wav = self.get_audio(audiopath)
sid = torch.LongTensor([int(self.spk_map[sid])])
emo = torch.FloatTensor(np.load(audiopath.replace(".wav", ".emo.npy")))
return (phones, spec, wav, sid, tone, language, bert, ja_bert, en_bert, emo)
def get_audio(self, filename):
audio, sampling_rate = load_wav_to_torch(filename)
if sampling_rate != self.sampling_rate:
raise ValueError(
"{} {} SR doesn't match target {} SR".format(
filename, sampling_rate, self.sampling_rate
)
)
audio_norm = audio / self.max_wav_value
audio_norm = audio_norm.unsqueeze(0)
spec_filename = filename.replace(".wav", ".spec.pt")
if self.use_mel_spec_posterior:
spec_filename = spec_filename.replace(".spec.pt", ".mel.pt")
try:
spec = torch.load(spec_filename)
except:
if self.use_mel_spec_posterior:
spec = mel_spectrogram_torch(
audio_norm,
self.filter_length,
self.n_mel_channels,
self.sampling_rate,
self.hop_length,
self.win_length,
self.hparams.mel_fmin,
self.hparams.mel_fmax,
center=False,
)
else:
spec = spectrogram_torch(
audio_norm,
self.filter_length,
self.sampling_rate,
self.hop_length,
self.win_length,
center=False,
)
spec = torch.squeeze(spec, 0)
if config.train_ms_config.spec_cache:
torch.save(spec, spec_filename)
return spec, audio_norm
def get_text(self, text, word2ph, phone, tone, language_str, wav_path):
phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
if self.add_blank:
phone = commons.intersperse(phone, 0)
tone = commons.intersperse(tone, 0)
language = commons.intersperse(language, 0)
for i in range(len(word2ph)):
word2ph[i] = word2ph[i] * 2
word2ph[0] += 1
bert_path = wav_path.replace(".wav", ".bert.pt")
try:
bert_ori = torch.load(bert_path)
assert bert_ori.shape[-1] == len(phone)
except Exception as e:
logger.warning("Bert load Failed")
logger.warning(e)
if language_str == "ZH":
bert = bert_ori
ja_bert = torch.zeros(1024, len(phone))
en_bert = torch.zeros(1024, len(phone))
elif language_str == "JP":
bert = torch.zeros(1024, len(phone))
ja_bert = bert_ori
en_bert = torch.zeros(1024, len(phone))
elif language_str == "EN":
bert = torch.zeros(1024, len(phone))
ja_bert = torch.zeros(1024, len(phone))
en_bert = bert_ori
phone = torch.LongTensor(phone)
tone = torch.LongTensor(tone)
language = torch.LongTensor(language)
return bert, ja_bert, en_bert, phone, tone, language
def get_sid(self, sid):
sid = torch.LongTensor([int(sid)])
return sid
def __getitem__(self, index):
return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
def __len__(self):
return len(self.audiopaths_sid_text)
class TextAudioSpeakerCollate:
"""Zero-pads model inputs and targets"""
def __init__(self, return_ids=False):
self.return_ids = return_ids
def __call__(self, batch):
"""Collate's training batch from normalized text, audio and speaker identities
PARAMS
------
batch: [text_normalized, spec_normalized, wav_normalized, sid]
"""
# Right zero-pad all one-hot text sequences to max input length
_, ids_sorted_decreasing = torch.sort(
torch.LongTensor([x[1].size(1) for x in batch]), dim=0, descending=True
)
max_text_len = max([len(x[0]) for x in batch])
max_spec_len = max([x[1].size(1) for x in batch])
max_wav_len = max([x[2].size(1) for x in batch])
text_lengths = torch.LongTensor(len(batch))
spec_lengths = torch.LongTensor(len(batch))
wav_lengths = torch.LongTensor(len(batch))
sid = torch.LongTensor(len(batch))
text_padded = torch.LongTensor(len(batch), max_text_len)
tone_padded = torch.LongTensor(len(batch), max_text_len)
language_padded = torch.LongTensor(len(batch), max_text_len)
bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)
ja_bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)
en_bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)
emo = torch.FloatTensor(len(batch), 1024)
spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
text_padded.zero_()
tone_padded.zero_()
language_padded.zero_()
spec_padded.zero_()
wav_padded.zero_()
bert_padded.zero_()
ja_bert_padded.zero_()
en_bert_padded.zero_()
emo.zero_()
for i in range(len(ids_sorted_decreasing)):
row = batch[ids_sorted_decreasing[i]]
text = row[0]
text_padded[i, : text.size(0)] = text
text_lengths[i] = text.size(0)
spec = row[1]
spec_padded[i, :, : spec.size(1)] = spec
spec_lengths[i] = spec.size(1)
wav = row[2]
wav_padded[i, :, : wav.size(1)] = wav
wav_lengths[i] = wav.size(1)
sid[i] = row[3]
tone = row[4]
tone_padded[i, : tone.size(0)] = tone
language = row[5]
language_padded[i, : language.size(0)] = language
bert = row[6]
bert_padded[i, :, : bert.size(1)] = bert
ja_bert = row[7]
ja_bert_padded[i, :, : ja_bert.size(1)] = ja_bert
en_bert = row[8]
en_bert_padded[i, :, : en_bert.size(1)] = en_bert
emo[i, :] = row[9]
return (
text_padded,
text_lengths,
spec_padded,
spec_lengths,
wav_padded,
wav_lengths,
sid,
tone_padded,
language_padded,
bert_padded,
ja_bert_padded,
en_bert_padded,
emo,
)
class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
"""
Maintain similar input lengths in a batch.
Length groups are specified by boundaries.
Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
It removes samples which are not included in the boundaries.
Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
"""
def __init__(
self,
dataset,
batch_size,
boundaries,
num_replicas=None,
rank=None,
shuffle=True,
):
super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
self.lengths = dataset.lengths
self.batch_size = batch_size
self.boundaries = boundaries
self.buckets, self.num_samples_per_bucket = self._create_buckets()
logger.info(f"Bucket info: {self.num_samples_per_bucket}")
logger.info(
f"Unuseful samples: {len(self.lengths) - sum(self.num_samples_per_bucket)}"
)
self.total_size = sum(self.num_samples_per_bucket)
self.num_samples = self.total_size // self.num_replicas
def _create_buckets(self):
buckets = [[] for _ in range(len(self.boundaries) - 1)]
for i in range(len(self.lengths)):
length = self.lengths[i]
idx_bucket = self._bisect(length)
if idx_bucket != -1:
buckets[idx_bucket].append(i)
try:
for i in range(len(buckets) - 1, 0, -1):
if len(buckets[i]) == 0:
buckets.pop(i)
self.boundaries.pop(i + 1)
assert all(len(bucket) > 0 for bucket in buckets)
# When one bucket is not traversed
except Exception as e:
print("Bucket warning ", e)
for i in range(len(buckets) - 1, -1, -1):
if len(buckets[i]) == 0:
buckets.pop(i)
self.boundaries.pop(i + 1)
num_samples_per_bucket = []
for i in range(len(buckets)):
len_bucket = len(buckets[i])
total_batch_size = self.num_replicas * self.batch_size
rem = (
total_batch_size - (len_bucket % total_batch_size)
) % total_batch_size
num_samples_per_bucket.append(len_bucket + rem)
return buckets, num_samples_per_bucket
def __iter__(self):
# deterministically shuffle based on epoch
g = torch.Generator()
g.manual_seed(self.epoch)
indices = []
if self.shuffle:
for bucket in self.buckets:
indices.append(torch.randperm(len(bucket), generator=g).tolist())
else:
for bucket in self.buckets:
indices.append(list(range(len(bucket))))
batches = []
for i in range(len(self.buckets)):
bucket = self.buckets[i]
len_bucket = len(bucket)
if len_bucket == 0:
continue
ids_bucket = indices[i]
num_samples_bucket = self.num_samples_per_bucket[i]
# add extra samples to make it evenly divisible
rem = num_samples_bucket - len_bucket
ids_bucket = (
ids_bucket
+ ids_bucket * (rem // len_bucket)
+ ids_bucket[: (rem % len_bucket)]
)
# subsample
ids_bucket = ids_bucket[self.rank :: self.num_replicas]
# batching
for j in range(len(ids_bucket) // self.batch_size):
batch = [
bucket[idx]
for idx in ids_bucket[
j * self.batch_size : (j + 1) * self.batch_size
]
]
batches.append(batch)
if self.shuffle:
batch_ids = torch.randperm(len(batches), generator=g).tolist()
batches = [batches[i] for i in batch_ids]
self.batches = batches
assert len(self.batches) * self.batch_size == self.num_samples
return iter(self.batches)
def _bisect(self, x, lo=0, hi=None):
if hi is None:
hi = len(self.boundaries) - 1
if hi > lo:
mid = (hi + lo) // 2
if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
return mid
elif x <= self.boundaries[mid]:
return self._bisect(x, lo, mid)
else:
return self._bisect(x, mid + 1, hi)
else:
return -1
def __len__(self):
return self.num_samples // self.batch_size

19
train_ms.py
View File

@@ -390,6 +390,15 @@ def run():
scheduler_wd.step()
if net_dur_disc is not None:
scheduler_dur_disc.step()
if epoch == hps.train.epochs:
utils.save_compressed_models_checkpoint(
net_g,
epoch,
os.path.join(
hps.model_dir,
f"release_{global_step}.pth",
),
)
def train_and_evaluate(
@@ -731,6 +740,16 @@ def train_and_evaluate(
n_ckpts_to_keep=keep_ckpts,
sort_by_time=True,
)
save_compressed_models = hps.train.save_compressed_models
if save_compressed_models:
utils.save_compressed_models_checkpoint(
net_g,
epoch,
os.path.join(
hps.model_dir,
f"release_{global_step}.pth",
),
)
global_step += 1

710
train_ms_V210.py Normal file
View File

@@ -0,0 +1,710 @@
# flake8: noqa: E402
import platform
import os
import torch
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.cuda.amp import autocast, GradScaler
from tqdm import tqdm
import logging
from config import config
import argparse
import datetime
import gc
logging.getLogger("numba").setLevel(logging.WARNING)
import commons
import utils
from oldVersion.V210.data_utils import (
TextAudioSpeakerLoader,
TextAudioSpeakerCollate,
DistributedBucketSampler,
)
from oldVersion.V210.models import (
SynthesizerTrn,
MultiPeriodDiscriminator,
DurationDiscriminator,
)
from losses import generator_loss, discriminator_loss, feature_loss, kl_loss
from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
from oldVersion.V210.text.symbols import symbols
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = (
True # If encontered training problem,please try to disable TF32.
)
torch.set_float32_matmul_precision("medium")
torch.backends.cuda.sdp_kernel("flash")
torch.backends.cuda.enable_flash_sdp(True)
torch.backends.cuda.enable_mem_efficient_sdp(
True
) # Not available if torch version is lower than 2.0
torch.backends.cuda.enable_math_sdp(True)
global_step = 0
def run():
# 环境变量解析
envs = config.train_ms_config.env
for env_name, env_value in envs.items():
if env_name not in os.environ.keys():
print("加载config中的配置{}".format(str(env_value)))
os.environ[env_name] = str(env_value)
print(
"加载环境变量 \nMASTER_ADDR: {},\nMASTER_PORT: {},\nWORLD_SIZE: {},\nRANK: {},\nLOCAL_RANK: {}".format(
os.environ["MASTER_ADDR"],
os.environ["MASTER_PORT"],
os.environ["WORLD_SIZE"],
os.environ["RANK"],
os.environ["LOCAL_RANK"],
)
)
backend = "nccl"
if platform.system() == "Windows":
backend = "gloo" # If Windows,switch to gloo backend.
dist.init_process_group(
backend=backend,
init_method="env://",
timeout=datetime.timedelta(seconds=300),
) # Use torchrun instead of mp.spawn
rank = dist.get_rank()
local_rank = int(os.environ["LOCAL_RANK"])
n_gpus = dist.get_world_size()
# 命令行/config.yml配置解析
# hps = utils.get_hparams()
parser = argparse.ArgumentParser()
# 非必要不建议使用命令行配置，请使用config.yml文件
parser.add_argument(
"-c",
"--config",
type=str,
default=config.train_ms_config.config_path,
help="JSON file for configuration",
)
parser.add_argument(
"-m",
"--model",
type=str,
help="数据集文件夹路径，请注意，数据不再默认放在/logs文件夹下。如果需要用命令行配置，请声明相对于根目录的路径",
default=config.dataset_path,
)
args = parser.parse_args()
model_dir = os.path.join(args.model, config.train_ms_config.model)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
hps = utils.get_hparams_from_file(args.config)
hps.model_dir = model_dir
# 比较路径是否相同
if os.path.realpath(args.config) != os.path.realpath(
config.train_ms_config.config_path
):
with open(args.config, "r", encoding="utf-8") as f:
data = f.read()
with open(config.train_ms_config.config_path, "w", encoding="utf-8") as f:
f.write(data)
torch.manual_seed(hps.train.seed)
torch.cuda.set_device(local_rank)
global global_step
if rank == 0:
logger = utils.get_logger(hps.model_dir)
logger.info(hps)
utils.check_git_hash(hps.model_dir)
writer = SummaryWriter(log_dir=hps.model_dir)
writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
train_sampler = DistributedBucketSampler(
train_dataset,
hps.train.batch_size,
[32, 300, 400, 500, 600, 700, 800, 900, 1000],
num_replicas=n_gpus,
rank=rank,
shuffle=True,
)
collate_fn = TextAudioSpeakerCollate()
train_loader = DataLoader(
train_dataset,
num_workers=min(config.train_ms_config.num_workers, os.cpu_count() - 1),
shuffle=False,
pin_memory=True,
collate_fn=collate_fn,
batch_sampler=train_sampler,
persistent_workers=True,
prefetch_factor=4,
) # DataLoader config could be adjusted.
if rank == 0:
eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
eval_loader = DataLoader(
eval_dataset,
num_workers=0,
shuffle=False,
batch_size=1,
pin_memory=True,
drop_last=False,
collate_fn=collate_fn,
)
if (
"use_noise_scaled_mas" in hps.model.keys()
and hps.model.use_noise_scaled_mas is True
):
print("Using noise scaled MAS for VITS2")
mas_noise_scale_initial = 0.01
noise_scale_delta = 2e-6
else:
print("Using normal MAS for VITS1")
mas_noise_scale_initial = 0.0
noise_scale_delta = 0.0
if (
"use_duration_discriminator" in hps.model.keys()
and hps.model.use_duration_discriminator is True
):
print("Using duration discriminator for VITS2")
net_dur_disc = DurationDiscriminator(
hps.model.hidden_channels,
hps.model.hidden_channels,
3,
0.1,
gin_channels=hps.model.gin_channels if hps.data.n_speakers != 0 else 0,
).cuda(local_rank)
if (
"use_spk_conditioned_encoder" in hps.model.keys()
and hps.model.use_spk_conditioned_encoder is True
):
if hps.data.n_speakers == 0:
raise ValueError(
"n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model"
)
else:
print("Using normal encoder for VITS1")
net_g = SynthesizerTrn(
len(symbols),
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
mas_noise_scale_initial=mas_noise_scale_initial,
noise_scale_delta=noise_scale_delta,
**hps.model,
).cuda(local_rank)
net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(local_rank)
optim_g = torch.optim.AdamW(
filter(lambda p: p.requires_grad, net_g.parameters()),
hps.train.learning_rate,
betas=hps.train.betas,
eps=hps.train.eps,
)
optim_d = torch.optim.AdamW(
net_d.parameters(),
hps.train.learning_rate,
betas=hps.train.betas,
eps=hps.train.eps,
)
if net_dur_disc is not None:
optim_dur_disc = torch.optim.AdamW(
net_dur_disc.parameters(),
hps.train.learning_rate,
betas=hps.train.betas,
eps=hps.train.eps,
)
else:
optim_dur_disc = None
net_g = DDP(net_g, device_ids=[local_rank])
net_d = DDP(net_d, device_ids=[local_rank])
dur_resume_lr = None
if net_dur_disc is not None:
net_dur_disc = DDP(
net_dur_disc, device_ids=[local_rank], find_unused_parameters=True
)
# 下载底模
if config.train_ms_config.base["use_base_model"]:
utils.download_checkpoint(
hps.model_dir,
config.train_ms_config.base,
token=config.openi_token,
mirror=config.mirror,
)
try:
if net_dur_disc is not None:
_, _, dur_resume_lr, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path(hps.model_dir, "DUR_*.pth"),
net_dur_disc,
optim_dur_disc,
skip_optimizer=hps.train.skip_optimizer
if "skip_optimizer" in hps.train
else True,
)
_, optim_g, g_resume_lr, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"),
net_g,
optim_g,
skip_optimizer=hps.train.skip_optimizer
if "skip_optimizer" in hps.train
else True,
)
_, optim_d, d_resume_lr, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"),
net_d,
optim_d,
skip_optimizer=hps.train.skip_optimizer
if "skip_optimizer" in hps.train
else True,
)
if not optim_g.param_groups[0].get("initial_lr"):
optim_g.param_groups[0]["initial_lr"] = g_resume_lr
if not optim_d.param_groups[0].get("initial_lr"):
optim_d.param_groups[0]["initial_lr"] = d_resume_lr
if not optim_dur_disc.param_groups[0].get("initial_lr"):
optim_dur_disc.param_groups[0]["initial_lr"] = dur_resume_lr
epoch_str = max(epoch_str, 1)
# global_step = (epoch_str - 1) * len(train_loader)
global_step = int(
utils.get_steps(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"))
)
print(
f"******************检测到模型存在，epoch为 {epoch_str}，gloabl step为 {global_step}*********************"
)
except Exception as e:
print(e)
epoch_str = 1
global_step = 0
scheduler_g = torch.optim.lr_scheduler.ExponentialLR(
optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
)
scheduler_d = torch.optim.lr_scheduler.ExponentialLR(
optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
)
if net_dur_disc is not None:
if not optim_dur_disc.param_groups[0].get("initial_lr"):
optim_dur_disc.param_groups[0]["initial_lr"] = dur_resume_lr
scheduler_dur_disc = torch.optim.lr_scheduler.ExponentialLR(
optim_dur_disc, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
)
else:
scheduler_dur_disc = None
scaler = GradScaler(enabled=hps.train.fp16_run)
for epoch in range(epoch_str, hps.train.epochs + 1):
if rank == 0:
train_and_evaluate(
rank,
local_rank,
epoch,
hps,
[net_g, net_d, net_dur_disc],
[optim_g, optim_d, optim_dur_disc],
[scheduler_g, scheduler_d, scheduler_dur_disc],
scaler,
[train_loader, eval_loader],
logger,
[writer, writer_eval],
)
else:
train_and_evaluate(
rank,
local_rank,
epoch,
hps,
[net_g, net_d, net_dur_disc],
[optim_g, optim_d, optim_dur_disc],
[scheduler_g, scheduler_d, scheduler_dur_disc],
scaler,
[train_loader, None],
None,
None,
)
scheduler_g.step()
scheduler_d.step()
if net_dur_disc is not None:
scheduler_dur_disc.step()
def train_and_evaluate(
rank,
local_rank,
epoch,
hps,
nets,
optims,
schedulers,
scaler,
loaders,
logger,
writers,
):
net_g, net_d, net_dur_disc = nets
optim_g, optim_d, optim_dur_disc = optims
scheduler_g, scheduler_d, scheduler_dur_disc = schedulers
train_loader, eval_loader = loaders
if writers is not None:
writer, writer_eval = writers
train_loader.batch_sampler.set_epoch(epoch)
global global_step
net_g.train()
net_d.train()
if net_dur_disc is not None:
net_dur_disc.train()
for batch_idx, (
x,
x_lengths,
spec,
spec_lengths,
y,
y_lengths,
speakers,
tone,
language,
bert,
ja_bert,
en_bert,
emo,
) in enumerate(tqdm(train_loader)):
if net_g.module.use_noise_scaled_mas:
current_mas_noise_scale = (
net_g.module.mas_noise_scale_initial
- net_g.module.noise_scale_delta * global_step
)
net_g.module.current_mas_noise_scale = max(current_mas_noise_scale, 0.0)
x, x_lengths = x.cuda(local_rank, non_blocking=True), x_lengths.cuda(
local_rank, non_blocking=True
)
spec, spec_lengths = spec.cuda(
local_rank, non_blocking=True
), spec_lengths.cuda(local_rank, non_blocking=True)
y, y_lengths = y.cuda(local_rank, non_blocking=True), y_lengths.cuda(
local_rank, non_blocking=True
)
speakers = speakers.cuda(local_rank, non_blocking=True)
tone = tone.cuda(local_rank, non_blocking=True)
language = language.cuda(local_rank, non_blocking=True)
bert = bert.cuda(local_rank, non_blocking=True)
ja_bert = ja_bert.cuda(local_rank, non_blocking=True)
en_bert = en_bert.cuda(local_rank, non_blocking=True)
emo = emo.cuda(local_rank, non_blocking=True)
with autocast(enabled=hps.train.fp16_run):
(
y_hat,
l_length,
attn,
ids_slice,
x_mask,
z_mask,
(z, z_p, m_p, logs_p, m_q, logs_q),
(hidden_x, logw, logw_),
loss_commit,
) = net_g(
x,
x_lengths,
spec,
spec_lengths,
speakers,
tone,
language,
bert,
ja_bert,
en_bert,
emo,
)
mel = spec_to_mel_torch(
spec,
hps.data.filter_length,
hps.data.n_mel_channels,
hps.data.sampling_rate,
hps.data.mel_fmin,
hps.data.mel_fmax,
)
y_mel = commons.slice_segments(
mel, ids_slice, hps.train.segment_size // hps.data.hop_length
)
y_hat_mel = mel_spectrogram_torch(
y_hat.squeeze(1),
hps.data.filter_length,
hps.data.n_mel_channels,
hps.data.sampling_rate,
hps.data.hop_length,
hps.data.win_length,
hps.data.mel_fmin,
hps.data.mel_fmax,
)
y = commons.slice_segments(
y, ids_slice * hps.data.hop_length, hps.train.segment_size
) # slice
# Discriminator
y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
with autocast(enabled=False):
loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(
y_d_hat_r, y_d_hat_g
)
loss_disc_all = loss_disc
if net_dur_disc is not None:
y_dur_hat_r, y_dur_hat_g = net_dur_disc(
hidden_x.detach(), x_mask.detach(), logw.detach(), logw_.detach()
)
with autocast(enabled=False):
# TODO: I think need to mean using the mask, but for now, just mean all
(
loss_dur_disc,
losses_dur_disc_r,
losses_dur_disc_g,
) = discriminator_loss(y_dur_hat_r, y_dur_hat_g)
loss_dur_disc_all = loss_dur_disc
optim_dur_disc.zero_grad()
scaler.scale(loss_dur_disc_all).backward()
scaler.unscale_(optim_dur_disc)
commons.clip_grad_value_(net_dur_disc.parameters(), None)
scaler.step(optim_dur_disc)
optim_d.zero_grad()
scaler.scale(loss_disc_all).backward()
scaler.unscale_(optim_d)
grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
scaler.step(optim_d)
with autocast(enabled=hps.train.fp16_run):
# Generator
y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
if net_dur_disc is not None:
y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw, logw_)
with autocast(enabled=False):
loss_dur = torch.sum(l_length.float())
loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
loss_fm = feature_loss(fmap_r, fmap_g)
loss_gen, losses_gen = generator_loss(y_d_hat_g)
loss_gen_all = (
loss_gen + loss_fm + loss_mel + loss_dur + loss_kl + loss_commit
)
if net_dur_disc is not None:
loss_dur_gen, losses_dur_gen = generator_loss(y_dur_hat_g)
loss_gen_all += loss_dur_gen
optim_g.zero_grad()
scaler.scale(loss_gen_all).backward()
scaler.unscale_(optim_g)
grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
scaler.step(optim_g)
scaler.update()
if rank == 0:
if global_step % hps.train.log_interval == 0:
lr = optim_g.param_groups[0]["lr"]
losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl]
logger.info(
"Train Epoch: {} [{:.0f}%]".format(
epoch, 100.0 * batch_idx / len(train_loader)
)
)
logger.info([x.item() for x in losses] + [global_step, lr])
scalar_dict = {
"loss/g/total": loss_gen_all,
"loss/d/total": loss_disc_all,
"learning_rate": lr,
"grad_norm_d": grad_norm_d,
"grad_norm_g": grad_norm_g,
}
scalar_dict.update(
{
"loss/g/fm": loss_fm,
"loss/g/mel": loss_mel,
"loss/g/dur": loss_dur,
"loss/g/kl": loss_kl,
}
)
scalar_dict.update(
{"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)}
)
scalar_dict.update(
{"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)}
)
scalar_dict.update(
{"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)}
)
image_dict = {
"slice/mel_org": utils.plot_spectrogram_to_numpy(
y_mel[0].data.cpu().numpy()
),
"slice/mel_gen": utils.plot_spectrogram_to_numpy(
y_hat_mel[0].data.cpu().numpy()
),
"all/mel": utils.plot_spectrogram_to_numpy(
mel[0].data.cpu().numpy()
),
"all/attn": utils.plot_alignment_to_numpy(
attn[0, 0].data.cpu().numpy()
),
}
utils.summarize(
writer=writer,
global_step=global_step,
images=image_dict,
scalars=scalar_dict,
)
if global_step % hps.train.eval_interval == 0:
evaluate(hps, net_g, eval_loader, writer_eval)
utils.save_checkpoint(
net_g,
optim_g,
hps.train.learning_rate,
epoch,
os.path.join(hps.model_dir, "G_{}.pth".format(global_step)),
)
utils.save_checkpoint(
net_d,
optim_d,
hps.train.learning_rate,
epoch,
os.path.join(hps.model_dir, "D_{}.pth".format(global_step)),
)
if net_dur_disc is not None:
utils.save_checkpoint(
net_dur_disc,
optim_dur_disc,
hps.train.learning_rate,
epoch,
os.path.join(hps.model_dir, "DUR_{}.pth".format(global_step)),
)
keep_ckpts = config.train_ms_config.keep_ckpts
if keep_ckpts > 0:
utils.clean_checkpoints(
path_to_models=hps.model_dir,
n_ckpts_to_keep=keep_ckpts,
sort_by_time=True,
)
save_compressed_models = hps.train.save_compressed_models
if save_compressed_models:
utils.save_compressed_models_checkpoint(
net_g,
epoch,
os.path.join(
hps.model_dir,
f"release_{global_step}.pth",
),
)
global_step += 1
gc.collect()
torch.cuda.empty_cache()
if rank == 0:
logger.info("====> Epoch: {}".format(epoch))
def evaluate(hps, generator, eval_loader, writer_eval):
generator.eval()
image_dict = {}
audio_dict = {}
print("Evaluating ...")
with torch.no_grad():
for batch_idx, (
x,
x_lengths,
spec,
spec_lengths,
y,
y_lengths,
speakers,
tone,
language,
bert,
ja_bert,
en_bert,
emo,
) in enumerate(eval_loader):
x, x_lengths = x.cuda(), x_lengths.cuda()
spec, spec_lengths = spec.cuda(), spec_lengths.cuda()
y, y_lengths = y.cuda(), y_lengths.cuda()
speakers = speakers.cuda()
bert = bert.cuda()
ja_bert = ja_bert.cuda()
en_bert = en_bert.cuda()
tone = tone.cuda()
language = language.cuda()
emo = emo.cuda()
for use_sdp in [True, False]:
y_hat, attn, mask, *_ = generator.module.infer(
x,
x_lengths,
speakers,
tone,
language,
bert,
ja_bert,
en_bert,
emo,
y=spec,
max_len=1000,
sdp_ratio=0.0 if not use_sdp else 1.0,
)
y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length
mel = spec_to_mel_torch(
spec,
hps.data.filter_length,
hps.data.n_mel_channels,
hps.data.sampling_rate,
hps.data.mel_fmin,
hps.data.mel_fmax,
)
y_hat_mel = mel_spectrogram_torch(
y_hat.squeeze(1).float(),
hps.data.filter_length,
hps.data.n_mel_channels,
hps.data.sampling_rate,
hps.data.hop_length,
hps.data.win_length,
hps.data.mel_fmin,
hps.data.mel_fmax,
)
image_dict.update(
{
f"gen/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(
y_hat_mel[0].cpu().numpy()
)
}
)
audio_dict.update(
{
f"gen/audio_{batch_idx}_{use_sdp}": y_hat[
0, :, : y_hat_lengths[0]
]
}
)
image_dict.update(
{
f"gt/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(
mel[0].cpu().numpy()
)
}
)
audio_dict.update({f"gt/audio_{batch_idx}": y[0, :, : y_lengths[0]]})
utils.summarize(
writer=writer_eval,
global_step=global_step,
images=image_dict,
audios=audio_dict,
audio_sampling_rate=hps.data.sampling_rate,
)
generator.train()
if __name__ == "__main__":
run()

37
utils.py
View File

@@ -10,6 +10,7 @@ from huggingface_hub import hf_hub_download
from scipy.io.wavfile import read
import torch
import re
from collections import OrderedDict
MATPLOTLIB_FLAG = False
@@ -141,6 +142,35 @@ def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path)
)
def save_compressed_models_checkpoint(model, iteration, checkpoint_path, ishalf=False):
logger.info(
f"Saving compressed model at iteration {iteration} to {checkpoint_path}"
)
if hasattr(model, "module"):
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
keys = []
for k in state_dict:
if "enc_q" in k:
continue # noqa: E701
keys.append(k)
new_dict_g = (
{k: state_dict[k].half() for k in keys}
if ishalf
else {k: state_dict[k] for k in keys}
)
torch.save(
{
"model": new_dict_g,
"iteration": iteration,
"learning_rate": 0,
},
checkpoint_path,
)
def summarize(
writer,
global_step,
@@ -302,9 +332,10 @@ def clean_checkpoints(path_to_models="logs/44k/", n_ckpts_to_keep=2, sort_by_tim
to_del = [
os.path.join(path_to_models, fn)
for fn in (
x_sorted("G")[:-n_ckpts_to_keep]
+ x_sorted("D")[:-n_ckpts_to_keep]
+ x_sorted("WD")[:-n_ckpts_to_keep]
x_sorted("G_")[:-n_ckpts_to_keep]
+ x_sorted("D_")[:-n_ckpts_to_keep]
+ x_sorted("WD_")[:-n_ckpts_to_keep]
+ x_sorted("DUR_")[:-n_ckpts_to_keep]
)
]