make a structure first
Browse files- src/f5_tts/api.py +5 -6
- src/f5_tts/data/Emilia_ZH_EN_pinyin/vocab.txt +0 -2545
- src/f5_tts/data/inference-cli.toml +0 -10
- src/f5_tts/{scripts → eval}/eval_infer_batch.py +0 -0
- src/f5_tts/{scripts → eval}/eval_infer_batch.sh +0 -0
- src/f5_tts/{scripts → eval}/eval_librispeech_test_clean.py +0 -0
- src/f5_tts/{scripts → eval}/eval_seedtts_testset.py +0 -0
- src/f5_tts/{data → eval/eval_testset}/librispeech_pc_test_clean_cross_sentence.lst +0 -0
- src/f5_tts/{inference_cli.py → infer/infer_cli.py} +1 -1
- src/f5_tts/{gradio_app.py → infer/infer_gradio.py} +0 -0
- src/f5_tts/{speech_edit.py → infer/speech_edit.py} +0 -0
- src/f5_tts/scripts/count_params_gflops.py +2 -2
- src/f5_tts/{finetune_cli.py → train/finetune_cli.py} +128 -128
- src/f5_tts/{finetune_gradio.py → train/finetune_gradio.py} +944 -944
- src/f5_tts/{train.py → train/train.py} +0 -0
src/f5_tts/api.py
CHANGED
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import soundfile as sf
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import torch
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import tqdm
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from cached_path import cached_path
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from f5_tts.model import DiT, UNetT
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from f5_tts.model.utils import save_spectrogram
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from f5_tts.model.utils_infer import load_vocoder, load_model, infer_process, remove_silence_for_generated_wav
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from f5_tts.model.utils import seed_everything
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import random
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import sys
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class F5TTS:
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import random
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import sys
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import tqdm
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import soundfile as sf
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import torch
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from cached_path import cached_path
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from f5_tts.model import DiT, UNetT
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from f5_tts.model.utils import seed_everything, save_spectrogram
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from f5_tts.model.utils_infer import load_vocoder, load_model, infer_process, remove_silence_for_generated_wav
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class F5TTS:
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src/f5_tts/data/Emilia_ZH_EN_pinyin/vocab.txt
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-
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!
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"
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#
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$
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%
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&
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'
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(
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*
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=
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>
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?
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@
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A
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B
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C
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D
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E
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F
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G
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H
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I
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J
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K
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L
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M
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N
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O
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P
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Q
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R
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S
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T
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U
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V
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W
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X
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Y
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Z
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[
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\
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]
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_
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a
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a1
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an1
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chi1
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chong1
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chou1
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chuang1
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chui1
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cong1
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cun1
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cuo1
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dai1
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dan1
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dang1
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dao1
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de
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de1
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dei3
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deng1
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di1
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diao1
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die1
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ding1
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diu1
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dong1
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dou1
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he2
|
| 463 |
-
he4
|
| 464 |
-
hei1
|
| 465 |
-
hen2
|
| 466 |
-
hen3
|
| 467 |
-
hen4
|
| 468 |
-
heng1
|
| 469 |
-
heng2
|
| 470 |
-
heng4
|
| 471 |
-
hong1
|
| 472 |
-
hong2
|
| 473 |
-
hong3
|
| 474 |
-
hong4
|
| 475 |
-
hou1
|
| 476 |
-
hou2
|
| 477 |
-
hou3
|
| 478 |
-
hou4
|
| 479 |
-
hu1
|
| 480 |
-
hu2
|
| 481 |
-
hu3
|
| 482 |
-
hu4
|
| 483 |
-
hua1
|
| 484 |
-
hua2
|
| 485 |
-
hua4
|
| 486 |
-
huai2
|
| 487 |
-
huai4
|
| 488 |
-
huan1
|
| 489 |
-
huan2
|
| 490 |
-
huan3
|
| 491 |
-
huan4
|
| 492 |
-
huang1
|
| 493 |
-
huang2
|
| 494 |
-
huang3
|
| 495 |
-
huang4
|
| 496 |
-
hui1
|
| 497 |
-
hui2
|
| 498 |
-
hui3
|
| 499 |
-
hui4
|
| 500 |
-
hun1
|
| 501 |
-
hun2
|
| 502 |
-
hun4
|
| 503 |
-
huo
|
| 504 |
-
huo1
|
| 505 |
-
huo2
|
| 506 |
-
huo3
|
| 507 |
-
huo4
|
| 508 |
-
i
|
| 509 |
-
j
|
| 510 |
-
ji1
|
| 511 |
-
ji2
|
| 512 |
-
ji3
|
| 513 |
-
ji4
|
| 514 |
-
jia
|
| 515 |
-
jia1
|
| 516 |
-
jia2
|
| 517 |
-
jia3
|
| 518 |
-
jia4
|
| 519 |
-
jian1
|
| 520 |
-
jian2
|
| 521 |
-
jian3
|
| 522 |
-
jian4
|
| 523 |
-
jiang1
|
| 524 |
-
jiang2
|
| 525 |
-
jiang3
|
| 526 |
-
jiang4
|
| 527 |
-
jiao1
|
| 528 |
-
jiao2
|
| 529 |
-
jiao3
|
| 530 |
-
jiao4
|
| 531 |
-
jie1
|
| 532 |
-
jie2
|
| 533 |
-
jie3
|
| 534 |
-
jie4
|
| 535 |
-
jin1
|
| 536 |
-
jin2
|
| 537 |
-
jin3
|
| 538 |
-
jin4
|
| 539 |
-
jing1
|
| 540 |
-
jing2
|
| 541 |
-
jing3
|
| 542 |
-
jing4
|
| 543 |
-
jiong3
|
| 544 |
-
jiu1
|
| 545 |
-
jiu2
|
| 546 |
-
jiu3
|
| 547 |
-
jiu4
|
| 548 |
-
ju1
|
| 549 |
-
ju2
|
| 550 |
-
ju3
|
| 551 |
-
ju4
|
| 552 |
-
juan1
|
| 553 |
-
juan2
|
| 554 |
-
juan3
|
| 555 |
-
juan4
|
| 556 |
-
jue1
|
| 557 |
-
jue2
|
| 558 |
-
jue4
|
| 559 |
-
jun1
|
| 560 |
-
jun4
|
| 561 |
-
k
|
| 562 |
-
ka1
|
| 563 |
-
ka2
|
| 564 |
-
ka3
|
| 565 |
-
kai1
|
| 566 |
-
kai2
|
| 567 |
-
kai3
|
| 568 |
-
kai4
|
| 569 |
-
kan1
|
| 570 |
-
kan2
|
| 571 |
-
kan3
|
| 572 |
-
kan4
|
| 573 |
-
kang1
|
| 574 |
-
kang2
|
| 575 |
-
kang4
|
| 576 |
-
kao1
|
| 577 |
-
kao2
|
| 578 |
-
kao3
|
| 579 |
-
kao4
|
| 580 |
-
ke1
|
| 581 |
-
ke2
|
| 582 |
-
ke3
|
| 583 |
-
ke4
|
| 584 |
-
ken3
|
| 585 |
-
keng1
|
| 586 |
-
kong1
|
| 587 |
-
kong3
|
| 588 |
-
kong4
|
| 589 |
-
kou1
|
| 590 |
-
kou2
|
| 591 |
-
kou3
|
| 592 |
-
kou4
|
| 593 |
-
ku1
|
| 594 |
-
ku2
|
| 595 |
-
ku3
|
| 596 |
-
ku4
|
| 597 |
-
kua1
|
| 598 |
-
kua3
|
| 599 |
-
kua4
|
| 600 |
-
kuai3
|
| 601 |
-
kuai4
|
| 602 |
-
kuan1
|
| 603 |
-
kuan2
|
| 604 |
-
kuan3
|
| 605 |
-
kuang1
|
| 606 |
-
kuang2
|
| 607 |
-
kuang4
|
| 608 |
-
kui1
|
| 609 |
-
kui2
|
| 610 |
-
kui3
|
| 611 |
-
kui4
|
| 612 |
-
kun1
|
| 613 |
-
kun3
|
| 614 |
-
kun4
|
| 615 |
-
kuo4
|
| 616 |
-
l
|
| 617 |
-
la
|
| 618 |
-
la1
|
| 619 |
-
la2
|
| 620 |
-
la3
|
| 621 |
-
la4
|
| 622 |
-
lai2
|
| 623 |
-
lai4
|
| 624 |
-
lan2
|
| 625 |
-
lan3
|
| 626 |
-
lan4
|
| 627 |
-
lang1
|
| 628 |
-
lang2
|
| 629 |
-
lang3
|
| 630 |
-
lang4
|
| 631 |
-
lao1
|
| 632 |
-
lao2
|
| 633 |
-
lao3
|
| 634 |
-
lao4
|
| 635 |
-
le
|
| 636 |
-
le1
|
| 637 |
-
le4
|
| 638 |
-
lei
|
| 639 |
-
lei1
|
| 640 |
-
lei2
|
| 641 |
-
lei3
|
| 642 |
-
lei4
|
| 643 |
-
leng1
|
| 644 |
-
leng2
|
| 645 |
-
leng3
|
| 646 |
-
leng4
|
| 647 |
-
li
|
| 648 |
-
li1
|
| 649 |
-
li2
|
| 650 |
-
li3
|
| 651 |
-
li4
|
| 652 |
-
lia3
|
| 653 |
-
lian2
|
| 654 |
-
lian3
|
| 655 |
-
lian4
|
| 656 |
-
liang2
|
| 657 |
-
liang3
|
| 658 |
-
liang4
|
| 659 |
-
liao1
|
| 660 |
-
liao2
|
| 661 |
-
liao3
|
| 662 |
-
liao4
|
| 663 |
-
lie1
|
| 664 |
-
lie2
|
| 665 |
-
lie3
|
| 666 |
-
lie4
|
| 667 |
-
lin1
|
| 668 |
-
lin2
|
| 669 |
-
lin3
|
| 670 |
-
lin4
|
| 671 |
-
ling2
|
| 672 |
-
ling3
|
| 673 |
-
ling4
|
| 674 |
-
liu1
|
| 675 |
-
liu2
|
| 676 |
-
liu3
|
| 677 |
-
liu4
|
| 678 |
-
long1
|
| 679 |
-
long2
|
| 680 |
-
long3
|
| 681 |
-
long4
|
| 682 |
-
lou1
|
| 683 |
-
lou2
|
| 684 |
-
lou3
|
| 685 |
-
lou4
|
| 686 |
-
lu1
|
| 687 |
-
lu2
|
| 688 |
-
lu3
|
| 689 |
-
lu4
|
| 690 |
-
luan2
|
| 691 |
-
luan3
|
| 692 |
-
luan4
|
| 693 |
-
lun1
|
| 694 |
-
lun2
|
| 695 |
-
lun4
|
| 696 |
-
luo1
|
| 697 |
-
luo2
|
| 698 |
-
luo3
|
| 699 |
-
luo4
|
| 700 |
-
lv2
|
| 701 |
-
lv3
|
| 702 |
-
lv4
|
| 703 |
-
lve3
|
| 704 |
-
lve4
|
| 705 |
-
m
|
| 706 |
-
ma
|
| 707 |
-
ma1
|
| 708 |
-
ma2
|
| 709 |
-
ma3
|
| 710 |
-
ma4
|
| 711 |
-
mai2
|
| 712 |
-
mai3
|
| 713 |
-
mai4
|
| 714 |
-
man1
|
| 715 |
-
man2
|
| 716 |
-
man3
|
| 717 |
-
man4
|
| 718 |
-
mang2
|
| 719 |
-
mang3
|
| 720 |
-
mao1
|
| 721 |
-
mao2
|
| 722 |
-
mao3
|
| 723 |
-
mao4
|
| 724 |
-
me
|
| 725 |
-
mei2
|
| 726 |
-
mei3
|
| 727 |
-
mei4
|
| 728 |
-
men
|
| 729 |
-
men1
|
| 730 |
-
men2
|
| 731 |
-
men4
|
| 732 |
-
meng
|
| 733 |
-
meng1
|
| 734 |
-
meng2
|
| 735 |
-
meng3
|
| 736 |
-
meng4
|
| 737 |
-
mi1
|
| 738 |
-
mi2
|
| 739 |
-
mi3
|
| 740 |
-
mi4
|
| 741 |
-
mian2
|
| 742 |
-
mian3
|
| 743 |
-
mian4
|
| 744 |
-
miao1
|
| 745 |
-
miao2
|
| 746 |
-
miao3
|
| 747 |
-
miao4
|
| 748 |
-
mie1
|
| 749 |
-
mie4
|
| 750 |
-
min2
|
| 751 |
-
min3
|
| 752 |
-
ming2
|
| 753 |
-
ming3
|
| 754 |
-
ming4
|
| 755 |
-
miu4
|
| 756 |
-
mo1
|
| 757 |
-
mo2
|
| 758 |
-
mo3
|
| 759 |
-
mo4
|
| 760 |
-
mou1
|
| 761 |
-
mou2
|
| 762 |
-
mou3
|
| 763 |
-
mu2
|
| 764 |
-
mu3
|
| 765 |
-
mu4
|
| 766 |
-
n
|
| 767 |
-
n2
|
| 768 |
-
na1
|
| 769 |
-
na2
|
| 770 |
-
na3
|
| 771 |
-
na4
|
| 772 |
-
nai2
|
| 773 |
-
nai3
|
| 774 |
-
nai4
|
| 775 |
-
nan1
|
| 776 |
-
nan2
|
| 777 |
-
nan3
|
| 778 |
-
nan4
|
| 779 |
-
nang1
|
| 780 |
-
nang2
|
| 781 |
-
nang3
|
| 782 |
-
nao1
|
| 783 |
-
nao2
|
| 784 |
-
nao3
|
| 785 |
-
nao4
|
| 786 |
-
ne
|
| 787 |
-
ne2
|
| 788 |
-
ne4
|
| 789 |
-
nei3
|
| 790 |
-
nei4
|
| 791 |
-
nen4
|
| 792 |
-
neng2
|
| 793 |
-
ni1
|
| 794 |
-
ni2
|
| 795 |
-
ni3
|
| 796 |
-
ni4
|
| 797 |
-
nian1
|
| 798 |
-
nian2
|
| 799 |
-
nian3
|
| 800 |
-
nian4
|
| 801 |
-
niang2
|
| 802 |
-
niang4
|
| 803 |
-
niao2
|
| 804 |
-
niao3
|
| 805 |
-
niao4
|
| 806 |
-
nie1
|
| 807 |
-
nie4
|
| 808 |
-
nin2
|
| 809 |
-
ning2
|
| 810 |
-
ning3
|
| 811 |
-
ning4
|
| 812 |
-
niu1
|
| 813 |
-
niu2
|
| 814 |
-
niu3
|
| 815 |
-
niu4
|
| 816 |
-
nong2
|
| 817 |
-
nong4
|
| 818 |
-
nou4
|
| 819 |
-
nu2
|
| 820 |
-
nu3
|
| 821 |
-
nu4
|
| 822 |
-
nuan3
|
| 823 |
-
nuo2
|
| 824 |
-
nuo4
|
| 825 |
-
nv2
|
| 826 |
-
nv3
|
| 827 |
-
nve4
|
| 828 |
-
o
|
| 829 |
-
o1
|
| 830 |
-
o2
|
| 831 |
-
ou1
|
| 832 |
-
ou2
|
| 833 |
-
ou3
|
| 834 |
-
ou4
|
| 835 |
-
p
|
| 836 |
-
pa1
|
| 837 |
-
pa2
|
| 838 |
-
pa4
|
| 839 |
-
pai1
|
| 840 |
-
pai2
|
| 841 |
-
pai3
|
| 842 |
-
pai4
|
| 843 |
-
pan1
|
| 844 |
-
pan2
|
| 845 |
-
pan4
|
| 846 |
-
pang1
|
| 847 |
-
pang2
|
| 848 |
-
pang4
|
| 849 |
-
pao1
|
| 850 |
-
pao2
|
| 851 |
-
pao3
|
| 852 |
-
pao4
|
| 853 |
-
pei1
|
| 854 |
-
pei2
|
| 855 |
-
pei4
|
| 856 |
-
pen1
|
| 857 |
-
pen2
|
| 858 |
-
pen4
|
| 859 |
-
peng1
|
| 860 |
-
peng2
|
| 861 |
-
peng3
|
| 862 |
-
peng4
|
| 863 |
-
pi1
|
| 864 |
-
pi2
|
| 865 |
-
pi3
|
| 866 |
-
pi4
|
| 867 |
-
pian1
|
| 868 |
-
pian2
|
| 869 |
-
pian4
|
| 870 |
-
piao1
|
| 871 |
-
piao2
|
| 872 |
-
piao3
|
| 873 |
-
piao4
|
| 874 |
-
pie1
|
| 875 |
-
pie2
|
| 876 |
-
pie3
|
| 877 |
-
pin1
|
| 878 |
-
pin2
|
| 879 |
-
pin3
|
| 880 |
-
pin4
|
| 881 |
-
ping1
|
| 882 |
-
ping2
|
| 883 |
-
po1
|
| 884 |
-
po2
|
| 885 |
-
po3
|
| 886 |
-
po4
|
| 887 |
-
pou1
|
| 888 |
-
pu1
|
| 889 |
-
pu2
|
| 890 |
-
pu3
|
| 891 |
-
pu4
|
| 892 |
-
q
|
| 893 |
-
qi1
|
| 894 |
-
qi2
|
| 895 |
-
qi3
|
| 896 |
-
qi4
|
| 897 |
-
qia1
|
| 898 |
-
qia3
|
| 899 |
-
qia4
|
| 900 |
-
qian1
|
| 901 |
-
qian2
|
| 902 |
-
qian3
|
| 903 |
-
qian4
|
| 904 |
-
qiang1
|
| 905 |
-
qiang2
|
| 906 |
-
qiang3
|
| 907 |
-
qiang4
|
| 908 |
-
qiao1
|
| 909 |
-
qiao2
|
| 910 |
-
qiao3
|
| 911 |
-
qiao4
|
| 912 |
-
qie1
|
| 913 |
-
qie2
|
| 914 |
-
qie3
|
| 915 |
-
qie4
|
| 916 |
-
qin1
|
| 917 |
-
qin2
|
| 918 |
-
qin3
|
| 919 |
-
qin4
|
| 920 |
-
qing1
|
| 921 |
-
qing2
|
| 922 |
-
qing3
|
| 923 |
-
qing4
|
| 924 |
-
qiong1
|
| 925 |
-
qiong2
|
| 926 |
-
qiu1
|
| 927 |
-
qiu2
|
| 928 |
-
qiu3
|
| 929 |
-
qu1
|
| 930 |
-
qu2
|
| 931 |
-
qu3
|
| 932 |
-
qu4
|
| 933 |
-
quan1
|
| 934 |
-
quan2
|
| 935 |
-
quan3
|
| 936 |
-
quan4
|
| 937 |
-
que1
|
| 938 |
-
que2
|
| 939 |
-
que4
|
| 940 |
-
qun2
|
| 941 |
-
r
|
| 942 |
-
ran2
|
| 943 |
-
ran3
|
| 944 |
-
rang1
|
| 945 |
-
rang2
|
| 946 |
-
rang3
|
| 947 |
-
rang4
|
| 948 |
-
rao2
|
| 949 |
-
rao3
|
| 950 |
-
rao4
|
| 951 |
-
re2
|
| 952 |
-
re3
|
| 953 |
-
re4
|
| 954 |
-
ren2
|
| 955 |
-
ren3
|
| 956 |
-
ren4
|
| 957 |
-
reng1
|
| 958 |
-
reng2
|
| 959 |
-
ri4
|
| 960 |
-
rong1
|
| 961 |
-
rong2
|
| 962 |
-
rong3
|
| 963 |
-
rou2
|
| 964 |
-
rou4
|
| 965 |
-
ru2
|
| 966 |
-
ru3
|
| 967 |
-
ru4
|
| 968 |
-
ruan2
|
| 969 |
-
ruan3
|
| 970 |
-
rui3
|
| 971 |
-
rui4
|
| 972 |
-
run4
|
| 973 |
-
ruo4
|
| 974 |
-
s
|
| 975 |
-
sa1
|
| 976 |
-
sa2
|
| 977 |
-
sa3
|
| 978 |
-
sa4
|
| 979 |
-
sai1
|
| 980 |
-
sai4
|
| 981 |
-
san1
|
| 982 |
-
san2
|
| 983 |
-
san3
|
| 984 |
-
san4
|
| 985 |
-
sang1
|
| 986 |
-
sang3
|
| 987 |
-
sang4
|
| 988 |
-
sao1
|
| 989 |
-
sao2
|
| 990 |
-
sao3
|
| 991 |
-
sao4
|
| 992 |
-
se4
|
| 993 |
-
sen1
|
| 994 |
-
seng1
|
| 995 |
-
sha1
|
| 996 |
-
sha2
|
| 997 |
-
sha3
|
| 998 |
-
sha4
|
| 999 |
-
shai1
|
| 1000 |
-
shai2
|
| 1001 |
-
shai3
|
| 1002 |
-
shai4
|
| 1003 |
-
shan1
|
| 1004 |
-
shan3
|
| 1005 |
-
shan4
|
| 1006 |
-
shang
|
| 1007 |
-
shang1
|
| 1008 |
-
shang3
|
| 1009 |
-
shang4
|
| 1010 |
-
shao1
|
| 1011 |
-
shao2
|
| 1012 |
-
shao3
|
| 1013 |
-
shao4
|
| 1014 |
-
she1
|
| 1015 |
-
she2
|
| 1016 |
-
she3
|
| 1017 |
-
she4
|
| 1018 |
-
shei2
|
| 1019 |
-
shen1
|
| 1020 |
-
shen2
|
| 1021 |
-
shen3
|
| 1022 |
-
shen4
|
| 1023 |
-
sheng1
|
| 1024 |
-
sheng2
|
| 1025 |
-
sheng3
|
| 1026 |
-
sheng4
|
| 1027 |
-
shi
|
| 1028 |
-
shi1
|
| 1029 |
-
shi2
|
| 1030 |
-
shi3
|
| 1031 |
-
shi4
|
| 1032 |
-
shou1
|
| 1033 |
-
shou2
|
| 1034 |
-
shou3
|
| 1035 |
-
shou4
|
| 1036 |
-
shu1
|
| 1037 |
-
shu2
|
| 1038 |
-
shu3
|
| 1039 |
-
shu4
|
| 1040 |
-
shua1
|
| 1041 |
-
shua2
|
| 1042 |
-
shua3
|
| 1043 |
-
shua4
|
| 1044 |
-
shuai1
|
| 1045 |
-
shuai3
|
| 1046 |
-
shuai4
|
| 1047 |
-
shuan1
|
| 1048 |
-
shuan4
|
| 1049 |
-
shuang1
|
| 1050 |
-
shuang3
|
| 1051 |
-
shui2
|
| 1052 |
-
shui3
|
| 1053 |
-
shui4
|
| 1054 |
-
shun3
|
| 1055 |
-
shun4
|
| 1056 |
-
shuo1
|
| 1057 |
-
shuo4
|
| 1058 |
-
si1
|
| 1059 |
-
si2
|
| 1060 |
-
si3
|
| 1061 |
-
si4
|
| 1062 |
-
song1
|
| 1063 |
-
song3
|
| 1064 |
-
song4
|
| 1065 |
-
sou1
|
| 1066 |
-
sou3
|
| 1067 |
-
sou4
|
| 1068 |
-
su1
|
| 1069 |
-
su2
|
| 1070 |
-
su4
|
| 1071 |
-
suan1
|
| 1072 |
-
suan4
|
| 1073 |
-
sui1
|
| 1074 |
-
sui2
|
| 1075 |
-
sui3
|
| 1076 |
-
sui4
|
| 1077 |
-
sun1
|
| 1078 |
-
sun3
|
| 1079 |
-
suo
|
| 1080 |
-
suo1
|
| 1081 |
-
suo2
|
| 1082 |
-
suo3
|
| 1083 |
-
t
|
| 1084 |
-
ta1
|
| 1085 |
-
ta2
|
| 1086 |
-
ta3
|
| 1087 |
-
ta4
|
| 1088 |
-
tai1
|
| 1089 |
-
tai2
|
| 1090 |
-
tai4
|
| 1091 |
-
tan1
|
| 1092 |
-
tan2
|
| 1093 |
-
tan3
|
| 1094 |
-
tan4
|
| 1095 |
-
tang1
|
| 1096 |
-
tang2
|
| 1097 |
-
tang3
|
| 1098 |
-
tang4
|
| 1099 |
-
tao1
|
| 1100 |
-
tao2
|
| 1101 |
-
tao3
|
| 1102 |
-
tao4
|
| 1103 |
-
te4
|
| 1104 |
-
teng2
|
| 1105 |
-
ti1
|
| 1106 |
-
ti2
|
| 1107 |
-
ti3
|
| 1108 |
-
ti4
|
| 1109 |
-
tian1
|
| 1110 |
-
tian2
|
| 1111 |
-
tian3
|
| 1112 |
-
tiao1
|
| 1113 |
-
tiao2
|
| 1114 |
-
tiao3
|
| 1115 |
-
tiao4
|
| 1116 |
-
tie1
|
| 1117 |
-
tie2
|
| 1118 |
-
tie3
|
| 1119 |
-
tie4
|
| 1120 |
-
ting1
|
| 1121 |
-
ting2
|
| 1122 |
-
ting3
|
| 1123 |
-
tong1
|
| 1124 |
-
tong2
|
| 1125 |
-
tong3
|
| 1126 |
-
tong4
|
| 1127 |
-
tou
|
| 1128 |
-
tou1
|
| 1129 |
-
tou2
|
| 1130 |
-
tou4
|
| 1131 |
-
tu1
|
| 1132 |
-
tu2
|
| 1133 |
-
tu3
|
| 1134 |
-
tu4
|
| 1135 |
-
tuan1
|
| 1136 |
-
tuan2
|
| 1137 |
-
tui1
|
| 1138 |
-
tui2
|
| 1139 |
-
tui3
|
| 1140 |
-
tui4
|
| 1141 |
-
tun1
|
| 1142 |
-
tun2
|
| 1143 |
-
tun4
|
| 1144 |
-
tuo1
|
| 1145 |
-
tuo2
|
| 1146 |
-
tuo3
|
| 1147 |
-
tuo4
|
| 1148 |
-
u
|
| 1149 |
-
v
|
| 1150 |
-
w
|
| 1151 |
-
wa
|
| 1152 |
-
wa1
|
| 1153 |
-
wa2
|
| 1154 |
-
wa3
|
| 1155 |
-
wa4
|
| 1156 |
-
wai1
|
| 1157 |
-
wai3
|
| 1158 |
-
wai4
|
| 1159 |
-
wan1
|
| 1160 |
-
wan2
|
| 1161 |
-
wan3
|
| 1162 |
-
wan4
|
| 1163 |
-
wang1
|
| 1164 |
-
wang2
|
| 1165 |
-
wang3
|
| 1166 |
-
wang4
|
| 1167 |
-
wei1
|
| 1168 |
-
wei2
|
| 1169 |
-
wei3
|
| 1170 |
-
wei4
|
| 1171 |
-
wen1
|
| 1172 |
-
wen2
|
| 1173 |
-
wen3
|
| 1174 |
-
wen4
|
| 1175 |
-
weng1
|
| 1176 |
-
weng4
|
| 1177 |
-
wo1
|
| 1178 |
-
wo2
|
| 1179 |
-
wo3
|
| 1180 |
-
wo4
|
| 1181 |
-
wu1
|
| 1182 |
-
wu2
|
| 1183 |
-
wu3
|
| 1184 |
-
wu4
|
| 1185 |
-
x
|
| 1186 |
-
xi1
|
| 1187 |
-
xi2
|
| 1188 |
-
xi3
|
| 1189 |
-
xi4
|
| 1190 |
-
xia1
|
| 1191 |
-
xia2
|
| 1192 |
-
xia4
|
| 1193 |
-
xian1
|
| 1194 |
-
xian2
|
| 1195 |
-
xian3
|
| 1196 |
-
xian4
|
| 1197 |
-
xiang1
|
| 1198 |
-
xiang2
|
| 1199 |
-
xiang3
|
| 1200 |
-
xiang4
|
| 1201 |
-
xiao1
|
| 1202 |
-
xiao2
|
| 1203 |
-
xiao3
|
| 1204 |
-
xiao4
|
| 1205 |
-
xie1
|
| 1206 |
-
xie2
|
| 1207 |
-
xie3
|
| 1208 |
-
xie4
|
| 1209 |
-
xin1
|
| 1210 |
-
xin2
|
| 1211 |
-
xin4
|
| 1212 |
-
xing1
|
| 1213 |
-
xing2
|
| 1214 |
-
xing3
|
| 1215 |
-
xing4
|
| 1216 |
-
xiong1
|
| 1217 |
-
xiong2
|
| 1218 |
-
xiu1
|
| 1219 |
-
xiu3
|
| 1220 |
-
xiu4
|
| 1221 |
-
xu
|
| 1222 |
-
xu1
|
| 1223 |
-
xu2
|
| 1224 |
-
xu3
|
| 1225 |
-
xu4
|
| 1226 |
-
xuan1
|
| 1227 |
-
xuan2
|
| 1228 |
-
xuan3
|
| 1229 |
-
xuan4
|
| 1230 |
-
xue1
|
| 1231 |
-
xue2
|
| 1232 |
-
xue3
|
| 1233 |
-
xue4
|
| 1234 |
-
xun1
|
| 1235 |
-
xun2
|
| 1236 |
-
xun4
|
| 1237 |
-
y
|
| 1238 |
-
ya
|
| 1239 |
-
ya1
|
| 1240 |
-
ya2
|
| 1241 |
-
ya3
|
| 1242 |
-
ya4
|
| 1243 |
-
yan1
|
| 1244 |
-
yan2
|
| 1245 |
-
yan3
|
| 1246 |
-
yan4
|
| 1247 |
-
yang1
|
| 1248 |
-
yang2
|
| 1249 |
-
yang3
|
| 1250 |
-
yang4
|
| 1251 |
-
yao1
|
| 1252 |
-
yao2
|
| 1253 |
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|
| 1254 |
-
yao4
|
| 1255 |
-
ye1
|
| 1256 |
-
ye2
|
| 1257 |
-
ye3
|
| 1258 |
-
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|
| 1259 |
-
yi
|
| 1260 |
-
yi1
|
| 1261 |
-
yi2
|
| 1262 |
-
yi3
|
| 1263 |
-
yi4
|
| 1264 |
-
yin1
|
| 1265 |
-
yin2
|
| 1266 |
-
yin3
|
| 1267 |
-
yin4
|
| 1268 |
-
ying1
|
| 1269 |
-
ying2
|
| 1270 |
-
ying3
|
| 1271 |
-
ying4
|
| 1272 |
-
yo1
|
| 1273 |
-
yong1
|
| 1274 |
-
yong2
|
| 1275 |
-
yong3
|
| 1276 |
-
yong4
|
| 1277 |
-
you1
|
| 1278 |
-
you2
|
| 1279 |
-
you3
|
| 1280 |
-
you4
|
| 1281 |
-
yu1
|
| 1282 |
-
yu2
|
| 1283 |
-
yu3
|
| 1284 |
-
yu4
|
| 1285 |
-
yuan1
|
| 1286 |
-
yuan2
|
| 1287 |
-
yuan3
|
| 1288 |
-
yuan4
|
| 1289 |
-
yue1
|
| 1290 |
-
yue4
|
| 1291 |
-
yun1
|
| 1292 |
-
yun2
|
| 1293 |
-
yun3
|
| 1294 |
-
yun4
|
| 1295 |
-
z
|
| 1296 |
-
za1
|
| 1297 |
-
za2
|
| 1298 |
-
za3
|
| 1299 |
-
zai1
|
| 1300 |
-
zai3
|
| 1301 |
-
zai4
|
| 1302 |
-
zan1
|
| 1303 |
-
zan2
|
| 1304 |
-
zan3
|
| 1305 |
-
zan4
|
| 1306 |
-
zang1
|
| 1307 |
-
zang4
|
| 1308 |
-
zao1
|
| 1309 |
-
zao2
|
| 1310 |
-
zao3
|
| 1311 |
-
zao4
|
| 1312 |
-
ze2
|
| 1313 |
-
ze4
|
| 1314 |
-
zei2
|
| 1315 |
-
zen3
|
| 1316 |
-
zeng1
|
| 1317 |
-
zeng4
|
| 1318 |
-
zha1
|
| 1319 |
-
zha2
|
| 1320 |
-
zha3
|
| 1321 |
-
zha4
|
| 1322 |
-
zhai1
|
| 1323 |
-
zhai2
|
| 1324 |
-
zhai3
|
| 1325 |
-
zhai4
|
| 1326 |
-
zhan1
|
| 1327 |
-
zhan2
|
| 1328 |
-
zhan3
|
| 1329 |
-
zhan4
|
| 1330 |
-
zhang1
|
| 1331 |
-
zhang2
|
| 1332 |
-
zhang3
|
| 1333 |
-
zhang4
|
| 1334 |
-
zhao1
|
| 1335 |
-
zhao2
|
| 1336 |
-
zhao3
|
| 1337 |
-
zhao4
|
| 1338 |
-
zhe
|
| 1339 |
-
zhe1
|
| 1340 |
-
zhe2
|
| 1341 |
-
zhe3
|
| 1342 |
-
zhe4
|
| 1343 |
-
zhen1
|
| 1344 |
-
zhen2
|
| 1345 |
-
zhen3
|
| 1346 |
-
zhen4
|
| 1347 |
-
zheng1
|
| 1348 |
-
zheng2
|
| 1349 |
-
zheng3
|
| 1350 |
-
zheng4
|
| 1351 |
-
zhi1
|
| 1352 |
-
zhi2
|
| 1353 |
-
zhi3
|
| 1354 |
-
zhi4
|
| 1355 |
-
zhong1
|
| 1356 |
-
zhong2
|
| 1357 |
-
zhong3
|
| 1358 |
-
zhong4
|
| 1359 |
-
zhou1
|
| 1360 |
-
zhou2
|
| 1361 |
-
zhou3
|
| 1362 |
-
zhou4
|
| 1363 |
-
zhu1
|
| 1364 |
-
zhu2
|
| 1365 |
-
zhu3
|
| 1366 |
-
zhu4
|
| 1367 |
-
zhua1
|
| 1368 |
-
zhua2
|
| 1369 |
-
zhua3
|
| 1370 |
-
zhuai1
|
| 1371 |
-
zhuai3
|
| 1372 |
-
zhuai4
|
| 1373 |
-
zhuan1
|
| 1374 |
-
zhuan2
|
| 1375 |
-
zhuan3
|
| 1376 |
-
zhuan4
|
| 1377 |
-
zhuang1
|
| 1378 |
-
zhuang4
|
| 1379 |
-
zhui1
|
| 1380 |
-
zhui4
|
| 1381 |
-
zhun1
|
| 1382 |
-
zhun2
|
| 1383 |
-
zhun3
|
| 1384 |
-
zhuo1
|
| 1385 |
-
zhuo2
|
| 1386 |
-
zi
|
| 1387 |
-
zi1
|
| 1388 |
-
zi2
|
| 1389 |
-
zi3
|
| 1390 |
-
zi4
|
| 1391 |
-
zong1
|
| 1392 |
-
zong2
|
| 1393 |
-
zong3
|
| 1394 |
-
zong4
|
| 1395 |
-
zou1
|
| 1396 |
-
zou2
|
| 1397 |
-
zou3
|
| 1398 |
-
zou4
|
| 1399 |
-
zu1
|
| 1400 |
-
zu2
|
| 1401 |
-
zu3
|
| 1402 |
-
zuan1
|
| 1403 |
-
zuan3
|
| 1404 |
-
zuan4
|
| 1405 |
-
zui2
|
| 1406 |
-
zui3
|
| 1407 |
-
zui4
|
| 1408 |
-
zun1
|
| 1409 |
-
zuo
|
| 1410 |
-
zuo1
|
| 1411 |
-
zuo2
|
| 1412 |
-
zuo3
|
| 1413 |
-
zuo4
|
| 1414 |
-
{
|
| 1415 |
-
~
|
| 1416 |
-
¡
|
| 1417 |
-
¢
|
| 1418 |
-
£
|
| 1419 |
-
¥
|
| 1420 |
-
§
|
| 1421 |
-
¨
|
| 1422 |
-
©
|
| 1423 |
-
«
|
| 1424 |
-
®
|
| 1425 |
-
¯
|
| 1426 |
-
°
|
| 1427 |
-
±
|
| 1428 |
-
²
|
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-
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|
| 1430 |
-
´
|
| 1431 |
-
µ
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| 1432 |
-
·
|
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-
¹
|
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¼
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½
|
| 1438 |
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¾
|
| 1439 |
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| 1440 |
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À
|
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-
Á
|
| 1442 |
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Â
|
| 1443 |
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|
| 1444 |
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Ä
|
| 1445 |
-
Å
|
| 1446 |
-
Æ
|
| 1447 |
-
Ç
|
| 1448 |
-
È
|
| 1449 |
-
É
|
| 1450 |
-
Ê
|
| 1451 |
-
Í
|
| 1452 |
-
Î
|
| 1453 |
-
Ñ
|
| 1454 |
-
Ó
|
| 1455 |
-
Ö
|
| 1456 |
-
×
|
| 1457 |
-
Ø
|
| 1458 |
-
Ú
|
| 1459 |
-
Ü
|
| 1460 |
-
Ý
|
| 1461 |
-
Þ
|
| 1462 |
-
ß
|
| 1463 |
-
à
|
| 1464 |
-
á
|
| 1465 |
-
â
|
| 1466 |
-
ã
|
| 1467 |
-
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|
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|
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|
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|
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|
| 1890 |
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|
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|
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|
| 1894 |
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|
| 1895 |
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|
| 1896 |
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|
| 1897 |
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|
| 1898 |
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|
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|
| 1900 |
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|
| 1901 |
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|
| 1902 |
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|
| 1903 |
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|
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|
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|
| 1906 |
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|
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|
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|
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|
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|
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|
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|
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|
| 1917 |
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|
| 1918 |
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|
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|
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|
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|
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|
| 1924 |
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|
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|
| 1926 |
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|
| 1927 |
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|
| 1928 |
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|
| 1929 |
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|
| 1930 |
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つ
|
| 1931 |
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で
|
| 1932 |
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と
|
| 1933 |
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ど
|
| 1934 |
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|
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|
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の
|
| 1938 |
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|
| 1939 |
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|
| 1940 |
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|
| 1941 |
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ぶ
|
| 1942 |
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|
| 1943 |
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べ
|
| 1944 |
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ま
|
| 1945 |
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|
| 1946 |
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|
| 1947 |
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|
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|
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|
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|
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|
| 1952 |
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|
| 1953 |
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|
| 1954 |
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|
| 1955 |
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|
| 1956 |
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|
| 1957 |
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|
| 1958 |
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|
| 1959 |
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|
| 1960 |
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|
| 1961 |
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|
| 1962 |
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|
| 1963 |
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ア
|
| 1964 |
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|
| 1965 |
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|
| 1966 |
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|
| 1967 |
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|
| 1968 |
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|
| 1969 |
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|
| 1970 |
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| 1972 |
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|
| 1973 |
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| 1976 |
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| 1977 |
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|
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|
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| 1980 |
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|
| 1981 |
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|
| 1982 |
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|
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|
| 1984 |
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|
| 1985 |
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|
| 1986 |
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|
| 1987 |
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|
| 1988 |
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チ
|
| 1989 |
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|
| 1990 |
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|
| 1991 |
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テ
|
| 1992 |
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デ
|
| 1993 |
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ト
|
| 1994 |
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ド
|
| 1995 |
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ナ
|
| 1996 |
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ニ
|
| 1997 |
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ネ
|
| 1998 |
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ノ
|
| 1999 |
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|
| 2000 |
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|
| 2001 |
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ビ
|
| 2002 |
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ピ
|
| 2003 |
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フ
|
| 2004 |
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プ
|
| 2005 |
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ヘ
|
| 2006 |
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ベ
|
| 2007 |
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|
| 2008 |
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ホ
|
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기
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깍
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깔
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| 2110 |
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| 2111 |
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| 2118 |
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| 2119 |
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| 2120 |
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| 2121 |
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난
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| 2122 |
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날
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| 2123 |
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남
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| 2124 |
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납
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| 2125 |
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내
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| 2126 |
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냐
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| 2127 |
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냥
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| 2128 |
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너
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| 2129 |
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넘
|
| 2130 |
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넣
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| 2131 |
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네
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| 2132 |
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녁
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| 2133 |
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년
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| 2134 |
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녕
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| 2135 |
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노
|
| 2136 |
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녹
|
| 2137 |
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놀
|
| 2138 |
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누
|
| 2139 |
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눈
|
| 2140 |
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느
|
| 2141 |
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는
|
| 2142 |
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늘
|
| 2143 |
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니
|
| 2144 |
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님
|
| 2145 |
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닙
|
| 2146 |
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다
|
| 2147 |
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닥
|
| 2148 |
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단
|
| 2149 |
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달
|
| 2150 |
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닭
|
| 2151 |
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당
|
| 2152 |
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대
|
| 2153 |
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더
|
| 2154 |
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덕
|
| 2155 |
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던
|
| 2156 |
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덥
|
| 2157 |
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데
|
| 2158 |
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도
|
| 2159 |
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독
|
| 2160 |
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동
|
| 2161 |
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돼
|
| 2162 |
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됐
|
| 2163 |
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되
|
| 2164 |
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된
|
| 2165 |
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될
|
| 2166 |
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두
|
| 2167 |
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둑
|
| 2168 |
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둥
|
| 2169 |
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드
|
| 2170 |
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들
|
| 2171 |
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등
|
| 2172 |
-
디
|
| 2173 |
-
따
|
| 2174 |
-
딱
|
| 2175 |
-
딸
|
| 2176 |
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땅
|
| 2177 |
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때
|
| 2178 |
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떤
|
| 2179 |
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떨
|
| 2180 |
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떻
|
| 2181 |
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또
|
| 2182 |
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똑
|
| 2183 |
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뚱
|
| 2184 |
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뛰
|
| 2185 |
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뜻
|
| 2186 |
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띠
|
| 2187 |
-
라
|
| 2188 |
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락
|
| 2189 |
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란
|
| 2190 |
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람
|
| 2191 |
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랍
|
| 2192 |
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랑
|
| 2193 |
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래
|
| 2194 |
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랜
|
| 2195 |
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러
|
| 2196 |
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런
|
| 2197 |
-
럼
|
| 2198 |
-
렇
|
| 2199 |
-
레
|
| 2200 |
-
려
|
| 2201 |
-
력
|
| 2202 |
-
렵
|
| 2203 |
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렸
|
| 2204 |
-
로
|
| 2205 |
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록
|
| 2206 |
-
롬
|
| 2207 |
-
루
|
| 2208 |
-
르
|
| 2209 |
-
른
|
| 2210 |
-
를
|
| 2211 |
-
름
|
| 2212 |
-
릉
|
| 2213 |
-
리
|
| 2214 |
-
릴
|
| 2215 |
-
림
|
| 2216 |
-
마
|
| 2217 |
-
막
|
| 2218 |
-
만
|
| 2219 |
-
많
|
| 2220 |
-
말
|
| 2221 |
-
맑
|
| 2222 |
-
맙
|
| 2223 |
-
맛
|
| 2224 |
-
매
|
| 2225 |
-
머
|
| 2226 |
-
먹
|
| 2227 |
-
멍
|
| 2228 |
-
메
|
| 2229 |
-
면
|
| 2230 |
-
명
|
| 2231 |
-
몇
|
| 2232 |
-
모
|
| 2233 |
-
목
|
| 2234 |
-
몸
|
| 2235 |
-
못
|
| 2236 |
-
무
|
| 2237 |
-
문
|
| 2238 |
-
물
|
| 2239 |
-
뭐
|
| 2240 |
-
뭘
|
| 2241 |
-
미
|
| 2242 |
-
민
|
| 2243 |
-
밌
|
| 2244 |
-
밑
|
| 2245 |
-
바
|
| 2246 |
-
박
|
| 2247 |
-
밖
|
| 2248 |
-
반
|
| 2249 |
-
받
|
| 2250 |
-
발
|
| 2251 |
-
밤
|
| 2252 |
-
밥
|
| 2253 |
-
방
|
| 2254 |
-
배
|
| 2255 |
-
백
|
| 2256 |
-
밸
|
| 2257 |
-
뱀
|
| 2258 |
-
버
|
| 2259 |
-
번
|
| 2260 |
-
벌
|
| 2261 |
-
벚
|
| 2262 |
-
베
|
| 2263 |
-
벼
|
| 2264 |
-
벽
|
| 2265 |
-
별
|
| 2266 |
-
병
|
| 2267 |
-
보
|
| 2268 |
-
복
|
| 2269 |
-
본
|
| 2270 |
-
볼
|
| 2271 |
-
봐
|
| 2272 |
-
봤
|
| 2273 |
-
부
|
| 2274 |
-
분
|
| 2275 |
-
불
|
| 2276 |
-
비
|
| 2277 |
-
빔
|
| 2278 |
-
빛
|
| 2279 |
-
빠
|
| 2280 |
-
빨
|
| 2281 |
-
뼈
|
| 2282 |
-
뽀
|
| 2283 |
-
뿅
|
| 2284 |
-
쁘
|
| 2285 |
-
사
|
| 2286 |
-
산
|
| 2287 |
-
살
|
| 2288 |
-
삼
|
| 2289 |
-
샀
|
| 2290 |
-
상
|
| 2291 |
-
새
|
| 2292 |
-
색
|
| 2293 |
-
생
|
| 2294 |
-
서
|
| 2295 |
-
선
|
| 2296 |
-
설
|
| 2297 |
-
섭
|
| 2298 |
-
섰
|
| 2299 |
-
성
|
| 2300 |
-
세
|
| 2301 |
-
셔
|
| 2302 |
-
션
|
| 2303 |
-
셨
|
| 2304 |
-
소
|
| 2305 |
-
속
|
| 2306 |
-
손
|
| 2307 |
-
송
|
| 2308 |
-
수
|
| 2309 |
-
숙
|
| 2310 |
-
순
|
| 2311 |
-
술
|
| 2312 |
-
숫
|
| 2313 |
-
숭
|
| 2314 |
-
숲
|
| 2315 |
-
쉬
|
| 2316 |
-
쉽
|
| 2317 |
-
스
|
| 2318 |
-
슨
|
| 2319 |
-
습
|
| 2320 |
-
슷
|
| 2321 |
-
시
|
| 2322 |
-
식
|
| 2323 |
-
신
|
| 2324 |
-
실
|
| 2325 |
-
싫
|
| 2326 |
-
심
|
| 2327 |
-
십
|
| 2328 |
-
싶
|
| 2329 |
-
싸
|
| 2330 |
-
써
|
| 2331 |
-
쓰
|
| 2332 |
-
쓴
|
| 2333 |
-
씌
|
| 2334 |
-
씨
|
| 2335 |
-
씩
|
| 2336 |
-
씬
|
| 2337 |
-
아
|
| 2338 |
-
악
|
| 2339 |
-
안
|
| 2340 |
-
않
|
| 2341 |
-
알
|
| 2342 |
-
야
|
| 2343 |
-
약
|
| 2344 |
-
얀
|
| 2345 |
-
양
|
| 2346 |
-
얘
|
| 2347 |
-
어
|
| 2348 |
-
언
|
| 2349 |
-
얼
|
| 2350 |
-
엄
|
| 2351 |
-
업
|
| 2352 |
-
없
|
| 2353 |
-
었
|
| 2354 |
-
엉
|
| 2355 |
-
에
|
| 2356 |
-
여
|
| 2357 |
-
역
|
| 2358 |
-
연
|
| 2359 |
-
염
|
| 2360 |
-
엽
|
| 2361 |
-
영
|
| 2362 |
-
옆
|
| 2363 |
-
예
|
| 2364 |
-
옛
|
| 2365 |
-
오
|
| 2366 |
-
온
|
| 2367 |
-
올
|
| 2368 |
-
옷
|
| 2369 |
-
옹
|
| 2370 |
-
와
|
| 2371 |
-
왔
|
| 2372 |
-
왜
|
| 2373 |
-
요
|
| 2374 |
-
욕
|
| 2375 |
-
용
|
| 2376 |
-
우
|
| 2377 |
-
운
|
| 2378 |
-
울
|
| 2379 |
-
웃
|
| 2380 |
-
워
|
| 2381 |
-
원
|
| 2382 |
-
월
|
| 2383 |
-
웠
|
| 2384 |
-
위
|
| 2385 |
-
윙
|
| 2386 |
-
유
|
| 2387 |
-
육
|
| 2388 |
-
윤
|
| 2389 |
-
으
|
| 2390 |
-
은
|
| 2391 |
-
을
|
| 2392 |
-
음
|
| 2393 |
-
응
|
| 2394 |
-
의
|
| 2395 |
-
이
|
| 2396 |
-
익
|
| 2397 |
-
인
|
| 2398 |
-
일
|
| 2399 |
-
읽
|
| 2400 |
-
임
|
| 2401 |
-
입
|
| 2402 |
-
있
|
| 2403 |
-
자
|
| 2404 |
-
작
|
| 2405 |
-
잔
|
| 2406 |
-
잖
|
| 2407 |
-
잘
|
| 2408 |
-
잡
|
| 2409 |
-
잤
|
| 2410 |
-
장
|
| 2411 |
-
재
|
| 2412 |
-
저
|
| 2413 |
-
전
|
| 2414 |
-
점
|
| 2415 |
-
정
|
| 2416 |
-
제
|
| 2417 |
-
져
|
| 2418 |
-
졌
|
| 2419 |
-
조
|
| 2420 |
-
족
|
| 2421 |
-
좀
|
| 2422 |
-
종
|
| 2423 |
-
좋
|
| 2424 |
-
죠
|
| 2425 |
-
주
|
| 2426 |
-
준
|
| 2427 |
-
줄
|
| 2428 |
-
중
|
| 2429 |
-
줘
|
| 2430 |
-
즈
|
| 2431 |
-
즐
|
| 2432 |
-
즘
|
| 2433 |
-
지
|
| 2434 |
-
진
|
| 2435 |
-
집
|
| 2436 |
-
짜
|
| 2437 |
-
짝
|
| 2438 |
-
쩌
|
| 2439 |
-
쪼
|
| 2440 |
-
쪽
|
| 2441 |
-
쫌
|
| 2442 |
-
쭈
|
| 2443 |
-
쯔
|
| 2444 |
-
찌
|
| 2445 |
-
찍
|
| 2446 |
-
차
|
| 2447 |
-
착
|
| 2448 |
-
찾
|
| 2449 |
-
책
|
| 2450 |
-
처
|
| 2451 |
-
천
|
| 2452 |
-
철
|
| 2453 |
-
체
|
| 2454 |
-
쳐
|
| 2455 |
-
쳤
|
| 2456 |
-
초
|
| 2457 |
-
촌
|
| 2458 |
-
추
|
| 2459 |
-
출
|
| 2460 |
-
춤
|
| 2461 |
-
춥
|
| 2462 |
-
춰
|
| 2463 |
-
치
|
| 2464 |
-
친
|
| 2465 |
-
칠
|
| 2466 |
-
침
|
| 2467 |
-
칩
|
| 2468 |
-
칼
|
| 2469 |
-
커
|
| 2470 |
-
켓
|
| 2471 |
-
코
|
| 2472 |
-
콩
|
| 2473 |
-
쿠
|
| 2474 |
-
퀴
|
| 2475 |
-
크
|
| 2476 |
-
큰
|
| 2477 |
-
큽
|
| 2478 |
-
키
|
| 2479 |
-
킨
|
| 2480 |
-
타
|
| 2481 |
-
태
|
| 2482 |
-
터
|
| 2483 |
-
턴
|
| 2484 |
-
털
|
| 2485 |
-
테
|
| 2486 |
-
토
|
| 2487 |
-
통
|
| 2488 |
-
투
|
| 2489 |
-
트
|
| 2490 |
-
특
|
| 2491 |
-
튼
|
| 2492 |
-
틀
|
| 2493 |
-
티
|
| 2494 |
-
팀
|
| 2495 |
-
파
|
| 2496 |
-
팔
|
| 2497 |
-
패
|
| 2498 |
-
페
|
| 2499 |
-
펜
|
| 2500 |
-
펭
|
| 2501 |
-
평
|
| 2502 |
-
포
|
| 2503 |
-
폭
|
| 2504 |
-
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| 2518 |
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| 2523 |
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| 2526 |
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| 2527 |
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혼
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| 2528 |
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| 2529 |
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| 2530 |
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| 2531 |
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| 2532 |
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| 2533 |
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| 2541 |
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| 2542 |
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| 2544 |
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|
src/f5_tts/data/inference-cli.toml
DELETED
|
@@ -1,10 +0,0 @@
|
|
| 1 |
-
# F5-TTS | E2-TTS
|
| 2 |
-
model = "F5-TTS"
|
| 3 |
-
ref_audio = "tests/ref_audio/test_en_1_ref_short.wav"
|
| 4 |
-
# If an empty "", transcribes the reference audio automatically.
|
| 5 |
-
ref_text = "Some call me nature, others call me mother nature."
|
| 6 |
-
gen_text = "I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring. Respect me and I'll nurture you; ignore me and you shall face the consequences."
|
| 7 |
-
# File with text to generate. Ignores the text above.
|
| 8 |
-
gen_file = ""
|
| 9 |
-
remove_silence = false
|
| 10 |
-
output_dir = "tests"
|
|
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|
|
src/f5_tts/{scripts → eval}/eval_infer_batch.py
RENAMED
|
File without changes
|
src/f5_tts/{scripts → eval}/eval_infer_batch.sh
RENAMED
|
File without changes
|
src/f5_tts/{scripts → eval}/eval_librispeech_test_clean.py
RENAMED
|
File without changes
|
src/f5_tts/{scripts → eval}/eval_seedtts_testset.py
RENAMED
|
File without changes
|
src/f5_tts/{data → eval/eval_testset}/librispeech_pc_test_clean_cross_sentence.lst
RENAMED
|
File without changes
|
src/f5_tts/{inference_cli.py → infer/infer_cli.py}
RENAMED
|
@@ -1,7 +1,7 @@
|
|
| 1 |
import argparse
|
| 2 |
import codecs
|
| 3 |
-
import re
|
| 4 |
import os
|
|
|
|
| 5 |
from pathlib import Path
|
| 6 |
from importlib.resources import files
|
| 7 |
|
|
|
|
| 1 |
import argparse
|
| 2 |
import codecs
|
|
|
|
| 3 |
import os
|
| 4 |
+
import re
|
| 5 |
from pathlib import Path
|
| 6 |
from importlib.resources import files
|
| 7 |
|
src/f5_tts/{gradio_app.py → infer/infer_gradio.py}
RENAMED
|
File without changes
|
src/f5_tts/{speech_edit.py → infer/speech_edit.py}
RENAMED
|
File without changes
|
src/f5_tts/scripts/count_params_gflops.py
CHANGED
|
@@ -3,7 +3,7 @@ import os
|
|
| 3 |
|
| 4 |
sys.path.append(os.getcwd())
|
| 5 |
|
| 6 |
-
from f5_tts.model import
|
| 7 |
|
| 8 |
import torch
|
| 9 |
import thop
|
|
@@ -24,7 +24,7 @@ import thop
|
|
| 24 |
transformer = DiT(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
|
| 25 |
|
| 26 |
|
| 27 |
-
model =
|
| 28 |
target_sample_rate = 24000
|
| 29 |
n_mel_channels = 100
|
| 30 |
hop_length = 256
|
|
|
|
| 3 |
|
| 4 |
sys.path.append(os.getcwd())
|
| 5 |
|
| 6 |
+
from f5_tts.model import CFM, DiT
|
| 7 |
|
| 8 |
import torch
|
| 9 |
import thop
|
|
|
|
| 24 |
transformer = DiT(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
|
| 25 |
|
| 26 |
|
| 27 |
+
model = CFM(transformer=transformer)
|
| 28 |
target_sample_rate = 24000
|
| 29 |
n_mel_channels = 100
|
| 30 |
hop_length = 256
|
src/f5_tts/{finetune_cli.py → train/finetune_cli.py}
RENAMED
|
@@ -1,128 +1,128 @@
|
|
| 1 |
-
import argparse
|
| 2 |
-
import os
|
| 3 |
-
import shutil
|
| 4 |
-
|
| 5 |
-
from cached_path import cached_path
|
| 6 |
-
from f5_tts.model import CFM, UNetT, DiT, Trainer
|
| 7 |
-
from f5_tts.model.utils import get_tokenizer
|
| 8 |
-
from f5_tts.model.dataset import load_dataset
|
| 9 |
-
|
| 10 |
-
# -------------------------- Dataset Settings --------------------------- #
|
| 11 |
-
target_sample_rate = 24000
|
| 12 |
-
n_mel_channels = 100
|
| 13 |
-
hop_length = 256
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
# -------------------------- Argument Parsing --------------------------- #
|
| 17 |
-
def parse_args():
|
| 18 |
-
parser = argparse.ArgumentParser(description="Train CFM Model")
|
| 19 |
-
|
| 20 |
-
parser.add_argument(
|
| 21 |
-
"--exp_name", type=str, default="F5TTS_Base", choices=["F5TTS_Base", "E2TTS_Base"], help="Experiment name"
|
| 22 |
-
)
|
| 23 |
-
parser.add_argument("--dataset_name", type=str, default="Emilia_ZH_EN", help="Name of the dataset to use")
|
| 24 |
-
parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate for training")
|
| 25 |
-
parser.add_argument("--batch_size_per_gpu", type=int, default=256, help="Batch size per GPU")
|
| 26 |
-
parser.add_argument(
|
| 27 |
-
"--batch_size_type", type=str, default="frame", choices=["frame", "sample"], help="Batch size type"
|
| 28 |
-
)
|
| 29 |
-
parser.add_argument("--max_samples", type=int, default=16, help="Max sequences per batch")
|
| 30 |
-
parser.add_argument("--grad_accumulation_steps", type=int, default=1, help="Gradient accumulation steps")
|
| 31 |
-
parser.add_argument("--max_grad_norm", type=float, default=1.0, help="Max gradient norm for clipping")
|
| 32 |
-
parser.add_argument("--epochs", type=int, default=10, help="Number of training epochs")
|
| 33 |
-
parser.add_argument("--num_warmup_updates", type=int, default=5, help="Warmup steps")
|
| 34 |
-
parser.add_argument("--save_per_updates", type=int, default=10, help="Save checkpoint every X steps")
|
| 35 |
-
parser.add_argument("--last_per_steps", type=int, default=10, help="Save last checkpoint every X steps")
|
| 36 |
-
parser.add_argument("--finetune", type=bool, default=True, help="Use Finetune")
|
| 37 |
-
|
| 38 |
-
parser.add_argument(
|
| 39 |
-
"--tokenizer", type=str, default="pinyin", choices=["pinyin", "char", "custom"], help="Tokenizer type"
|
| 40 |
-
)
|
| 41 |
-
parser.add_argument(
|
| 42 |
-
"--tokenizer_path",
|
| 43 |
-
type=str,
|
| 44 |
-
default=None,
|
| 45 |
-
help="Path to custom tokenizer vocab file (only used if tokenizer = 'custom')",
|
| 46 |
-
)
|
| 47 |
-
|
| 48 |
-
return parser.parse_args()
|
| 49 |
-
|
| 50 |
-
|
| 51 |
-
# -------------------------- Training Settings -------------------------- #
|
| 52 |
-
|
| 53 |
-
|
| 54 |
-
def main():
|
| 55 |
-
args = parse_args()
|
| 56 |
-
|
| 57 |
-
# Model parameters based on experiment name
|
| 58 |
-
if args.exp_name == "F5TTS_Base":
|
| 59 |
-
wandb_resume_id = None
|
| 60 |
-
model_cls = DiT
|
| 61 |
-
model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
|
| 62 |
-
if args.finetune:
|
| 63 |
-
ckpt_path = str(cached_path("hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.pt"))
|
| 64 |
-
elif args.exp_name == "E2TTS_Base":
|
| 65 |
-
wandb_resume_id = None
|
| 66 |
-
model_cls = UNetT
|
| 67 |
-
model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
|
| 68 |
-
if args.finetune:
|
| 69 |
-
ckpt_path = str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.pt"))
|
| 70 |
-
|
| 71 |
-
if args.finetune:
|
| 72 |
-
path_ckpt = os.path.join("ckpts", args.dataset_name)
|
| 73 |
-
if not os.path.isdir(path_ckpt):
|
| 74 |
-
os.makedirs(path_ckpt, exist_ok=True)
|
| 75 |
-
shutil.copy2(ckpt_path, os.path.join(path_ckpt, os.path.basename(ckpt_path)))
|
| 76 |
-
|
| 77 |
-
checkpoint_path = os.path.join("ckpts", args.dataset_name)
|
| 78 |
-
|
| 79 |
-
# Use the tokenizer and tokenizer_path provided in the command line arguments
|
| 80 |
-
tokenizer = args.tokenizer
|
| 81 |
-
if tokenizer == "custom":
|
| 82 |
-
if not args.tokenizer_path:
|
| 83 |
-
raise ValueError("Custom tokenizer selected, but no tokenizer_path provided.")
|
| 84 |
-
tokenizer_path = args.tokenizer_path
|
| 85 |
-
else:
|
| 86 |
-
tokenizer_path = args.dataset_name
|
| 87 |
-
|
| 88 |
-
vocab_char_map, vocab_size = get_tokenizer(tokenizer_path, tokenizer)
|
| 89 |
-
|
| 90 |
-
mel_spec_kwargs = dict(
|
| 91 |
-
target_sample_rate=target_sample_rate,
|
| 92 |
-
n_mel_channels=n_mel_channels,
|
| 93 |
-
hop_length=hop_length,
|
| 94 |
-
)
|
| 95 |
-
|
| 96 |
-
e2tts = CFM(
|
| 97 |
-
transformer=model_cls(**model_cfg, text_num_embeds=vocab_size, mel_dim=n_mel_channels),
|
| 98 |
-
mel_spec_kwargs=mel_spec_kwargs,
|
| 99 |
-
vocab_char_map=vocab_char_map,
|
| 100 |
-
)
|
| 101 |
-
|
| 102 |
-
trainer = Trainer(
|
| 103 |
-
e2tts,
|
| 104 |
-
args.epochs,
|
| 105 |
-
args.learning_rate,
|
| 106 |
-
num_warmup_updates=args.num_warmup_updates,
|
| 107 |
-
save_per_updates=args.save_per_updates,
|
| 108 |
-
checkpoint_path=checkpoint_path,
|
| 109 |
-
batch_size=args.batch_size_per_gpu,
|
| 110 |
-
batch_size_type=args.batch_size_type,
|
| 111 |
-
max_samples=args.max_samples,
|
| 112 |
-
grad_accumulation_steps=args.grad_accumulation_steps,
|
| 113 |
-
max_grad_norm=args.max_grad_norm,
|
| 114 |
-
wandb_project="CFM-TTS",
|
| 115 |
-
wandb_run_name=args.exp_name,
|
| 116 |
-
wandb_resume_id=wandb_resume_id,
|
| 117 |
-
last_per_steps=args.last_per_steps,
|
| 118 |
-
)
|
| 119 |
-
|
| 120 |
-
train_dataset = load_dataset(args.dataset_name, tokenizer, mel_spec_kwargs=mel_spec_kwargs)
|
| 121 |
-
trainer.train(
|
| 122 |
-
train_dataset,
|
| 123 |
-
resumable_with_seed=666, # seed for shuffling dataset
|
| 124 |
-
)
|
| 125 |
-
|
| 126 |
-
|
| 127 |
-
if __name__ == "__main__":
|
| 128 |
-
main()
|
|
|
|
| 1 |
+
import argparse
|
| 2 |
+
import os
|
| 3 |
+
import shutil
|
| 4 |
+
|
| 5 |
+
from cached_path import cached_path
|
| 6 |
+
from f5_tts.model import CFM, UNetT, DiT, Trainer
|
| 7 |
+
from f5_tts.model.utils import get_tokenizer
|
| 8 |
+
from f5_tts.model.dataset import load_dataset
|
| 9 |
+
|
| 10 |
+
# -------------------------- Dataset Settings --------------------------- #
|
| 11 |
+
target_sample_rate = 24000
|
| 12 |
+
n_mel_channels = 100
|
| 13 |
+
hop_length = 256
|
| 14 |
+
|
| 15 |
+
|
| 16 |
+
# -------------------------- Argument Parsing --------------------------- #
|
| 17 |
+
def parse_args():
|
| 18 |
+
parser = argparse.ArgumentParser(description="Train CFM Model")
|
| 19 |
+
|
| 20 |
+
parser.add_argument(
|
| 21 |
+
"--exp_name", type=str, default="F5TTS_Base", choices=["F5TTS_Base", "E2TTS_Base"], help="Experiment name"
|
| 22 |
+
)
|
| 23 |
+
parser.add_argument("--dataset_name", type=str, default="Emilia_ZH_EN", help="Name of the dataset to use")
|
| 24 |
+
parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate for training")
|
| 25 |
+
parser.add_argument("--batch_size_per_gpu", type=int, default=256, help="Batch size per GPU")
|
| 26 |
+
parser.add_argument(
|
| 27 |
+
"--batch_size_type", type=str, default="frame", choices=["frame", "sample"], help="Batch size type"
|
| 28 |
+
)
|
| 29 |
+
parser.add_argument("--max_samples", type=int, default=16, help="Max sequences per batch")
|
| 30 |
+
parser.add_argument("--grad_accumulation_steps", type=int, default=1, help="Gradient accumulation steps")
|
| 31 |
+
parser.add_argument("--max_grad_norm", type=float, default=1.0, help="Max gradient norm for clipping")
|
| 32 |
+
parser.add_argument("--epochs", type=int, default=10, help="Number of training epochs")
|
| 33 |
+
parser.add_argument("--num_warmup_updates", type=int, default=5, help="Warmup steps")
|
| 34 |
+
parser.add_argument("--save_per_updates", type=int, default=10, help="Save checkpoint every X steps")
|
| 35 |
+
parser.add_argument("--last_per_steps", type=int, default=10, help="Save last checkpoint every X steps")
|
| 36 |
+
parser.add_argument("--finetune", type=bool, default=True, help="Use Finetune")
|
| 37 |
+
|
| 38 |
+
parser.add_argument(
|
| 39 |
+
"--tokenizer", type=str, default="pinyin", choices=["pinyin", "char", "custom"], help="Tokenizer type"
|
| 40 |
+
)
|
| 41 |
+
parser.add_argument(
|
| 42 |
+
"--tokenizer_path",
|
| 43 |
+
type=str,
|
| 44 |
+
default=None,
|
| 45 |
+
help="Path to custom tokenizer vocab file (only used if tokenizer = 'custom')",
|
| 46 |
+
)
|
| 47 |
+
|
| 48 |
+
return parser.parse_args()
|
| 49 |
+
|
| 50 |
+
|
| 51 |
+
# -------------------------- Training Settings -------------------------- #
|
| 52 |
+
|
| 53 |
+
|
| 54 |
+
def main():
|
| 55 |
+
args = parse_args()
|
| 56 |
+
|
| 57 |
+
# Model parameters based on experiment name
|
| 58 |
+
if args.exp_name == "F5TTS_Base":
|
| 59 |
+
wandb_resume_id = None
|
| 60 |
+
model_cls = DiT
|
| 61 |
+
model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
|
| 62 |
+
if args.finetune:
|
| 63 |
+
ckpt_path = str(cached_path("hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.pt"))
|
| 64 |
+
elif args.exp_name == "E2TTS_Base":
|
| 65 |
+
wandb_resume_id = None
|
| 66 |
+
model_cls = UNetT
|
| 67 |
+
model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
|
| 68 |
+
if args.finetune:
|
| 69 |
+
ckpt_path = str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.pt"))
|
| 70 |
+
|
| 71 |
+
if args.finetune:
|
| 72 |
+
path_ckpt = os.path.join("ckpts", args.dataset_name)
|
| 73 |
+
if not os.path.isdir(path_ckpt):
|
| 74 |
+
os.makedirs(path_ckpt, exist_ok=True)
|
| 75 |
+
shutil.copy2(ckpt_path, os.path.join(path_ckpt, os.path.basename(ckpt_path)))
|
| 76 |
+
|
| 77 |
+
checkpoint_path = os.path.join("ckpts", args.dataset_name)
|
| 78 |
+
|
| 79 |
+
# Use the tokenizer and tokenizer_path provided in the command line arguments
|
| 80 |
+
tokenizer = args.tokenizer
|
| 81 |
+
if tokenizer == "custom":
|
| 82 |
+
if not args.tokenizer_path:
|
| 83 |
+
raise ValueError("Custom tokenizer selected, but no tokenizer_path provided.")
|
| 84 |
+
tokenizer_path = args.tokenizer_path
|
| 85 |
+
else:
|
| 86 |
+
tokenizer_path = args.dataset_name
|
| 87 |
+
|
| 88 |
+
vocab_char_map, vocab_size = get_tokenizer(tokenizer_path, tokenizer)
|
| 89 |
+
|
| 90 |
+
mel_spec_kwargs = dict(
|
| 91 |
+
target_sample_rate=target_sample_rate,
|
| 92 |
+
n_mel_channels=n_mel_channels,
|
| 93 |
+
hop_length=hop_length,
|
| 94 |
+
)
|
| 95 |
+
|
| 96 |
+
e2tts = CFM(
|
| 97 |
+
transformer=model_cls(**model_cfg, text_num_embeds=vocab_size, mel_dim=n_mel_channels),
|
| 98 |
+
mel_spec_kwargs=mel_spec_kwargs,
|
| 99 |
+
vocab_char_map=vocab_char_map,
|
| 100 |
+
)
|
| 101 |
+
|
| 102 |
+
trainer = Trainer(
|
| 103 |
+
e2tts,
|
| 104 |
+
args.epochs,
|
| 105 |
+
args.learning_rate,
|
| 106 |
+
num_warmup_updates=args.num_warmup_updates,
|
| 107 |
+
save_per_updates=args.save_per_updates,
|
| 108 |
+
checkpoint_path=checkpoint_path,
|
| 109 |
+
batch_size=args.batch_size_per_gpu,
|
| 110 |
+
batch_size_type=args.batch_size_type,
|
| 111 |
+
max_samples=args.max_samples,
|
| 112 |
+
grad_accumulation_steps=args.grad_accumulation_steps,
|
| 113 |
+
max_grad_norm=args.max_grad_norm,
|
| 114 |
+
wandb_project="CFM-TTS",
|
| 115 |
+
wandb_run_name=args.exp_name,
|
| 116 |
+
wandb_resume_id=wandb_resume_id,
|
| 117 |
+
last_per_steps=args.last_per_steps,
|
| 118 |
+
)
|
| 119 |
+
|
| 120 |
+
train_dataset = load_dataset(args.dataset_name, tokenizer, mel_spec_kwargs=mel_spec_kwargs)
|
| 121 |
+
trainer.train(
|
| 122 |
+
train_dataset,
|
| 123 |
+
resumable_with_seed=666, # seed for shuffling dataset
|
| 124 |
+
)
|
| 125 |
+
|
| 126 |
+
|
| 127 |
+
if __name__ == "__main__":
|
| 128 |
+
main()
|
src/f5_tts/{finetune_gradio.py → train/finetune_gradio.py}
RENAMED
|
@@ -1,944 +1,944 @@
|
|
| 1 |
-
import
|
| 2 |
-
import
|
| 3 |
-
|
| 4 |
-
import
|
| 5 |
-
import
|
| 6 |
-
|
| 7 |
-
import
|
| 8 |
-
import
|
| 9 |
-
import
|
| 10 |
-
import
|
| 11 |
-
import
|
| 12 |
-
|
| 13 |
-
import
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
import
|
| 17 |
-
import
|
| 18 |
-
|
| 19 |
-
import
|
| 20 |
-
|
| 21 |
-
import
|
| 22 |
-
import
|
| 23 |
-
import
|
| 24 |
-
import
|
| 25 |
-
|
| 26 |
-
from
|
| 27 |
-
from f5_tts.
|
| 28 |
-
|
| 29 |
-
|
| 30 |
-
training_process = None
|
| 31 |
-
system = platform.system()
|
| 32 |
-
python_executable = sys.executable or "python"
|
| 33 |
-
tts_api = None
|
| 34 |
-
last_checkpoint = ""
|
| 35 |
-
last_device = ""
|
| 36 |
-
|
| 37 |
-
path_data = "data"
|
| 38 |
-
|
| 39 |
-
device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
|
| 40 |
-
|
| 41 |
-
pipe = None
|
| 42 |
-
|
| 43 |
-
|
| 44 |
-
# Load metadata
|
| 45 |
-
def get_audio_duration(audio_path):
|
| 46 |
-
"""Calculate the duration of an audio file."""
|
| 47 |
-
audio, sample_rate = torchaudio.load(audio_path)
|
| 48 |
-
num_channels = audio.shape[0]
|
| 49 |
-
return audio.shape[1] / (sample_rate * num_channels)
|
| 50 |
-
|
| 51 |
-
|
| 52 |
-
def clear_text(text):
|
| 53 |
-
"""Clean and prepare text by lowering the case and stripping whitespace."""
|
| 54 |
-
return text.lower().strip()
|
| 55 |
-
|
| 56 |
-
|
| 57 |
-
def get_rms(
|
| 58 |
-
y,
|
| 59 |
-
frame_length=2048,
|
| 60 |
-
hop_length=512,
|
| 61 |
-
pad_mode="constant",
|
| 62 |
-
): # https://github.com/RVC-Boss/GPT-SoVITS/blob/main/tools/slicer2.py
|
| 63 |
-
padding = (int(frame_length // 2), int(frame_length // 2))
|
| 64 |
-
y = np.pad(y, padding, mode=pad_mode)
|
| 65 |
-
|
| 66 |
-
axis = -1
|
| 67 |
-
# put our new within-frame axis at the end for now
|
| 68 |
-
out_strides = y.strides + tuple([y.strides[axis]])
|
| 69 |
-
# Reduce the shape on the framing axis
|
| 70 |
-
x_shape_trimmed = list(y.shape)
|
| 71 |
-
x_shape_trimmed[axis] -= frame_length - 1
|
| 72 |
-
out_shape = tuple(x_shape_trimmed) + tuple([frame_length])
|
| 73 |
-
xw = np.lib.stride_tricks.as_strided(y, shape=out_shape, strides=out_strides)
|
| 74 |
-
if axis < 0:
|
| 75 |
-
target_axis = axis - 1
|
| 76 |
-
else:
|
| 77 |
-
target_axis = axis + 1
|
| 78 |
-
xw = np.moveaxis(xw, -1, target_axis)
|
| 79 |
-
# Downsample along the target axis
|
| 80 |
-
slices = [slice(None)] * xw.ndim
|
| 81 |
-
slices[axis] = slice(0, None, hop_length)
|
| 82 |
-
x = xw[tuple(slices)]
|
| 83 |
-
|
| 84 |
-
# Calculate power
|
| 85 |
-
power = np.mean(np.abs(x) ** 2, axis=-2, keepdims=True)
|
| 86 |
-
|
| 87 |
-
return np.sqrt(power)
|
| 88 |
-
|
| 89 |
-
|
| 90 |
-
class Slicer: # https://github.com/RVC-Boss/GPT-SoVITS/blob/main/tools/slicer2.py
|
| 91 |
-
def __init__(
|
| 92 |
-
self,
|
| 93 |
-
sr: int,
|
| 94 |
-
threshold: float = -40.0,
|
| 95 |
-
min_length: int = 2000,
|
| 96 |
-
min_interval: int = 300,
|
| 97 |
-
hop_size: int = 20,
|
| 98 |
-
max_sil_kept: int = 2000,
|
| 99 |
-
):
|
| 100 |
-
if not min_length >= min_interval >= hop_size:
|
| 101 |
-
raise ValueError("The following condition must be satisfied: min_length >= min_interval >= hop_size")
|
| 102 |
-
if not max_sil_kept >= hop_size:
|
| 103 |
-
raise ValueError("The following condition must be satisfied: max_sil_kept >= hop_size")
|
| 104 |
-
min_interval = sr * min_interval / 1000
|
| 105 |
-
self.threshold = 10 ** (threshold / 20.0)
|
| 106 |
-
self.hop_size = round(sr * hop_size / 1000)
|
| 107 |
-
self.win_size = min(round(min_interval), 4 * self.hop_size)
|
| 108 |
-
self.min_length = round(sr * min_length / 1000 / self.hop_size)
|
| 109 |
-
self.min_interval = round(min_interval / self.hop_size)
|
| 110 |
-
self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size)
|
| 111 |
-
|
| 112 |
-
def _apply_slice(self, waveform, begin, end):
|
| 113 |
-
if len(waveform.shape) > 1:
|
| 114 |
-
return waveform[:, begin * self.hop_size : min(waveform.shape[1], end * self.hop_size)]
|
| 115 |
-
else:
|
| 116 |
-
return waveform[begin * self.hop_size : min(waveform.shape[0], end * self.hop_size)]
|
| 117 |
-
|
| 118 |
-
# @timeit
|
| 119 |
-
def slice(self, waveform):
|
| 120 |
-
if len(waveform.shape) > 1:
|
| 121 |
-
samples = waveform.mean(axis=0)
|
| 122 |
-
else:
|
| 123 |
-
samples = waveform
|
| 124 |
-
if samples.shape[0] <= self.min_length:
|
| 125 |
-
return [waveform]
|
| 126 |
-
rms_list = get_rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0)
|
| 127 |
-
sil_tags = []
|
| 128 |
-
silence_start = None
|
| 129 |
-
clip_start = 0
|
| 130 |
-
for i, rms in enumerate(rms_list):
|
| 131 |
-
# Keep looping while frame is silent.
|
| 132 |
-
if rms < self.threshold:
|
| 133 |
-
# Record start of silent frames.
|
| 134 |
-
if silence_start is None:
|
| 135 |
-
silence_start = i
|
| 136 |
-
continue
|
| 137 |
-
# Keep looping while frame is not silent and silence start has not been recorded.
|
| 138 |
-
if silence_start is None:
|
| 139 |
-
continue
|
| 140 |
-
# Clear recorded silence start if interval is not enough or clip is too short
|
| 141 |
-
is_leading_silence = silence_start == 0 and i > self.max_sil_kept
|
| 142 |
-
need_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_length
|
| 143 |
-
if not is_leading_silence and not need_slice_middle:
|
| 144 |
-
silence_start = None
|
| 145 |
-
continue
|
| 146 |
-
# Need slicing. Record the range of silent frames to be removed.
|
| 147 |
-
if i - silence_start <= self.max_sil_kept:
|
| 148 |
-
pos = rms_list[silence_start : i + 1].argmin() + silence_start
|
| 149 |
-
if silence_start == 0:
|
| 150 |
-
sil_tags.append((0, pos))
|
| 151 |
-
else:
|
| 152 |
-
sil_tags.append((pos, pos))
|
| 153 |
-
clip_start = pos
|
| 154 |
-
elif i - silence_start <= self.max_sil_kept * 2:
|
| 155 |
-
pos = rms_list[i - self.max_sil_kept : silence_start + self.max_sil_kept + 1].argmin()
|
| 156 |
-
pos += i - self.max_sil_kept
|
| 157 |
-
pos_l = rms_list[silence_start : silence_start + self.max_sil_kept + 1].argmin() + silence_start
|
| 158 |
-
pos_r = rms_list[i - self.max_sil_kept : i + 1].argmin() + i - self.max_sil_kept
|
| 159 |
-
if silence_start == 0:
|
| 160 |
-
sil_tags.append((0, pos_r))
|
| 161 |
-
clip_start = pos_r
|
| 162 |
-
else:
|
| 163 |
-
sil_tags.append((min(pos_l, pos), max(pos_r, pos)))
|
| 164 |
-
clip_start = max(pos_r, pos)
|
| 165 |
-
else:
|
| 166 |
-
pos_l = rms_list[silence_start : silence_start + self.max_sil_kept + 1].argmin() + silence_start
|
| 167 |
-
pos_r = rms_list[i - self.max_sil_kept : i + 1].argmin() + i - self.max_sil_kept
|
| 168 |
-
if silence_start == 0:
|
| 169 |
-
sil_tags.append((0, pos_r))
|
| 170 |
-
else:
|
| 171 |
-
sil_tags.append((pos_l, pos_r))
|
| 172 |
-
clip_start = pos_r
|
| 173 |
-
silence_start = None
|
| 174 |
-
# Deal with trailing silence.
|
| 175 |
-
total_frames = rms_list.shape[0]
|
| 176 |
-
if silence_start is not None and total_frames - silence_start >= self.min_interval:
|
| 177 |
-
silence_end = min(total_frames, silence_start + self.max_sil_kept)
|
| 178 |
-
pos = rms_list[silence_start : silence_end + 1].argmin() + silence_start
|
| 179 |
-
sil_tags.append((pos, total_frames + 1))
|
| 180 |
-
# Apply and return slices.
|
| 181 |
-
####音频+起始时间+终止时间
|
| 182 |
-
if len(sil_tags) == 0:
|
| 183 |
-
return [[waveform, 0, int(total_frames * self.hop_size)]]
|
| 184 |
-
else:
|
| 185 |
-
chunks = []
|
| 186 |
-
if sil_tags[0][0] > 0:
|
| 187 |
-
chunks.append([self._apply_slice(waveform, 0, sil_tags[0][0]), 0, int(sil_tags[0][0] * self.hop_size)])
|
| 188 |
-
for i in range(len(sil_tags) - 1):
|
| 189 |
-
chunks.append(
|
| 190 |
-
[
|
| 191 |
-
self._apply_slice(waveform, sil_tags[i][1], sil_tags[i + 1][0]),
|
| 192 |
-
int(sil_tags[i][1] * self.hop_size),
|
| 193 |
-
int(sil_tags[i + 1][0] * self.hop_size),
|
| 194 |
-
]
|
| 195 |
-
)
|
| 196 |
-
if sil_tags[-1][1] < total_frames:
|
| 197 |
-
chunks.append(
|
| 198 |
-
[
|
| 199 |
-
self._apply_slice(waveform, sil_tags[-1][1], total_frames),
|
| 200 |
-
int(sil_tags[-1][1] * self.hop_size),
|
| 201 |
-
int(total_frames * self.hop_size),
|
| 202 |
-
]
|
| 203 |
-
)
|
| 204 |
-
return chunks
|
| 205 |
-
|
| 206 |
-
|
| 207 |
-
# terminal
|
| 208 |
-
def terminate_process_tree(pid, including_parent=True):
|
| 209 |
-
try:
|
| 210 |
-
parent = psutil.Process(pid)
|
| 211 |
-
except psutil.NoSuchProcess:
|
| 212 |
-
# Process already terminated
|
| 213 |
-
return
|
| 214 |
-
|
| 215 |
-
children = parent.children(recursive=True)
|
| 216 |
-
for child in children:
|
| 217 |
-
try:
|
| 218 |
-
os.kill(child.pid, signal.SIGTERM) # or signal.SIGKILL
|
| 219 |
-
except OSError:
|
| 220 |
-
pass
|
| 221 |
-
if including_parent:
|
| 222 |
-
try:
|
| 223 |
-
os.kill(parent.pid, signal.SIGTERM) # or signal.SIGKILL
|
| 224 |
-
except OSError:
|
| 225 |
-
pass
|
| 226 |
-
|
| 227 |
-
|
| 228 |
-
def terminate_process(pid):
|
| 229 |
-
if system == "Windows":
|
| 230 |
-
cmd = f"taskkill /t /f /pid {pid}"
|
| 231 |
-
os.system(cmd)
|
| 232 |
-
else:
|
| 233 |
-
terminate_process_tree(pid)
|
| 234 |
-
|
| 235 |
-
|
| 236 |
-
def start_training(
|
| 237 |
-
dataset_name="",
|
| 238 |
-
exp_name="F5TTS_Base",
|
| 239 |
-
learning_rate=1e-4,
|
| 240 |
-
batch_size_per_gpu=400,
|
| 241 |
-
batch_size_type="frame",
|
| 242 |
-
max_samples=64,
|
| 243 |
-
grad_accumulation_steps=1,
|
| 244 |
-
max_grad_norm=1.0,
|
| 245 |
-
epochs=11,
|
| 246 |
-
num_warmup_updates=200,
|
| 247 |
-
save_per_updates=400,
|
| 248 |
-
last_per_steps=800,
|
| 249 |
-
finetune=True,
|
| 250 |
-
):
|
| 251 |
-
global training_process, tts_api
|
| 252 |
-
|
| 253 |
-
if tts_api is not None:
|
| 254 |
-
del tts_api
|
| 255 |
-
gc.collect()
|
| 256 |
-
torch.cuda.empty_cache()
|
| 257 |
-
tts_api = None
|
| 258 |
-
|
| 259 |
-
path_project = os.path.join(path_data, dataset_name + "_pinyin")
|
| 260 |
-
|
| 261 |
-
if not os.path.isdir(path_project):
|
| 262 |
-
yield (
|
| 263 |
-
f"There is not project with name {dataset_name}",
|
| 264 |
-
gr.update(interactive=True),
|
| 265 |
-
gr.update(interactive=False),
|
| 266 |
-
)
|
| 267 |
-
return
|
| 268 |
-
|
| 269 |
-
file_raw = os.path.join(path_project, "raw.arrow")
|
| 270 |
-
if not os.path.isfile(file_raw):
|
| 271 |
-
yield f"There is no file {file_raw}", gr.update(interactive=True), gr.update(interactive=False)
|
| 272 |
-
return
|
| 273 |
-
|
| 274 |
-
# Check if a training process is already running
|
| 275 |
-
if training_process is not None:
|
| 276 |
-
return "Train run already!", gr.update(interactive=False), gr.update(interactive=True)
|
| 277 |
-
|
| 278 |
-
yield "start train", gr.update(interactive=False), gr.update(interactive=False)
|
| 279 |
-
|
| 280 |
-
# Command to run the training script with the specified arguments
|
| 281 |
-
cmd = (
|
| 282 |
-
f"accelerate launch finetune-cli.py --exp_name {exp_name} "
|
| 283 |
-
f"--learning_rate {learning_rate} "
|
| 284 |
-
f"--batch_size_per_gpu {batch_size_per_gpu} "
|
| 285 |
-
f"--batch_size_type {batch_size_type} "
|
| 286 |
-
f"--max_samples {max_samples} "
|
| 287 |
-
f"--grad_accumulation_steps {grad_accumulation_steps} "
|
| 288 |
-
f"--max_grad_norm {max_grad_norm} "
|
| 289 |
-
f"--epochs {epochs} "
|
| 290 |
-
f"--num_warmup_updates {num_warmup_updates} "
|
| 291 |
-
f"--save_per_updates {save_per_updates} "
|
| 292 |
-
f"--last_per_steps {last_per_steps} "
|
| 293 |
-
f"--dataset_name {dataset_name}"
|
| 294 |
-
)
|
| 295 |
-
if finetune:
|
| 296 |
-
cmd += f" --finetune {finetune}"
|
| 297 |
-
|
| 298 |
-
print(cmd)
|
| 299 |
-
|
| 300 |
-
try:
|
| 301 |
-
# Start the training process
|
| 302 |
-
training_process = subprocess.Popen(cmd, shell=True)
|
| 303 |
-
|
| 304 |
-
time.sleep(5)
|
| 305 |
-
yield "train start", gr.update(interactive=False), gr.update(interactive=True)
|
| 306 |
-
|
| 307 |
-
# Wait for the training process to finish
|
| 308 |
-
training_process.wait()
|
| 309 |
-
time.sleep(1)
|
| 310 |
-
|
| 311 |
-
if training_process is None:
|
| 312 |
-
text_info = "train stop"
|
| 313 |
-
else:
|
| 314 |
-
text_info = "train complete !"
|
| 315 |
-
|
| 316 |
-
except Exception as e: # Catch all exceptions
|
| 317 |
-
# Ensure that we reset the training process variable in case of an error
|
| 318 |
-
text_info = f"An error occurred: {str(e)}"
|
| 319 |
-
|
| 320 |
-
training_process = None
|
| 321 |
-
|
| 322 |
-
yield text_info, gr.update(interactive=True), gr.update(interactive=False)
|
| 323 |
-
|
| 324 |
-
|
| 325 |
-
def stop_training():
|
| 326 |
-
global training_process
|
| 327 |
-
if training_process is None:
|
| 328 |
-
return "Train not run !", gr.update(interactive=True), gr.update(interactive=False)
|
| 329 |
-
terminate_process_tree(training_process.pid)
|
| 330 |
-
training_process = None
|
| 331 |
-
return "train stop", gr.update(interactive=True), gr.update(interactive=False)
|
| 332 |
-
|
| 333 |
-
|
| 334 |
-
def create_data_project(name):
|
| 335 |
-
name += "_pinyin"
|
| 336 |
-
os.makedirs(os.path.join(path_data, name), exist_ok=True)
|
| 337 |
-
os.makedirs(os.path.join(path_data, name, "dataset"), exist_ok=True)
|
| 338 |
-
|
| 339 |
-
|
| 340 |
-
def transcribe(file_audio, language="english"):
|
| 341 |
-
global pipe
|
| 342 |
-
|
| 343 |
-
if pipe is None:
|
| 344 |
-
pipe = pipeline(
|
| 345 |
-
"automatic-speech-recognition",
|
| 346 |
-
model="openai/whisper-large-v3-turbo",
|
| 347 |
-
torch_dtype=torch.float16,
|
| 348 |
-
device=device,
|
| 349 |
-
)
|
| 350 |
-
|
| 351 |
-
text_transcribe = pipe(
|
| 352 |
-
file_audio,
|
| 353 |
-
chunk_length_s=30,
|
| 354 |
-
batch_size=128,
|
| 355 |
-
generate_kwargs={"task": "transcribe", "language": language},
|
| 356 |
-
return_timestamps=False,
|
| 357 |
-
)["text"].strip()
|
| 358 |
-
return text_transcribe
|
| 359 |
-
|
| 360 |
-
|
| 361 |
-
def transcribe_all(name_project, audio_files, language, user=False, progress=gr.Progress()):
|
| 362 |
-
name_project += "_pinyin"
|
| 363 |
-
path_project = os.path.join(path_data, name_project)
|
| 364 |
-
path_dataset = os.path.join(path_project, "dataset")
|
| 365 |
-
path_project_wavs = os.path.join(path_project, "wavs")
|
| 366 |
-
file_metadata = os.path.join(path_project, "metadata.csv")
|
| 367 |
-
|
| 368 |
-
if audio_files is None:
|
| 369 |
-
return "You need to load an audio file."
|
| 370 |
-
|
| 371 |
-
if os.path.isdir(path_project_wavs):
|
| 372 |
-
shutil.rmtree(path_project_wavs)
|
| 373 |
-
|
| 374 |
-
if os.path.isfile(file_metadata):
|
| 375 |
-
os.remove(file_metadata)
|
| 376 |
-
|
| 377 |
-
os.makedirs(path_project_wavs, exist_ok=True)
|
| 378 |
-
|
| 379 |
-
if user:
|
| 380 |
-
file_audios = [
|
| 381 |
-
file
|
| 382 |
-
for format in ("*.wav", "*.ogg", "*.opus", "*.mp3", "*.flac")
|
| 383 |
-
for file in glob(os.path.join(path_dataset, format))
|
| 384 |
-
]
|
| 385 |
-
if file_audios == []:
|
| 386 |
-
return "No audio file was found in the dataset."
|
| 387 |
-
else:
|
| 388 |
-
file_audios = audio_files
|
| 389 |
-
|
| 390 |
-
alpha = 0.5
|
| 391 |
-
_max = 1.0
|
| 392 |
-
slicer = Slicer(24000)
|
| 393 |
-
|
| 394 |
-
num = 0
|
| 395 |
-
error_num = 0
|
| 396 |
-
data = ""
|
| 397 |
-
for file_audio in progress.tqdm(file_audios, desc="transcribe files", total=len((file_audios))):
|
| 398 |
-
audio, _ = librosa.load(file_audio, sr=24000, mono=True)
|
| 399 |
-
|
| 400 |
-
list_slicer = slicer.slice(audio)
|
| 401 |
-
for chunk, start, end in progress.tqdm(list_slicer, total=len(list_slicer), desc="slicer files"):
|
| 402 |
-
name_segment = os.path.join(f"segment_{num}")
|
| 403 |
-
file_segment = os.path.join(path_project_wavs, f"{name_segment}.wav")
|
| 404 |
-
|
| 405 |
-
tmp_max = np.abs(chunk).max()
|
| 406 |
-
if tmp_max > 1:
|
| 407 |
-
chunk /= tmp_max
|
| 408 |
-
chunk = (chunk / tmp_max * (_max * alpha)) + (1 - alpha) * chunk
|
| 409 |
-
wavfile.write(file_segment, 24000, (chunk * 32767).astype(np.int16))
|
| 410 |
-
|
| 411 |
-
try:
|
| 412 |
-
text = transcribe(file_segment, language)
|
| 413 |
-
text = text.lower().strip().replace('"', "")
|
| 414 |
-
|
| 415 |
-
data += f"{name_segment}|{text}\n"
|
| 416 |
-
|
| 417 |
-
num += 1
|
| 418 |
-
except: # noqa: E722
|
| 419 |
-
error_num += 1
|
| 420 |
-
|
| 421 |
-
with open(file_metadata, "w", encoding="utf-8") as f:
|
| 422 |
-
f.write(data)
|
| 423 |
-
|
| 424 |
-
if error_num != []:
|
| 425 |
-
error_text = f"\nerror files : {error_num}"
|
| 426 |
-
else:
|
| 427 |
-
error_text = ""
|
| 428 |
-
|
| 429 |
-
return f"transcribe complete samples : {num}\npath : {path_project_wavs}{error_text}"
|
| 430 |
-
|
| 431 |
-
|
| 432 |
-
def format_seconds_to_hms(seconds):
|
| 433 |
-
hours = int(seconds / 3600)
|
| 434 |
-
minutes = int((seconds % 3600) / 60)
|
| 435 |
-
seconds = seconds % 60
|
| 436 |
-
return "{:02d}:{:02d}:{:02d}".format(hours, minutes, int(seconds))
|
| 437 |
-
|
| 438 |
-
|
| 439 |
-
def create_metadata(name_project, progress=gr.Progress()):
|
| 440 |
-
name_project += "_pinyin"
|
| 441 |
-
path_project = os.path.join(path_data, name_project)
|
| 442 |
-
path_project_wavs = os.path.join(path_project, "wavs")
|
| 443 |
-
file_metadata = os.path.join(path_project, "metadata.csv")
|
| 444 |
-
file_raw = os.path.join(path_project, "raw.arrow")
|
| 445 |
-
file_duration = os.path.join(path_project, "duration.json")
|
| 446 |
-
file_vocab = os.path.join(path_project, "vocab.txt")
|
| 447 |
-
|
| 448 |
-
if not os.path.isfile(file_metadata):
|
| 449 |
-
return "The file was not found in " + file_metadata
|
| 450 |
-
|
| 451 |
-
with open(file_metadata, "r", encoding="utf-8") as f:
|
| 452 |
-
data = f.read()
|
| 453 |
-
|
| 454 |
-
audio_path_list = []
|
| 455 |
-
text_list = []
|
| 456 |
-
duration_list = []
|
| 457 |
-
|
| 458 |
-
count = data.split("\n")
|
| 459 |
-
lenght = 0
|
| 460 |
-
result = []
|
| 461 |
-
error_files = []
|
| 462 |
-
for line in progress.tqdm(data.split("\n"), total=count):
|
| 463 |
-
sp_line = line.split("|")
|
| 464 |
-
if len(sp_line) != 2:
|
| 465 |
-
continue
|
| 466 |
-
name_audio, text = sp_line[:2]
|
| 467 |
-
|
| 468 |
-
file_audio = os.path.join(path_project_wavs, name_audio + ".wav")
|
| 469 |
-
|
| 470 |
-
if not os.path.isfile(file_audio):
|
| 471 |
-
error_files.append(file_audio)
|
| 472 |
-
continue
|
| 473 |
-
|
| 474 |
-
duraction = get_audio_duration(file_audio)
|
| 475 |
-
if duraction < 2 and duraction > 15:
|
| 476 |
-
continue
|
| 477 |
-
if len(text) < 4:
|
| 478 |
-
continue
|
| 479 |
-
|
| 480 |
-
text = clear_text(text)
|
| 481 |
-
text = convert_char_to_pinyin([text], polyphone=True)[0]
|
| 482 |
-
|
| 483 |
-
audio_path_list.append(file_audio)
|
| 484 |
-
duration_list.append(duraction)
|
| 485 |
-
text_list.append(text)
|
| 486 |
-
|
| 487 |
-
result.append({"audio_path": file_audio, "text": text, "duration": duraction})
|
| 488 |
-
|
| 489 |
-
lenght += duraction
|
| 490 |
-
|
| 491 |
-
if duration_list == []:
|
| 492 |
-
error_files_text = "\n".join(error_files)
|
| 493 |
-
return f"Error: No audio files found in the specified path : \n{error_files_text}"
|
| 494 |
-
|
| 495 |
-
min_second = round(min(duration_list), 2)
|
| 496 |
-
max_second = round(max(duration_list), 2)
|
| 497 |
-
|
| 498 |
-
with ArrowWriter(path=file_raw, writer_batch_size=1) as writer:
|
| 499 |
-
for line in progress.tqdm(result, total=len(result), desc="prepare data"):
|
| 500 |
-
writer.write(line)
|
| 501 |
-
|
| 502 |
-
with open(file_duration, "w", encoding="utf-8") as f:
|
| 503 |
-
json.dump({"duration": duration_list}, f, ensure_ascii=False)
|
| 504 |
-
|
| 505 |
-
file_vocab_finetune = "data/Emilia_ZH_EN_pinyin/vocab.txt"
|
| 506 |
-
if not os.path.isfile(file_vocab_finetune):
|
| 507 |
-
return "Error: Vocabulary file 'Emilia_ZH_EN_pinyin' not found!"
|
| 508 |
-
shutil.copy2(file_vocab_finetune, file_vocab)
|
| 509 |
-
|
| 510 |
-
if error_files != []:
|
| 511 |
-
error_text = "error files\n" + "\n".join(error_files)
|
| 512 |
-
else:
|
| 513 |
-
error_text = ""
|
| 514 |
-
|
| 515 |
-
return f"prepare complete \nsamples : {len(text_list)}\ntime data : {format_seconds_to_hms(lenght)}\nmin sec : {min_second}\nmax sec : {max_second}\nfile_arrow : {file_raw}\n{error_text}"
|
| 516 |
-
|
| 517 |
-
|
| 518 |
-
def check_user(value):
|
| 519 |
-
return gr.update(visible=not value), gr.update(visible=value)
|
| 520 |
-
|
| 521 |
-
|
| 522 |
-
def calculate_train(
|
| 523 |
-
name_project,
|
| 524 |
-
batch_size_type,
|
| 525 |
-
max_samples,
|
| 526 |
-
learning_rate,
|
| 527 |
-
num_warmup_updates,
|
| 528 |
-
save_per_updates,
|
| 529 |
-
last_per_steps,
|
| 530 |
-
finetune,
|
| 531 |
-
):
|
| 532 |
-
name_project += "_pinyin"
|
| 533 |
-
path_project = os.path.join(path_data, name_project)
|
| 534 |
-
file_duraction = os.path.join(path_project, "duration.json")
|
| 535 |
-
|
| 536 |
-
if not os.path.isfile(file_duraction):
|
| 537 |
-
return (
|
| 538 |
-
1000,
|
| 539 |
-
max_samples,
|
| 540 |
-
num_warmup_updates,
|
| 541 |
-
save_per_updates,
|
| 542 |
-
last_per_steps,
|
| 543 |
-
"project not found !",
|
| 544 |
-
learning_rate,
|
| 545 |
-
)
|
| 546 |
-
|
| 547 |
-
with open(file_duraction, "r") as file:
|
| 548 |
-
data = json.load(file)
|
| 549 |
-
|
| 550 |
-
duration_list = data["duration"]
|
| 551 |
-
|
| 552 |
-
samples = len(duration_list)
|
| 553 |
-
|
| 554 |
-
if torch.cuda.is_available():
|
| 555 |
-
gpu_properties = torch.cuda.get_device_properties(0)
|
| 556 |
-
total_memory = gpu_properties.total_memory / (1024**3)
|
| 557 |
-
elif torch.backends.mps.is_available():
|
| 558 |
-
total_memory = psutil.virtual_memory().available / (1024**3)
|
| 559 |
-
|
| 560 |
-
if batch_size_type == "frame":
|
| 561 |
-
batch = int(total_memory * 0.5)
|
| 562 |
-
batch = (lambda num: num + 1 if num % 2 != 0 else num)(batch)
|
| 563 |
-
batch_size_per_gpu = int(38400 / batch)
|
| 564 |
-
else:
|
| 565 |
-
batch_size_per_gpu = int(total_memory / 8)
|
| 566 |
-
batch_size_per_gpu = (lambda num: num + 1 if num % 2 != 0 else num)(batch_size_per_gpu)
|
| 567 |
-
batch = batch_size_per_gpu
|
| 568 |
-
|
| 569 |
-
if batch_size_per_gpu <= 0:
|
| 570 |
-
batch_size_per_gpu = 1
|
| 571 |
-
|
| 572 |
-
if samples < 64:
|
| 573 |
-
max_samples = int(samples * 0.25)
|
| 574 |
-
else:
|
| 575 |
-
max_samples = 64
|
| 576 |
-
|
| 577 |
-
num_warmup_updates = int(samples * 0.05)
|
| 578 |
-
save_per_updates = int(samples * 0.10)
|
| 579 |
-
last_per_steps = int(save_per_updates * 5)
|
| 580 |
-
|
| 581 |
-
max_samples = (lambda num: num + 1 if num % 2 != 0 else num)(max_samples)
|
| 582 |
-
num_warmup_updates = (lambda num: num + 1 if num % 2 != 0 else num)(num_warmup_updates)
|
| 583 |
-
save_per_updates = (lambda num: num + 1 if num % 2 != 0 else num)(save_per_updates)
|
| 584 |
-
last_per_steps = (lambda num: num + 1 if num % 2 != 0 else num)(last_per_steps)
|
| 585 |
-
|
| 586 |
-
if finetune:
|
| 587 |
-
learning_rate = 1e-5
|
| 588 |
-
else:
|
| 589 |
-
learning_rate = 7.5e-5
|
| 590 |
-
|
| 591 |
-
return batch_size_per_gpu, max_samples, num_warmup_updates, save_per_updates, last_per_steps, samples, learning_rate
|
| 592 |
-
|
| 593 |
-
|
| 594 |
-
def extract_and_save_ema_model(checkpoint_path: str, new_checkpoint_path: str) -> None:
|
| 595 |
-
try:
|
| 596 |
-
checkpoint = torch.load(checkpoint_path)
|
| 597 |
-
print("Original Checkpoint Keys:", checkpoint.keys())
|
| 598 |
-
|
| 599 |
-
ema_model_state_dict = checkpoint.get("ema_model_state_dict", None)
|
| 600 |
-
|
| 601 |
-
if ema_model_state_dict is not None:
|
| 602 |
-
new_checkpoint = {"ema_model_state_dict": ema_model_state_dict}
|
| 603 |
-
torch.save(new_checkpoint, new_checkpoint_path)
|
| 604 |
-
return f"New checkpoint saved at: {new_checkpoint_path}"
|
| 605 |
-
else:
|
| 606 |
-
return "No 'ema_model_state_dict' found in the checkpoint."
|
| 607 |
-
|
| 608 |
-
except Exception as e:
|
| 609 |
-
return f"An error occurred: {e}"
|
| 610 |
-
|
| 611 |
-
|
| 612 |
-
def vocab_check(project_name):
|
| 613 |
-
name_project = project_name + "_pinyin"
|
| 614 |
-
path_project = os.path.join(path_data, name_project)
|
| 615 |
-
|
| 616 |
-
file_metadata = os.path.join(path_project, "metadata.csv")
|
| 617 |
-
|
| 618 |
-
file_vocab = "data/Emilia_ZH_EN_pinyin/vocab.txt"
|
| 619 |
-
if not os.path.isfile(file_vocab):
|
| 620 |
-
return f"the file {file_vocab} not found !"
|
| 621 |
-
|
| 622 |
-
with open(file_vocab, "r", encoding="utf-8") as f:
|
| 623 |
-
data = f.read()
|
| 624 |
-
|
| 625 |
-
vocab = data.split("\n")
|
| 626 |
-
|
| 627 |
-
if not os.path.isfile(file_metadata):
|
| 628 |
-
return f"the file {file_metadata} not found !"
|
| 629 |
-
|
| 630 |
-
with open(file_metadata, "r", encoding="utf-8") as f:
|
| 631 |
-
data = f.read()
|
| 632 |
-
|
| 633 |
-
miss_symbols = []
|
| 634 |
-
miss_symbols_keep = {}
|
| 635 |
-
for item in data.split("\n"):
|
| 636 |
-
sp = item.split("|")
|
| 637 |
-
if len(sp) != 2:
|
| 638 |
-
continue
|
| 639 |
-
|
| 640 |
-
text = sp[1].lower().strip()
|
| 641 |
-
|
| 642 |
-
for t in text:
|
| 643 |
-
if t not in vocab and t not in miss_symbols_keep:
|
| 644 |
-
miss_symbols.append(t)
|
| 645 |
-
miss_symbols_keep[t] = t
|
| 646 |
-
if miss_symbols == []:
|
| 647 |
-
info = "You can train using your language !"
|
| 648 |
-
else:
|
| 649 |
-
info = f"The following symbols are missing in your language : {len(miss_symbols)}\n\n" + "\n".join(miss_symbols)
|
| 650 |
-
|
| 651 |
-
return info
|
| 652 |
-
|
| 653 |
-
|
| 654 |
-
def get_random_sample_prepare(project_name):
|
| 655 |
-
name_project = project_name + "_pinyin"
|
| 656 |
-
path_project = os.path.join(path_data, name_project)
|
| 657 |
-
file_arrow = os.path.join(path_project, "raw.arrow")
|
| 658 |
-
if not os.path.isfile(file_arrow):
|
| 659 |
-
return "", None
|
| 660 |
-
dataset = Dataset_.from_file(file_arrow)
|
| 661 |
-
random_sample = dataset.shuffle(seed=random.randint(0, 1000)).select([0])
|
| 662 |
-
text = "[" + " , ".join(["' " + t + " '" for t in random_sample["text"][0]]) + "]"
|
| 663 |
-
audio_path = random_sample["audio_path"][0]
|
| 664 |
-
return text, audio_path
|
| 665 |
-
|
| 666 |
-
|
| 667 |
-
def get_random_sample_transcribe(project_name):
|
| 668 |
-
name_project = project_name + "_pinyin"
|
| 669 |
-
path_project = os.path.join(path_data, name_project)
|
| 670 |
-
file_metadata = os.path.join(path_project, "metadata.csv")
|
| 671 |
-
if not os.path.isfile(file_metadata):
|
| 672 |
-
return "", None
|
| 673 |
-
|
| 674 |
-
data = ""
|
| 675 |
-
with open(file_metadata, "r", encoding="utf-8") as f:
|
| 676 |
-
data = f.read()
|
| 677 |
-
|
| 678 |
-
list_data = []
|
| 679 |
-
for item in data.split("\n"):
|
| 680 |
-
sp = item.split("|")
|
| 681 |
-
if len(sp) != 2:
|
| 682 |
-
continue
|
| 683 |
-
list_data.append([os.path.join(path_project, "wavs", sp[0] + ".wav"), sp[1]])
|
| 684 |
-
|
| 685 |
-
if list_data == []:
|
| 686 |
-
return "", None
|
| 687 |
-
|
| 688 |
-
random_item = random.choice(list_data)
|
| 689 |
-
|
| 690 |
-
return random_item[1], random_item[0]
|
| 691 |
-
|
| 692 |
-
|
| 693 |
-
def get_random_sample_infer(project_name):
|
| 694 |
-
text, audio = get_random_sample_transcribe(project_name)
|
| 695 |
-
return (
|
| 696 |
-
text,
|
| 697 |
-
text,
|
| 698 |
-
audio,
|
| 699 |
-
)
|
| 700 |
-
|
| 701 |
-
|
| 702 |
-
def infer(file_checkpoint, exp_name, ref_text, ref_audio, gen_text, nfe_step):
|
| 703 |
-
global last_checkpoint, last_device, tts_api
|
| 704 |
-
|
| 705 |
-
if not os.path.isfile(file_checkpoint):
|
| 706 |
-
return None
|
| 707 |
-
|
| 708 |
-
if training_process is not None:
|
| 709 |
-
device_test = "cpu"
|
| 710 |
-
else:
|
| 711 |
-
device_test = None
|
| 712 |
-
|
| 713 |
-
if last_checkpoint != file_checkpoint or last_device != device_test:
|
| 714 |
-
if last_checkpoint != file_checkpoint:
|
| 715 |
-
last_checkpoint = file_checkpoint
|
| 716 |
-
if last_device != device_test:
|
| 717 |
-
last_device = device_test
|
| 718 |
-
|
| 719 |
-
tts_api = F5TTS(model_type=exp_name, ckpt_file=file_checkpoint, device=device_test)
|
| 720 |
-
|
| 721 |
-
print("update", device_test, file_checkpoint)
|
| 722 |
-
|
| 723 |
-
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
|
| 724 |
-
tts_api.infer(gen_text=gen_text, ref_text=ref_text, ref_file=ref_audio, nfe_step=nfe_step, file_wave=f.name)
|
| 725 |
-
return f.name
|
| 726 |
-
|
| 727 |
-
|
| 728 |
-
with gr.Blocks() as app:
|
| 729 |
-
with gr.Row():
|
| 730 |
-
project_name = gr.Textbox(label="project name", value="my_speak")
|
| 731 |
-
bt_create = gr.Button("create new project")
|
| 732 |
-
|
| 733 |
-
bt_create.click(fn=create_data_project, inputs=[project_name])
|
| 734 |
-
|
| 735 |
-
with gr.Tabs():
|
| 736 |
-
with gr.TabItem("transcribe Data"):
|
| 737 |
-
ch_manual = gr.Checkbox(label="user", value=False)
|
| 738 |
-
|
| 739 |
-
mark_info_transcribe = gr.Markdown(
|
| 740 |
-
"""```plaintext
|
| 741 |
-
Place your 'wavs' folder and 'metadata.csv' file in the {your_project_name}' directory.
|
| 742 |
-
|
| 743 |
-
my_speak/
|
| 744 |
-
│
|
| 745 |
-
└── dataset/
|
| 746 |
-
├── audio1.wav
|
| 747 |
-
└── audio2.wav
|
| 748 |
-
...
|
| 749 |
-
```""",
|
| 750 |
-
visible=False,
|
| 751 |
-
)
|
| 752 |
-
|
| 753 |
-
audio_speaker = gr.File(label="voice", type="filepath", file_count="multiple")
|
| 754 |
-
txt_lang = gr.Text(label="Language", value="english")
|
| 755 |
-
bt_transcribe = bt_create = gr.Button("transcribe")
|
| 756 |
-
txt_info_transcribe = gr.Text(label="info", value="")
|
| 757 |
-
bt_transcribe.click(
|
| 758 |
-
fn=transcribe_all,
|
| 759 |
-
inputs=[project_name, audio_speaker, txt_lang, ch_manual],
|
| 760 |
-
outputs=[txt_info_transcribe],
|
| 761 |
-
)
|
| 762 |
-
ch_manual.change(fn=check_user, inputs=[ch_manual], outputs=[audio_speaker, mark_info_transcribe])
|
| 763 |
-
|
| 764 |
-
random_sample_transcribe = gr.Button("random sample")
|
| 765 |
-
|
| 766 |
-
with gr.Row():
|
| 767 |
-
random_text_transcribe = gr.Text(label="Text")
|
| 768 |
-
random_audio_transcribe = gr.Audio(label="Audio", type="filepath")
|
| 769 |
-
|
| 770 |
-
random_sample_transcribe.click(
|
| 771 |
-
fn=get_random_sample_transcribe,
|
| 772 |
-
inputs=[project_name],
|
| 773 |
-
outputs=[random_text_transcribe, random_audio_transcribe],
|
| 774 |
-
)
|
| 775 |
-
|
| 776 |
-
with gr.TabItem("prepare Data"):
|
| 777 |
-
gr.Markdown(
|
| 778 |
-
"""```plaintext
|
| 779 |
-
place all your wavs folder and your metadata.csv file in {your name project}
|
| 780 |
-
my_speak/
|
| 781 |
-
│
|
| 782 |
-
├── wavs/
|
| 783 |
-
│ ├── audio1.wav
|
| 784 |
-
│ └── audio2.wav
|
| 785 |
-
| ...
|
| 786 |
-
│
|
| 787 |
-
└── metadata.csv
|
| 788 |
-
|
| 789 |
-
file format metadata.csv
|
| 790 |
-
|
| 791 |
-
audio1|text1
|
| 792 |
-
audio2|text1
|
| 793 |
-
...
|
| 794 |
-
|
| 795 |
-
```"""
|
| 796 |
-
)
|
| 797 |
-
|
| 798 |
-
bt_prepare = bt_create = gr.Button("prepare")
|
| 799 |
-
txt_info_prepare = gr.Text(label="info", value="")
|
| 800 |
-
bt_prepare.click(fn=create_metadata, inputs=[project_name], outputs=[txt_info_prepare])
|
| 801 |
-
|
| 802 |
-
random_sample_prepare = gr.Button("random sample")
|
| 803 |
-
|
| 804 |
-
with gr.Row():
|
| 805 |
-
random_text_prepare = gr.Text(label="Pinyin")
|
| 806 |
-
random_audio_prepare = gr.Audio(label="Audio", type="filepath")
|
| 807 |
-
|
| 808 |
-
random_sample_prepare.click(
|
| 809 |
-
fn=get_random_sample_prepare, inputs=[project_name], outputs=[random_text_prepare, random_audio_prepare]
|
| 810 |
-
)
|
| 811 |
-
|
| 812 |
-
with gr.TabItem("train Data"):
|
| 813 |
-
with gr.Row():
|
| 814 |
-
bt_calculate = bt_create = gr.Button("Auto Settings")
|
| 815 |
-
ch_finetune = bt_create = gr.Checkbox(label="finetune", value=True)
|
| 816 |
-
lb_samples = gr.Label(label="samples")
|
| 817 |
-
batch_size_type = gr.Radio(label="Batch Size Type", choices=["frame", "sample"], value="frame")
|
| 818 |
-
|
| 819 |
-
with gr.Row():
|
| 820 |
-
exp_name = gr.Radio(label="Model", choices=["F5TTS_Base", "E2TTS_Base"], value="F5TTS_Base")
|
| 821 |
-
learning_rate = gr.Number(label="Learning Rate", value=1e-5, step=1e-5)
|
| 822 |
-
|
| 823 |
-
with gr.Row():
|
| 824 |
-
batch_size_per_gpu = gr.Number(label="Batch Size per GPU", value=1000)
|
| 825 |
-
max_samples = gr.Number(label="Max Samples", value=64)
|
| 826 |
-
|
| 827 |
-
with gr.Row():
|
| 828 |
-
grad_accumulation_steps = gr.Number(label="Gradient Accumulation Steps", value=1)
|
| 829 |
-
max_grad_norm = gr.Number(label="Max Gradient Norm", value=1.0)
|
| 830 |
-
|
| 831 |
-
with gr.Row():
|
| 832 |
-
epochs = gr.Number(label="Epochs", value=10)
|
| 833 |
-
num_warmup_updates = gr.Number(label="Warmup Updates", value=5)
|
| 834 |
-
|
| 835 |
-
with gr.Row():
|
| 836 |
-
save_per_updates = gr.Number(label="Save per Updates", value=10)
|
| 837 |
-
last_per_steps = gr.Number(label="Last per Steps", value=50)
|
| 838 |
-
|
| 839 |
-
with gr.Row():
|
| 840 |
-
start_button = gr.Button("Start Training")
|
| 841 |
-
stop_button = gr.Button("Stop Training", interactive=False)
|
| 842 |
-
|
| 843 |
-
txt_info_train = gr.Text(label="info", value="")
|
| 844 |
-
start_button.click(
|
| 845 |
-
fn=start_training,
|
| 846 |
-
inputs=[
|
| 847 |
-
project_name,
|
| 848 |
-
exp_name,
|
| 849 |
-
learning_rate,
|
| 850 |
-
batch_size_per_gpu,
|
| 851 |
-
batch_size_type,
|
| 852 |
-
max_samples,
|
| 853 |
-
grad_accumulation_steps,
|
| 854 |
-
max_grad_norm,
|
| 855 |
-
epochs,
|
| 856 |
-
num_warmup_updates,
|
| 857 |
-
save_per_updates,
|
| 858 |
-
last_per_steps,
|
| 859 |
-
ch_finetune,
|
| 860 |
-
],
|
| 861 |
-
outputs=[txt_info_train, start_button, stop_button],
|
| 862 |
-
)
|
| 863 |
-
stop_button.click(fn=stop_training, outputs=[txt_info_train, start_button, stop_button])
|
| 864 |
-
bt_calculate.click(
|
| 865 |
-
fn=calculate_train,
|
| 866 |
-
inputs=[
|
| 867 |
-
project_name,
|
| 868 |
-
batch_size_type,
|
| 869 |
-
max_samples,
|
| 870 |
-
learning_rate,
|
| 871 |
-
num_warmup_updates,
|
| 872 |
-
save_per_updates,
|
| 873 |
-
last_per_steps,
|
| 874 |
-
ch_finetune,
|
| 875 |
-
],
|
| 876 |
-
outputs=[
|
| 877 |
-
batch_size_per_gpu,
|
| 878 |
-
max_samples,
|
| 879 |
-
num_warmup_updates,
|
| 880 |
-
save_per_updates,
|
| 881 |
-
last_per_steps,
|
| 882 |
-
lb_samples,
|
| 883 |
-
learning_rate,
|
| 884 |
-
],
|
| 885 |
-
)
|
| 886 |
-
|
| 887 |
-
with gr.TabItem("reduse checkpoint"):
|
| 888 |
-
txt_path_checkpoint = gr.Text(label="path checkpoint :")
|
| 889 |
-
txt_path_checkpoint_small = gr.Text(label="path output :")
|
| 890 |
-
txt_info_reduse = gr.Text(label="info", value="")
|
| 891 |
-
reduse_button = gr.Button("reduse")
|
| 892 |
-
reduse_button.click(
|
| 893 |
-
fn=extract_and_save_ema_model,
|
| 894 |
-
inputs=[txt_path_checkpoint, txt_path_checkpoint_small],
|
| 895 |
-
outputs=[txt_info_reduse],
|
| 896 |
-
)
|
| 897 |
-
|
| 898 |
-
with gr.TabItem("vocab check experiment"):
|
| 899 |
-
check_button = gr.Button("check vocab")
|
| 900 |
-
txt_info_check = gr.Text(label="info", value="")
|
| 901 |
-
check_button.click(fn=vocab_check, inputs=[project_name], outputs=[txt_info_check])
|
| 902 |
-
|
| 903 |
-
with gr.TabItem("test model"):
|
| 904 |
-
exp_name = gr.Radio(label="Model", choices=["F5-TTS", "E2-TTS"], value="F5-TTS")
|
| 905 |
-
nfe_step = gr.Number(label="n_step", value=32)
|
| 906 |
-
file_checkpoint_pt = gr.Textbox(label="Checkpoint", value="")
|
| 907 |
-
|
| 908 |
-
random_sample_infer = gr.Button("random sample")
|
| 909 |
-
|
| 910 |
-
ref_text = gr.Textbox(label="ref text")
|
| 911 |
-
ref_audio = gr.Audio(label="audio ref", type="filepath")
|
| 912 |
-
gen_text = gr.Textbox(label="gen text")
|
| 913 |
-
random_sample_infer.click(
|
| 914 |
-
fn=get_random_sample_infer, inputs=[project_name], outputs=[ref_text, gen_text, ref_audio]
|
| 915 |
-
)
|
| 916 |
-
check_button_infer = gr.Button("infer")
|
| 917 |
-
gen_audio = gr.Audio(label="audio gen", type="filepath")
|
| 918 |
-
|
| 919 |
-
check_button_infer.click(
|
| 920 |
-
fn=infer,
|
| 921 |
-
inputs=[file_checkpoint_pt, exp_name, ref_text, ref_audio, gen_text, nfe_step],
|
| 922 |
-
outputs=[gen_audio],
|
| 923 |
-
)
|
| 924 |
-
|
| 925 |
-
|
| 926 |
-
@click.command()
|
| 927 |
-
@click.option("--port", "-p", default=None, type=int, help="Port to run the app on")
|
| 928 |
-
@click.option("--host", "-H", default=None, help="Host to run the app on")
|
| 929 |
-
@click.option(
|
| 930 |
-
"--share",
|
| 931 |
-
"-s",
|
| 932 |
-
default=False,
|
| 933 |
-
is_flag=True,
|
| 934 |
-
help="Share the app via Gradio share link",
|
| 935 |
-
)
|
| 936 |
-
@click.option("--api", "-a", default=True, is_flag=True, help="Allow API access")
|
| 937 |
-
def main(port, host, share, api):
|
| 938 |
-
global app
|
| 939 |
-
print("Starting app...")
|
| 940 |
-
app.queue(api_open=api).launch(server_name=host, server_port=port, share=share, show_api=api)
|
| 941 |
-
|
| 942 |
-
|
| 943 |
-
if __name__ == "__main__":
|
| 944 |
-
main()
|
|
|
|
| 1 |
+
import gc
|
| 2 |
+
import json
|
| 3 |
+
import os
|
| 4 |
+
import platform
|
| 5 |
+
import psutil
|
| 6 |
+
import random
|
| 7 |
+
import signal
|
| 8 |
+
import shutil
|
| 9 |
+
import subprocess
|
| 10 |
+
import sys
|
| 11 |
+
import tempfile
|
| 12 |
+
import time
|
| 13 |
+
from glob import glob
|
| 14 |
+
|
| 15 |
+
import click
|
| 16 |
+
import gradio as gr
|
| 17 |
+
import librosa
|
| 18 |
+
import numpy as np
|
| 19 |
+
import torch
|
| 20 |
+
import torchaudio
|
| 21 |
+
from datasets import Dataset as Dataset_
|
| 22 |
+
from datasets.arrow_writer import ArrowWriter
|
| 23 |
+
from scipy.io import wavfile
|
| 24 |
+
from transformers import pipeline
|
| 25 |
+
|
| 26 |
+
from f5_tts.api import F5TTS
|
| 27 |
+
from f5_tts.model.utils import convert_char_to_pinyin
|
| 28 |
+
|
| 29 |
+
|
| 30 |
+
training_process = None
|
| 31 |
+
system = platform.system()
|
| 32 |
+
python_executable = sys.executable or "python"
|
| 33 |
+
tts_api = None
|
| 34 |
+
last_checkpoint = ""
|
| 35 |
+
last_device = ""
|
| 36 |
+
|
| 37 |
+
path_data = "data"
|
| 38 |
+
|
| 39 |
+
device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
|
| 40 |
+
|
| 41 |
+
pipe = None
|
| 42 |
+
|
| 43 |
+
|
| 44 |
+
# Load metadata
|
| 45 |
+
def get_audio_duration(audio_path):
|
| 46 |
+
"""Calculate the duration of an audio file."""
|
| 47 |
+
audio, sample_rate = torchaudio.load(audio_path)
|
| 48 |
+
num_channels = audio.shape[0]
|
| 49 |
+
return audio.shape[1] / (sample_rate * num_channels)
|
| 50 |
+
|
| 51 |
+
|
| 52 |
+
def clear_text(text):
|
| 53 |
+
"""Clean and prepare text by lowering the case and stripping whitespace."""
|
| 54 |
+
return text.lower().strip()
|
| 55 |
+
|
| 56 |
+
|
| 57 |
+
def get_rms(
|
| 58 |
+
y,
|
| 59 |
+
frame_length=2048,
|
| 60 |
+
hop_length=512,
|
| 61 |
+
pad_mode="constant",
|
| 62 |
+
): # https://github.com/RVC-Boss/GPT-SoVITS/blob/main/tools/slicer2.py
|
| 63 |
+
padding = (int(frame_length // 2), int(frame_length // 2))
|
| 64 |
+
y = np.pad(y, padding, mode=pad_mode)
|
| 65 |
+
|
| 66 |
+
axis = -1
|
| 67 |
+
# put our new within-frame axis at the end for now
|
| 68 |
+
out_strides = y.strides + tuple([y.strides[axis]])
|
| 69 |
+
# Reduce the shape on the framing axis
|
| 70 |
+
x_shape_trimmed = list(y.shape)
|
| 71 |
+
x_shape_trimmed[axis] -= frame_length - 1
|
| 72 |
+
out_shape = tuple(x_shape_trimmed) + tuple([frame_length])
|
| 73 |
+
xw = np.lib.stride_tricks.as_strided(y, shape=out_shape, strides=out_strides)
|
| 74 |
+
if axis < 0:
|
| 75 |
+
target_axis = axis - 1
|
| 76 |
+
else:
|
| 77 |
+
target_axis = axis + 1
|
| 78 |
+
xw = np.moveaxis(xw, -1, target_axis)
|
| 79 |
+
# Downsample along the target axis
|
| 80 |
+
slices = [slice(None)] * xw.ndim
|
| 81 |
+
slices[axis] = slice(0, None, hop_length)
|
| 82 |
+
x = xw[tuple(slices)]
|
| 83 |
+
|
| 84 |
+
# Calculate power
|
| 85 |
+
power = np.mean(np.abs(x) ** 2, axis=-2, keepdims=True)
|
| 86 |
+
|
| 87 |
+
return np.sqrt(power)
|
| 88 |
+
|
| 89 |
+
|
| 90 |
+
class Slicer: # https://github.com/RVC-Boss/GPT-SoVITS/blob/main/tools/slicer2.py
|
| 91 |
+
def __init__(
|
| 92 |
+
self,
|
| 93 |
+
sr: int,
|
| 94 |
+
threshold: float = -40.0,
|
| 95 |
+
min_length: int = 2000,
|
| 96 |
+
min_interval: int = 300,
|
| 97 |
+
hop_size: int = 20,
|
| 98 |
+
max_sil_kept: int = 2000,
|
| 99 |
+
):
|
| 100 |
+
if not min_length >= min_interval >= hop_size:
|
| 101 |
+
raise ValueError("The following condition must be satisfied: min_length >= min_interval >= hop_size")
|
| 102 |
+
if not max_sil_kept >= hop_size:
|
| 103 |
+
raise ValueError("The following condition must be satisfied: max_sil_kept >= hop_size")
|
| 104 |
+
min_interval = sr * min_interval / 1000
|
| 105 |
+
self.threshold = 10 ** (threshold / 20.0)
|
| 106 |
+
self.hop_size = round(sr * hop_size / 1000)
|
| 107 |
+
self.win_size = min(round(min_interval), 4 * self.hop_size)
|
| 108 |
+
self.min_length = round(sr * min_length / 1000 / self.hop_size)
|
| 109 |
+
self.min_interval = round(min_interval / self.hop_size)
|
| 110 |
+
self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size)
|
| 111 |
+
|
| 112 |
+
def _apply_slice(self, waveform, begin, end):
|
| 113 |
+
if len(waveform.shape) > 1:
|
| 114 |
+
return waveform[:, begin * self.hop_size : min(waveform.shape[1], end * self.hop_size)]
|
| 115 |
+
else:
|
| 116 |
+
return waveform[begin * self.hop_size : min(waveform.shape[0], end * self.hop_size)]
|
| 117 |
+
|
| 118 |
+
# @timeit
|
| 119 |
+
def slice(self, waveform):
|
| 120 |
+
if len(waveform.shape) > 1:
|
| 121 |
+
samples = waveform.mean(axis=0)
|
| 122 |
+
else:
|
| 123 |
+
samples = waveform
|
| 124 |
+
if samples.shape[0] <= self.min_length:
|
| 125 |
+
return [waveform]
|
| 126 |
+
rms_list = get_rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0)
|
| 127 |
+
sil_tags = []
|
| 128 |
+
silence_start = None
|
| 129 |
+
clip_start = 0
|
| 130 |
+
for i, rms in enumerate(rms_list):
|
| 131 |
+
# Keep looping while frame is silent.
|
| 132 |
+
if rms < self.threshold:
|
| 133 |
+
# Record start of silent frames.
|
| 134 |
+
if silence_start is None:
|
| 135 |
+
silence_start = i
|
| 136 |
+
continue
|
| 137 |
+
# Keep looping while frame is not silent and silence start has not been recorded.
|
| 138 |
+
if silence_start is None:
|
| 139 |
+
continue
|
| 140 |
+
# Clear recorded silence start if interval is not enough or clip is too short
|
| 141 |
+
is_leading_silence = silence_start == 0 and i > self.max_sil_kept
|
| 142 |
+
need_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_length
|
| 143 |
+
if not is_leading_silence and not need_slice_middle:
|
| 144 |
+
silence_start = None
|
| 145 |
+
continue
|
| 146 |
+
# Need slicing. Record the range of silent frames to be removed.
|
| 147 |
+
if i - silence_start <= self.max_sil_kept:
|
| 148 |
+
pos = rms_list[silence_start : i + 1].argmin() + silence_start
|
| 149 |
+
if silence_start == 0:
|
| 150 |
+
sil_tags.append((0, pos))
|
| 151 |
+
else:
|
| 152 |
+
sil_tags.append((pos, pos))
|
| 153 |
+
clip_start = pos
|
| 154 |
+
elif i - silence_start <= self.max_sil_kept * 2:
|
| 155 |
+
pos = rms_list[i - self.max_sil_kept : silence_start + self.max_sil_kept + 1].argmin()
|
| 156 |
+
pos += i - self.max_sil_kept
|
| 157 |
+
pos_l = rms_list[silence_start : silence_start + self.max_sil_kept + 1].argmin() + silence_start
|
| 158 |
+
pos_r = rms_list[i - self.max_sil_kept : i + 1].argmin() + i - self.max_sil_kept
|
| 159 |
+
if silence_start == 0:
|
| 160 |
+
sil_tags.append((0, pos_r))
|
| 161 |
+
clip_start = pos_r
|
| 162 |
+
else:
|
| 163 |
+
sil_tags.append((min(pos_l, pos), max(pos_r, pos)))
|
| 164 |
+
clip_start = max(pos_r, pos)
|
| 165 |
+
else:
|
| 166 |
+
pos_l = rms_list[silence_start : silence_start + self.max_sil_kept + 1].argmin() + silence_start
|
| 167 |
+
pos_r = rms_list[i - self.max_sil_kept : i + 1].argmin() + i - self.max_sil_kept
|
| 168 |
+
if silence_start == 0:
|
| 169 |
+
sil_tags.append((0, pos_r))
|
| 170 |
+
else:
|
| 171 |
+
sil_tags.append((pos_l, pos_r))
|
| 172 |
+
clip_start = pos_r
|
| 173 |
+
silence_start = None
|
| 174 |
+
# Deal with trailing silence.
|
| 175 |
+
total_frames = rms_list.shape[0]
|
| 176 |
+
if silence_start is not None and total_frames - silence_start >= self.min_interval:
|
| 177 |
+
silence_end = min(total_frames, silence_start + self.max_sil_kept)
|
| 178 |
+
pos = rms_list[silence_start : silence_end + 1].argmin() + silence_start
|
| 179 |
+
sil_tags.append((pos, total_frames + 1))
|
| 180 |
+
# Apply and return slices.
|
| 181 |
+
####音频+起始时间+终止时间
|
| 182 |
+
if len(sil_tags) == 0:
|
| 183 |
+
return [[waveform, 0, int(total_frames * self.hop_size)]]
|
| 184 |
+
else:
|
| 185 |
+
chunks = []
|
| 186 |
+
if sil_tags[0][0] > 0:
|
| 187 |
+
chunks.append([self._apply_slice(waveform, 0, sil_tags[0][0]), 0, int(sil_tags[0][0] * self.hop_size)])
|
| 188 |
+
for i in range(len(sil_tags) - 1):
|
| 189 |
+
chunks.append(
|
| 190 |
+
[
|
| 191 |
+
self._apply_slice(waveform, sil_tags[i][1], sil_tags[i + 1][0]),
|
| 192 |
+
int(sil_tags[i][1] * self.hop_size),
|
| 193 |
+
int(sil_tags[i + 1][0] * self.hop_size),
|
| 194 |
+
]
|
| 195 |
+
)
|
| 196 |
+
if sil_tags[-1][1] < total_frames:
|
| 197 |
+
chunks.append(
|
| 198 |
+
[
|
| 199 |
+
self._apply_slice(waveform, sil_tags[-1][1], total_frames),
|
| 200 |
+
int(sil_tags[-1][1] * self.hop_size),
|
| 201 |
+
int(total_frames * self.hop_size),
|
| 202 |
+
]
|
| 203 |
+
)
|
| 204 |
+
return chunks
|
| 205 |
+
|
| 206 |
+
|
| 207 |
+
# terminal
|
| 208 |
+
def terminate_process_tree(pid, including_parent=True):
|
| 209 |
+
try:
|
| 210 |
+
parent = psutil.Process(pid)
|
| 211 |
+
except psutil.NoSuchProcess:
|
| 212 |
+
# Process already terminated
|
| 213 |
+
return
|
| 214 |
+
|
| 215 |
+
children = parent.children(recursive=True)
|
| 216 |
+
for child in children:
|
| 217 |
+
try:
|
| 218 |
+
os.kill(child.pid, signal.SIGTERM) # or signal.SIGKILL
|
| 219 |
+
except OSError:
|
| 220 |
+
pass
|
| 221 |
+
if including_parent:
|
| 222 |
+
try:
|
| 223 |
+
os.kill(parent.pid, signal.SIGTERM) # or signal.SIGKILL
|
| 224 |
+
except OSError:
|
| 225 |
+
pass
|
| 226 |
+
|
| 227 |
+
|
| 228 |
+
def terminate_process(pid):
|
| 229 |
+
if system == "Windows":
|
| 230 |
+
cmd = f"taskkill /t /f /pid {pid}"
|
| 231 |
+
os.system(cmd)
|
| 232 |
+
else:
|
| 233 |
+
terminate_process_tree(pid)
|
| 234 |
+
|
| 235 |
+
|
| 236 |
+
def start_training(
|
| 237 |
+
dataset_name="",
|
| 238 |
+
exp_name="F5TTS_Base",
|
| 239 |
+
learning_rate=1e-4,
|
| 240 |
+
batch_size_per_gpu=400,
|
| 241 |
+
batch_size_type="frame",
|
| 242 |
+
max_samples=64,
|
| 243 |
+
grad_accumulation_steps=1,
|
| 244 |
+
max_grad_norm=1.0,
|
| 245 |
+
epochs=11,
|
| 246 |
+
num_warmup_updates=200,
|
| 247 |
+
save_per_updates=400,
|
| 248 |
+
last_per_steps=800,
|
| 249 |
+
finetune=True,
|
| 250 |
+
):
|
| 251 |
+
global training_process, tts_api
|
| 252 |
+
|
| 253 |
+
if tts_api is not None:
|
| 254 |
+
del tts_api
|
| 255 |
+
gc.collect()
|
| 256 |
+
torch.cuda.empty_cache()
|
| 257 |
+
tts_api = None
|
| 258 |
+
|
| 259 |
+
path_project = os.path.join(path_data, dataset_name + "_pinyin")
|
| 260 |
+
|
| 261 |
+
if not os.path.isdir(path_project):
|
| 262 |
+
yield (
|
| 263 |
+
f"There is not project with name {dataset_name}",
|
| 264 |
+
gr.update(interactive=True),
|
| 265 |
+
gr.update(interactive=False),
|
| 266 |
+
)
|
| 267 |
+
return
|
| 268 |
+
|
| 269 |
+
file_raw = os.path.join(path_project, "raw.arrow")
|
| 270 |
+
if not os.path.isfile(file_raw):
|
| 271 |
+
yield f"There is no file {file_raw}", gr.update(interactive=True), gr.update(interactive=False)
|
| 272 |
+
return
|
| 273 |
+
|
| 274 |
+
# Check if a training process is already running
|
| 275 |
+
if training_process is not None:
|
| 276 |
+
return "Train run already!", gr.update(interactive=False), gr.update(interactive=True)
|
| 277 |
+
|
| 278 |
+
yield "start train", gr.update(interactive=False), gr.update(interactive=False)
|
| 279 |
+
|
| 280 |
+
# Command to run the training script with the specified arguments
|
| 281 |
+
cmd = (
|
| 282 |
+
f"accelerate launch finetune-cli.py --exp_name {exp_name} "
|
| 283 |
+
f"--learning_rate {learning_rate} "
|
| 284 |
+
f"--batch_size_per_gpu {batch_size_per_gpu} "
|
| 285 |
+
f"--batch_size_type {batch_size_type} "
|
| 286 |
+
f"--max_samples {max_samples} "
|
| 287 |
+
f"--grad_accumulation_steps {grad_accumulation_steps} "
|
| 288 |
+
f"--max_grad_norm {max_grad_norm} "
|
| 289 |
+
f"--epochs {epochs} "
|
| 290 |
+
f"--num_warmup_updates {num_warmup_updates} "
|
| 291 |
+
f"--save_per_updates {save_per_updates} "
|
| 292 |
+
f"--last_per_steps {last_per_steps} "
|
| 293 |
+
f"--dataset_name {dataset_name}"
|
| 294 |
+
)
|
| 295 |
+
if finetune:
|
| 296 |
+
cmd += f" --finetune {finetune}"
|
| 297 |
+
|
| 298 |
+
print(cmd)
|
| 299 |
+
|
| 300 |
+
try:
|
| 301 |
+
# Start the training process
|
| 302 |
+
training_process = subprocess.Popen(cmd, shell=True)
|
| 303 |
+
|
| 304 |
+
time.sleep(5)
|
| 305 |
+
yield "train start", gr.update(interactive=False), gr.update(interactive=True)
|
| 306 |
+
|
| 307 |
+
# Wait for the training process to finish
|
| 308 |
+
training_process.wait()
|
| 309 |
+
time.sleep(1)
|
| 310 |
+
|
| 311 |
+
if training_process is None:
|
| 312 |
+
text_info = "train stop"
|
| 313 |
+
else:
|
| 314 |
+
text_info = "train complete !"
|
| 315 |
+
|
| 316 |
+
except Exception as e: # Catch all exceptions
|
| 317 |
+
# Ensure that we reset the training process variable in case of an error
|
| 318 |
+
text_info = f"An error occurred: {str(e)}"
|
| 319 |
+
|
| 320 |
+
training_process = None
|
| 321 |
+
|
| 322 |
+
yield text_info, gr.update(interactive=True), gr.update(interactive=False)
|
| 323 |
+
|
| 324 |
+
|
| 325 |
+
def stop_training():
|
| 326 |
+
global training_process
|
| 327 |
+
if training_process is None:
|
| 328 |
+
return "Train not run !", gr.update(interactive=True), gr.update(interactive=False)
|
| 329 |
+
terminate_process_tree(training_process.pid)
|
| 330 |
+
training_process = None
|
| 331 |
+
return "train stop", gr.update(interactive=True), gr.update(interactive=False)
|
| 332 |
+
|
| 333 |
+
|
| 334 |
+
def create_data_project(name):
|
| 335 |
+
name += "_pinyin"
|
| 336 |
+
os.makedirs(os.path.join(path_data, name), exist_ok=True)
|
| 337 |
+
os.makedirs(os.path.join(path_data, name, "dataset"), exist_ok=True)
|
| 338 |
+
|
| 339 |
+
|
| 340 |
+
def transcribe(file_audio, language="english"):
|
| 341 |
+
global pipe
|
| 342 |
+
|
| 343 |
+
if pipe is None:
|
| 344 |
+
pipe = pipeline(
|
| 345 |
+
"automatic-speech-recognition",
|
| 346 |
+
model="openai/whisper-large-v3-turbo",
|
| 347 |
+
torch_dtype=torch.float16,
|
| 348 |
+
device=device,
|
| 349 |
+
)
|
| 350 |
+
|
| 351 |
+
text_transcribe = pipe(
|
| 352 |
+
file_audio,
|
| 353 |
+
chunk_length_s=30,
|
| 354 |
+
batch_size=128,
|
| 355 |
+
generate_kwargs={"task": "transcribe", "language": language},
|
| 356 |
+
return_timestamps=False,
|
| 357 |
+
)["text"].strip()
|
| 358 |
+
return text_transcribe
|
| 359 |
+
|
| 360 |
+
|
| 361 |
+
def transcribe_all(name_project, audio_files, language, user=False, progress=gr.Progress()):
|
| 362 |
+
name_project += "_pinyin"
|
| 363 |
+
path_project = os.path.join(path_data, name_project)
|
| 364 |
+
path_dataset = os.path.join(path_project, "dataset")
|
| 365 |
+
path_project_wavs = os.path.join(path_project, "wavs")
|
| 366 |
+
file_metadata = os.path.join(path_project, "metadata.csv")
|
| 367 |
+
|
| 368 |
+
if audio_files is None:
|
| 369 |
+
return "You need to load an audio file."
|
| 370 |
+
|
| 371 |
+
if os.path.isdir(path_project_wavs):
|
| 372 |
+
shutil.rmtree(path_project_wavs)
|
| 373 |
+
|
| 374 |
+
if os.path.isfile(file_metadata):
|
| 375 |
+
os.remove(file_metadata)
|
| 376 |
+
|
| 377 |
+
os.makedirs(path_project_wavs, exist_ok=True)
|
| 378 |
+
|
| 379 |
+
if user:
|
| 380 |
+
file_audios = [
|
| 381 |
+
file
|
| 382 |
+
for format in ("*.wav", "*.ogg", "*.opus", "*.mp3", "*.flac")
|
| 383 |
+
for file in glob(os.path.join(path_dataset, format))
|
| 384 |
+
]
|
| 385 |
+
if file_audios == []:
|
| 386 |
+
return "No audio file was found in the dataset."
|
| 387 |
+
else:
|
| 388 |
+
file_audios = audio_files
|
| 389 |
+
|
| 390 |
+
alpha = 0.5
|
| 391 |
+
_max = 1.0
|
| 392 |
+
slicer = Slicer(24000)
|
| 393 |
+
|
| 394 |
+
num = 0
|
| 395 |
+
error_num = 0
|
| 396 |
+
data = ""
|
| 397 |
+
for file_audio in progress.tqdm(file_audios, desc="transcribe files", total=len((file_audios))):
|
| 398 |
+
audio, _ = librosa.load(file_audio, sr=24000, mono=True)
|
| 399 |
+
|
| 400 |
+
list_slicer = slicer.slice(audio)
|
| 401 |
+
for chunk, start, end in progress.tqdm(list_slicer, total=len(list_slicer), desc="slicer files"):
|
| 402 |
+
name_segment = os.path.join(f"segment_{num}")
|
| 403 |
+
file_segment = os.path.join(path_project_wavs, f"{name_segment}.wav")
|
| 404 |
+
|
| 405 |
+
tmp_max = np.abs(chunk).max()
|
| 406 |
+
if tmp_max > 1:
|
| 407 |
+
chunk /= tmp_max
|
| 408 |
+
chunk = (chunk / tmp_max * (_max * alpha)) + (1 - alpha) * chunk
|
| 409 |
+
wavfile.write(file_segment, 24000, (chunk * 32767).astype(np.int16))
|
| 410 |
+
|
| 411 |
+
try:
|
| 412 |
+
text = transcribe(file_segment, language)
|
| 413 |
+
text = text.lower().strip().replace('"', "")
|
| 414 |
+
|
| 415 |
+
data += f"{name_segment}|{text}\n"
|
| 416 |
+
|
| 417 |
+
num += 1
|
| 418 |
+
except: # noqa: E722
|
| 419 |
+
error_num += 1
|
| 420 |
+
|
| 421 |
+
with open(file_metadata, "w", encoding="utf-8") as f:
|
| 422 |
+
f.write(data)
|
| 423 |
+
|
| 424 |
+
if error_num != []:
|
| 425 |
+
error_text = f"\nerror files : {error_num}"
|
| 426 |
+
else:
|
| 427 |
+
error_text = ""
|
| 428 |
+
|
| 429 |
+
return f"transcribe complete samples : {num}\npath : {path_project_wavs}{error_text}"
|
| 430 |
+
|
| 431 |
+
|
| 432 |
+
def format_seconds_to_hms(seconds):
|
| 433 |
+
hours = int(seconds / 3600)
|
| 434 |
+
minutes = int((seconds % 3600) / 60)
|
| 435 |
+
seconds = seconds % 60
|
| 436 |
+
return "{:02d}:{:02d}:{:02d}".format(hours, minutes, int(seconds))
|
| 437 |
+
|
| 438 |
+
|
| 439 |
+
def create_metadata(name_project, progress=gr.Progress()):
|
| 440 |
+
name_project += "_pinyin"
|
| 441 |
+
path_project = os.path.join(path_data, name_project)
|
| 442 |
+
path_project_wavs = os.path.join(path_project, "wavs")
|
| 443 |
+
file_metadata = os.path.join(path_project, "metadata.csv")
|
| 444 |
+
file_raw = os.path.join(path_project, "raw.arrow")
|
| 445 |
+
file_duration = os.path.join(path_project, "duration.json")
|
| 446 |
+
file_vocab = os.path.join(path_project, "vocab.txt")
|
| 447 |
+
|
| 448 |
+
if not os.path.isfile(file_metadata):
|
| 449 |
+
return "The file was not found in " + file_metadata
|
| 450 |
+
|
| 451 |
+
with open(file_metadata, "r", encoding="utf-8") as f:
|
| 452 |
+
data = f.read()
|
| 453 |
+
|
| 454 |
+
audio_path_list = []
|
| 455 |
+
text_list = []
|
| 456 |
+
duration_list = []
|
| 457 |
+
|
| 458 |
+
count = data.split("\n")
|
| 459 |
+
lenght = 0
|
| 460 |
+
result = []
|
| 461 |
+
error_files = []
|
| 462 |
+
for line in progress.tqdm(data.split("\n"), total=count):
|
| 463 |
+
sp_line = line.split("|")
|
| 464 |
+
if len(sp_line) != 2:
|
| 465 |
+
continue
|
| 466 |
+
name_audio, text = sp_line[:2]
|
| 467 |
+
|
| 468 |
+
file_audio = os.path.join(path_project_wavs, name_audio + ".wav")
|
| 469 |
+
|
| 470 |
+
if not os.path.isfile(file_audio):
|
| 471 |
+
error_files.append(file_audio)
|
| 472 |
+
continue
|
| 473 |
+
|
| 474 |
+
duraction = get_audio_duration(file_audio)
|
| 475 |
+
if duraction < 2 and duraction > 15:
|
| 476 |
+
continue
|
| 477 |
+
if len(text) < 4:
|
| 478 |
+
continue
|
| 479 |
+
|
| 480 |
+
text = clear_text(text)
|
| 481 |
+
text = convert_char_to_pinyin([text], polyphone=True)[0]
|
| 482 |
+
|
| 483 |
+
audio_path_list.append(file_audio)
|
| 484 |
+
duration_list.append(duraction)
|
| 485 |
+
text_list.append(text)
|
| 486 |
+
|
| 487 |
+
result.append({"audio_path": file_audio, "text": text, "duration": duraction})
|
| 488 |
+
|
| 489 |
+
lenght += duraction
|
| 490 |
+
|
| 491 |
+
if duration_list == []:
|
| 492 |
+
error_files_text = "\n".join(error_files)
|
| 493 |
+
return f"Error: No audio files found in the specified path : \n{error_files_text}"
|
| 494 |
+
|
| 495 |
+
min_second = round(min(duration_list), 2)
|
| 496 |
+
max_second = round(max(duration_list), 2)
|
| 497 |
+
|
| 498 |
+
with ArrowWriter(path=file_raw, writer_batch_size=1) as writer:
|
| 499 |
+
for line in progress.tqdm(result, total=len(result), desc="prepare data"):
|
| 500 |
+
writer.write(line)
|
| 501 |
+
|
| 502 |
+
with open(file_duration, "w", encoding="utf-8") as f:
|
| 503 |
+
json.dump({"duration": duration_list}, f, ensure_ascii=False)
|
| 504 |
+
|
| 505 |
+
file_vocab_finetune = "data/Emilia_ZH_EN_pinyin/vocab.txt"
|
| 506 |
+
if not os.path.isfile(file_vocab_finetune):
|
| 507 |
+
return "Error: Vocabulary file 'Emilia_ZH_EN_pinyin' not found!"
|
| 508 |
+
shutil.copy2(file_vocab_finetune, file_vocab)
|
| 509 |
+
|
| 510 |
+
if error_files != []:
|
| 511 |
+
error_text = "error files\n" + "\n".join(error_files)
|
| 512 |
+
else:
|
| 513 |
+
error_text = ""
|
| 514 |
+
|
| 515 |
+
return f"prepare complete \nsamples : {len(text_list)}\ntime data : {format_seconds_to_hms(lenght)}\nmin sec : {min_second}\nmax sec : {max_second}\nfile_arrow : {file_raw}\n{error_text}"
|
| 516 |
+
|
| 517 |
+
|
| 518 |
+
def check_user(value):
|
| 519 |
+
return gr.update(visible=not value), gr.update(visible=value)
|
| 520 |
+
|
| 521 |
+
|
| 522 |
+
def calculate_train(
|
| 523 |
+
name_project,
|
| 524 |
+
batch_size_type,
|
| 525 |
+
max_samples,
|
| 526 |
+
learning_rate,
|
| 527 |
+
num_warmup_updates,
|
| 528 |
+
save_per_updates,
|
| 529 |
+
last_per_steps,
|
| 530 |
+
finetune,
|
| 531 |
+
):
|
| 532 |
+
name_project += "_pinyin"
|
| 533 |
+
path_project = os.path.join(path_data, name_project)
|
| 534 |
+
file_duraction = os.path.join(path_project, "duration.json")
|
| 535 |
+
|
| 536 |
+
if not os.path.isfile(file_duraction):
|
| 537 |
+
return (
|
| 538 |
+
1000,
|
| 539 |
+
max_samples,
|
| 540 |
+
num_warmup_updates,
|
| 541 |
+
save_per_updates,
|
| 542 |
+
last_per_steps,
|
| 543 |
+
"project not found !",
|
| 544 |
+
learning_rate,
|
| 545 |
+
)
|
| 546 |
+
|
| 547 |
+
with open(file_duraction, "r") as file:
|
| 548 |
+
data = json.load(file)
|
| 549 |
+
|
| 550 |
+
duration_list = data["duration"]
|
| 551 |
+
|
| 552 |
+
samples = len(duration_list)
|
| 553 |
+
|
| 554 |
+
if torch.cuda.is_available():
|
| 555 |
+
gpu_properties = torch.cuda.get_device_properties(0)
|
| 556 |
+
total_memory = gpu_properties.total_memory / (1024**3)
|
| 557 |
+
elif torch.backends.mps.is_available():
|
| 558 |
+
total_memory = psutil.virtual_memory().available / (1024**3)
|
| 559 |
+
|
| 560 |
+
if batch_size_type == "frame":
|
| 561 |
+
batch = int(total_memory * 0.5)
|
| 562 |
+
batch = (lambda num: num + 1 if num % 2 != 0 else num)(batch)
|
| 563 |
+
batch_size_per_gpu = int(38400 / batch)
|
| 564 |
+
else:
|
| 565 |
+
batch_size_per_gpu = int(total_memory / 8)
|
| 566 |
+
batch_size_per_gpu = (lambda num: num + 1 if num % 2 != 0 else num)(batch_size_per_gpu)
|
| 567 |
+
batch = batch_size_per_gpu
|
| 568 |
+
|
| 569 |
+
if batch_size_per_gpu <= 0:
|
| 570 |
+
batch_size_per_gpu = 1
|
| 571 |
+
|
| 572 |
+
if samples < 64:
|
| 573 |
+
max_samples = int(samples * 0.25)
|
| 574 |
+
else:
|
| 575 |
+
max_samples = 64
|
| 576 |
+
|
| 577 |
+
num_warmup_updates = int(samples * 0.05)
|
| 578 |
+
save_per_updates = int(samples * 0.10)
|
| 579 |
+
last_per_steps = int(save_per_updates * 5)
|
| 580 |
+
|
| 581 |
+
max_samples = (lambda num: num + 1 if num % 2 != 0 else num)(max_samples)
|
| 582 |
+
num_warmup_updates = (lambda num: num + 1 if num % 2 != 0 else num)(num_warmup_updates)
|
| 583 |
+
save_per_updates = (lambda num: num + 1 if num % 2 != 0 else num)(save_per_updates)
|
| 584 |
+
last_per_steps = (lambda num: num + 1 if num % 2 != 0 else num)(last_per_steps)
|
| 585 |
+
|
| 586 |
+
if finetune:
|
| 587 |
+
learning_rate = 1e-5
|
| 588 |
+
else:
|
| 589 |
+
learning_rate = 7.5e-5
|
| 590 |
+
|
| 591 |
+
return batch_size_per_gpu, max_samples, num_warmup_updates, save_per_updates, last_per_steps, samples, learning_rate
|
| 592 |
+
|
| 593 |
+
|
| 594 |
+
def extract_and_save_ema_model(checkpoint_path: str, new_checkpoint_path: str) -> None:
|
| 595 |
+
try:
|
| 596 |
+
checkpoint = torch.load(checkpoint_path)
|
| 597 |
+
print("Original Checkpoint Keys:", checkpoint.keys())
|
| 598 |
+
|
| 599 |
+
ema_model_state_dict = checkpoint.get("ema_model_state_dict", None)
|
| 600 |
+
|
| 601 |
+
if ema_model_state_dict is not None:
|
| 602 |
+
new_checkpoint = {"ema_model_state_dict": ema_model_state_dict}
|
| 603 |
+
torch.save(new_checkpoint, new_checkpoint_path)
|
| 604 |
+
return f"New checkpoint saved at: {new_checkpoint_path}"
|
| 605 |
+
else:
|
| 606 |
+
return "No 'ema_model_state_dict' found in the checkpoint."
|
| 607 |
+
|
| 608 |
+
except Exception as e:
|
| 609 |
+
return f"An error occurred: {e}"
|
| 610 |
+
|
| 611 |
+
|
| 612 |
+
def vocab_check(project_name):
|
| 613 |
+
name_project = project_name + "_pinyin"
|
| 614 |
+
path_project = os.path.join(path_data, name_project)
|
| 615 |
+
|
| 616 |
+
file_metadata = os.path.join(path_project, "metadata.csv")
|
| 617 |
+
|
| 618 |
+
file_vocab = "data/Emilia_ZH_EN_pinyin/vocab.txt"
|
| 619 |
+
if not os.path.isfile(file_vocab):
|
| 620 |
+
return f"the file {file_vocab} not found !"
|
| 621 |
+
|
| 622 |
+
with open(file_vocab, "r", encoding="utf-8") as f:
|
| 623 |
+
data = f.read()
|
| 624 |
+
|
| 625 |
+
vocab = data.split("\n")
|
| 626 |
+
|
| 627 |
+
if not os.path.isfile(file_metadata):
|
| 628 |
+
return f"the file {file_metadata} not found !"
|
| 629 |
+
|
| 630 |
+
with open(file_metadata, "r", encoding="utf-8") as f:
|
| 631 |
+
data = f.read()
|
| 632 |
+
|
| 633 |
+
miss_symbols = []
|
| 634 |
+
miss_symbols_keep = {}
|
| 635 |
+
for item in data.split("\n"):
|
| 636 |
+
sp = item.split("|")
|
| 637 |
+
if len(sp) != 2:
|
| 638 |
+
continue
|
| 639 |
+
|
| 640 |
+
text = sp[1].lower().strip()
|
| 641 |
+
|
| 642 |
+
for t in text:
|
| 643 |
+
if t not in vocab and t not in miss_symbols_keep:
|
| 644 |
+
miss_symbols.append(t)
|
| 645 |
+
miss_symbols_keep[t] = t
|
| 646 |
+
if miss_symbols == []:
|
| 647 |
+
info = "You can train using your language !"
|
| 648 |
+
else:
|
| 649 |
+
info = f"The following symbols are missing in your language : {len(miss_symbols)}\n\n" + "\n".join(miss_symbols)
|
| 650 |
+
|
| 651 |
+
return info
|
| 652 |
+
|
| 653 |
+
|
| 654 |
+
def get_random_sample_prepare(project_name):
|
| 655 |
+
name_project = project_name + "_pinyin"
|
| 656 |
+
path_project = os.path.join(path_data, name_project)
|
| 657 |
+
file_arrow = os.path.join(path_project, "raw.arrow")
|
| 658 |
+
if not os.path.isfile(file_arrow):
|
| 659 |
+
return "", None
|
| 660 |
+
dataset = Dataset_.from_file(file_arrow)
|
| 661 |
+
random_sample = dataset.shuffle(seed=random.randint(0, 1000)).select([0])
|
| 662 |
+
text = "[" + " , ".join(["' " + t + " '" for t in random_sample["text"][0]]) + "]"
|
| 663 |
+
audio_path = random_sample["audio_path"][0]
|
| 664 |
+
return text, audio_path
|
| 665 |
+
|
| 666 |
+
|
| 667 |
+
def get_random_sample_transcribe(project_name):
|
| 668 |
+
name_project = project_name + "_pinyin"
|
| 669 |
+
path_project = os.path.join(path_data, name_project)
|
| 670 |
+
file_metadata = os.path.join(path_project, "metadata.csv")
|
| 671 |
+
if not os.path.isfile(file_metadata):
|
| 672 |
+
return "", None
|
| 673 |
+
|
| 674 |
+
data = ""
|
| 675 |
+
with open(file_metadata, "r", encoding="utf-8") as f:
|
| 676 |
+
data = f.read()
|
| 677 |
+
|
| 678 |
+
list_data = []
|
| 679 |
+
for item in data.split("\n"):
|
| 680 |
+
sp = item.split("|")
|
| 681 |
+
if len(sp) != 2:
|
| 682 |
+
continue
|
| 683 |
+
list_data.append([os.path.join(path_project, "wavs", sp[0] + ".wav"), sp[1]])
|
| 684 |
+
|
| 685 |
+
if list_data == []:
|
| 686 |
+
return "", None
|
| 687 |
+
|
| 688 |
+
random_item = random.choice(list_data)
|
| 689 |
+
|
| 690 |
+
return random_item[1], random_item[0]
|
| 691 |
+
|
| 692 |
+
|
| 693 |
+
def get_random_sample_infer(project_name):
|
| 694 |
+
text, audio = get_random_sample_transcribe(project_name)
|
| 695 |
+
return (
|
| 696 |
+
text,
|
| 697 |
+
text,
|
| 698 |
+
audio,
|
| 699 |
+
)
|
| 700 |
+
|
| 701 |
+
|
| 702 |
+
def infer(file_checkpoint, exp_name, ref_text, ref_audio, gen_text, nfe_step):
|
| 703 |
+
global last_checkpoint, last_device, tts_api
|
| 704 |
+
|
| 705 |
+
if not os.path.isfile(file_checkpoint):
|
| 706 |
+
return None
|
| 707 |
+
|
| 708 |
+
if training_process is not None:
|
| 709 |
+
device_test = "cpu"
|
| 710 |
+
else:
|
| 711 |
+
device_test = None
|
| 712 |
+
|
| 713 |
+
if last_checkpoint != file_checkpoint or last_device != device_test:
|
| 714 |
+
if last_checkpoint != file_checkpoint:
|
| 715 |
+
last_checkpoint = file_checkpoint
|
| 716 |
+
if last_device != device_test:
|
| 717 |
+
last_device = device_test
|
| 718 |
+
|
| 719 |
+
tts_api = F5TTS(model_type=exp_name, ckpt_file=file_checkpoint, device=device_test)
|
| 720 |
+
|
| 721 |
+
print("update", device_test, file_checkpoint)
|
| 722 |
+
|
| 723 |
+
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
|
| 724 |
+
tts_api.infer(gen_text=gen_text, ref_text=ref_text, ref_file=ref_audio, nfe_step=nfe_step, file_wave=f.name)
|
| 725 |
+
return f.name
|
| 726 |
+
|
| 727 |
+
|
| 728 |
+
with gr.Blocks() as app:
|
| 729 |
+
with gr.Row():
|
| 730 |
+
project_name = gr.Textbox(label="project name", value="my_speak")
|
| 731 |
+
bt_create = gr.Button("create new project")
|
| 732 |
+
|
| 733 |
+
bt_create.click(fn=create_data_project, inputs=[project_name])
|
| 734 |
+
|
| 735 |
+
with gr.Tabs():
|
| 736 |
+
with gr.TabItem("transcribe Data"):
|
| 737 |
+
ch_manual = gr.Checkbox(label="user", value=False)
|
| 738 |
+
|
| 739 |
+
mark_info_transcribe = gr.Markdown(
|
| 740 |
+
"""```plaintext
|
| 741 |
+
Place your 'wavs' folder and 'metadata.csv' file in the {your_project_name}' directory.
|
| 742 |
+
|
| 743 |
+
my_speak/
|
| 744 |
+
│
|
| 745 |
+
└── dataset/
|
| 746 |
+
├── audio1.wav
|
| 747 |
+
└── audio2.wav
|
| 748 |
+
...
|
| 749 |
+
```""",
|
| 750 |
+
visible=False,
|
| 751 |
+
)
|
| 752 |
+
|
| 753 |
+
audio_speaker = gr.File(label="voice", type="filepath", file_count="multiple")
|
| 754 |
+
txt_lang = gr.Text(label="Language", value="english")
|
| 755 |
+
bt_transcribe = bt_create = gr.Button("transcribe")
|
| 756 |
+
txt_info_transcribe = gr.Text(label="info", value="")
|
| 757 |
+
bt_transcribe.click(
|
| 758 |
+
fn=transcribe_all,
|
| 759 |
+
inputs=[project_name, audio_speaker, txt_lang, ch_manual],
|
| 760 |
+
outputs=[txt_info_transcribe],
|
| 761 |
+
)
|
| 762 |
+
ch_manual.change(fn=check_user, inputs=[ch_manual], outputs=[audio_speaker, mark_info_transcribe])
|
| 763 |
+
|
| 764 |
+
random_sample_transcribe = gr.Button("random sample")
|
| 765 |
+
|
| 766 |
+
with gr.Row():
|
| 767 |
+
random_text_transcribe = gr.Text(label="Text")
|
| 768 |
+
random_audio_transcribe = gr.Audio(label="Audio", type="filepath")
|
| 769 |
+
|
| 770 |
+
random_sample_transcribe.click(
|
| 771 |
+
fn=get_random_sample_transcribe,
|
| 772 |
+
inputs=[project_name],
|
| 773 |
+
outputs=[random_text_transcribe, random_audio_transcribe],
|
| 774 |
+
)
|
| 775 |
+
|
| 776 |
+
with gr.TabItem("prepare Data"):
|
| 777 |
+
gr.Markdown(
|
| 778 |
+
"""```plaintext
|
| 779 |
+
place all your wavs folder and your metadata.csv file in {your name project}
|
| 780 |
+
my_speak/
|
| 781 |
+
│
|
| 782 |
+
├── wavs/
|
| 783 |
+
│ ├── audio1.wav
|
| 784 |
+
│ └── audio2.wav
|
| 785 |
+
| ...
|
| 786 |
+
│
|
| 787 |
+
└── metadata.csv
|
| 788 |
+
|
| 789 |
+
file format metadata.csv
|
| 790 |
+
|
| 791 |
+
audio1|text1
|
| 792 |
+
audio2|text1
|
| 793 |
+
...
|
| 794 |
+
|
| 795 |
+
```"""
|
| 796 |
+
)
|
| 797 |
+
|
| 798 |
+
bt_prepare = bt_create = gr.Button("prepare")
|
| 799 |
+
txt_info_prepare = gr.Text(label="info", value="")
|
| 800 |
+
bt_prepare.click(fn=create_metadata, inputs=[project_name], outputs=[txt_info_prepare])
|
| 801 |
+
|
| 802 |
+
random_sample_prepare = gr.Button("random sample")
|
| 803 |
+
|
| 804 |
+
with gr.Row():
|
| 805 |
+
random_text_prepare = gr.Text(label="Pinyin")
|
| 806 |
+
random_audio_prepare = gr.Audio(label="Audio", type="filepath")
|
| 807 |
+
|
| 808 |
+
random_sample_prepare.click(
|
| 809 |
+
fn=get_random_sample_prepare, inputs=[project_name], outputs=[random_text_prepare, random_audio_prepare]
|
| 810 |
+
)
|
| 811 |
+
|
| 812 |
+
with gr.TabItem("train Data"):
|
| 813 |
+
with gr.Row():
|
| 814 |
+
bt_calculate = bt_create = gr.Button("Auto Settings")
|
| 815 |
+
ch_finetune = bt_create = gr.Checkbox(label="finetune", value=True)
|
| 816 |
+
lb_samples = gr.Label(label="samples")
|
| 817 |
+
batch_size_type = gr.Radio(label="Batch Size Type", choices=["frame", "sample"], value="frame")
|
| 818 |
+
|
| 819 |
+
with gr.Row():
|
| 820 |
+
exp_name = gr.Radio(label="Model", choices=["F5TTS_Base", "E2TTS_Base"], value="F5TTS_Base")
|
| 821 |
+
learning_rate = gr.Number(label="Learning Rate", value=1e-5, step=1e-5)
|
| 822 |
+
|
| 823 |
+
with gr.Row():
|
| 824 |
+
batch_size_per_gpu = gr.Number(label="Batch Size per GPU", value=1000)
|
| 825 |
+
max_samples = gr.Number(label="Max Samples", value=64)
|
| 826 |
+
|
| 827 |
+
with gr.Row():
|
| 828 |
+
grad_accumulation_steps = gr.Number(label="Gradient Accumulation Steps", value=1)
|
| 829 |
+
max_grad_norm = gr.Number(label="Max Gradient Norm", value=1.0)
|
| 830 |
+
|
| 831 |
+
with gr.Row():
|
| 832 |
+
epochs = gr.Number(label="Epochs", value=10)
|
| 833 |
+
num_warmup_updates = gr.Number(label="Warmup Updates", value=5)
|
| 834 |
+
|
| 835 |
+
with gr.Row():
|
| 836 |
+
save_per_updates = gr.Number(label="Save per Updates", value=10)
|
| 837 |
+
last_per_steps = gr.Number(label="Last per Steps", value=50)
|
| 838 |
+
|
| 839 |
+
with gr.Row():
|
| 840 |
+
start_button = gr.Button("Start Training")
|
| 841 |
+
stop_button = gr.Button("Stop Training", interactive=False)
|
| 842 |
+
|
| 843 |
+
txt_info_train = gr.Text(label="info", value="")
|
| 844 |
+
start_button.click(
|
| 845 |
+
fn=start_training,
|
| 846 |
+
inputs=[
|
| 847 |
+
project_name,
|
| 848 |
+
exp_name,
|
| 849 |
+
learning_rate,
|
| 850 |
+
batch_size_per_gpu,
|
| 851 |
+
batch_size_type,
|
| 852 |
+
max_samples,
|
| 853 |
+
grad_accumulation_steps,
|
| 854 |
+
max_grad_norm,
|
| 855 |
+
epochs,
|
| 856 |
+
num_warmup_updates,
|
| 857 |
+
save_per_updates,
|
| 858 |
+
last_per_steps,
|
| 859 |
+
ch_finetune,
|
| 860 |
+
],
|
| 861 |
+
outputs=[txt_info_train, start_button, stop_button],
|
| 862 |
+
)
|
| 863 |
+
stop_button.click(fn=stop_training, outputs=[txt_info_train, start_button, stop_button])
|
| 864 |
+
bt_calculate.click(
|
| 865 |
+
fn=calculate_train,
|
| 866 |
+
inputs=[
|
| 867 |
+
project_name,
|
| 868 |
+
batch_size_type,
|
| 869 |
+
max_samples,
|
| 870 |
+
learning_rate,
|
| 871 |
+
num_warmup_updates,
|
| 872 |
+
save_per_updates,
|
| 873 |
+
last_per_steps,
|
| 874 |
+
ch_finetune,
|
| 875 |
+
],
|
| 876 |
+
outputs=[
|
| 877 |
+
batch_size_per_gpu,
|
| 878 |
+
max_samples,
|
| 879 |
+
num_warmup_updates,
|
| 880 |
+
save_per_updates,
|
| 881 |
+
last_per_steps,
|
| 882 |
+
lb_samples,
|
| 883 |
+
learning_rate,
|
| 884 |
+
],
|
| 885 |
+
)
|
| 886 |
+
|
| 887 |
+
with gr.TabItem("reduse checkpoint"):
|
| 888 |
+
txt_path_checkpoint = gr.Text(label="path checkpoint :")
|
| 889 |
+
txt_path_checkpoint_small = gr.Text(label="path output :")
|
| 890 |
+
txt_info_reduse = gr.Text(label="info", value="")
|
| 891 |
+
reduse_button = gr.Button("reduse")
|
| 892 |
+
reduse_button.click(
|
| 893 |
+
fn=extract_and_save_ema_model,
|
| 894 |
+
inputs=[txt_path_checkpoint, txt_path_checkpoint_small],
|
| 895 |
+
outputs=[txt_info_reduse],
|
| 896 |
+
)
|
| 897 |
+
|
| 898 |
+
with gr.TabItem("vocab check experiment"):
|
| 899 |
+
check_button = gr.Button("check vocab")
|
| 900 |
+
txt_info_check = gr.Text(label="info", value="")
|
| 901 |
+
check_button.click(fn=vocab_check, inputs=[project_name], outputs=[txt_info_check])
|
| 902 |
+
|
| 903 |
+
with gr.TabItem("test model"):
|
| 904 |
+
exp_name = gr.Radio(label="Model", choices=["F5-TTS", "E2-TTS"], value="F5-TTS")
|
| 905 |
+
nfe_step = gr.Number(label="n_step", value=32)
|
| 906 |
+
file_checkpoint_pt = gr.Textbox(label="Checkpoint", value="")
|
| 907 |
+
|
| 908 |
+
random_sample_infer = gr.Button("random sample")
|
| 909 |
+
|
| 910 |
+
ref_text = gr.Textbox(label="ref text")
|
| 911 |
+
ref_audio = gr.Audio(label="audio ref", type="filepath")
|
| 912 |
+
gen_text = gr.Textbox(label="gen text")
|
| 913 |
+
random_sample_infer.click(
|
| 914 |
+
fn=get_random_sample_infer, inputs=[project_name], outputs=[ref_text, gen_text, ref_audio]
|
| 915 |
+
)
|
| 916 |
+
check_button_infer = gr.Button("infer")
|
| 917 |
+
gen_audio = gr.Audio(label="audio gen", type="filepath")
|
| 918 |
+
|
| 919 |
+
check_button_infer.click(
|
| 920 |
+
fn=infer,
|
| 921 |
+
inputs=[file_checkpoint_pt, exp_name, ref_text, ref_audio, gen_text, nfe_step],
|
| 922 |
+
outputs=[gen_audio],
|
| 923 |
+
)
|
| 924 |
+
|
| 925 |
+
|
| 926 |
+
@click.command()
|
| 927 |
+
@click.option("--port", "-p", default=None, type=int, help="Port to run the app on")
|
| 928 |
+
@click.option("--host", "-H", default=None, help="Host to run the app on")
|
| 929 |
+
@click.option(
|
| 930 |
+
"--share",
|
| 931 |
+
"-s",
|
| 932 |
+
default=False,
|
| 933 |
+
is_flag=True,
|
| 934 |
+
help="Share the app via Gradio share link",
|
| 935 |
+
)
|
| 936 |
+
@click.option("--api", "-a", default=True, is_flag=True, help="Allow API access")
|
| 937 |
+
def main(port, host, share, api):
|
| 938 |
+
global app
|
| 939 |
+
print("Starting app...")
|
| 940 |
+
app.queue(api_open=api).launch(server_name=host, server_port=port, share=share, show_api=api)
|
| 941 |
+
|
| 942 |
+
|
| 943 |
+
if __name__ == "__main__":
|
| 944 |
+
main()
|
src/f5_tts/{train.py → train/train.py}
RENAMED
|
File without changes
|