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warren-buffett-speech/tacotron-training/run-June-26-2023_05+28AM-00.../trainer_0_log.txt

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Add tacotron unsuccessful training, but barely legible at loss of 0.1432
2 years ago
> Training Environment:
| > Current device: 0
| > Num. of GPUs: 1
| > Num. of CPUs: 1
| > Num. of Torch Threads: 1
| > Torch seed: 54321
| > Torch CUDNN: True
| > Torch CUDNN deterministic: False
| > Torch CUDNN benchmark: False
> Start Tensorboard: tensorboard --logdir=/home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
> Model has 28610257 parameters
 > EPOCH: 0/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 05:28:42) 
 --> STEP: 0/406 -- GLOBAL_STEP: 0
| > current_lr: 0.00000
| > step_time: 79.16690 (79.16692)
| > loader_time: 5.74650 (5.74649)
 --> STEP: 25/406 -- GLOBAL_STEP: 25
| > loss: 3.94976 (3.78406)
| > log_mle: 0.82513 (0.82313)
| > loss_dur: 3.12463 (2.96093)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 11.25827 (10.15753)
| > current_lr: 0.00000
| > step_time: 0.59400 (2.96324)
| > loader_time: 1.51590 (2.03268)
 --> STEP: 50/406 -- GLOBAL_STEP: 50
| > loss: 3.82559 (3.74907)
| > log_mle: 0.82458 (0.82480)
| > loss_dur: 3.00102 (2.92427)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.99044 (10.56035)
| > current_lr: 0.00000
| > step_time: 0.38640 (1.75328)
| > loader_time: 1.81580 (2.14834)
 --> STEP: 75/406 -- GLOBAL_STEP: 75
| > loss: 3.81644 (3.74541)
| > log_mle: 0.83158 (0.82491)
| > loss_dur: 2.98486 (2.92050)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.94210 (10.67450)
| > current_lr: 0.00000
| > step_time: 0.44110 (1.31910)
| > loader_time: 2.26730 (2.16377)
 --> STEP: 100/406 -- GLOBAL_STEP: 100
| > loss: 3.74355 (3.73695)
| > log_mle: 0.83085 (0.82495)
| > loss_dur: 2.91271 (2.91200)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.89170 (10.71725)
| > current_lr: 0.00000
| > step_time: 0.47930 (1.11325)
| > loader_time: 1.94830 (2.16166)
 --> STEP: 125/406 -- GLOBAL_STEP: 125
| > loss: 3.71598 (3.73164)
| > log_mle: 0.82804 (0.82474)
| > loss_dur: 2.88794 (2.90691)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.74962 (10.73911)
| > current_lr: 0.00000
| > step_time: 0.51620 (0.99739)
| > loader_time: 2.05700 (2.18027)
 --> STEP: 150/406 -- GLOBAL_STEP: 150
| > loss: 3.71293 (3.72814)
| > log_mle: 0.82601 (0.82459)
| > loss_dur: 2.88692 (2.90355)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.88592 (10.74974)
| > current_lr: 0.00000
| > step_time: 0.59460 (0.92529)
| > loader_time: 2.90450 (2.24011)
 --> STEP: 175/406 -- GLOBAL_STEP: 175
| > loss: 3.72749 (3.72533)
| > log_mle: 0.82417 (0.82436)
| > loss_dur: 2.90331 (2.90097)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.79335 (10.75303)
| > current_lr: 0.00000
| > step_time: 0.62420 (0.87525)
| > loader_time: 3.14640 (2.34661)
 --> STEP: 200/406 -- GLOBAL_STEP: 200
| > loss: 3.68366 (3.72319)
| > log_mle: 0.82602 (0.82422)
| > loss_dur: 2.85764 (2.89897)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.71187 (10.75198)
| > current_lr: 0.00000
| > step_time: 0.61540 (0.84143)
| > loader_time: 2.58790 (2.41791)
 --> STEP: 225/406 -- GLOBAL_STEP: 225
| > loss: 3.69834 (3.71959)
| > log_mle: 0.82262 (0.82419)
| > loss_dur: 2.87573 (2.89540)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.67498 (10.74564)
| > current_lr: 0.00000
| > step_time: 0.67190 (0.82011)
| > loader_time: 2.88800 (2.47938)
 --> STEP: 250/406 -- GLOBAL_STEP: 250
| > loss: 3.72714 (3.71780)
| > log_mle: 0.82394 (0.82417)
| > loss_dur: 2.90320 (2.89363)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.66663 (10.74011)
| > current_lr: 0.00000
| > step_time: 1.09020 (0.81621)
| > loader_time: 3.26330 (2.52943)
 --> STEP: 275/406 -- GLOBAL_STEP: 275
| > loss: 3.71350 (3.71480)
| > log_mle: 0.82473 (0.82383)
| > loss_dur: 2.88877 (2.89096)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.71189 (10.73030)
| > current_lr: 0.00000
| > step_time: 1.17890 (0.81986)
| > loader_time: 2.63030 (2.56145)
 --> STEP: 300/406 -- GLOBAL_STEP: 300
| > loss: 3.68987 (3.71059)
| > log_mle: 0.82339 (0.82367)
| > loss_dur: 2.86647 (2.88692)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.60767 (10.71504)
| > current_lr: 0.00000
| > step_time: 0.79850 (0.82163)
| > loader_time: 2.36810 (2.58497)
 --> STEP: 325/406 -- GLOBAL_STEP: 325
| > loss: 3.64184 (3.70649)
| > log_mle: 0.82212 (0.82346)
| > loss_dur: 2.81972 (2.88303)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.46201 (10.69954)
| > current_lr: 0.00000
| > step_time: 0.89300 (0.82556)
| > loader_time: 3.39350 (2.62371)
 --> STEP: 350/406 -- GLOBAL_STEP: 350
| > loss: 3.60014 (3.70447)
| > log_mle: 0.81764 (0.82326)
| > loss_dur: 2.78251 (2.88121)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.33894 (10.68521)
| > current_lr: 0.00000
| > step_time: 0.97850 (0.83363)
| > loader_time: 3.19540 (2.66616)
 --> STEP: 375/406 -- GLOBAL_STEP: 375
| > loss: 3.68202 (3.69995)
| > log_mle: 0.81569 (0.82298)
| > loss_dur: 2.86632 (2.87697)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.45899 (10.66681)
| > current_lr: 0.00000
| > step_time: 0.81860 (0.84132)
| > loader_time: 3.41660 (2.70840)
 --> STEP: 400/406 -- GLOBAL_STEP: 400
| > loss: 3.62027 (3.69524)
| > log_mle: 0.81756 (0.82280)
| > loss_dur: 2.80270 (2.87244)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.27854 (10.64640)
| > current_lr: 0.00000
| > step_time: 1.14200 (0.85254)
| > loader_time: 3.23870 (2.74440)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 2.00473 (+0.00000)
| > avg_loss: 3.65562 (+0.00000)
| > avg_log_mle: 0.81726 (+0.00000)
| > avg_loss_dur: 2.83836 (+0.00000)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_406.pth
 > EPOCH: 1/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 05:55:46) 
 --> STEP: 19/406 -- GLOBAL_STEP: 425
| > loss: 3.65962 (3.68245)
| > log_mle: 0.81672 (0.81579)
| > loss_dur: 2.84290 (2.86665)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.34681 (10.20771)
| > current_lr: 0.00000
| > step_time: 0.31550 (0.34176)
| > loader_time: 2.30600 (1.67893)
 --> STEP: 44/406 -- GLOBAL_STEP: 450
| > loss: 3.53872 (3.63671)
| > log_mle: 0.82075 (0.81779)
| > loss_dur: 2.71797 (2.81892)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.01404 (10.17054)
| > current_lr: 0.00000
| > step_time: 0.33360 (0.35092)
| > loader_time: 2.36250 (1.92916)
 --> STEP: 69/406 -- GLOBAL_STEP: 475
| > loss: 3.65216 (3.62507)
| > log_mle: 0.82403 (0.81814)
| > loss_dur: 2.82813 (2.80693)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.18318 (10.14952)
| > current_lr: 0.00000
| > step_time: 0.44810 (0.37412)
| > loader_time: 1.93760 (2.01139)
 --> STEP: 94/406 -- GLOBAL_STEP: 500
| > loss: 3.55572 (3.61176)
| > log_mle: 0.81874 (0.81788)
| > loss_dur: 2.73698 (2.79388)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.00861 (10.11863)
| > current_lr: 0.00000
| > step_time: 35.74170 (0.79270)
| > loader_time: 9.70000 (2.29221)
 --> STEP: 119/406 -- GLOBAL_STEP: 525
| > loss: 3.56023 (3.59951)
| > log_mle: 0.81293 (0.81769)
| > loss_dur: 2.74730 (2.78182)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 9.96277 (10.08136)
| > current_lr: 0.00000
| > step_time: 0.52790 (0.75709)
| > loader_time: 2.49130 (2.36663)
 --> STEP: 144/406 -- GLOBAL_STEP: 550
| > loss: 3.53837 (3.59365)
| > log_mle: 0.81460 (0.81737)
| > loss_dur: 2.72377 (2.77628)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 9.80126 (10.05367)
| > current_lr: 0.00000
| > step_time: 0.59860 (0.72906)
| > loader_time: 2.25370 (2.36543)
 --> STEP: 169/406 -- GLOBAL_STEP: 575
| > loss: 3.56250 (3.58757)
| > log_mle: 0.82209 (0.81706)
| > loss_dur: 2.74041 (2.77051)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 9.79955 (10.02330)
| > current_lr: 0.00000
| > step_time: 0.58670 (0.70945)
| > loader_time: 2.40860 (2.40226)
 --> STEP: 194/406 -- GLOBAL_STEP: 600
| > loss: 3.57549 (3.58195)
| > log_mle: 0.81772 (0.81671)
| > loss_dur: 2.75778 (2.76524)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 9.84931 (9.99123)
| > current_lr: 0.00000
| > step_time: 0.66660 (0.70359)
| > loader_time: 2.58820 (2.44928)
 --> STEP: 219/406 -- GLOBAL_STEP: 625
| > loss: 3.44302 (3.57462)
| > log_mle: 0.81825 (0.81648)
| > loss_dur: 2.62477 (2.75814)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 9.52012 (9.95608)
| > current_lr: 0.00000
| > step_time: 0.67770 (0.70093)
| > loader_time: 2.60570 (2.48239)
 --> STEP: 244/406 -- GLOBAL_STEP: 650
| > loss: 3.55473 (3.56906)
| > log_mle: 0.81139 (0.81628)
| > loss_dur: 2.74335 (2.75278)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 9.70034 (9.92165)
| > current_lr: 0.00000
| > step_time: 0.75780 (0.70252)
| > loader_time: 2.57140 (2.53127)
 --> STEP: 269/406 -- GLOBAL_STEP: 675
| > loss: 3.45832 (3.56188)
| > log_mle: 0.81790 (0.81585)
| > loss_dur: 2.64042 (2.74603)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 9.41434 (9.88276)
| > current_lr: 0.00000
| > step_time: 0.70590 (0.71185)
| > loader_time: 2.68980 (2.57178)
 --> STEP: 294/406 -- GLOBAL_STEP: 700
| > loss: 3.47896 (3.55637)
| > log_mle: 0.81275 (0.81550)
| > loss_dur: 2.66621 (2.74086)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 9.34280 (9.84752)
| > current_lr: 0.00000
| > step_time: 1.14030 (0.72968)
| > loader_time: 2.62780 (2.59764)
 --> STEP: 319/406 -- GLOBAL_STEP: 725
| > loss: 3.45616 (3.54955)
| > log_mle: 0.80658 (0.81518)
| > loss_dur: 2.64957 (2.73437)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 9.27808 (9.80871)
| > current_lr: 0.00000
| > step_time: 0.90380 (0.74402)
| > loader_time: 3.07470 (2.63182)
 --> STEP: 344/406 -- GLOBAL_STEP: 750
| > loss: 3.43717 (3.54397)
| > log_mle: 0.80948 (0.81482)
| > loss_dur: 2.62770 (2.72915)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 9.14162 (9.76920)
| > current_lr: 0.00000
| > step_time: 0.94130 (0.76317)
| > loader_time: 3.76150 (2.67482)
 --> STEP: 369/406 -- GLOBAL_STEP: 775
| > loss: 3.38919 (3.53834)
| > log_mle: 0.80674 (0.81440)
| > loss_dur: 2.58245 (2.72393)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 9.08179 (9.73207)
| > current_lr: 0.00000
| > step_time: 1.22510 (0.78618)
| > loader_time: 3.38900 (2.70520)
 --> STEP: 394/406 -- GLOBAL_STEP: 800
| > loss: 3.44702 (3.53122)
| > log_mle: 0.81215 (0.81407)
| > loss_dur: 2.63487 (2.71715)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 9.08220 (9.68997)
| > current_lr: 0.00000
| > step_time: 0.91670 (0.88850)
| > loader_time: 2.40840 (2.71484)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.14245 (-0.86228)
| > avg_loss: 3.43748 (-0.21814)
| > avg_log_mle: 0.80588 (-0.01138)
| > avg_loss_dur: 2.63160 (-0.20676)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_812.pth
 > EPOCH: 2/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 06:21:20) 
 --> STEP: 13/406 -- GLOBAL_STEP: 825
| > loss: 3.37253 (3.46965)
| > log_mle: 0.79783 (0.80486)
| > loss_dur: 2.57470 (2.66479)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 8.74653 (8.86658)
| > current_lr: 0.00000
| > step_time: 1.25280 (0.91477)
| > loader_time: 1.01610 (1.06082)
 --> STEP: 38/406 -- GLOBAL_STEP: 850
| > loss: 3.46289 (3.42593)
| > log_mle: 0.80458 (0.80601)
| > loss_dur: 2.65831 (2.61992)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 8.86531 (8.80118)
| > current_lr: 0.00000
| > step_time: 0.70610 (0.80214)
| > loader_time: 1.79500 (1.42786)
 --> STEP: 63/406 -- GLOBAL_STEP: 875
| > loss: 3.44279 (3.40220)
| > log_mle: 0.80185 (0.80557)
| > loss_dur: 2.64094 (2.59663)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 8.75761 (8.71679)
| > current_lr: 0.00000
| > step_time: 0.51010 (0.80132)
| > loader_time: 1.59220 (1.56598)
 --> STEP: 88/406 -- GLOBAL_STEP: 900
| > loss: 3.29054 (3.38590)
| > log_mle: 0.80431 (0.80497)
| > loss_dur: 2.48623 (2.58093)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 8.30320 (8.63729)
| > current_lr: 0.00000
| > step_time: 0.78830 (0.80160)
| > loader_time: 2.12130 (1.68984)
 --> STEP: 113/406 -- GLOBAL_STEP: 925
| > loss: 3.32069 (3.36672)
| > log_mle: 0.80243 (0.80420)
| > loss_dur: 2.51826 (2.56253)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 8.15448 (8.54417)
| > current_lr: 0.00000
| > step_time: 0.47090 (0.74851)
| > loader_time: 2.15950 (1.80201)
 --> STEP: 138/406 -- GLOBAL_STEP: 950
| > loss: 3.25842 (3.35393)
| > log_mle: 0.79439 (0.80315)
| > loss_dur: 2.46403 (2.55078)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 7.86542 (8.45998)
| > current_lr: 0.00000
| > step_time: 0.53800 (0.70738)
| > loader_time: 2.58990 (1.88918)
 --> STEP: 163/406 -- GLOBAL_STEP: 975
| > loss: 3.25329 (3.34086)
| > log_mle: 0.79624 (0.80204)
| > loss_dur: 2.45705 (2.53882)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 7.75901 (8.37276)
| > current_lr: 0.00000
| > step_time: 0.51680 (0.68493)
| > loader_time: 2.52300 (1.99339)
 --> STEP: 188/406 -- GLOBAL_STEP: 1000
| > loss: 3.30118 (3.32816)
| > log_mle: 0.79379 (0.80102)
| > loss_dur: 2.50739 (2.52714)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 7.66504 (8.28391)
| > current_lr: 0.00000
| > step_time: 0.56480 (0.67324)
| > loader_time: 2.79130 (2.10796)
 --> STEP: 213/406 -- GLOBAL_STEP: 1025
| > loss: 3.17445 (3.31586)
| > log_mle: 0.78797 (0.80000)
| > loss_dur: 2.38647 (2.51587)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 7.41179 (8.19825)
| > current_lr: 0.00000
| > step_time: 0.61190 (0.66794)
| > loader_time: 2.81980 (2.19283)
 --> STEP: 238/406 -- GLOBAL_STEP: 1050
| > loss: 3.25268 (3.30448)
| > log_mle: 0.79221 (0.79915)
| > loss_dur: 2.46047 (2.50533)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 7.41109 (8.11253)
| > current_lr: 0.00000
| > step_time: 0.66550 (0.67002)
| > loader_time: 2.49960 (2.27166)
 --> STEP: 263/406 -- GLOBAL_STEP: 1075
| > loss: 3.15904 (3.29416)
| > log_mle: 0.78633 (0.79799)
| > loss_dur: 2.37270 (2.49617)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 7.08219 (8.02792)
| > current_lr: 0.00000
| > step_time: 0.67600 (0.67596)
| > loader_time: 2.53420 (2.33123)
 --> STEP: 288/406 -- GLOBAL_STEP: 1100
| > loss: 3.11665 (3.28435)
| > log_mle: 0.78100 (0.79680)
| > loss_dur: 2.33564 (2.48755)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 6.88171 (7.94364)
| > current_lr: 0.00000
| > step_time: 0.89500 (0.68578)
| > loader_time: 2.95730 (2.38636)
 --> STEP: 313/406 -- GLOBAL_STEP: 1125
| > loss: 3.13315 (3.27499)
| > log_mle: 0.78020 (0.79574)
| > loss_dur: 2.35295 (2.47926)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 6.76387 (7.86089)
| > current_lr: 0.00000
| > step_time: 0.74600 (0.69217)
| > loader_time: 2.83380 (2.42366)
 --> STEP: 338/406 -- GLOBAL_STEP: 1150
| > loss: 3.15742 (3.26641)
| > log_mle: 0.77964 (0.79451)
| > loss_dur: 2.37778 (2.47190)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 6.68452 (7.78001)
| > current_lr: 0.00000
| > step_time: 0.87090 (0.71013)
| > loader_time: 3.16410 (2.48275)
 --> STEP: 363/406 -- GLOBAL_STEP: 1175
| > loss: 3.13071 (3.25907)
| > log_mle: 0.77394 (0.79327)
| > loss_dur: 2.35676 (2.46580)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 6.54849 (7.70225)
| > current_lr: 0.00000
| > step_time: 1.05840 (0.73340)
| > loader_time: 2.99350 (2.53001)
 --> STEP: 388/406 -- GLOBAL_STEP: 1200
| > loss: 3.10237 (3.25019)
| > log_mle: 0.77581 (0.79206)
| > loss_dur: 2.32656 (2.45813)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 6.39775 (7.62358)
| > current_lr: 0.00000
| > step_time: 1.80780 (0.76340)
| > loader_time: 0.81420 (2.56988)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.16578 (+0.02333)
| > avg_loss: 3.08469 (-0.35279)
| > avg_log_mle: 0.76893 (-0.03695)
| > avg_loss_dur: 2.31576 (-0.31584)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_1218.pth
 > EPOCH: 3/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 06:44:50) 
 --> STEP: 7/406 -- GLOBAL_STEP: 1225
| > loss: 3.00657 (3.13080)
| > log_mle: 0.77299 (0.77032)
| > loss_dur: 2.23359 (2.36048)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 6.09316 (6.30876)
| > current_lr: 0.00000
| > step_time: 0.23340 (0.36592)
| > loader_time: 1.24660 (1.62296)
 --> STEP: 32/406 -- GLOBAL_STEP: 1250
| > loss: 3.20895 (3.12256)
| > log_mle: 0.76949 (0.77058)
| > loss_dur: 2.43945 (2.35198)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 6.40050 (6.28856)
| > current_lr: 0.00000
| > step_time: 0.30770 (0.32807)
| > loader_time: 1.71630 (1.79281)
 --> STEP: 57/406 -- GLOBAL_STEP: 1275
| > loss: 3.12532 (3.11343)
| > log_mle: 0.76483 (0.76951)
| > loss_dur: 2.36049 (2.34391)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 6.15928 (6.22955)
| > current_lr: 0.00000
| > step_time: 0.43540 (0.35620)
| > loader_time: 2.23450 (1.96996)
 --> STEP: 82/406 -- GLOBAL_STEP: 1300
| > loss: 3.04734 (3.11453)
| > log_mle: 0.76212 (0.76764)
| > loss_dur: 2.28522 (2.34689)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 5.93814 (6.19482)
| > current_lr: 0.00000
| > step_time: 0.44260 (0.38278)
| > loader_time: 2.55000 (2.02718)
 --> STEP: 107/406 -- GLOBAL_STEP: 1325
| > loss: 3.01948 (3.10480)
| > log_mle: 0.75827 (0.76545)
| > loss_dur: 2.26121 (2.33935)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 5.90751 (6.14338)
| > current_lr: 0.00000
| > step_time: 0.49910 (0.40926)
| > loader_time: 2.48440 (2.06977)
 --> STEP: 132/406 -- GLOBAL_STEP: 1350
| > loss: 3.10430 (3.09711)
| > log_mle: 0.74660 (0.76298)
| > loss_dur: 2.35770 (2.33413)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 5.98590 (6.09694)
| > current_lr: 0.00000
| > step_time: 0.52950 (0.43270)
| > loader_time: 2.43140 (2.12237)
 --> STEP: 157/406 -- GLOBAL_STEP: 1375
| > loss: 3.14100 (3.09132)
| > log_mle: 0.74779 (0.76067)
| > loss_dur: 2.39321 (2.33065)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 5.94515 (6.05538)
| > current_lr: 0.00000
| > step_time: 0.55210 (0.45586)
| > loader_time: 2.89750 (2.19223)
 --> STEP: 182/406 -- GLOBAL_STEP: 1400
| > loss: 3.05443 (3.08360)
| > log_mle: 0.73520 (0.75825)
| > loss_dur: 2.31924 (2.32535)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 5.76718 (6.01263)
| > current_lr: 0.00000
| > step_time: 0.65590 (0.48784)
| > loader_time: 2.56450 (2.26756)
 --> STEP: 207/406 -- GLOBAL_STEP: 1425
| > loss: 3.02998 (3.07539)
| > log_mle: 0.73455 (0.75598)
| > loss_dur: 2.29543 (2.31941)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 5.66429 (5.97146)
| > current_lr: 0.00000
| > step_time: 0.67860 (0.50683)
| > loader_time: 2.83310 (2.31920)
 --> STEP: 232/406 -- GLOBAL_STEP: 1450
| > loss: 2.97410 (3.06646)
| > log_mle: 0.72626 (0.75371)
| > loss_dur: 2.24784 (2.31275)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 5.57358 (5.93199)
| > current_lr: 0.00000
| > step_time: 0.70400 (0.52790)
| > loader_time: 2.48750 (2.35931)
 --> STEP: 257/406 -- GLOBAL_STEP: 1475
| > loss: 2.98643 (3.05869)
| > log_mle: 0.72606 (0.75142)
| > loss_dur: 2.26038 (2.30727)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 5.56014 (5.89580)
| > current_lr: 0.00000
| > step_time: 0.70830 (0.54720)
| > loader_time: 2.70140 (2.39842)
 --> STEP: 282/406 -- GLOBAL_STEP: 1500
| > loss: 2.95733 (3.04967)
| > log_mle: 0.72684 (0.74906)
| > loss_dur: 2.23049 (2.30061)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 5.50368 (5.86039)
| > current_lr: 0.00000
| > step_time: 0.77950 (0.56854)
| > loader_time: 3.01980 (2.43420)
 --> STEP: 307/406 -- GLOBAL_STEP: 1525
| > loss: 2.93742 (3.04123)
| > log_mle: 0.71872 (0.74675)
| > loss_dur: 2.21870 (2.29448)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 5.44606 (5.82725)
| > current_lr: 0.00000
| > step_time: 0.80860 (0.58726)
| > loader_time: 2.78570 (2.46268)
 --> STEP: 332/406 -- GLOBAL_STEP: 1550
| > loss: 2.88498 (3.03201)
| > log_mle: 0.70957 (0.74435)
| > loss_dur: 2.17540 (2.28766)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 5.30959 (5.79475)
| > current_lr: 0.00000
| > step_time: 0.83630 (0.60682)
| > loader_time: 2.66760 (2.51126)
 --> STEP: 357/406 -- GLOBAL_STEP: 1575
| > loss: 2.87253 (3.02456)
| > log_mle: 0.71189 (0.74192)
| > loss_dur: 2.16064 (2.28264)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 5.29678 (5.76602)
| > current_lr: 0.00000
| > step_time: 0.91770 (0.62535)
| > loader_time: 3.48580 (2.56067)
 --> STEP: 382/406 -- GLOBAL_STEP: 1600
| > loss: 2.87543 (3.01428)
| > log_mle: 0.70275 (0.73953)
| > loss_dur: 2.17267 (2.27476)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 5.26271 (5.73362)
| > current_lr: 0.00000
| > step_time: 1.08450 (0.64811)
| > loader_time: 3.32250 (2.61155)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.21142 (+0.04564)
| > avg_loss: 2.80198 (-0.28271)
| > avg_log_mle: 0.69711 (-0.07182)
| > avg_loss_dur: 2.10487 (-0.21089)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_1624.pth
 > EPOCH: 4/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 07:08:06) 
 --> STEP: 1/406 -- GLOBAL_STEP: 1625
| > loss: 2.87741 (2.87741)
| > log_mle: 0.70139 (0.70139)
| > loss_dur: 2.17602 (2.17602)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 5.25176 (5.25176)
| > current_lr: 0.00000
| > step_time: 0.27200 (0.27203)
| > loader_time: 1.52990 (1.52985)
 --> STEP: 26/406 -- GLOBAL_STEP: 1650
| > loss: 2.80450 (2.81334)
| > log_mle: 0.70088 (0.70594)
| > loss_dur: 2.10362 (2.10740)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 5.16089 (5.16732)
| > current_lr: 0.00000
| > step_time: 0.33400 (0.31330)
| > loader_time: 1.90390 (1.84449)
 --> STEP: 51/406 -- GLOBAL_STEP: 1675
| > loss: 2.75381 (2.80003)
| > log_mle: 0.69207 (0.70356)
| > loss_dur: 2.06174 (2.09647)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.97318 (5.14317)
| > current_lr: 0.00000
| > step_time: 0.43440 (0.34511)
| > loader_time: 1.94680 (1.97950)
 --> STEP: 76/406 -- GLOBAL_STEP: 1700
| > loss: 2.78072 (2.79140)
| > log_mle: 0.69540 (0.69977)
| > loss_dur: 2.08532 (2.09162)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 5.09202 (5.11958)
| > current_lr: 0.00000
| > step_time: 0.48170 (0.37742)
| > loader_time: 2.30280 (2.05244)
 --> STEP: 101/406 -- GLOBAL_STEP: 1725
| > loss: 2.75655 (2.77599)
| > log_mle: 0.67863 (0.69598)
| > loss_dur: 2.07792 (2.08001)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.98949 (5.08328)
| > current_lr: 0.00000
| > step_time: 0.53030 (0.40527)
| > loader_time: 2.46830 (2.15380)
 --> STEP: 126/406 -- GLOBAL_STEP: 1750
| > loss: 2.62607 (2.76086)
| > log_mle: 0.67366 (0.69207)
| > loss_dur: 1.95241 (2.06879)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.77410 (5.04698)
| > current_lr: 0.00000
| > step_time: 0.54830 (0.43114)
| > loader_time: 2.29170 (2.19794)
 --> STEP: 151/406 -- GLOBAL_STEP: 1775
| > loss: 2.67206 (2.75022)
| > log_mle: 0.66885 (0.68824)
| > loss_dur: 2.00320 (2.06197)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.79990 (5.01798)
| > current_lr: 0.00000
| > step_time: 0.56980 (0.45234)
| > loader_time: 2.26750 (2.20793)
 --> STEP: 176/406 -- GLOBAL_STEP: 1800
| > loss: 2.64068 (2.73596)
| > log_mle: 0.65875 (0.68452)
| > loss_dur: 1.98193 (2.05144)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.75045 (4.98149)
| > current_lr: 0.00000
| > step_time: 0.56340 (0.47237)
| > loader_time: 2.90420 (2.27342)
 --> STEP: 201/406 -- GLOBAL_STEP: 1825
| > loss: 2.57232 (2.72281)
| > log_mle: 0.65837 (0.68091)
| > loss_dur: 1.91395 (2.04189)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.60655 (4.94764)
| > current_lr: 0.00000
| > step_time: 0.67540 (0.49286)
| > loader_time: 2.63220 (2.31817)
 --> STEP: 226/406 -- GLOBAL_STEP: 1850
| > loss: 2.58292 (2.70914)
| > log_mle: 0.64114 (0.67736)
| > loss_dur: 1.94178 (2.03178)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.59125 (4.91463)
| > current_lr: 0.00000
| > step_time: 0.65050 (0.51328)
| > loader_time: 2.87230 (2.36121)
 --> STEP: 251/406 -- GLOBAL_STEP: 1875
| > loss: 2.62632 (2.69743)
| > log_mle: 0.63875 (0.67391)
| > loss_dur: 1.98757 (2.02352)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.69390 (4.88464)
| > current_lr: 0.00000
| > step_time: 0.71500 (0.53115)
| > loader_time: 2.33360 (2.39324)
 --> STEP: 276/406 -- GLOBAL_STEP: 1900
| > loss: 2.53220 (2.68452)
| > log_mle: 0.63297 (0.67031)
| > loss_dur: 1.89923 (2.01420)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.51055 (4.85343)
| > current_lr: 0.00000
| > step_time: 0.77160 (0.55069)
| > loader_time: 2.90480 (2.42575)
 --> STEP: 301/406 -- GLOBAL_STEP: 1925
| > loss: 2.53341 (2.67228)
| > log_mle: 0.62215 (0.66689)
| > loss_dur: 1.91126 (2.00539)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.43819 (4.82291)
| > current_lr: 0.00000
| > step_time: 0.77920 (0.57104)
| > loader_time: 2.71130 (2.45730)
 --> STEP: 326/406 -- GLOBAL_STEP: 1950
| > loss: 2.46752 (2.65957)
| > log_mle: 0.61535 (0.66345)
| > loss_dur: 1.85218 (1.99612)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.35987 (4.79345)
| > current_lr: 0.00000
| > step_time: 0.83070 (0.58932)
| > loader_time: 2.98860 (2.48715)
 --> STEP: 351/406 -- GLOBAL_STEP: 1975
| > loss: 2.50517 (2.64904)
| > log_mle: 0.61377 (0.65999)
| > loss_dur: 1.89140 (1.98905)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.41112 (4.76648)
| > current_lr: 0.00000
| > step_time: 0.85910 (0.60910)
| > loader_time: 2.99990 (2.53192)
 --> STEP: 376/406 -- GLOBAL_STEP: 2000
| > loss: 2.43601 (2.63688)
| > log_mle: 0.60993 (0.65655)
| > loss_dur: 1.82608 (1.98032)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.26871 (4.73842)
| > current_lr: 0.00000
| > step_time: 0.91590 (0.62982)
| > loader_time: 2.86120 (2.57925)
 --> STEP: 401/406 -- GLOBAL_STEP: 2025
| > loss: 2.41235 (2.62498)
| > log_mle: 0.60259 (0.65324)
| > loss_dur: 1.80976 (1.97174)
| > amp_scaler: 32768.00000 (16996.86783)
| > grad_norm: 4.21382 (4.71053)
| > current_lr: 0.00000
| > step_time: 1.16610 (0.66068)
| > loader_time: 3.42280 (2.62975)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.18578 (-0.02564)
| > avg_loss: 2.34633 (-0.45565)
| > avg_log_mle: 0.59978 (-0.09732)
| > avg_loss_dur: 1.74655 (-0.35832)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_2030.pth
 > EPOCH: 5/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 07:31:11) 
 --> STEP: 20/406 -- GLOBAL_STEP: 2050
| > loss: 2.32632 (2.39752)
| > log_mle: 0.62514 (0.61732)
| > loss_dur: 1.70118 (1.78020)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 4.13014 (4.16269)
| > current_lr: 0.00000
| > step_time: 0.61280 (0.30739)
| > loader_time: 2.20610 (1.74694)
 --> STEP: 45/406 -- GLOBAL_STEP: 2075
| > loss: 2.37442 (2.37738)
| > log_mle: 0.61136 (0.61411)
| > loss_dur: 1.76306 (1.76327)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 4.12018 (4.14276)
| > current_lr: 0.00000
| > step_time: 0.45660 (0.34062)
| > loader_time: 2.55210 (1.96832)
 --> STEP: 70/406 -- GLOBAL_STEP: 2100
| > loss: 2.31845 (2.36988)
| > log_mle: 0.58928 (0.60870)
| > loss_dur: 1.72917 (1.76118)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 4.01500 (4.12811)
| > current_lr: 0.00000
| > step_time: 0.41150 (0.37417)
| > loader_time: 2.44440 (2.02950)
 --> STEP: 95/406 -- GLOBAL_STEP: 2125
| > loss: 2.27078 (2.35859)
| > log_mle: 0.58644 (0.60354)
| > loss_dur: 1.68433 (1.75505)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.98636 (4.11155)
| > current_lr: 0.00000
| > step_time: 0.44350 (0.40353)
| > loader_time: 2.15350 (2.08449)
 --> STEP: 120/406 -- GLOBAL_STEP: 2150
| > loss: 2.28870 (2.34508)
| > log_mle: 0.57209 (0.59878)
| > loss_dur: 1.71661 (1.74630)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 4.00643 (4.08450)
| > current_lr: 0.00000
| > step_time: 0.54960 (0.43083)
| > loader_time: 2.14420 (2.09590)
 --> STEP: 145/406 -- GLOBAL_STEP: 2175
| > loss: 2.21042 (2.33611)
| > log_mle: 0.57014 (0.59404)
| > loss_dur: 1.64029 (1.74207)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.86027 (4.06902)
| > current_lr: 0.00000
| > step_time: 0.59390 (0.45372)
| > loader_time: 2.34310 (2.12068)
 --> STEP: 170/406 -- GLOBAL_STEP: 2200
| > loss: 2.24571 (2.32535)
| > log_mle: 0.55950 (0.58987)
| > loss_dur: 1.68621 (1.73548)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.90860 (4.04696)
| > current_lr: 0.00000
| > step_time: 0.53940 (0.47304)
| > loader_time: 2.58390 (2.18627)
 --> STEP: 195/406 -- GLOBAL_STEP: 2225
| > loss: 2.23841 (2.31468)
| > log_mle: 0.56599 (0.58576)
| > loss_dur: 1.67242 (1.72891)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.86637 (4.02464)
| > current_lr: 0.00000
| > step_time: 0.62200 (0.49047)
| > loader_time: 2.60360 (2.26200)
 --> STEP: 220/406 -- GLOBAL_STEP: 2250
| > loss: 2.22180 (2.30328)
| > log_mle: 0.53867 (0.58184)
| > loss_dur: 1.68313 (1.72145)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.81014 (4.00162)
| > current_lr: 0.00000
| > step_time: 0.63750 (0.50699)
| > loader_time: 2.61390 (2.32729)
 --> STEP: 245/406 -- GLOBAL_STEP: 2275
| > loss: 2.20323 (2.29394)
| > log_mle: 0.54924 (0.57805)
| > loss_dur: 1.65400 (1.71589)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.79550 (3.98261)
| > current_lr: 0.00000
| > step_time: 0.68220 (0.52334)
| > loader_time: 3.00010 (2.38975)
 --> STEP: 270/406 -- GLOBAL_STEP: 2300
| > loss: 2.18591 (2.28371)
| > log_mle: 0.53678 (0.57434)
| > loss_dur: 1.64914 (1.70937)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.74888 (3.96214)
| > current_lr: 0.00000
| > step_time: 0.72950 (0.54111)
| > loader_time: 2.66320 (2.42543)
 --> STEP: 295/406 -- GLOBAL_STEP: 2325
| > loss: 2.16205 (2.27391)
| > log_mle: 0.53249 (0.57075)
| > loss_dur: 1.62956 (1.70315)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.71011 (3.94190)
| > current_lr: 0.00000
| > step_time: 0.80610 (0.56175)
| > loader_time: 2.86540 (2.44789)
 --> STEP: 320/406 -- GLOBAL_STEP: 2350
| > loss: 2.13325 (2.26440)
| > log_mle: 0.53064 (0.56733)
| > loss_dur: 1.60261 (1.69707)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.68975 (3.92360)
| > current_lr: 0.00000
| > step_time: 0.75920 (0.57878)
| > loader_time: 2.99530 (2.47226)
 --> STEP: 345/406 -- GLOBAL_STEP: 2375
| > loss: 2.16305 (2.25604)
| > log_mle: 0.51063 (0.56382)
| > loss_dur: 1.65242 (1.69222)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.72070 (3.90668)
| > current_lr: 0.00000
| > step_time: 0.83260 (0.59704)
| > loader_time: 2.99060 (2.51815)
 --> STEP: 370/406 -- GLOBAL_STEP: 2400
| > loss: 2.09848 (2.24708)
| > log_mle: 0.50447 (0.56045)
| > loss_dur: 1.59401 (1.68663)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.59549 (3.89014)
| > current_lr: 0.00000
| > step_time: 1.01260 (0.61554)
| > loader_time: 2.78670 (2.56226)
 --> STEP: 395/406 -- GLOBAL_STEP: 2425
| > loss: 2.10066 (2.23806)
| > log_mle: 0.50259 (0.55727)
| > loss_dur: 1.59806 (1.68080)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.65250 (3.87293)
| > current_lr: 0.00000
| > step_time: 1.20210 (0.64160)
| > loader_time: 3.23820 (2.60756)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.10739 (-0.07839)
| > avg_loss: 2.01928 (-0.32706)
| > avg_log_mle: 0.50687 (-0.09291)
| > avg_loss_dur: 1.51240 (-0.23415)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_2436.pth
 > EPOCH: 6/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 07:54:00) 
 --> STEP: 14/406 -- GLOBAL_STEP: 2450
| > loss: 2.07627 (2.08896)
| > log_mle: 0.51377 (0.52882)
| > loss_dur: 1.56250 (1.56014)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.59623 (3.55583)
| > current_lr: 0.00000
| > step_time: 0.23050 (0.27280)
| > loader_time: 1.48280 (1.40424)
 --> STEP: 39/406 -- GLOBAL_STEP: 2475
| > loss: 2.04526 (2.06515)
| > log_mle: 0.52449 (0.52664)
| > loss_dur: 1.52077 (1.53851)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.42434 (3.52871)
| > current_lr: 0.00000
| > step_time: 0.39060 (0.31894)
| > loader_time: 2.39020 (1.67910)
 --> STEP: 64/406 -- GLOBAL_STEP: 2500
| > loss: 1.95181 (2.05596)
| > log_mle: 0.52202 (0.52228)
| > loss_dur: 1.42979 (1.53368)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.33448 (3.52275)
| > current_lr: 0.00000
| > step_time: 0.45940 (0.35870)
| > loader_time: 1.88890 (1.78787)
 --> STEP: 89/406 -- GLOBAL_STEP: 2525
| > loss: 2.04461 (2.05266)
| > log_mle: 0.50531 (0.51754)
| > loss_dur: 1.53930 (1.53512)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.49105 (3.52939)
| > current_lr: 0.00000
| > step_time: 0.47620 (0.39132)
| > loader_time: 2.16490 (1.86330)
 --> STEP: 114/406 -- GLOBAL_STEP: 2550
| > loss: 2.01263 (2.04373)
| > log_mle: 0.48527 (0.51295)
| > loss_dur: 1.52736 (1.53078)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.46784 (3.52018)
| > current_lr: 0.00000
| > step_time: 0.55040 (0.42217)
| > loader_time: 1.94480 (1.89878)
 --> STEP: 139/406 -- GLOBAL_STEP: 2575
| > loss: 2.04148 (2.03901)
| > log_mle: 0.48638 (0.50869)
| > loss_dur: 1.55510 (1.53033)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.52272 (3.51866)
| > current_lr: 0.00000
| > step_time: 0.52860 (0.44776)
| > loader_time: 2.08100 (1.94636)
 --> STEP: 164/406 -- GLOBAL_STEP: 2600
| > loss: 2.01093 (2.03404)
| > log_mle: 0.48942 (0.50497)
| > loss_dur: 1.52151 (1.52907)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.47391 (3.51444)
| > current_lr: 0.00000
| > step_time: 0.57730 (0.47040)
| > loader_time: 2.80420 (2.04028)
 --> STEP: 189/406 -- GLOBAL_STEP: 2625
| > loss: 2.00893 (2.02843)
| > log_mle: 0.47288 (0.50142)
| > loss_dur: 1.53605 (1.52701)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.45250 (3.50698)
| > current_lr: 0.00000
| > step_time: 0.61870 (0.49225)
| > loader_time: 2.55990 (2.12734)
 --> STEP: 214/406 -- GLOBAL_STEP: 2650
| > loss: 1.95254 (2.02227)
| > log_mle: 0.47246 (0.49824)
| > loss_dur: 1.48008 (1.52404)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.41300 (3.49969)
| > current_lr: 0.00000
| > step_time: 0.67090 (0.51047)
| > loader_time: 2.60850 (2.17820)
 --> STEP: 239/406 -- GLOBAL_STEP: 2675
| > loss: 2.00469 (2.01757)
| > log_mle: 0.45472 (0.49510)
| > loss_dur: 1.54998 (1.52247)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.51217 (3.49622)
| > current_lr: 0.00000
| > step_time: 0.69440 (0.53091)
| > loader_time: 2.76690 (2.23221)
 --> STEP: 264/406 -- GLOBAL_STEP: 2700
| > loss: 1.98347 (2.01273)
| > log_mle: 0.46317 (0.49212)
| > loss_dur: 1.52030 (1.52061)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.46322 (3.49149)
| > current_lr: 0.00000
| > step_time: 0.75000 (0.55010)
| > loader_time: 2.48280 (2.26869)
 --> STEP: 289/406 -- GLOBAL_STEP: 2725
| > loss: 1.91773 (2.00786)
| > log_mle: 0.45712 (0.48919)
| > loss_dur: 1.46061 (1.51867)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.39969 (3.48598)
| > current_lr: 0.00000
| > step_time: 0.74230 (0.57001)
| > loader_time: 2.43200 (2.29983)
 --> STEP: 314/406 -- GLOBAL_STEP: 2750
| > loss: 1.91721 (2.00348)
| > log_mle: 0.44756 (0.48651)
| > loss_dur: 1.46965 (1.51697)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.39172 (3.48175)
| > current_lr: 0.00000
| > step_time: 0.80250 (0.59083)
| > loader_time: 3.22490 (2.33684)
 --> STEP: 339/406 -- GLOBAL_STEP: 2775
| > loss: 1.92716 (1.99946)
| > log_mle: 0.44777 (0.48377)
| > loss_dur: 1.47939 (1.51569)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.41049 (3.47910)
| > current_lr: 0.00000
| > step_time: 0.85750 (0.61535)
| > loader_time: 3.68520 (2.39520)
 --> STEP: 364/406 -- GLOBAL_STEP: 2800
| > loss: 1.94982 (1.99564)
| > log_mle: 0.44866 (0.48105)
| > loss_dur: 1.50116 (1.51459)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.45689 (3.47657)
| > current_lr: 0.00000
| > step_time: 0.90490 (0.63895)
| > loader_time: 2.73300 (2.43614)
 --> STEP: 389/406 -- GLOBAL_STEP: 2825
| > loss: 1.92435 (1.99092)
| > log_mle: 0.43361 (0.47854)
| > loss_dur: 1.49074 (1.51237)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.44428 (3.47275)
| > current_lr: 0.00000
| > step_time: 0.93950 (0.65929)
| > loader_time: 2.84510 (2.46847)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.05160 (-0.05579)
| > avg_loss: 1.84788 (-0.17140)
| > avg_log_mle: 0.44069 (-0.06619)
| > avg_loss_dur: 1.40719 (-0.10521)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_2842.pth
 > EPOCH: 7/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 08:16:03) 
 --> STEP: 8/406 -- GLOBAL_STEP: 2850
| > loss: 1.96911 (1.96349)
| > log_mle: 0.46093 (0.46795)
| > loss_dur: 1.50819 (1.49554)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.48551 (3.47797)
| > current_lr: 0.00000
| > step_time: 0.28110 (0.27917)
| > loader_time: 1.49990 (1.29088)
 --> STEP: 33/406 -- GLOBAL_STEP: 2875
| > loss: 1.94136 (1.92067)
| > log_mle: 0.45958 (0.46323)
| > loss_dur: 1.48178 (1.45744)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.47601 (3.42534)
| > current_lr: 0.00000
| > step_time: 0.37700 (0.31595)
| > loader_time: 2.39950 (1.66749)
 --> STEP: 58/406 -- GLOBAL_STEP: 2900
| > loss: 1.93873 (1.90948)
| > log_mle: 0.45079 (0.45991)
| > loss_dur: 1.48793 (1.44957)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.46104 (3.40913)
| > current_lr: 0.00000
| > step_time: 0.38810 (0.35485)
| > loader_time: 2.20780 (1.79926)
 --> STEP: 83/406 -- GLOBAL_STEP: 2925
| > loss: 1.90904 (1.90316)
| > log_mle: 0.43886 (0.45571)
| > loss_dur: 1.47018 (1.44745)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.47968 (3.40773)
| > current_lr: 0.00000
| > step_time: 0.48130 (0.38773)
| > loader_time: 2.24530 (1.88041)
 --> STEP: 108/406 -- GLOBAL_STEP: 2950
| > loss: 1.82908 (1.89354)
| > log_mle: 0.43417 (0.45163)
| > loss_dur: 1.39490 (1.44191)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.30387 (3.39798)
| > current_lr: 0.00000
| > step_time: 0.47450 (0.41515)
| > loader_time: 2.42770 (1.94447)
 --> STEP: 133/406 -- GLOBAL_STEP: 2975
| > loss: 1.86270 (1.88712)
| > log_mle: 0.42606 (0.44763)
| > loss_dur: 1.43665 (1.43949)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.42021 (3.39358)
| > current_lr: 0.00000
| > step_time: 0.51100 (0.43871)
| > loader_time: 1.89930 (1.99278)
 --> STEP: 158/406 -- GLOBAL_STEP: 3000
| > loss: 1.86250 (1.88376)
| > log_mle: 0.41751 (0.44437)
| > loss_dur: 1.44499 (1.43939)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.43428 (3.39362)
| > current_lr: 0.00000
| > step_time: 0.51520 (0.46005)
| > loader_time: 2.52260 (2.04280)
 --> STEP: 183/406 -- GLOBAL_STEP: 3025
| > loss: 1.83292 (1.87877)
| > log_mle: 0.41893 (0.44134)
| > loss_dur: 1.41399 (1.43743)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.36544 (3.38969)
| > current_lr: 0.00000
| > step_time: 0.62150 (0.47857)
| > loader_time: 2.50240 (2.12756)
 --> STEP: 208/406 -- GLOBAL_STEP: 3050
| > loss: 1.78827 (1.87383)
| > log_mle: 0.40539 (0.43844)
| > loss_dur: 1.38288 (1.43539)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.32563 (3.38419)
| > current_lr: 0.00000
| > step_time: 0.63180 (0.49634)
| > loader_time: 2.38040 (2.16925)
 --> STEP: 233/406 -- GLOBAL_STEP: 3075
| > loss: 1.83761 (1.86837)
| > log_mle: 0.41203 (0.43569)
| > loss_dur: 1.42558 (1.43268)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.33179 (3.37878)
| > current_lr: 0.00000
| > step_time: 0.65630 (0.51538)
| > loader_time: 2.37380 (2.19834)
 --> STEP: 258/406 -- GLOBAL_STEP: 3100
| > loss: 1.85241 (1.86407)
| > log_mle: 0.40924 (0.43311)
| > loss_dur: 1.44316 (1.43096)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.39369 (3.37558)
| > current_lr: 0.00000
| > step_time: 0.74990 (0.53692)
| > loader_time: 2.80250 (2.23514)
 --> STEP: 283/406 -- GLOBAL_STEP: 3125
| > loss: 1.79562 (1.85885)
| > log_mle: 0.39832 (0.43056)
| > loss_dur: 1.39730 (1.42829)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.30349 (3.36852)
| > current_lr: 0.00000
| > step_time: 0.74230 (0.55632)
| > loader_time: 2.65480 (2.27261)
 --> STEP: 308/406 -- GLOBAL_STEP: 3150
| > loss: 1.78168 (1.85450)
| > log_mle: 0.39840 (0.42827)
| > loss_dur: 1.38327 (1.42624)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.29173 (3.36365)
| > current_lr: 0.00000
| > step_time: 0.73790 (0.57741)
| > loader_time: 3.03090 (2.32942)
 --> STEP: 333/406 -- GLOBAL_STEP: 3175
| > loss: 1.83408 (1.84947)
| > log_mle: 0.39156 (0.42598)
| > loss_dur: 1.44252 (1.42349)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.37923 (3.35843)
| > current_lr: 0.00000
| > step_time: 0.76510 (0.59347)
| > loader_time: 3.18340 (2.39377)
 --> STEP: 358/406 -- GLOBAL_STEP: 3200
| > loss: 1.71611 (1.84539)
| > log_mle: 0.37494 (0.42362)
| > loss_dur: 1.34117 (1.42177)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.19474 (3.35654)
| > current_lr: 0.00000
| > step_time: 0.87450 (0.61329)
| > loader_time: 3.12700 (2.43973)
 --> STEP: 383/406 -- GLOBAL_STEP: 3225
| > loss: 1.77768 (1.83987)
| > log_mle: 0.39020 (0.42149)
| > loss_dur: 1.38748 (1.41838)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.25907 (3.34852)
| > current_lr: 0.00000
| > step_time: 1.49200 (0.63222)
| > loader_time: 3.90140 (2.47648)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.14087 (+0.08927)
| > avg_loss: 1.68739 (-0.16049)
| > avg_log_mle: 0.39035 (-0.05033)
| > avg_loss_dur: 1.29703 (-0.11016)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_3248.pth
 > EPOCH: 8/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 08:38:02) 
 --> STEP: 2/406 -- GLOBAL_STEP: 3250
| > loss: 1.84794 (1.84990)
| > log_mle: 0.42129 (0.41605)
| > loss_dur: 1.42665 (1.43385)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.44622 (3.49590)
| > current_lr: 0.00000
| > step_time: 0.28890 (0.28988)
| > loader_time: 1.56430 (1.40669)
 --> STEP: 27/406 -- GLOBAL_STEP: 3275
| > loss: 1.70309 (1.76733)
| > log_mle: 0.41906 (0.41654)
| > loss_dur: 1.28403 (1.35079)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.18575 (3.28146)
| > current_lr: 0.00000
| > step_time: 0.28630 (0.29704)
| > loader_time: 1.72530 (1.60817)
 --> STEP: 52/406 -- GLOBAL_STEP: 3300
| > loss: 1.71644 (1.74735)
| > log_mle: 0.40328 (0.41352)
| > loss_dur: 1.31316 (1.33383)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.11168 (3.22014)
| > current_lr: 0.00000
| > step_time: 0.45840 (0.34316)
| > loader_time: 1.89590 (1.79496)
 --> STEP: 77/406 -- GLOBAL_STEP: 3325
| > loss: 1.73448 (1.73563)
| > log_mle: 0.40530 (0.40922)
| > loss_dur: 1.32918 (1.32641)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.16931 (3.19617)
| > current_lr: 0.00000
| > step_time: 0.53400 (0.37952)
| > loader_time: 2.17930 (1.88825)
 --> STEP: 102/406 -- GLOBAL_STEP: 3350
| > loss: 1.70873 (1.72368)
| > log_mle: 0.37598 (0.40487)
| > loss_dur: 1.33275 (1.31881)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.18634 (3.17941)
| > current_lr: 0.00000
| > step_time: 0.52590 (0.40938)
| > loader_time: 2.17620 (1.93153)
 --> STEP: 127/406 -- GLOBAL_STEP: 3375
| > loss: 1.67640 (1.71273)
| > log_mle: 0.37993 (0.40098)
| > loss_dur: 1.29646 (1.31175)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.16501 (3.15668)
| > current_lr: 0.00000
| > step_time: 0.51500 (0.43232)
| > loader_time: 2.42380 (1.97267)
 --> STEP: 152/406 -- GLOBAL_STEP: 3400
| > loss: 1.65853 (1.70660)
| > log_mle: 0.37716 (0.39754)
| > loss_dur: 1.28137 (1.30906)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.01159 (3.14658)
| > current_lr: 0.00000
| > step_time: 0.57630 (0.45423)
| > loader_time: 2.37160 (2.01706)
 --> STEP: 177/406 -- GLOBAL_STEP: 3425
| > loss: 1.64946 (1.69962)
| > log_mle: 0.36582 (0.39452)
| > loss_dur: 1.28364 (1.30510)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.07432 (3.13534)
| > current_lr: 0.00000
| > step_time: 0.64430 (0.47297)
| > loader_time: 2.60340 (2.12425)
 --> STEP: 202/406 -- GLOBAL_STEP: 3450
| > loss: 1.63416 (1.69252)
| > log_mle: 0.37142 (0.39168)
| > loss_dur: 1.26274 (1.30084)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.97809 (3.11988)
| > current_lr: 0.00000
| > step_time: 0.61060 (0.49242)
| > loader_time: 2.80140 (2.18575)
 --> STEP: 227/406 -- GLOBAL_STEP: 3475
| > loss: 1.62764 (1.68514)
| > log_mle: 0.37852 (0.38900)
| > loss_dur: 1.24913 (1.29614)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.92160 (3.10267)
| > current_lr: 0.00000
| > step_time: 0.65890 (0.51124)
| > loader_time: 2.34810 (2.22160)
 --> STEP: 252/406 -- GLOBAL_STEP: 3500
| > loss: 1.61722 (1.67925)
| > log_mle: 0.35895 (0.38643)
| > loss_dur: 1.25827 (1.29282)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.89135 (3.08984)
| > current_lr: 0.00000
| > step_time: 0.71100 (0.53094)
| > loader_time: 2.59120 (2.24913)
 --> STEP: 277/406 -- GLOBAL_STEP: 3525
| > loss: 1.59856 (1.67291)
| > log_mle: 0.36055 (0.38393)
| > loss_dur: 1.23801 (1.28898)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.92326 (3.07682)
| > current_lr: 0.00000
| > step_time: 0.78250 (0.55436)
| > loader_time: 2.32180 (2.27453)
 --> STEP: 302/406 -- GLOBAL_STEP: 3550
| > loss: 1.59327 (1.66705)
| > log_mle: 0.35343 (0.38167)
| > loss_dur: 1.23984 (1.28538)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.92698 (3.06434)
| > current_lr: 0.00000
| > step_time: 0.78710 (0.57367)
| > loader_time: 2.35590 (2.29975)
 --> STEP: 327/406 -- GLOBAL_STEP: 3575
| > loss: 1.58036 (1.66084)
| > log_mle: 0.35204 (0.37953)
| > loss_dur: 1.22833 (1.28131)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.97569 (3.05265)
| > current_lr: 0.00000
| > step_time: 0.78480 (0.59303)
| > loader_time: 3.39530 (2.33705)
 --> STEP: 352/406 -- GLOBAL_STEP: 3600
| > loss: 1.58020 (1.65628)
| > log_mle: 0.34938 (0.37730)
| > loss_dur: 1.23082 (1.27899)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.89964 (3.04428)
| > current_lr: 0.00000
| > step_time: 0.87750 (0.61284)
| > loader_time: 2.76590 (2.37554)
 --> STEP: 377/406 -- GLOBAL_STEP: 3625
| > loss: 1.58888 (1.65010)
| > log_mle: 0.34446 (0.37511)
| > loss_dur: 1.24442 (1.27499)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.95227 (3.03088)
| > current_lr: 0.00000
| > step_time: 0.96200 (0.63442)
| > loader_time: 3.11010 (2.41044)
 --> STEP: 402/406 -- GLOBAL_STEP: 3650
| > loss: 1.53839 (1.64419)
| > log_mle: 0.33520 (0.37307)
| > loss_dur: 1.20319 (1.27112)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.84542 (3.01778)
| > current_lr: 0.00000
| > step_time: 1.04060 (0.66372)
| > loader_time: 3.07020 (2.44366)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.07849 (-0.06238)
| > avg_loss: 1.49742 (-0.18997)
| > avg_log_mle: 0.34547 (-0.04488)
| > avg_loss_dur: 1.15194 (-0.14509)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_3654.pth
 > EPOCH: 9/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 08:59:42) 
 --> STEP: 21/406 -- GLOBAL_STEP: 3675
| > loss: 1.49872 (1.57799)
| > log_mle: 0.36793 (0.37336)
| > loss_dur: 1.13079 (1.20463)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.73898 (2.91897)
| > current_lr: 0.00000
| > step_time: 0.26220 (0.31256)
| > loader_time: 1.60250 (1.40686)
 --> STEP: 46/406 -- GLOBAL_STEP: 3700
| > loss: 1.56281 (1.55377)
| > log_mle: 0.36506 (0.37138)
| > loss_dur: 1.19775 (1.18238)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.76536 (2.83050)
| > current_lr: 0.00000
| > step_time: 0.39350 (0.34399)
| > loader_time: 1.68410 (1.64451)
 --> STEP: 71/406 -- GLOBAL_STEP: 3725
| > loss: 1.51894 (1.54579)
| > log_mle: 0.35790 (0.36630)
| > loss_dur: 1.16105 (1.17950)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.99218 (2.82838)
| > current_lr: 0.00000
| > step_time: 0.48710 (0.37561)
| > loader_time: 1.78850 (1.73678)
 --> STEP: 96/406 -- GLOBAL_STEP: 3750
| > loss: 1.53003 (1.53496)
| > log_mle: 0.34705 (0.36160)
| > loss_dur: 1.18298 (1.17336)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.92493 (2.80869)
| > current_lr: 0.00000
| > step_time: 0.51530 (0.40348)
| > loader_time: 2.02150 (1.78785)
 --> STEP: 121/406 -- GLOBAL_STEP: 3775
| > loss: 1.51401 (1.52598)
| > log_mle: 0.34361 (0.35769)
| > loss_dur: 1.17040 (1.16829)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.78462 (2.78650)
| > current_lr: 0.00000
| > step_time: 0.56140 (0.44794)
| > loader_time: 1.77920 (1.81979)
 --> STEP: 146/406 -- GLOBAL_STEP: 3800
| > loss: 1.48767 (1.52002)
| > log_mle: 0.34445 (0.35402)
| > loss_dur: 1.14323 (1.16600)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.71470 (2.78053)
| > current_lr: 0.00000
| > step_time: 0.60390 (0.46943)
| > loader_time: 2.48690 (1.87265)
 --> STEP: 171/406 -- GLOBAL_STEP: 3825
| > loss: 1.45805 (1.51447)
| > log_mle: 0.32992 (0.35096)
| > loss_dur: 1.12812 (1.16351)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.66298 (2.76658)
| > current_lr: 0.00000
| > step_time: 0.61050 (0.48842)
| > loader_time: 2.72310 (1.98629)
 --> STEP: 196/406 -- GLOBAL_STEP: 3850
| > loss: 1.45783 (1.50941)
| > log_mle: 0.32256 (0.34799)
| > loss_dur: 1.13528 (1.16142)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.65209 (2.75529)
| > current_lr: 0.00000
| > step_time: 0.66210 (0.50768)
| > loader_time: 2.71610 (2.07140)
 --> STEP: 221/406 -- GLOBAL_STEP: 3875
| > loss: 1.47014 (1.50357)
| > log_mle: 0.33055 (0.34530)
| > loss_dur: 1.13959 (1.15827)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.65247 (2.74201)
| > current_lr: 0.00000
| > step_time: 0.60850 (0.52547)
| > loader_time: 2.63390 (2.11945)
 --> STEP: 246/406 -- GLOBAL_STEP: 3900
| > loss: 1.43280 (1.49877)
| > log_mle: 0.31848 (0.34271)
| > loss_dur: 1.11432 (1.15606)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.69570 (2.73298)
| > current_lr: 0.00000
| > step_time: 0.71550 (0.54282)
| > loader_time: 2.29480 (2.15506)
 --> STEP: 271/406 -- GLOBAL_STEP: 3925
| > loss: 1.45462 (1.49424)
| > log_mle: 0.30196 (0.34024)
| > loss_dur: 1.15267 (1.15400)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.92253 (2.72909)
| > current_lr: 0.00000
| > step_time: 0.72700 (0.56259)
| > loader_time: 2.39850 (2.19385)
 --> STEP: 296/406 -- GLOBAL_STEP: 3950
| > loss: 1.46072 (1.49001)
| > log_mle: 0.31431 (0.33795)
| > loss_dur: 1.14641 (1.15205)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.61953 (2.72566)
| > current_lr: 0.00000
| > step_time: 0.76590 (0.58155)
| > loader_time: 2.15570 (2.22230)
 --> STEP: 321/406 -- GLOBAL_STEP: 3975
| > loss: 1.42671 (1.48536)
| > log_mle: 0.30693 (0.33581)
| > loss_dur: 1.11977 (1.14955)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.61382 (2.72431)
| > current_lr: 0.00000
| > step_time: 0.79400 (0.60064)
| > loader_time: 3.23400 (2.27174)
 --> STEP: 346/406 -- GLOBAL_STEP: 4000
| > loss: 1.41643 (1.48190)
| > log_mle: 0.30933 (0.33356)
| > loss_dur: 1.10709 (1.14835)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.54829 (2.71890)
| > current_lr: 0.00000
| > step_time: 0.90700 (0.61771)
| > loader_time: 3.31090 (2.33149)
 --> STEP: 371/406 -- GLOBAL_STEP: 4025
| > loss: 1.41167 (1.47760)
| > log_mle: 0.29983 (0.33137)
| > loss_dur: 1.11184 (1.14623)
| > amp_scaler: 32768.00000 (32856.32345)
| > grad_norm: 2.54190 (2.70327)
| > current_lr: 0.00000
| > step_time: 0.88050 (0.63619)
| > loader_time: 3.20440 (2.37874)
 --> STEP: 396/406 -- GLOBAL_STEP: 4050
| > loss: 1.40678 (1.47328)
| > log_mle: 0.28764 (0.32938)
| > loss_dur: 1.11914 (1.14391)
| > amp_scaler: 32768.00000 (32850.74747)
| > grad_norm: 2.59690 (2.69736)
| > current_lr: 0.00000
| > step_time: 1.15280 (0.66131)
| > loader_time: 3.57930 (2.41747)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.09103 (+0.01254)
| > avg_loss: 1.36604 (-0.13137)
| > avg_log_mle: 0.30225 (-0.04322)
| > avg_loss_dur: 1.06379 (-0.08816)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_4060.pth
 > EPOCH: 10/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 09:21:17) 
 --> STEP: 15/406 -- GLOBAL_STEP: 4075
| > loss: 1.42459 (1.44612)
| > log_mle: 0.32219 (0.33019)
| > loss_dur: 1.10240 (1.11593)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.65287 (2.73155)
| > current_lr: 0.00000
| > step_time: 0.29900 (0.28946)
| > loader_time: 1.53250 (1.39406)
 --> STEP: 40/406 -- GLOBAL_STEP: 4100
| > loss: 1.36887 (1.41465)
| > log_mle: 0.33738 (0.32948)
| > loss_dur: 1.03149 (1.08517)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.42406 (2.60906)
| > current_lr: 0.00000
| > step_time: 0.38680 (0.33503)
| > loader_time: 1.90710 (1.66163)
 --> STEP: 65/406 -- GLOBAL_STEP: 4125
| > loss: 1.41261 (1.40471)
| > log_mle: 0.31738 (0.32524)
| > loss_dur: 1.09523 (1.07947)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.51945 (2.58182)
| > current_lr: 0.00000
| > step_time: 0.50620 (0.37851)
| > loader_time: 1.78520 (1.75945)
 --> STEP: 90/406 -- GLOBAL_STEP: 4150
| > loss: 1.37216 (1.39692)
| > log_mle: 0.30640 (0.32073)
| > loss_dur: 1.06575 (1.07619)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.50864 (2.56483)
| > current_lr: 0.00000
| > step_time: 0.51750 (0.40811)
| > loader_time: 2.37720 (1.85806)
 --> STEP: 115/406 -- GLOBAL_STEP: 4175
| > loss: 1.38216 (1.38773)
| > log_mle: 0.31031 (0.31631)
| > loss_dur: 1.07185 (1.07142)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.46989 (2.55213)
| > current_lr: 0.00000
| > step_time: 0.58110 (0.43439)
| > loader_time: 2.13700 (1.88739)
 --> STEP: 140/406 -- GLOBAL_STEP: 4200
| > loss: 1.34752 (1.38247)
| > log_mle: 0.28762 (0.31243)
| > loss_dur: 1.05990 (1.07005)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.55189 (2.55990)
| > current_lr: 0.00000
| > step_time: 0.56830 (0.45717)
| > loader_time: 2.08320 (1.90449)
 --> STEP: 165/406 -- GLOBAL_STEP: 4225
| > loss: 1.34131 (1.37790)
| > log_mle: 0.28218 (0.30929)
| > loss_dur: 1.05912 (1.06861)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.48666 (2.55559)
| > current_lr: 0.00000
| > step_time: 0.57980 (0.47895)
| > loader_time: 2.76290 (1.97593)
 --> STEP: 190/406 -- GLOBAL_STEP: 4250
| > loss: 1.35345 (1.37374)
| > log_mle: 0.27890 (0.30625)
| > loss_dur: 1.07454 (1.06749)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.69455 (2.56084)
| > current_lr: 0.00000
| > step_time: 0.65310 (0.49803)
| > loader_time: 2.38080 (2.04050)
 --> STEP: 215/406 -- GLOBAL_STEP: 4275
| > loss: 1.32825 (1.36860)
| > log_mle: 0.28016 (0.30369)
| > loss_dur: 1.04809 (1.06491)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.58756 (2.55561)
| > current_lr: 0.00000
| > step_time: 0.61060 (0.51656)
| > loader_time: 2.56090 (2.10172)
 --> STEP: 240/406 -- GLOBAL_STEP: 4300
| > loss: 1.31147 (1.36449)
| > log_mle: 0.27203 (0.30111)
| > loss_dur: 1.03944 (1.06338)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.44012 (2.54704)
| > current_lr: 0.00000
| > step_time: 0.63150 (0.53437)
| > loader_time: 2.68430 (2.15565)
 --> STEP: 265/406 -- GLOBAL_STEP: 4325
| > loss: 1.32583 (1.36066)
| > log_mle: 0.27015 (0.29873)
| > loss_dur: 1.05569 (1.06193)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.50196 (2.53991)
| > current_lr: 0.00000
| > step_time: 0.67330 (0.55169)
| > loader_time: 2.65600 (2.19620)
 --> STEP: 290/406 -- GLOBAL_STEP: 4350
| > loss: 1.30914 (1.35701)
| > log_mle: 0.26071 (0.29641)
| > loss_dur: 1.04843 (1.06060)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.42991 (2.53274)
| > current_lr: 0.00000
| > step_time: 0.78430 (0.57045)
| > loader_time: 2.43250 (2.22027)
 --> STEP: 315/406 -- GLOBAL_STEP: 4375
| > loss: 1.28599 (1.35367)
| > log_mle: 0.25376 (0.29437)
| > loss_dur: 1.03223 (1.05930)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.40107 (2.52525)
| > current_lr: 0.00000
| > step_time: 0.77590 (0.59181)
| > loader_time: 2.79670 (2.25139)
 --> STEP: 340/406 -- GLOBAL_STEP: 4400
| > loss: 1.32872 (1.35074)
| > log_mle: 0.26691 (0.29233)
| > loss_dur: 1.06181 (1.05841)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.86391 (2.53015)
| > current_lr: 0.00000
| > step_time: 0.87200 (0.61023)
| > loader_time: 2.70990 (2.29055)
 --> STEP: 365/406 -- GLOBAL_STEP: 4425
| > loss: 1.25242 (1.34737)
| > log_mle: 0.26373 (0.29020)
| > loss_dur: 0.98869 (1.05717)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.29143 (2.53519)
| > current_lr: 0.00000
| > step_time: 0.89890 (0.62864)
| > loader_time: 2.39630 (2.33699)
 --> STEP: 390/406 -- GLOBAL_STEP: 4450
| > loss: 1.29040 (1.34365)
| > log_mle: 0.25125 (0.28826)
| > loss_dur: 1.03915 (1.05539)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.54572 (2.53367)
| > current_lr: 0.00000
| > step_time: 1.07500 (0.65068)
| > loader_time: 2.78960 (2.36752)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.07129 (-0.01974)
| > avg_loss: 1.25202 (-0.11402)
| > avg_log_mle: 0.26251 (-0.03974)
| > avg_loss_dur: 0.98951 (-0.07428)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_4466.pth
 > EPOCH: 11/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 09:42:36) 
 --> STEP: 9/406 -- GLOBAL_STEP: 4475
| > loss: 1.34257 (1.33919)
| > log_mle: 0.29600 (0.29549)
| > loss_dur: 1.04657 (1.04370)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.53248 (2.58129)
| > current_lr: 0.00000
| > step_time: 0.30140 (0.29187)
| > loader_time: 1.26050 (1.29966)
 --> STEP: 34/406 -- GLOBAL_STEP: 4500
| > loss: 1.28956 (1.29893)
| > log_mle: 0.30031 (0.29030)
| > loss_dur: 0.98925 (1.00863)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.38411 (2.47360)
| > current_lr: 0.00000
| > step_time: 0.39080 (0.32303)
| > loader_time: 1.99910 (1.60834)
 --> STEP: 59/406 -- GLOBAL_STEP: 4525
| > loss: 1.25389 (1.28784)
| > log_mle: 0.26517 (0.28617)
| > loss_dur: 0.98872 (1.00167)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.72886 (2.45463)
| > current_lr: 0.00000
| > step_time: 0.49210 (0.36166)
| > loader_time: 1.84130 (1.71455)
 --> STEP: 84/406 -- GLOBAL_STEP: 4550
| > loss: 1.25189 (1.28053)
| > log_mle: 0.27123 (0.28208)
| > loss_dur: 0.98067 (0.99846)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.35870 (2.45077)
| > current_lr: 0.00000
| > step_time: 0.52550 (0.39617)
| > loader_time: 1.70420 (1.80977)
 --> STEP: 109/406 -- GLOBAL_STEP: 4575
| > loss: 1.23211 (1.27106)
| > log_mle: 0.25285 (0.27778)
| > loss_dur: 0.97926 (0.99328)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.57709 (2.43590)
| > current_lr: 0.00000
| > step_time: 0.54800 (0.42151)
| > loader_time: 2.06720 (1.85622)
 --> STEP: 134/406 -- GLOBAL_STEP: 4600
| > loss: 1.25460 (1.26537)
| > log_mle: 0.25480 (0.27382)
| > loss_dur: 0.99980 (0.99155)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.38852 (2.44857)
| > current_lr: 0.00000
| > step_time: 0.55990 (0.44367)
| > loader_time: 2.09420 (1.87612)
 --> STEP: 159/406 -- GLOBAL_STEP: 4625
| > loss: 1.23639 (1.26185)
| > log_mle: 0.25376 (0.27070)
| > loss_dur: 0.98263 (0.99115)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.26595 (2.44123)
| > current_lr: 0.00000
| > step_time: 0.55710 (0.46468)
| > loader_time: 2.69590 (1.92138)
 --> STEP: 184/406 -- GLOBAL_STEP: 4650
| > loss: 1.27520 (1.25801)
| > log_mle: 0.23324 (0.26770)
| > loss_dur: 1.04196 (0.99032)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.44802 (2.43277)
| > current_lr: 0.00000
| > step_time: 0.67700 (0.48534)
| > loader_time: 2.04390 (1.98034)
 --> STEP: 209/406 -- GLOBAL_STEP: 4675
| > loss: 1.23888 (1.25394)
| > log_mle: 0.23535 (0.26500)
| > loss_dur: 1.00353 (0.98894)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.33587 (2.43368)
| > current_lr: 0.00000
| > step_time: 0.58790 (0.50466)
| > loader_time: 2.40440 (2.01515)
 --> STEP: 234/406 -- GLOBAL_STEP: 4700
| > loss: 1.22883 (1.24960)
| > log_mle: 0.23179 (0.26233)
| > loss_dur: 0.99704 (0.98727)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.42160 (2.42645)
| > current_lr: 0.00000
| > step_time: 0.67960 (0.52404)
| > loader_time: 2.51970 (2.05319)
 --> STEP: 259/406 -- GLOBAL_STEP: 4725
| > loss: 1.20521 (1.24649)
| > log_mle: 0.22357 (0.25988)
| > loss_dur: 0.98164 (0.98661)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.34066 (2.41780)
| > current_lr: 0.00000
| > step_time: 0.79690 (0.54303)
| > loader_time: 3.15780 (2.10299)
 --> STEP: 284/406 -- GLOBAL_STEP: 4750
| > loss: 1.21444 (1.24298)
| > log_mle: 0.22858 (0.25755)
| > loss_dur: 0.98587 (0.98543)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.41069 (2.41238)
| > current_lr: 0.00000
| > step_time: 0.77780 (0.56307)
| > loader_time: 2.44820 (2.14532)
 --> STEP: 309/406 -- GLOBAL_STEP: 4775
| > loss: 1.18498 (1.24003)
| > log_mle: 0.23236 (0.25556)
| > loss_dur: 0.95262 (0.98447)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.46981 (2.41025)
| > current_lr: 0.00000
| > step_time: 0.89260 (0.58288)
| > loader_time: 2.55140 (2.18204)
 --> STEP: 334/406 -- GLOBAL_STEP: 4800
| > loss: 1.23048 (1.23705)
| > log_mle: 0.22302 (0.25355)
| > loss_dur: 1.00746 (0.98350)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.51359 (2.40824)
| > current_lr: 0.00000
| > step_time: 0.86220 (0.60250)
| > loader_time: 2.72580 (2.22189)
 --> STEP: 359/406 -- GLOBAL_STEP: 4825
| > loss: 1.17302 (1.23441)
| > log_mle: 0.22890 (0.25148)
| > loss_dur: 0.94412 (0.98293)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.30157 (2.41084)
| > current_lr: 0.00000
| > step_time: 0.90490 (0.62134)
| > loader_time: 2.82770 (2.27640)
 --> STEP: 384/406 -- GLOBAL_STEP: 4850
| > loss: 1.16129 (1.23105)
| > log_mle: 0.22923 (0.24960)
| > loss_dur: 0.93205 (0.98145)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.29501 (2.40804)
| > current_lr: 0.00000
| > step_time: 0.89280 (0.64200)
| > loader_time: 2.94830 (2.32658)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.09873 (+0.02744)
| > avg_loss: 1.14620 (-0.10582)
| > avg_log_mle: 0.22456 (-0.03795)
| > avg_loss_dur: 0.92164 (-0.06786)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_4872.pth
 > EPOCH: 12/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 10:03:30) 
 --> STEP: 3/406 -- GLOBAL_STEP: 4875
| > loss: 1.25120 (1.24528)
| > log_mle: 0.25092 (0.25100)
| > loss_dur: 1.00027 (0.99428)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.56650 (2.90351)
| > current_lr: 0.00000
| > step_time: 0.30070 (0.30155)
| > loader_time: 1.31660 (1.23587)
 --> STEP: 28/406 -- GLOBAL_STEP: 4900
| > loss: 1.17654 (1.18992)
| > log_mle: 0.24867 (0.25250)
| > loss_dur: 0.92787 (0.93742)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.17389 (2.50973)
| > current_lr: 0.00000
| > step_time: 0.36380 (0.31142)
| > loader_time: 1.93740 (1.48728)
 --> STEP: 53/406 -- GLOBAL_STEP: 4925
| > loss: 1.15333 (1.18015)
| > log_mle: 0.24664 (0.24935)
| > loss_dur: 0.90669 (0.93079)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.41848 (2.39002)
| > current_lr: 0.00000
| > step_time: 0.45560 (0.35889)
| > loader_time: 2.34120 (1.67044)
 --> STEP: 78/406 -- GLOBAL_STEP: 4950
| > loss: 1.12611 (1.17304)
| > log_mle: 0.23645 (0.24497)
| > loss_dur: 0.88966 (0.92807)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.14772 (2.35734)
| > current_lr: 0.00000
| > step_time: 0.38000 (0.39158)
| > loader_time: 1.65000 (1.72278)
 --> STEP: 103/406 -- GLOBAL_STEP: 4975
| > loss: 1.13303 (1.16578)
| > log_mle: 0.20101 (0.24031)
| > loss_dur: 0.93202 (0.92547)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.96942 (2.33709)
| > current_lr: 0.00000
| > step_time: 0.40750 (0.41898)
| > loader_time: 1.57430 (1.74268)
 --> STEP: 128/406 -- GLOBAL_STEP: 5000
| > loss: 1.17277 (1.15911)
| > log_mle: 0.21362 (0.23656)
| > loss_dur: 0.95915 (0.92256)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.28186 (2.36087)
| > current_lr: 0.00000
| > step_time: 0.58060 (0.44209)
| > loader_time: 1.85400 (1.77511)
 --> STEP: 153/406 -- GLOBAL_STEP: 5025
| > loss: 1.13765 (1.15570)
| > log_mle: 0.22049 (0.23332)
| > loss_dur: 0.91716 (0.92238)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.63199 (2.35428)
| > current_lr: 0.00000
| > step_time: 0.52450 (0.46280)
| > loader_time: 2.55750 (1.85892)
 --> STEP: 178/406 -- GLOBAL_STEP: 5050
| > loss: 1.12247 (1.15198)
| > log_mle: 0.22145 (0.23040)
| > loss_dur: 0.90103 (0.92158)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.72802 (2.36214)
| > current_lr: 0.00000
| > step_time: 0.55690 (0.48195)
| > loader_time: 2.03050 (1.93703)
 --> STEP: 203/406 -- GLOBAL_STEP: 5075
| > loss: 1.12946 (1.14824)
| > log_mle: 0.21736 (0.22768)
| > loss_dur: 0.91210 (0.92056)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.96276 (2.43284)
| > current_lr: 0.00000
| > step_time: 0.60150 (0.50045)
| > loader_time: 2.27760 (1.99759)
 --> STEP: 228/406 -- GLOBAL_STEP: 5100
| > loss: 1.13766 (1.14430)
| > log_mle: 0.19594 (0.22500)
| > loss_dur: 0.94172 (0.91930)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.57340 (2.44162)
| > current_lr: 0.00000
| > step_time: 0.69190 (0.52080)
| > loader_time: 2.16150 (2.04930)
 --> STEP: 253/406 -- GLOBAL_STEP: 5125
| > loss: 1.12917 (1.14129)
| > log_mle: 0.18708 (0.22253)
| > loss_dur: 0.94209 (0.91876)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.26057 (2.43592)
| > current_lr: 0.00000
| > step_time: 0.67690 (0.54006)
| > loader_time: 2.27300 (2.09187)
 --> STEP: 278/406 -- GLOBAL_STEP: 5150
| > loss: 1.09002 (1.13790)
| > log_mle: 0.21044 (0.22030)
| > loss_dur: 0.87958 (0.91760)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.08396 (2.42829)
| > current_lr: 0.00000
| > step_time: 0.78320 (0.56019)
| > loader_time: 2.29870 (2.11545)
 --> STEP: 303/406 -- GLOBAL_STEP: 5175
| > loss: 1.11645 (1.13511)
| > log_mle: 0.20075 (0.21823)
| > loss_dur: 0.91570 (0.91687)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.23935 (2.41150)
| > current_lr: 0.00000
| > step_time: 0.77750 (0.58203)
| > loader_time: 2.19950 (2.13815)
 --> STEP: 328/406 -- GLOBAL_STEP: 5200
| > loss: 1.13125 (1.13186)
| > log_mle: 0.19120 (0.21629)
| > loss_dur: 0.94005 (0.91558)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.18570 (2.39925)
| > current_lr: 0.00000
| > step_time: 0.83390 (0.60036)
| > loader_time: 2.76730 (2.17399)
 --> STEP: 353/406 -- GLOBAL_STEP: 5225
| > loss: 1.08862 (1.12975)
| > log_mle: 0.17697 (0.21424)
| > loss_dur: 0.91165 (0.91551)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.25039 (2.40600)
| > current_lr: 0.00000
| > step_time: 0.93980 (0.62179)
| > loader_time: 2.76200 (2.21695)
 --> STEP: 378/406 -- GLOBAL_STEP: 5250
| > loss: 1.08523 (1.12648)
| > log_mle: 0.19200 (0.21225)
| > loss_dur: 0.89322 (0.91424)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.18938 (2.39856)
| > current_lr: 0.00000
| > step_time: 0.85410 (0.64245)
| > loader_time: 3.08040 (2.25452)
 --> STEP: 403/406 -- GLOBAL_STEP: 5275
| > loss: 1.04455 (1.12316)
| > log_mle: 0.18261 (0.21035)
| > loss_dur: 0.86194 (0.91281)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.10067 (2.38826)
| > current_lr: 0.00000
| > step_time: 0.95120 (0.66131)
| > loader_time: 3.06690 (2.28946)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.10477 (+0.00604)
| > avg_loss: 1.04789 (-0.09831)
| > avg_log_mle: 0.18673 (-0.03783)
| > avg_loss_dur: 0.86116 (-0.06048)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_5278.pth
 > EPOCH: 13/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 10:24:02) 
 --> STEP: 22/406 -- GLOBAL_STEP: 5300
| > loss: 1.02650 (1.08775)
| > log_mle: 0.21650 (0.21637)
| > loss_dur: 0.81000 (0.87138)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.22766 (2.27365)
| > current_lr: 0.00000
| > step_time: 0.37660 (0.30410)
| > loader_time: 1.80980 (1.45334)
 --> STEP: 47/406 -- GLOBAL_STEP: 5325
| > loss: 1.09366 (1.07700)
| > log_mle: 0.19734 (0.21395)
| > loss_dur: 0.89632 (0.86305)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.46328 (2.26078)
| > current_lr: 0.00000
| > step_time: 0.35240 (0.34568)
| > loader_time: 1.96100 (1.67364)
 --> STEP: 72/406 -- GLOBAL_STEP: 5350
| > loss: 1.04907 (1.07063)
| > log_mle: 0.20828 (0.20932)
| > loss_dur: 0.84079 (0.86130)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.35592 (2.28033)
| > current_lr: 0.00000
| > step_time: 0.45040 (0.37802)
| > loader_time: 1.60750 (1.71310)
 --> STEP: 97/406 -- GLOBAL_STEP: 5375
| > loss: 1.02715 (1.06330)
| > log_mle: 0.19248 (0.20456)
| > loss_dur: 0.83467 (0.85875)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.53288 (2.31411)
| > current_lr: 0.00000
| > step_time: 0.53980 (0.40724)
| > loader_time: 1.81930 (1.75946)
 --> STEP: 122/406 -- GLOBAL_STEP: 5400
| > loss: 1.00207 (1.05670)
| > log_mle: 0.19442 (0.20072)
| > loss_dur: 0.80765 (0.85598)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.15784 (2.30115)
| > current_lr: 0.00000
| > step_time: 0.50270 (0.43074)
| > loader_time: 1.84370 (1.76714)
 --> STEP: 147/406 -- GLOBAL_STEP: 5425
| > loss: 1.04106 (1.05313)
| > log_mle: 0.18111 (0.19718)
| > loss_dur: 0.85995 (0.85596)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.17534 (2.28178)
| > current_lr: 0.00000
| > step_time: 0.53130 (0.45351)
| > loader_time: 2.01570 (1.80956)
 --> STEP: 172/406 -- GLOBAL_STEP: 5450
| > loss: 1.01768 (1.04924)
| > log_mle: 0.17793 (0.19426)
| > loss_dur: 0.83975 (0.85498)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 1.94584 (2.27083)
| > current_lr: 0.00000
| > step_time: 0.59570 (0.47222)
| > loader_time: 2.20620 (1.89277)
 --> STEP: 197/406 -- GLOBAL_STEP: 5475
| > loss: 1.02426 (1.04599)
| > log_mle: 0.17513 (0.19150)
| > loss_dur: 0.84913 (0.85450)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.39347 (2.30118)
| > current_lr: 0.00000
| > step_time: 0.56600 (0.49419)
| > loader_time: 2.32850 (1.94858)
 --> STEP: 222/406 -- GLOBAL_STEP: 5500
| > loss: 0.99649 (1.04196)
| > log_mle: 0.16359 (0.18896)
| > loss_dur: 0.83290 (0.85299)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.07585 (2.32388)
| > current_lr: 0.00000
| > step_time: 0.69410 (0.51076)
| > loader_time: 2.37010 (2.00060)
 --> STEP: 247/406 -- GLOBAL_STEP: 5525
| > loss: 0.98053 (1.03852)
| > log_mle: 0.18364 (0.18658)
| > loss_dur: 0.79689 (0.85194)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.20051 (2.30746)
| > current_lr: 0.00000
| > step_time: 0.78100 (0.52999)
| > loader_time: 2.40530 (2.04282)
 --> STEP: 272/406 -- GLOBAL_STEP: 5550
| > loss: 0.98957 (1.03533)
| > log_mle: 0.15604 (0.18419)
| > loss_dur: 0.83354 (0.85113)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.01190 (2.29004)
| > current_lr: 0.00000
| > step_time: 0.74140 (0.54761)
| > loader_time: 2.65060 (2.07874)
 --> STEP: 297/406 -- GLOBAL_STEP: 5575
| > loss: 0.99033 (1.03238)
| > log_mle: 0.16163 (0.18217)
| > loss_dur: 0.82869 (0.85021)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.01919 (2.29530)
| > current_lr: 0.00000
| > step_time: 0.72900 (0.56645)
| > loader_time: 2.70180 (2.10757)
 --> STEP: 322/406 -- GLOBAL_STEP: 5600
| > loss: 1.02396 (1.02908)
| > log_mle: 0.15174 (0.18024)
| > loss_dur: 0.87222 (0.84884)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.08260 (2.33987)
| > current_lr: 0.00000
| > step_time: 0.77800 (0.58459)
| > loader_time: 2.85200 (2.13871)
 --> STEP: 347/406 -- GLOBAL_STEP: 5625
| > loss: 1.00185 (1.02673)
| > log_mle: 0.14444 (0.17826)
| > loss_dur: 0.85741 (0.84847)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.83399 (2.35129)
| > current_lr: 0.00000
| > step_time: 0.83290 (0.60329)
| > loader_time: 2.83170 (2.18575)
 --> STEP: 372/406 -- GLOBAL_STEP: 5650
| > loss: 0.97082 (1.02360)
| > log_mle: 0.15735 (0.17634)
| > loss_dur: 0.81347 (0.84725)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.07717 (2.33641)
| > current_lr: 0.00000
| > step_time: 0.90580 (0.62265)
| > loader_time: 2.82190 (2.22383)
 --> STEP: 397/406 -- GLOBAL_STEP: 5675
| > loss: 0.97134 (1.02044)
| > log_mle: 0.14968 (0.17451)
| > loss_dur: 0.82166 (0.84593)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.71902 (2.32580)
| > current_lr: 0.00000
| > step_time: 1.06880 (0.64658)
| > loader_time: 2.44430 (2.25423)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.07332 (-0.03146)
| > avg_loss: 0.94557 (-0.10233)
| > avg_log_mle: 0.15062 (-0.03611)
| > avg_loss_dur: 0.79495 (-0.06621)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_5684.pth
 > EPOCH: 14/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 10:44:16) 
 --> STEP: 16/406 -- GLOBAL_STEP: 5700
| > loss: 1.00190 (0.99342)
| > log_mle: 0.17435 (0.17936)
| > loss_dur: 0.82755 (0.81406)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.65827 (2.28969)
| > current_lr: 0.00000
| > step_time: 0.23920 (0.30457)
| > loader_time: 1.29470 (1.20192)
 --> STEP: 41/406 -- GLOBAL_STEP: 5725
| > loss: 0.95316 (0.97528)
| > log_mle: 0.18530 (0.17942)
| > loss_dur: 0.76786 (0.79586)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.19472 (2.17793)
| > current_lr: 0.00000
| > step_time: 0.30130 (0.34044)
| > loader_time: 1.42940 (1.49483)
 --> STEP: 66/406 -- GLOBAL_STEP: 5750
| > loss: 0.97173 (0.96810)
| > log_mle: 0.16291 (0.17498)
| > loss_dur: 0.80882 (0.79313)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 1.99384 (2.15365)
| > current_lr: 0.00000
| > step_time: 0.39430 (0.36878)
| > loader_time: 1.85950 (1.57305)
 --> STEP: 91/406 -- GLOBAL_STEP: 5775
| > loss: 0.92456 (0.96212)
| > log_mle: 0.14024 (0.17048)
| > loss_dur: 0.78431 (0.79164)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.01462 (2.18665)
| > current_lr: 0.00000
| > step_time: 0.42660 (0.40129)
| > loader_time: 1.70580 (1.63475)
 --> STEP: 116/406 -- GLOBAL_STEP: 5800
| > loss: 0.93057 (0.95528)
| > log_mle: 0.15790 (0.16647)
| > loss_dur: 0.77267 (0.78881)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.09909 (2.16073)
| > current_lr: 0.00000
| > step_time: 0.42500 (0.42816)
| > loader_time: 1.56950 (1.66000)
 --> STEP: 141/406 -- GLOBAL_STEP: 5825
| > loss: 0.94767 (0.95162)
| > log_mle: 0.14481 (0.16288)
| > loss_dur: 0.80286 (0.78875)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 1.99329 (2.19661)
| > current_lr: 0.00000
| > step_time: 0.57490 (0.45115)
| > loader_time: 1.83820 (1.68420)
 --> STEP: 166/406 -- GLOBAL_STEP: 5850
| > loss: 0.92328 (0.94796)
| > log_mle: 0.13067 (0.15995)
| > loss_dur: 0.79261 (0.78801)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.43192 (2.19069)
| > current_lr: 0.00000
| > step_time: 0.62530 (0.47144)
| > loader_time: 2.39350 (1.74462)
 --> STEP: 191/406 -- GLOBAL_STEP: 5875
| > loss: 0.90414 (0.94467)
| > log_mle: 0.13559 (0.15717)
| > loss_dur: 0.76855 (0.78750)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.30896 (2.19571)
| > current_lr: 0.00000
| > step_time: 0.67770 (0.49045)
| > loader_time: 2.53360 (1.81872)
 --> STEP: 216/406 -- GLOBAL_STEP: 5900
| > loss: 0.90236 (0.94085)
| > log_mle: 0.12707 (0.15477)
| > loss_dur: 0.77529 (0.78608)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.22816 (2.17132)
| > current_lr: 0.00000
| > step_time: 0.68690 (0.50939)
| > loader_time: 2.16130 (1.86900)
 --> STEP: 241/406 -- GLOBAL_STEP: 5925
| > loss: 0.89977 (0.93770)
| > log_mle: 0.14124 (0.15239)
| > loss_dur: 0.75853 (0.78531)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.68819 (2.16703)
| > current_lr: 0.00000
| > step_time: 0.75280 (0.52830)
| > loader_time: 2.39030 (1.91200)
 --> STEP: 266/406 -- GLOBAL_STEP: 5950
| > loss: 0.91375 (0.93460)
| > log_mle: 0.13438 (0.15013)
| > loss_dur: 0.77937 (0.78448)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 1.80741 (2.17355)
| > current_lr: 0.00000
| > step_time: 0.76090 (0.54783)
| > loader_time: 2.51310 (1.95816)
 --> STEP: 291/406 -- GLOBAL_STEP: 5975
| > loss: 0.92023 (0.93155)
| > log_mle: 0.13882 (0.14801)
| > loss_dur: 0.78141 (0.78354)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 1.84725 (2.16132)
| > current_lr: 0.00000
| > step_time: 0.73150 (0.56708)
| > loader_time: 2.43020 (2.00390)
 --> STEP: 316/406 -- GLOBAL_STEP: 6000
| > loss: 0.88624 (0.92860)
| > log_mle: 0.12356 (0.14609)
| > loss_dur: 0.76269 (0.78251)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 1.88216 (2.16313)
| > current_lr: 0.00000
| > step_time: 0.98110 (0.58724)
| > loader_time: 2.62660 (2.04164)
 --> STEP: 341/406 -- GLOBAL_STEP: 6025
| > loss: 0.88454 (0.92622)
| > log_mle: 0.10591 (0.14425)
| > loss_dur: 0.77863 (0.78197)
| > amp_scaler: 65536.00000 (34113.31378)
| > grad_norm: 5.70181 (2.18458)
| > current_lr: 0.00000
| > step_time: 0.83290 (0.61379)
| > loader_time: 2.90490 (2.09965)
 --> STEP: 366/406 -- GLOBAL_STEP: 6050
| > loss: 0.87949 (0.92343)
| > log_mle: 0.11251 (0.14233)
| > loss_dur: 0.76699 (0.78110)
| > amp_scaler: 65536.00000 (36259.67213)
| > grad_norm: 2.09164 (2.20260)
| > current_lr: 0.00000
| > step_time: 0.84170 (0.63250)
| > loader_time: 3.00110 (2.16151)
 --> STEP: 391/406 -- GLOBAL_STEP: 6075
| > loss: 0.87505 (0.92047)
| > log_mle: 0.10405 (0.14057)
| > loss_dur: 0.77100 (0.77990)
| > amp_scaler: 65536.00000 (38131.56010)
| > grad_norm: 2.09093 (2.20270)
| > current_lr: 0.00000
| > step_time: 0.88790 (0.65049)
| > loader_time: 2.75030 (2.21116)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.08898 (+0.01566)
| > avg_loss: 0.84867 (-0.09689)
| > avg_log_mle: 0.11730 (-0.03332)
| > avg_loss_dur: 0.73138 (-0.06357)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_6090.pth
 > EPOCH: 15/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 11:04:24) 
 --> STEP: 10/406 -- GLOBAL_STEP: 6100
| > loss: 0.88356 (0.90483)
| > log_mle: 0.14603 (0.15052)
| > loss_dur: 0.73753 (0.75431)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.04802 (2.38087)
| > current_lr: 0.00000
| > step_time: 0.31670 (0.30248)
| > loader_time: 1.67380 (1.21728)
 --> STEP: 35/406 -- GLOBAL_STEP: 6125
| > loss: 0.85852 (0.87719)
| > log_mle: 0.12792 (0.14565)
| > loss_dur: 0.73061 (0.73154)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.77435 (2.14139)
| > current_lr: 0.00000
| > step_time: 0.28180 (0.33797)
| > loader_time: 1.58960 (1.50426)
 --> STEP: 60/406 -- GLOBAL_STEP: 6150
| > loss: 0.83465 (0.87068)
| > log_mle: 0.13209 (0.14217)
| > loss_dur: 0.70255 (0.72851)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.79910 (2.03058)
| > current_lr: 0.00000
| > step_time: 0.33460 (0.37218)
| > loader_time: 1.63880 (1.63301)
 --> STEP: 85/406 -- GLOBAL_STEP: 6175
| > loss: 0.81600 (0.86602)
| > log_mle: 0.10938 (0.13816)
| > loss_dur: 0.70662 (0.72786)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.76565 (2.04513)
| > current_lr: 0.00000
| > step_time: 0.38260 (0.40036)
| > loader_time: 1.57370 (1.67453)
 --> STEP: 110/406 -- GLOBAL_STEP: 6200
| > loss: 0.85372 (0.85952)
| > log_mle: 0.10825 (0.13420)
| > loss_dur: 0.74546 (0.72532)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.81643 (2.03321)
| > current_lr: 0.00000
| > step_time: 0.51630 (0.42559)
| > loader_time: 1.67230 (1.69237)
 --> STEP: 135/406 -- GLOBAL_STEP: 6225
| > loss: 0.86273 (0.85547)
| > log_mle: 0.11569 (0.13065)
| > loss_dur: 0.74704 (0.72482)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.19288 (2.04049)
| > current_lr: 0.00000
| > step_time: 0.63130 (0.44694)
| > loader_time: 2.16290 (1.73840)
 --> STEP: 160/406 -- GLOBAL_STEP: 6250
| > loss: 0.82481 (0.85233)
| > log_mle: 0.11221 (0.12782)
| > loss_dur: 0.71260 (0.72451)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.99797 (2.04038)
| > current_lr: 0.00000
| > step_time: 0.63850 (0.46965)
| > loader_time: 2.55570 (1.81283)
 --> STEP: 185/406 -- GLOBAL_STEP: 6275
| > loss: 0.81827 (0.84992)
| > log_mle: 0.11121 (0.12515)
| > loss_dur: 0.70706 (0.72477)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.59162 (2.05876)
| > current_lr: 0.00000
| > step_time: 0.61430 (0.48937)
| > loader_time: 2.89050 (1.91495)
 --> STEP: 210/406 -- GLOBAL_STEP: 6300
| > loss: 0.80603 (0.84680)
| > log_mle: 0.10976 (0.12274)
| > loss_dur: 0.69627 (0.72406)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.02422 (2.08904)
| > current_lr: 0.00000
| > step_time: 0.62310 (0.50876)
| > loader_time: 2.48990 (1.99623)
 --> STEP: 235/406 -- GLOBAL_STEP: 6325
| > loss: 0.82942 (0.84340)
| > log_mle: 0.09985 (0.12023)
| > loss_dur: 0.72956 (0.72317)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.84982 (2.08347)
| > current_lr: 0.00000
| > step_time: 0.69420 (0.53567)
| > loader_time: 2.64490 (2.04457)
 --> STEP: 260/406 -- GLOBAL_STEP: 6350
| > loss: 0.77415 (0.84069)
| > log_mle: 0.08921 (0.11799)
| > loss_dur: 0.68494 (0.72270)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.10852 (2.07371)
| > current_lr: 0.00000
| > step_time: 0.69180 (0.55327)
| > loader_time: 2.30000 (2.08789)
 --> STEP: 285/406 -- GLOBAL_STEP: 6375
| > loss: 0.79432 (0.83785)
| > log_mle: 0.09805 (0.11598)
| > loss_dur: 0.69627 (0.72186)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.87403 (2.08823)
| > current_lr: 0.00000
| > step_time: 0.74010 (0.57177)
| > loader_time: 2.56100 (2.13342)
 --> STEP: 310/406 -- GLOBAL_STEP: 6400
| > loss: 0.77572 (0.83541)
| > log_mle: 0.10057 (0.11425)
| > loss_dur: 0.67514 (0.72116)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.71324 (2.09712)
| > current_lr: 0.00000
| > step_time: 0.77790 (0.59108)
| > loader_time: 2.56350 (2.16368)
 --> STEP: 335/406 -- GLOBAL_STEP: 6425
| > loss: 0.82341 (0.83299)
| > log_mle: 0.08130 (0.11246)
| > loss_dur: 0.74212 (0.72053)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.21265 (2.11747)
| > current_lr: 0.00000
| > step_time: 0.83680 (0.61075)
| > loader_time: 3.14040 (2.22320)
 --> STEP: 360/406 -- GLOBAL_STEP: 6450
| > loss: 0.79910 (0.83068)
| > log_mle: 0.08271 (0.11064)
| > loss_dur: 0.71639 (0.72004)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.66862 (2.14137)
| > current_lr: 0.00000
| > step_time: 0.81090 (0.62895)
| > loader_time: 2.96480 (2.27350)
 --> STEP: 385/406 -- GLOBAL_STEP: 6475
| > loss: 0.78327 (0.82792)
| > log_mle: 0.08866 (0.10897)
| > loss_dur: 0.69461 (0.71895)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.96199 (2.13289)
| > current_lr: 0.00000
| > step_time: 1.06950 (0.64796)
| > loader_time: 2.87730 (2.32215)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.17881 (+0.08984)
| > avg_loss: 0.75686 (-0.09181)
| > avg_log_mle: 0.08445 (-0.03285)
| > avg_loss_dur: 0.67242 (-0.05896)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_6496.pth
 > EPOCH: 16/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 11:25:31) 
 --> STEP: 4/406 -- GLOBAL_STEP: 6500
| > loss: 0.87785 (0.83530)
| > log_mle: 0.13780 (0.11983)
| > loss_dur: 0.74005 (0.71548)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.98770 (2.32815)
| > current_lr: 0.00000
| > step_time: 0.31970 (0.31894)
| > loader_time: 1.56830 (1.42207)
 --> STEP: 29/406 -- GLOBAL_STEP: 6525
| > loss: 0.78056 (0.78767)
| > log_mle: 0.11443 (0.11520)
| > loss_dur: 0.66613 (0.67247)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.87768 (1.93910)
| > current_lr: 0.00000
| > step_time: 0.34050 (0.34190)
| > loader_time: 1.41210 (1.55573)
 --> STEP: 54/406 -- GLOBAL_STEP: 6550
| > loss: 0.77674 (0.78266)
| > log_mle: 0.10479 (0.11223)
| > loss_dur: 0.67195 (0.67043)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.85235 (2.01726)
| > current_lr: 0.00000
| > step_time: 0.36580 (0.38226)
| > loader_time: 2.12140 (1.69044)
 --> STEP: 79/406 -- GLOBAL_STEP: 6575
| > loss: 0.76706 (0.77787)
| > log_mle: 0.09122 (0.10814)
| > loss_dur: 0.67584 (0.66973)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 3.15325 (2.03767)
| > current_lr: 0.00000
| > step_time: 0.49610 (0.41900)
| > loader_time: 1.91060 (1.76630)
 --> STEP: 104/406 -- GLOBAL_STEP: 6600
| > loss: 0.73074 (0.77290)
| > log_mle: 0.10001 (0.10409)
| > loss_dur: 0.63074 (0.66881)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.80878 (2.34393)
| > current_lr: 0.00000
| > step_time: 0.54880 (0.44265)
| > loader_time: 2.08630 (1.80504)
 --> STEP: 129/406 -- GLOBAL_STEP: 6625
| > loss: 0.75989 (0.76845)
| > log_mle: 0.09058 (0.10060)
| > loss_dur: 0.66931 (0.66785)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.88958 (2.28491)
| > current_lr: 0.00000
| > step_time: 0.48940 (0.46405)
| > loader_time: 2.12410 (1.84989)
 --> STEP: 154/406 -- GLOBAL_STEP: 6650
| > loss: 0.73025 (0.76597)
| > log_mle: 0.08493 (0.09769)
| > loss_dur: 0.64533 (0.66828)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.72769 (2.27288)
| > current_lr: 0.00000
| > step_time: 0.59740 (0.48275)
| > loader_time: 2.83590 (1.93054)
 --> STEP: 179/406 -- GLOBAL_STEP: 6675
| > loss: 0.74534 (0.76370)
| > log_mle: 0.07594 (0.09509)
| > loss_dur: 0.66940 (0.66861)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.75924 (2.19808)
| > current_lr: 0.00000
| > step_time: 0.65250 (0.50124)
| > loader_time: 2.93130 (2.04968)
 --> STEP: 204/406 -- GLOBAL_STEP: 6700
| > loss: 0.73245 (0.76125)
| > log_mle: 0.07699 (0.09270)
| > loss_dur: 0.65546 (0.66856)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.37404 (2.14524)
| > current_lr: 0.00000
| > step_time: 0.63340 (0.51811)
| > loader_time: 2.44330 (2.12738)
 --> STEP: 229/406 -- GLOBAL_STEP: 6725
| > loss: 0.72765 (0.75810)
| > log_mle: 0.07821 (0.09033)
| > loss_dur: 0.64943 (0.66777)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 3.40260 (2.13140)
| > current_lr: 0.00000
| > step_time: 0.67750 (0.53570)
| > loader_time: 2.59920 (2.18043)
 --> STEP: 254/406 -- GLOBAL_STEP: 6750
| > loss: 0.73265 (0.75592)
| > log_mle: 0.07578 (0.08814)
| > loss_dur: 0.65687 (0.66778)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.83874 (2.20232)
| > current_lr: 0.00000
| > step_time: 0.72420 (0.55353)
| > loader_time: 3.10140 (2.22796)
 --> STEP: 279/406 -- GLOBAL_STEP: 6775
| > loss: 0.74165 (0.75340)
| > log_mle: 0.07022 (0.08620)
| > loss_dur: 0.67143 (0.66720)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.55920 (2.16462)
| > current_lr: 0.00000
| > step_time: 0.68050 (0.57138)
| > loader_time: 2.51000 (2.25971)
 --> STEP: 304/406 -- GLOBAL_STEP: 6800
| > loss: 0.70784 (0.75124)
| > log_mle: 0.06112 (0.08440)
| > loss_dur: 0.64672 (0.66684)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.87951 (2.13850)
| > current_lr: 0.00000
| > step_time: 0.81100 (0.59237)
| > loader_time: 2.81840 (2.28761)
 --> STEP: 329/406 -- GLOBAL_STEP: 6825
| > loss: 0.74032 (0.74885)
| > log_mle: 0.07267 (0.08281)
| > loss_dur: 0.66766 (0.66603)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.45145 (2.11407)
| > current_lr: 0.00000
| > step_time: 0.87860 (0.61045)
| > loader_time: 3.09190 (2.34143)
 --> STEP: 354/406 -- GLOBAL_STEP: 6850
| > loss: 0.73188 (0.74728)
| > log_mle: 0.06169 (0.08109)
| > loss_dur: 0.67019 (0.66619)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.62111 (2.16132)
| > current_lr: 0.00000
| > step_time: 0.83370 (0.63011)
| > loader_time: 3.22000 (2.40898)
 --> STEP: 379/406 -- GLOBAL_STEP: 6875
| > loss: 0.70260 (0.74508)
| > log_mle: 0.05484 (0.07941)
| > loss_dur: 0.64776 (0.66568)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.61433 (2.18605)
| > current_lr: 0.00000
| > step_time: 0.97280 (0.65190)
| > loader_time: 3.66410 (2.46086)
 --> STEP: 404/406 -- GLOBAL_STEP: 6900
| > loss: 0.72052 (0.74279)
| > log_mle: 0.05505 (0.07779)
| > loss_dur: 0.66547 (0.66501)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.64656 (2.16262)
| > current_lr: 0.00000
| > step_time: 1.47790 (0.67909)
| > loader_time: 2.80880 (2.49844)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.09973 (-0.07909)
| > avg_loss: 0.68373 (-0.07313)
| > avg_log_mle: 0.05649 (-0.02796)
| > avg_loss_dur: 0.62725 (-0.04517)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_6902.pth
 > EPOCH: 17/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 11:47:41) 
 --> STEP: 23/406 -- GLOBAL_STEP: 6925
| > loss: 0.71056 (0.70971)
| > log_mle: 0.08638 (0.08723)
| > loss_dur: 0.62418 (0.62248)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.51052 (1.84014)
| > current_lr: 0.00000
| > step_time: 0.35220 (0.33722)
| > loader_time: 1.78860 (1.51293)
 --> STEP: 48/406 -- GLOBAL_STEP: 6950
| > loss: 0.70648 (0.70483)
| > log_mle: 0.07952 (0.08516)
| > loss_dur: 0.62696 (0.61967)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.83981 (1.77796)
| > current_lr: 0.00000
| > step_time: 0.41700 (0.36757)
| > loader_time: 1.83590 (1.74833)
 --> STEP: 73/406 -- GLOBAL_STEP: 6975
| > loss: 0.65369 (0.70146)
| > log_mle: 0.05432 (0.08080)
| > loss_dur: 0.59938 (0.62066)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.62392 (1.76317)
| > current_lr: 0.00000
| > step_time: 0.52030 (0.40221)
| > loader_time: 1.92010 (1.83229)
 --> STEP: 98/406 -- GLOBAL_STEP: 7000
| > loss: 0.69573 (0.69851)
| > log_mle: 0.07162 (0.07685)
| > loss_dur: 0.62411 (0.62166)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.63760 (1.97515)
| > current_lr: 0.00000
| > step_time: 0.53940 (0.42564)
| > loader_time: 1.94040 (1.87679)
 --> STEP: 123/406 -- GLOBAL_STEP: 7025
| > loss: 0.69146 (0.69416)
| > log_mle: 0.05086 (0.07329)
| > loss_dur: 0.64060 (0.62087)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.28379 (1.96829)
| > current_lr: 0.00000
| > step_time: 0.50340 (0.44822)
| > loader_time: 2.02610 (1.91093)
 --> STEP: 148/406 -- GLOBAL_STEP: 7050
| > loss: 0.68895 (0.69172)
| > log_mle: 0.04897 (0.07026)
| > loss_dur: 0.63998 (0.62145)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.73259 (2.00058)
| > current_lr: 0.00000
| > step_time: 0.51050 (0.47037)
| > loader_time: 2.36490 (1.95597)
 --> STEP: 173/406 -- GLOBAL_STEP: 7075
| > loss: 0.70867 (0.68947)
| > log_mle: 0.05488 (0.06780)
| > loss_dur: 0.65379 (0.62167)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.75028 (1.95688)
| > current_lr: 0.00000
| > step_time: 0.65330 (0.48941)
| > loader_time: 2.46120 (2.03509)
 --> STEP: 198/406 -- GLOBAL_STEP: 7100
| > loss: 0.66233 (0.68771)
| > log_mle: 0.04541 (0.06533)
| > loss_dur: 0.61692 (0.62238)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 3.03263 (1.96591)
| > current_lr: 0.00000
| > step_time: 0.59340 (0.51013)
| > loader_time: 2.48670 (2.09235)
 --> STEP: 223/406 -- GLOBAL_STEP: 7125
| > loss: 0.65232 (0.68508)
| > log_mle: 0.04797 (0.06315)
| > loss_dur: 0.60435 (0.62193)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.88420 (1.94327)
| > current_lr: 0.00000
| > step_time: 0.70150 (0.52743)
| > loader_time: 2.60140 (2.14472)
 --> STEP: 248/406 -- GLOBAL_STEP: 7150
| > loss: 0.66399 (0.68333)
| > log_mle: 0.04633 (0.06108)
| > loss_dur: 0.61766 (0.62224)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.14434 (1.92488)
| > current_lr: 0.00000
| > step_time: 0.67440 (0.54646)
| > loader_time: 2.19150 (2.17671)
 --> STEP: 273/406 -- GLOBAL_STEP: 7175
| > loss: 0.64944 (0.68150)
| > log_mle: 0.03404 (0.05903)
| > loss_dur: 0.61539 (0.62247)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.53517 (1.92908)
| > current_lr: 0.00000
| > step_time: 0.69360 (0.56628)
| > loader_time: 2.56480 (2.20391)
 --> STEP: 298/406 -- GLOBAL_STEP: 7200
| > loss: 0.65263 (0.67981)
| > log_mle: 0.04824 (0.05744)
| > loss_dur: 0.60439 (0.62237)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.38549 (1.91233)
| > current_lr: 0.00000
| > step_time: 0.89090 (0.58763)
| > loader_time: 2.56240 (2.22214)
 --> STEP: 323/406 -- GLOBAL_STEP: 7225
| > loss: 0.64757 (0.67777)
| > log_mle: 0.03365 (0.05581)
| > loss_dur: 0.61392 (0.62196)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.68997 (1.89856)
| > current_lr: 0.00000
| > step_time: 0.86480 (0.60658)
| > loader_time: 2.62690 (2.25353)
 --> STEP: 348/406 -- GLOBAL_STEP: 7250
| > loss: 0.67494 (0.67659)
| > log_mle: 0.04247 (0.05422)
| > loss_dur: 0.63247 (0.62238)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.36015 (1.92321)
| > current_lr: 0.00000
| > step_time: 0.92970 (0.62435)
| > loader_time: 2.78830 (2.30251)
 --> STEP: 373/406 -- GLOBAL_STEP: 7275
| > loss: 0.66912 (0.67484)
| > log_mle: 0.02665 (0.05261)
| > loss_dur: 0.64248 (0.62223)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.43369 (1.92862)
| > current_lr: 0.00000
| > step_time: 0.95130 (0.64709)
| > loader_time: 3.23020 (2.33729)
 --> STEP: 398/406 -- GLOBAL_STEP: 7300
| > loss: 0.64225 (0.67301)
| > log_mle: 0.02269 (0.05110)
| > loss_dur: 0.61956 (0.62192)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.60807 (1.93023)
| > current_lr: 0.00000
| > step_time: 0.96850 (0.66719)
| > loader_time: 2.15170 (2.36134)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.08366 (-0.01606)
| > avg_loss: 0.61616 (-0.06757)
| > avg_log_mle: 0.02942 (-0.02706)
| > avg_loss_dur: 0.58674 (-0.04051)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_7308.pth
 > EPOCH: 18/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 12:08:44) 
 --> STEP: 17/406 -- GLOBAL_STEP: 7325
| > loss: 0.63872 (0.64835)
| > log_mle: 0.05992 (0.05981)
| > loss_dur: 0.57881 (0.58854)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.30209 (1.92295)
| > current_lr: 0.00000
| > step_time: 0.32280 (0.33716)
| > loader_time: 1.29910 (1.18344)
 --> STEP: 42/406 -- GLOBAL_STEP: 7350
| > loss: 0.64193 (0.64025)
| > log_mle: 0.04849 (0.05973)
| > loss_dur: 0.59344 (0.58052)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.44911 (1.66454)
| > current_lr: 0.00000
| > step_time: 0.57020 (0.34879)
| > loader_time: 2.04360 (1.47474)
 --> STEP: 67/406 -- GLOBAL_STEP: 7375
| > loss: 0.61392 (0.63857)
| > log_mle: 0.04265 (0.05602)
| > loss_dur: 0.57127 (0.58255)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.42700 (1.62901)
| > current_lr: 0.00000
| > step_time: 0.47850 (0.37316)
| > loader_time: 2.15310 (1.57884)
 --> STEP: 92/406 -- GLOBAL_STEP: 7400
| > loss: 0.61065 (0.63646)
| > log_mle: 0.02913 (0.05198)
| > loss_dur: 0.58152 (0.58448)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.03155 (1.67857)
| > current_lr: 0.00000
| > step_time: 0.54800 (0.40569)
| > loader_time: 2.16040 (1.65677)
 --> STEP: 117/406 -- GLOBAL_STEP: 7425
| > loss: 0.62464 (0.63298)
| > log_mle: 0.03355 (0.04850)
| > loss_dur: 0.59109 (0.58447)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.87521 (1.73615)
| > current_lr: 0.00000
| > step_time: 0.56290 (0.43430)
| > loader_time: 1.96560 (1.70832)
 --> STEP: 142/406 -- GLOBAL_STEP: 7450
| > loss: 0.60952 (0.63092)
| > log_mle: 0.02627 (0.04533)
| > loss_dur: 0.58324 (0.58559)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.47522 (1.84581)
| > current_lr: 0.00000
| > step_time: 0.55820 (0.45551)
| > loader_time: 2.15820 (1.74474)
 --> STEP: 167/406 -- GLOBAL_STEP: 7475
| > loss: 0.61104 (0.62908)
| > log_mle: 0.02405 (0.04281)
| > loss_dur: 0.58699 (0.58627)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.17686 (1.90762)
| > current_lr: 0.00000
| > step_time: 0.65590 (0.47627)
| > loader_time: 1.73090 (1.79367)
 --> STEP: 192/406 -- GLOBAL_STEP: 7500
| > loss: 0.58775 (0.62772)
| > log_mle: 0.02374 (0.04045)
| > loss_dur: 0.56401 (0.58726)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 4.05972 (1.93778)
| > current_lr: 0.00000
| > step_time: 0.58240 (0.49603)
| > loader_time: 2.13860 (1.83047)
 --> STEP: 217/406 -- GLOBAL_STEP: 7525
| > loss: 0.59858 (0.62560)
| > log_mle: 0.02135 (0.03844)
| > loss_dur: 0.57723 (0.58716)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.90632 (2.02591)
| > current_lr: 0.00000
| > step_time: 0.69230 (0.51456)
| > loader_time: 1.95780 (1.86553)
 --> STEP: 242/406 -- GLOBAL_STEP: 7550
| > loss: 0.60832 (0.62402)
| > log_mle: 0.00271 (0.03634)
| > loss_dur: 0.60562 (0.58768)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 3.98438 (2.01341)
| > current_lr: 0.00000
| > step_time: 0.76270 (0.53452)
| > loader_time: 2.24230 (1.90874)
 --> STEP: 267/406 -- GLOBAL_STEP: 7575
| > loss: 0.61049 (0.62257)
| > log_mle: 0.01358 (0.03451)
| > loss_dur: 0.59691 (0.58806)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.74262 (1.99373)
| > current_lr: 0.00000
| > step_time: 0.66940 (0.55415)
| > loader_time: 2.50390 (1.95696)
 --> STEP: 292/406 -- GLOBAL_STEP: 7600
| > loss: 0.62413 (0.62122)
| > log_mle: 0.02137 (0.03283)
| > loss_dur: 0.60275 (0.58840)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 4.67019 (2.02127)
| > current_lr: 0.00000
| > step_time: 0.94210 (0.57847)
| > loader_time: 2.71760 (2.00176)
 --> STEP: 317/406 -- GLOBAL_STEP: 7625
| > loss: 0.60477 (0.61976)
| > log_mle: 0.01892 (0.03124)
| > loss_dur: 0.58585 (0.58851)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.54323 (2.11433)
| > current_lr: 0.00000
| > step_time: 0.83910 (0.59961)
| > loader_time: 2.54460 (2.05020)
 --> STEP: 342/406 -- GLOBAL_STEP: 7650
| > loss: 0.59575 (0.61862)
| > log_mle: 0.00370 (0.02976)
| > loss_dur: 0.59205 (0.58886)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.33530 (2.12415)
| > current_lr: 0.00000
| > step_time: 0.87640 (0.61630)
| > loader_time: 3.11590 (2.10629)
 --> STEP: 367/406 -- GLOBAL_STEP: 7675
| > loss: 0.58436 (0.61738)
| > log_mle: 0.00987 (0.02821)
| > loss_dur: 0.57449 (0.58916)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.84325 (2.12046)
| > current_lr: 0.00000
| > step_time: 0.93670 (0.63533)
| > loader_time: 2.76440 (2.15070)
 --> STEP: 392/406 -- GLOBAL_STEP: 7700
| > loss: 0.59386 (0.61596)
| > log_mle: 0.00449 (0.02675)
| > loss_dur: 0.58937 (0.58921)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.70919 (2.10098)
| > current_lr: 0.00000
| > step_time: 0.88090 (0.65878)
| > loader_time: 2.63470 (2.18224)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.06107 (-0.02259)
| > avg_loss: 0.56329 (-0.05288)
| > avg_log_mle: 0.00567 (-0.02375)
| > avg_loss_dur: 0.55761 (-0.02912)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_7714.pth
 > EPOCH: 19/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 12:28:42) 
 --> STEP: 11/406 -- GLOBAL_STEP: 7725
| > loss: 0.58882 (0.59666)
| > log_mle: 0.03454 (0.03932)
| > loss_dur: 0.55428 (0.55734)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.32118 (1.61813)
| > current_lr: 0.00000
| > step_time: 0.52920 (0.33579)
| > loader_time: 1.52020 (1.25481)
 --> STEP: 36/406 -- GLOBAL_STEP: 7750
| > loss: 0.56091 (0.58552)
| > log_mle: 0.04186 (0.03611)
| > loss_dur: 0.51906 (0.54940)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.27929 (1.63259)
| > current_lr: 0.00000
| > step_time: 0.45030 (0.35026)
| > loader_time: 1.90240 (1.46218)
 --> STEP: 61/406 -- GLOBAL_STEP: 7775
| > loss: 0.59122 (0.58571)
| > log_mle: 0.02340 (0.03284)
| > loss_dur: 0.56782 (0.55287)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.24682 (1.71721)
| > current_lr: 0.00000
| > step_time: 0.47650 (0.38474)
| > loader_time: 1.96000 (1.60891)
 --> STEP: 86/406 -- GLOBAL_STEP: 7800
| > loss: 0.57559 (0.58399)
| > log_mle: 0.02182 (0.02911)
| > loss_dur: 0.55377 (0.55488)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.84747 (1.75610)
| > current_lr: 0.00000
| > step_time: 0.53370 (0.41648)
| > loader_time: 1.90640 (1.69727)
 --> STEP: 111/406 -- GLOBAL_STEP: 7825
| > loss: 0.55674 (0.58076)
| > log_mle: 0.00221 (0.02536)
| > loss_dur: 0.55453 (0.55541)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.60324 (1.83055)
| > current_lr: 0.00000
| > step_time: 0.51040 (0.44547)
| > loader_time: 2.01750 (1.75981)
 --> STEP: 136/406 -- GLOBAL_STEP: 7850
| > loss: 0.59540 (0.57885)
| > log_mle: 0.00325 (0.02220)
| > loss_dur: 0.59216 (0.55666)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 3.25412 (1.92249)
| > current_lr: 0.00000
| > step_time: 0.59360 (0.46709)
| > loader_time: 2.10570 (1.82538)
 --> STEP: 161/406 -- GLOBAL_STEP: 7875
| > loss: 0.56041 (0.57736)
| > log_mle: 0.01209 (0.01982)
| > loss_dur: 0.54832 (0.55754)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.94852 (1.88740)
| > current_lr: 0.00000
| > step_time: 0.61980 (0.49023)
| > loader_time: 2.58430 (1.90035)
 --> STEP: 186/406 -- GLOBAL_STEP: 7900
| > loss: 0.56540 (0.57658)
| > log_mle: -0.00345 (0.01745)
| > loss_dur: 0.56884 (0.55912)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.65653 (1.91100)
| > current_lr: 0.00000
| > step_time: 0.66350 (0.50831)
| > loader_time: 2.64980 (1.98152)
 --> STEP: 211/406 -- GLOBAL_STEP: 7925
| > loss: 0.54561 (0.57497)
| > log_mle: -0.01409 (0.01545)
| > loss_dur: 0.55970 (0.55952)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.80544 (1.93103)
| > current_lr: 0.00000
| > step_time: 0.71230 (0.52639)
| > loader_time: 2.51740 (2.03852)
 --> STEP: 236/406 -- GLOBAL_STEP: 7950
| > loss: 0.56089 (0.57349)
| > log_mle: 0.01113 (0.01347)
| > loss_dur: 0.54976 (0.56002)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.48231 (1.92610)
| > current_lr: 0.00000
| > step_time: 0.65790 (0.54610)
| > loader_time: 2.40020 (2.09883)
 --> STEP: 261/406 -- GLOBAL_STEP: 7975
| > loss: 0.55260 (0.57209)
| > log_mle: -0.00323 (0.01155)
| > loss_dur: 0.55583 (0.56053)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.19003 (1.91823)
| > current_lr: 0.00000
| > step_time: 0.73340 (0.56303)
| > loader_time: 2.45730 (2.13701)
 --> STEP: 286/406 -- GLOBAL_STEP: 8000
| > loss: 0.55032 (0.57093)
| > log_mle: -0.00925 (0.00989)
| > loss_dur: 0.55957 (0.56104)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.28757 (1.97478)
| > current_lr: 0.00000
| > step_time: 0.68550 (0.57921)
| > loader_time: 2.23800 (2.16275)
 --> STEP: 311/406 -- GLOBAL_STEP: 8025
| > loss: 0.54205 (0.56976)
| > log_mle: -0.01420 (0.00846)
| > loss_dur: 0.55624 (0.56130)
| > amp_scaler: 131072.00000 (68486.17363)
| > grad_norm: 1.03420 (1.93226)
| > current_lr: 0.00000
| > step_time: 0.97000 (0.60080)
| > loader_time: 2.07070 (2.17714)
 --> STEP: 336/406 -- GLOBAL_STEP: 8050
| > loss: 0.56463 (0.56869)
| > log_mle: -0.00178 (0.00707)
| > loss_dur: 0.56640 (0.56162)
| > amp_scaler: 131072.00000 (73142.85714)
| > grad_norm: 2.09438 (1.91971)
| > current_lr: 0.00000
| > step_time: 0.90150 (0.61978)
| > loader_time: 2.64830 (2.21436)
 --> STEP: 361/406 -- GLOBAL_STEP: 8075
| > loss: 0.55958 (0.56762)
| > log_mle: -0.00656 (0.00563)
| > loss_dur: 0.56614 (0.56199)
| > amp_scaler: 131072.00000 (77154.57064)
| > grad_norm: 1.29841 (1.96584)
| > current_lr: 0.00000
| > step_time: 1.02780 (0.64132)
| > loader_time: 2.66280 (2.24640)
 --> STEP: 386/406 -- GLOBAL_STEP: 8100
| > loss: 0.55573 (0.56636)
| > log_mle: -0.01164 (0.00429)
| > loss_dur: 0.56737 (0.56207)
| > amp_scaler: 131072.00000 (80646.63212)
| > grad_norm: 2.72421 (1.99370)
| > current_lr: 0.00000
| > step_time: 1.03130 (0.66340)
| > loader_time: 2.53260 (2.27874)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.15752 (+0.09645)
| > avg_loss: 0.51247 (-0.05082)
| > avg_log_mle: -0.01619 (-0.02187)
| > avg_loss_dur: 0.52866 (-0.02895)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_8120.pth
 > EPOCH: 20/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 12:49:26) 
 --> STEP: 5/406 -- GLOBAL_STEP: 8125
| > loss: 0.54112 (0.56025)
| > log_mle: 0.02811 (0.02038)
| > loss_dur: 0.51301 (0.53987)
| > amp_scaler: 131072.00000 (131072.00000)
| > grad_norm: 1.35217 (1.75753)
| > current_lr: 0.00000
| > step_time: 0.31870 (0.33056)
| > loader_time: 0.98650 (1.12710)
 --> STEP: 30/406 -- GLOBAL_STEP: 8150
| > loss: 0.52730 (0.53469)
| > log_mle: 0.00650 (0.01467)
| > loss_dur: 0.52080 (0.52002)
| > amp_scaler: 131072.00000 (131072.00000)
| > grad_norm: 1.33410 (1.63881)
| > current_lr: 0.00000
| > step_time: 0.31310 (0.33610)
| > loader_time: 1.86560 (1.46628)
 --> STEP: 55/406 -- GLOBAL_STEP: 8175
| > loss: 0.52488 (0.53496)
| > log_mle: -0.00521 (0.01205)
| > loss_dur: 0.53009 (0.52290)
| > amp_scaler: 131072.00000 (131072.00000)
| > grad_norm: 1.94975 (1.68310)
| > current_lr: 0.00000
| > step_time: 0.41270 (0.37420)
| > loader_time: 1.73030 (1.59201)
 --> STEP: 80/406 -- GLOBAL_STEP: 8200
| > loss: 0.53786 (0.53242)
| > log_mle: 0.00958 (0.00873)
| > loss_dur: 0.52828 (0.52369)
| > amp_scaler: 131072.00000 (131072.00000)
| > grad_norm: 1.64994 (1.72548)
| > current_lr: 0.00000
| > step_time: 0.50570 (0.40678)
| > loader_time: 1.72620 (1.67181)
 --> STEP: 105/406 -- GLOBAL_STEP: 8225
| > loss: 0.50475 (0.52990)
| > log_mle: 0.00193 (0.00503)
| > loss_dur: 0.50282 (0.52487)
| > amp_scaler: 131072.00000 (131072.00000)
| > grad_norm: 2.52956 (1.79567)
| > current_lr: 0.00000
| > step_time: 0.58330 (0.43586)
| > loader_time: 1.82480 (1.71991)
 --> STEP: 130/406 -- GLOBAL_STEP: 8250
| > loss: 0.50816 (0.52745)
| > log_mle: -0.01206 (0.00190)
| > loss_dur: 0.52022 (0.52555)
| > amp_scaler: 131072.00000 (131072.00000)
| > grad_norm: 2.89429 (1.92243)
| > current_lr: 0.00000
| > step_time: 0.60610 (0.46230)
| > loader_time: 1.83340 (1.73046)
 --> STEP: 155/406 -- GLOBAL_STEP: 8275
| > loss: 0.50928 (0.52627)
| > log_mle: -0.00994 (-0.00057)
| > loss_dur: 0.51923 (0.52684)
| > amp_scaler: 131072.00000 (131072.00000)
| > grad_norm: 1.72901 (1.99829)
| > current_lr: 0.00000
| > step_time: 0.66550 (0.48411)
| > loader_time: 2.10200 (1.76054)
 --> STEP: 180/406 -- GLOBAL_STEP: 8300
| > loss: 0.49535 (0.52522)
| > log_mle: -0.01972 (-0.00279)
| > loss_dur: 0.51507 (0.52802)
| > amp_scaler: 131072.00000 (131072.00000)
| > grad_norm: 1.96062 (2.15771)
| > current_lr: 0.00000
| > step_time: 0.66740 (0.50356)
| > loader_time: 1.99280 (1.80756)
 --> STEP: 205/406 -- GLOBAL_STEP: 8325
| > loss: 0.51911 (0.52434)
| > log_mle: -0.02778 (-0.00485)
| > loss_dur: 0.54689 (0.52919)
| > amp_scaler: 131072.00000 (131072.00000)
| > grad_norm: 3.27878 (2.24755)
| > current_lr: 0.00000
| > step_time: 0.72940 (0.52313)
| > loader_time: 2.19860 (1.84532)
 --> STEP: 230/406 -- GLOBAL_STEP: 8350
| > loss: 0.50891 (0.52255)
| > log_mle: -0.02062 (-0.00682)
| > loss_dur: 0.52953 (0.52937)
| > amp_scaler: 131072.00000 (131072.00000)
| > grad_norm: 1.51235 (2.25042)
| > current_lr: 0.00000
| > step_time: 0.62670 (0.54067)
| > loader_time: 2.27200 (1.88650)
 --> STEP: 255/406 -- GLOBAL_STEP: 8375
| > loss: 0.49785 (0.52128)
| > log_mle: -0.02267 (-0.00869)
| > loss_dur: 0.52052 (0.52997)
| > amp_scaler: 65536.00000 (129529.97647)
| > grad_norm: 5.30793 (2.25012)
| > current_lr: 0.00000
| > step_time: 0.67000 (0.56041)
| > loader_time: 2.33160 (1.93510)
 --> STEP: 280/406 -- GLOBAL_STEP: 8400
| > loss: 0.50150 (0.51989)
| > log_mle: -0.03442 (-0.01031)
| > loss_dur: 0.53592 (0.53020)
| > amp_scaler: 65536.00000 (123816.22857)
| > grad_norm: 1.54717 (2.19474)
| > current_lr: 0.00000
| > step_time: 0.71210 (0.57721)
| > loader_time: 2.40450 (1.97214)
 --> STEP: 305/406 -- GLOBAL_STEP: 8425
| > loss: 0.51990 (0.51873)
| > log_mle: -0.02659 (-0.01178)
| > loss_dur: 0.54649 (0.53051)
| > amp_scaler: 65536.00000 (119039.16066)
| > grad_norm: 3.05272 (2.20973)
| > current_lr: 0.00000
| > step_time: 0.86470 (0.59574)
| > loader_time: 2.23510 (2.00074)
 --> STEP: 330/406 -- GLOBAL_STEP: 8450
| > loss: 0.51872 (0.51738)
| > log_mle: -0.04099 (-0.01309)
| > loss_dur: 0.55972 (0.53047)
| > amp_scaler: 65536.00000 (114985.89091)
| > grad_norm: 1.87560 (2.23172)
| > current_lr: 0.00000
| > step_time: 0.76450 (0.61426)
| > loader_time: 2.25090 (2.04283)
 --> STEP: 355/406 -- GLOBAL_STEP: 8475
| > loss: 0.48814 (0.51634)
| > log_mle: -0.04023 (-0.01450)
| > loss_dur: 0.52837 (0.53084)
| > amp_scaler: 65536.00000 (111503.50423)
| > grad_norm: 1.71652 (2.26772)
| > current_lr: 0.00000
| > step_time: 0.84790 (0.63389)
| > loader_time: 2.66870 (2.08504)
 --> STEP: 380/406 -- GLOBAL_STEP: 8500
| > loss: 0.50377 (0.51510)
| > log_mle: -0.03459 (-0.01588)
| > loss_dur: 0.53836 (0.53098)
| > amp_scaler: 65536.00000 (108479.32632)
| > grad_norm: 2.43367 (2.27153)
| > current_lr: 0.00000
| > step_time: 0.94090 (0.65358)
| > loader_time: 3.05420 (2.12025)
 --> STEP: 405/406 -- GLOBAL_STEP: 8525
| > loss: 0.47969 (0.51386)
| > log_mle: -0.03269 (-0.01717)
| > loss_dur: 0.51238 (0.53103)
| > amp_scaler: 65536.00000 (105828.50370)
| > grad_norm: 1.51056 (2.27091)
| > current_lr: 0.00000
| > step_time: 0.44990 (0.68305)
| > loader_time: 0.14410 (2.13752)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.00041 (-0.15710)
| > avg_loss: 0.46370 (-0.04877)
| > avg_log_mle: -0.03485 (-0.01866)
| > avg_loss_dur: 0.49855 (-0.03012)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_8526.pth
 > EPOCH: 21/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 13:09:14) 
 --> STEP: 24/406 -- GLOBAL_STEP: 8550
| > loss: 0.48894 (0.48691)
| > log_mle: -0.01141 (-0.00448)
| > loss_dur: 0.50035 (0.49139)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.58710 (1.70015)
| > current_lr: 0.00001
| > step_time: 0.40710 (0.31567)
| > loader_time: 1.76360 (1.24635)
 --> STEP: 49/406 -- GLOBAL_STEP: 8575
| > loss: 0.49617 (0.48580)
| > log_mle: -0.00939 (-0.00594)
| > loss_dur: 0.50556 (0.49174)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.40099 (1.68370)
| > current_lr: 0.00001
| > step_time: 0.44000 (0.35663)
| > loader_time: 1.80320 (1.45924)
 --> STEP: 74/406 -- GLOBAL_STEP: 8600
| > loss: 0.49183 (0.48349)
| > log_mle: -0.03804 (-0.01015)
| > loss_dur: 0.52987 (0.49364)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 3.56506 (1.95312)
| > current_lr: 0.00001
| > step_time: 0.35720 (0.40209)
| > loader_time: 1.58770 (1.53181)
 --> STEP: 99/406 -- GLOBAL_STEP: 8625
| > loss: 0.47969 (0.48211)
| > log_mle: -0.02114 (-0.01338)
| > loss_dur: 0.50083 (0.49550)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.01473 (2.16549)
| > current_lr: 0.00001
| > step_time: 0.45480 (0.42850)
| > loader_time: 1.89590 (1.58305)
 --> STEP: 124/406 -- GLOBAL_STEP: 8650
| > loss: 0.45893 (0.47971)
| > log_mle: -0.03054 (-0.01647)
| > loss_dur: 0.48947 (0.49618)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 3.59767 (2.37554)
| > current_lr: 0.00001
| > step_time: 0.60870 (0.44866)
| > loader_time: 1.76950 (1.61090)
 --> STEP: 149/406 -- GLOBAL_STEP: 8675
| > loss: 0.48400 (0.47860)
| > log_mle: -0.03577 (-0.01908)
| > loss_dur: 0.51977 (0.49768)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.97928 (2.43723)
| > current_lr: 0.00001
| > step_time: 0.65940 (0.47159)
| > loader_time: 1.84830 (1.66450)
 --> STEP: 174/406 -- GLOBAL_STEP: 8700
| > loss: 0.47599 (0.47764)
| > log_mle: -0.03409 (-0.02108)
| > loss_dur: 0.51009 (0.49871)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.70520 (2.39137)
| > current_lr: 0.00001
| > step_time: 0.65090 (0.49230)
| > loader_time: 2.28000 (1.75465)
 --> STEP: 199/406 -- GLOBAL_STEP: 8725
| > loss: 0.48088 (0.47660)
| > log_mle: -0.02838 (-0.02311)
| > loss_dur: 0.50927 (0.49971)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.21901 (2.38723)
| > current_lr: 0.00001
| > step_time: 1.24090 (0.51502)
| > loader_time: 2.96930 (1.82421)
 --> STEP: 224/406 -- GLOBAL_STEP: 8750
| > loss: 0.46393 (0.47502)
| > log_mle: -0.03686 (-0.02494)
| > loss_dur: 0.50079 (0.49996)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.96758 (2.34225)
| > current_lr: 0.00001
| > step_time: 0.63390 (0.53239)
| > loader_time: 2.02210 (1.85865)
 --> STEP: 249/406 -- GLOBAL_STEP: 8775
| > loss: 0.47546 (0.47393)
| > log_mle: -0.03558 (-0.02664)
| > loss_dur: 0.51104 (0.50057)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.07203 (2.31047)
| > current_lr: 0.00001
| > step_time: 0.68800 (0.55181)
| > loader_time: 1.90280 (1.87904)
 --> STEP: 274/406 -- GLOBAL_STEP: 8800
| > loss: 0.47242 (0.47263)
| > log_mle: -0.04635 (-0.02835)
| > loss_dur: 0.51876 (0.50098)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.68935 (2.40262)
| > current_lr: 0.00001
| > step_time: 0.70930 (0.57015)
| > loader_time: 1.72160 (1.88755)
 --> STEP: 299/406 -- GLOBAL_STEP: 8825
| > loss: 0.44551 (0.47150)
| > log_mle: -0.06188 (-0.02965)
| > loss_dur: 0.50739 (0.50114)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.35420 (2.38334)
| > current_lr: 0.00001
| > step_time: 0.76510 (0.59012)
| > loader_time: 2.12890 (1.89523)
 --> STEP: 324/406 -- GLOBAL_STEP: 8850
| > loss: 0.42839 (0.47048)
| > log_mle: -0.04799 (-0.03090)
| > loss_dur: 0.47638 (0.50138)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.42410 (2.35382)
| > current_lr: 0.00001
| > step_time: 0.78850 (0.61021)
| > loader_time: 2.21430 (1.91420)
 --> STEP: 349/406 -- GLOBAL_STEP: 8875
| > loss: 0.47783 (0.46987)
| > log_mle: -0.04596 (-0.03214)
| > loss_dur: 0.52378 (0.50201)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 3.43248 (2.41983)
| > current_lr: 0.00001
| > step_time: 0.80640 (0.63160)
| > loader_time: 2.22780 (1.95226)
 --> STEP: 374/406 -- GLOBAL_STEP: 8900
| > loss: 0.43511 (0.46859)
| > log_mle: -0.04974 (-0.03345)
| > loss_dur: 0.48485 (0.50203)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 3.81903 (2.50179)
| > current_lr: 0.00001
| > step_time: 1.07360 (0.65356)
| > loader_time: 2.77610 (1.98897)
 --> STEP: 399/406 -- GLOBAL_STEP: 8925
| > loss: 0.43970 (0.46732)
| > log_mle: -0.05321 (-0.03468)
| > loss_dur: 0.49291 (0.50200)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.23116 (2.52165)
| > current_lr: 0.00001
| > step_time: 1.17980 (0.68166)
| > loader_time: 2.56120 (2.02084)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.00212 (+0.00170)
| > avg_loss: 0.41871 (-0.04498)
| > avg_log_mle: -0.05128 (-0.01643)
| > avg_loss_dur: 0.46999 (-0.02855)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_8932.pth
 > EPOCH: 22/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 13:28:16) 
 --> STEP: 18/406 -- GLOBAL_STEP: 8950
| > loss: 0.44240 (0.44247)
| > log_mle: -0.01600 (-0.02190)
| > loss_dur: 0.45840 (0.46437)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.71502 (1.97383)
| > current_lr: 0.00001
| > step_time: 0.25090 (0.31114)
| > loader_time: 1.12030 (1.13777)
 --> STEP: 43/406 -- GLOBAL_STEP: 8975
| > loss: 0.41444 (0.43994)
| > log_mle: -0.01950 (-0.02192)
| > loss_dur: 0.43395 (0.46186)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.57268 (1.78726)
| > current_lr: 0.00001
| > step_time: 0.39240 (0.33734)
| > loader_time: 1.70940 (1.33818)
 --> STEP: 68/406 -- GLOBAL_STEP: 9000
| > loss: 0.44090 (0.43888)
| > log_mle: -0.03775 (-0.02555)
| > loss_dur: 0.47865 (0.46443)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.38417 (1.95818)
| > current_lr: 0.00001
| > step_time: 0.44870 (0.37483)
| > loader_time: 1.53250 (1.45698)
 --> STEP: 93/406 -- GLOBAL_STEP: 9025
| > loss: 0.40230 (0.43716)
| > log_mle: -0.05484 (-0.02939)
| > loss_dur: 0.45714 (0.46655)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 4.21489 (2.07380)
| > current_lr: 0.00001
| > step_time: 0.50630 (0.40192)
| > loader_time: 1.47310 (1.51128)
 --> STEP: 118/406 -- GLOBAL_STEP: 9050
| > loss: 0.42291 (0.43512)
| > log_mle: -0.04610 (-0.03232)
| > loss_dur: 0.46901 (0.46744)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.71564 (2.24132)
| > current_lr: 0.00001
| > step_time: 0.43780 (0.42596)
| > loader_time: 1.56450 (1.53057)
 --> STEP: 143/406 -- GLOBAL_STEP: 9075
| > loss: 0.41060 (0.43378)
| > log_mle: -0.05656 (-0.03517)
| > loss_dur: 0.46716 (0.46896)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.36880 (2.28439)
| > current_lr: 0.00001
| > step_time: 0.62560 (0.44735)
| > loader_time: 1.74930 (1.54928)
 --> STEP: 168/406 -- GLOBAL_STEP: 9100
| > loss: 0.44352 (0.43312)
| > log_mle: -0.03752 (-0.03711)
| > loss_dur: 0.48104 (0.47023)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.42391 (2.35159)
| > current_lr: 0.00001
| > step_time: 0.94950 (0.46844)
| > loader_time: 2.04350 (1.57618)
 --> STEP: 193/406 -- GLOBAL_STEP: 9125
| > loss: 0.43542 (0.43226)
| > log_mle: -0.05460 (-0.03917)
| > loss_dur: 0.49002 (0.47143)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.85163 (2.30182)
| > current_lr: 0.00001
| > step_time: 1.26710 (0.49063)
| > loader_time: 2.45420 (1.60772)
 --> STEP: 218/406 -- GLOBAL_STEP: 9150
| > loss: 0.42857 (0.43096)
| > log_mle: -0.05211 (-0.04082)
| > loss_dur: 0.48068 (0.47178)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 3.05389 (2.35978)
| > current_lr: 0.00001
| > step_time: 0.58120 (0.51123)
| > loader_time: 1.56980 (1.63353)
 --> STEP: 243/406 -- GLOBAL_STEP: 9175
| > loss: 0.43464 (0.43006)
| > log_mle: -0.06977 (-0.04265)
| > loss_dur: 0.50441 (0.47271)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 3.54497 (2.36709)
| > current_lr: 0.00001
| > step_time: 0.61630 (0.53159)
| > loader_time: 1.73780 (1.66797)
 --> STEP: 268/406 -- GLOBAL_STEP: 9200
| > loss: 0.41441 (0.42882)
| > log_mle: -0.06332 (-0.04411)
| > loss_dur: 0.47773 (0.47292)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.89973 (2.36712)
| > current_lr: 0.00001
| > step_time: 0.80450 (0.55197)
| > loader_time: 1.92410 (1.69369)
 --> STEP: 293/406 -- GLOBAL_STEP: 9225
| > loss: 0.40959 (0.42765)
| > log_mle: -0.06629 (-0.04548)
| > loss_dur: 0.47587 (0.47313)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.43670 (2.49035)
| > current_lr: 0.00001
| > step_time: 0.85970 (0.57266)
| > loader_time: 2.22040 (1.71912)
 --> STEP: 318/406 -- GLOBAL_STEP: 9250
| > loss: 0.40956 (0.42686)
| > log_mle: -0.05902 (-0.04678)
| > loss_dur: 0.46858 (0.47364)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.93656 (2.49432)
| > current_lr: 0.00001
| > step_time: 0.79330 (0.59440)
| > loader_time: 1.83800 (1.74383)
 --> STEP: 343/406 -- GLOBAL_STEP: 9275
| > loss: 0.41702 (0.42589)
| > log_mle: -0.07229 (-0.04801)
| > loss_dur: 0.48931 (0.47390)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 3.48275 (2.47523)
| > current_lr: 0.00001
| > step_time: 0.81610 (0.61567)
| > loader_time: 2.79710 (1.78776)
 --> STEP: 368/406 -- GLOBAL_STEP: 9300
| > loss: 0.41625 (0.42495)
| > log_mle: -0.07497 (-0.04927)
| > loss_dur: 0.49122 (0.47422)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 3.46743 (2.50782)
| > current_lr: 0.00001
| > step_time: 0.98540 (0.63526)
| > loader_time: 2.36790 (1.82862)
 --> STEP: 393/406 -- GLOBAL_STEP: 9325
| > loss: 0.39614 (0.42387)
| > log_mle: -0.06734 (-0.05043)
| > loss_dur: 0.46349 (0.47430)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.72832 (2.46536)
| > current_lr: 0.00001
| > step_time: 1.11520 (0.65767)
| > loader_time: 2.42140 (1.85823)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 0.93518 (-0.06694)
| > avg_loss: 0.37130 (-0.04741)
| > avg_log_mle: -0.06830 (-0.01702)
| > avg_loss_dur: 0.43961 (-0.03039)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_9338.pth
 > EPOCH: 23/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 13:45:59) 
 --> STEP: 12/406 -- GLOBAL_STEP: 9350
| > loss: 0.38791 (0.39473)
| > log_mle: -0.04485 (-0.03542)
| > loss_dur: 0.43276 (0.43015)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.19087 (1.78454)
| > current_lr: 0.00001
| > step_time: 0.32640 (0.38599)
| > loader_time: 1.15020 (0.99471)
 --> STEP: 37/406 -- GLOBAL_STEP: 9375
| > loss: 0.39531 (0.39706)
| > log_mle: -0.03403 (-0.03723)
| > loss_dur: 0.42934 (0.43428)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.63264 (1.81385)
| > current_lr: 0.00001
| > step_time: 0.37070 (0.34712)
| > loader_time: 1.29320 (1.20758)
 --> STEP: 62/406 -- GLOBAL_STEP: 9400
| > loss: 0.38496 (0.39591)
| > log_mle: -0.03214 (-0.04004)
| > loss_dur: 0.41710 (0.43594)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.61394 (1.89919)
| > current_lr: 0.00001
| > step_time: 0.43940 (0.37503)
| > loader_time: 1.36590 (1.30459)
 --> STEP: 87/406 -- GLOBAL_STEP: 9425
| > loss: 0.38425 (0.39573)
| > log_mle: -0.06857 (-0.04363)
| > loss_dur: 0.45282 (0.43936)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.75198 (2.05405)
| > current_lr: 0.00001
| > step_time: 0.39400 (0.39614)
| > loader_time: 1.27700 (1.36192)
 --> STEP: 112/406 -- GLOBAL_STEP: 9450
| > loss: 0.37497 (0.39397)
| > log_mle: -0.06556 (-0.04684)
| > loss_dur: 0.44053 (0.44081)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.85195 (2.26179)
| > current_lr: 0.00001
| > step_time: 0.59090 (0.41997)
| > loader_time: 1.84970 (1.42143)
 --> STEP: 137/406 -- GLOBAL_STEP: 9475
| > loss: 0.39241 (0.39312)
| > log_mle: -0.05889 (-0.04951)
| > loss_dur: 0.45130 (0.44264)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.52189 (2.31795)
| > current_lr: 0.00001
| > step_time: 0.46170 (0.44204)
| > loader_time: 1.62780 (1.44944)
 --> STEP: 162/406 -- GLOBAL_STEP: 9500
| > loss: 0.37089 (0.39199)
| > log_mle: -0.08145 (-0.05166)
| > loss_dur: 0.45234 (0.44365)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.44864 (2.37980)
| > current_lr: 0.00001
| > step_time: 0.64280 (0.46324)
| > loader_time: 1.90580 (1.49154)
 --> STEP: 187/406 -- GLOBAL_STEP: 9525
| > loss: 0.38590 (0.39128)
| > log_mle: -0.06725 (-0.05366)
| > loss_dur: 0.45315 (0.44494)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 4.61434 (2.37950)
| > current_lr: 0.00001
| > step_time: 0.51320 (0.48373)
| > loader_time: 1.71440 (1.54315)
 --> STEP: 212/406 -- GLOBAL_STEP: 9550
| > loss: 0.38170 (0.39004)
| > log_mle: -0.07341 (-0.05544)
| > loss_dur: 0.45511 (0.44547)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.47850 (2.40552)
| > current_lr: 0.00001
| > step_time: 0.67130 (0.50330)
| > loader_time: 1.67250 (1.58955)
 --> STEP: 237/406 -- GLOBAL_STEP: 9575
| > loss: 0.38200 (0.38884)
| > log_mle: -0.07860 (-0.05717)
| > loss_dur: 0.46059 (0.44601)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.57387 (2.38282)
| > current_lr: 0.00001
| > step_time: 0.73300 (0.52405)
| > loader_time: 1.85090 (1.62784)
 --> STEP: 262/406 -- GLOBAL_STEP: 9600
| > loss: 0.38771 (0.38763)
| > log_mle: -0.06133 (-0.05874)
| > loss_dur: 0.44904 (0.44637)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.09067 (2.49723)
| > current_lr: 0.00001
| > step_time: 0.77150 (0.54436)
| > loader_time: 2.16070 (1.65933)
 --> STEP: 287/406 -- GLOBAL_STEP: 9625
| > loss: 0.40202 (0.38646)
| > log_mle: -0.06810 (-0.06014)
| > loss_dur: 0.47012 (0.44659)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.81088 (2.51863)
| > current_lr: 0.00001
| > step_time: 0.82620 (0.56432)
| > loader_time: 2.11710 (1.69279)
 --> STEP: 312/406 -- GLOBAL_STEP: 9650
| > loss: 0.38367 (0.38579)
| > log_mle: -0.07072 (-0.06130)
| > loss_dur: 0.45439 (0.44709)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.66743 (2.58789)
| > current_lr: 0.00001
| > step_time: 1.23790 (0.58890)
| > loader_time: 2.50300 (1.71889)
 --> STEP: 337/406 -- GLOBAL_STEP: 9675
| > loss: 0.36348 (0.38491)
| > log_mle: -0.07947 (-0.06243)
| > loss_dur: 0.44295 (0.44734)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 3.73454 (2.59658)
| > current_lr: 0.00001
| > step_time: 0.78640 (0.60991)
| > loader_time: 2.12140 (1.75585)
 --> STEP: 362/406 -- GLOBAL_STEP: 9700
| > loss: 0.36710 (0.38401)
| > log_mle: -0.08624 (-0.06365)
| > loss_dur: 0.45333 (0.44766)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.18580 (2.55949)
| > current_lr: 0.00001
| > step_time: 0.93950 (0.62921)
| > loader_time: 2.45100 (1.79307)
 --> STEP: 387/406 -- GLOBAL_STEP: 9725
| > loss: 0.35423 (0.38302)
| > log_mle: -0.08144 (-0.06476)
| > loss_dur: 0.43567 (0.44778)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.62336 (2.56641)
| > current_lr: 0.00001
| > step_time: 1.27540 (0.65105)
| > loader_time: 3.08360 (1.83285)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 0.92831 (-0.00686)
| > avg_loss: 0.32833 (-0.04298)
| > avg_log_mle: -0.08256 (-0.01426)
| > avg_loss_dur: 0.41089 (-0.02871)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_9744.pth
 > EPOCH: 24/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 14:03:38) 
 --> STEP: 6/406 -- GLOBAL_STEP: 9750
| > loss: 0.33191 (0.36378)
| > log_mle: -0.05005 (-0.04733)
| > loss_dur: 0.38196 (0.41111)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 1.50832 (1.80549)
| > current_lr: 0.00001
| > step_time: 0.35650 (0.34220)
| > loader_time: 0.88390 (0.84133)
 --> STEP: 31/406 -- GLOBAL_STEP: 9775
| > loss: 0.36799 (0.35880)
| > log_mle: -0.04952 (-0.05113)
| > loss_dur: 0.41750 (0.40994)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.46473 (2.03499)
| > current_lr: 0.00001
| > step_time: 0.37760 (0.34920)
| > loader_time: 1.51190 (1.10560)
 --> STEP: 56/406 -- GLOBAL_STEP: 9800
| > loss: 0.33785 (0.35927)
| > log_mle: -0.06201 (-0.05351)
| > loss_dur: 0.39985 (0.41278)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 3.17897 (2.09753)
| > current_lr: 0.00001
| > step_time: 0.41270 (0.37794)
| > loader_time: 1.46680 (1.26402)
 --> STEP: 81/406 -- GLOBAL_STEP: 9825
| > loss: 0.35844 (0.35732)
| > log_mle: -0.07053 (-0.05649)
| > loss_dur: 0.42897 (0.41381)
| > amp_scaler: 65536.00000 (65536.00000)
| > grad_norm: 2.83049 (2.35064)
| > current_lr: 0.00001
| > step_time: 0.49360 (0.39425)
| > loader_time: 1.59840 (1.31740)
 --> STEP: 106/406 -- GLOBAL_STEP: 9850
| > loss: 0.34818 (0.35655)
| > log_mle: -0.06362 (-0.05974)
| > loss_dur: 0.41181 (0.41629)
| > amp_scaler: 32768.00000 (60899.01887)
| > grad_norm: 3.48686 (2.86929)
| > current_lr: 0.00001
| > step_time: 0.49440 (0.41798)
| > loader_time: 1.54340 (1.35995)
 --> STEP: 131/406 -- GLOBAL_STEP: 9875
| > loss: 0.31918 (0.35569)
| > log_mle: -0.07275 (-0.06261)
| > loss_dur: 0.39193 (0.41830)
| > amp_scaler: 32768.00000 (55530.50382)
| > grad_norm: 3.00620 (2.91054)
| > current_lr: 0.00001
| > step_time: 0.43620 (0.43700)
| > loader_time: 1.51380 (1.40150)
 --> STEP: 156/406 -- GLOBAL_STEP: 9900
| > loss: 0.33350 (0.35500)
| > log_mle: -0.07969 (-0.06480)
| > loss_dur: 0.41320 (0.41979)
| > amp_scaler: 32768.00000 (51882.66667)
| > grad_norm: 2.61029 (2.88036)
| > current_lr: 0.00001
| > step_time: 0.55390 (0.46004)
| > loader_time: 1.84800 (1.44713)
 --> STEP: 181/406 -- GLOBAL_STEP: 9925
| > loss: 0.34724 (0.35438)
| > log_mle: -0.07706 (-0.06668)
| > loss_dur: 0.42430 (0.42106)
| > amp_scaler: 32768.00000 (49242.51934)
| > grad_norm: 4.78213 (2.95219)
| > current_lr: 0.00001
| > step_time: 0.54730 (0.48138)
| > loader_time: 1.82450 (1.50200)
 --> STEP: 206/406 -- GLOBAL_STEP: 9950
| > loss: 0.36308 (0.35363)
| > log_mle: -0.07920 (-0.06841)
| > loss_dur: 0.44227 (0.42204)
| > amp_scaler: 32768.00000 (47243.18447)
| > grad_norm: 2.10551 (3.02059)
| > current_lr: 0.00001
| > step_time: 0.69500 (0.50209)
| > loader_time: 1.74930 (1.54559)
 --> STEP: 231/406 -- GLOBAL_STEP: 9975
| > loss: 0.34605 (0.35272)
| > log_mle: -0.08828 (-0.07012)
| > loss_dur: 0.43433 (0.42283)
| > amp_scaler: 32768.00000 (45676.60606)
| > grad_norm: 2.46758 (3.06158)
| > current_lr: 0.00001
| > step_time: 0.73770 (0.52130)
| > loader_time: 2.01620 (1.58745)
 --> STEP: 256/406 -- GLOBAL_STEP: 10000
| > loss: 0.33344 (0.35172)
| > log_mle: -0.08687 (-0.07168)
| > loss_dur: 0.42031 (0.42340)
| > amp_scaler: 32768.00000 (44416.00000)
| > grad_norm: 3.29178 (3.04318)
| > current_lr: 0.00001
| > step_time: 0.83360 (0.54332)
| > loader_time: 1.76890 (1.61219)
> CHECKPOINT : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/checkpoint_10000.pth
 --> STEP: 281/406 -- GLOBAL_STEP: 10025
| > loss: 0.34688 (0.35072)
| > log_mle: -0.09505 (-0.07304)
| > loss_dur: 0.44193 (0.42376)
| > amp_scaler: 32768.00000 (43379.70107)
| > grad_norm: 2.75201 (3.04290)
| > current_lr: 0.00001
| > step_time: 0.83200 (0.56567)
| > loader_time: 2.06650 (1.62756)
 --> STEP: 306/406 -- GLOBAL_STEP: 10050
| > loss: 0.36296 (0.35010)
| > log_mle: -0.08582 (-0.07420)
| > loss_dur: 0.44878 (0.42431)
| > amp_scaler: 32768.00000 (42512.73203)
| > grad_norm: 2.21842 (3.07589)
| > current_lr: 0.00001
| > step_time: 0.73400 (0.58619)
| > loader_time: 2.21880 (1.65308)
 --> STEP: 331/406 -- GLOBAL_STEP: 10075
| > loss: 0.34699 (0.34948)
| > log_mle: -0.08820 (-0.07525)
| > loss_dur: 0.43519 (0.42473)
| > amp_scaler: 32768.00000 (41776.72508)
| > grad_norm: 2.17465 (3.02362)
| > current_lr: 0.00001
| > step_time: 0.91210 (0.60542)
| > loader_time: 2.25270 (1.69077)
 --> STEP: 356/406 -- GLOBAL_STEP: 10100
| > loss: 0.35376 (0.34882)
| > log_mle: -0.09512 (-0.07641)
| > loss_dur: 0.44887 (0.42522)
| > amp_scaler: 32768.00000 (41144.08989)
| > grad_norm: 3.12636 (2.99082)
| > current_lr: 0.00001
| > step_time: 0.91860 (0.62452)
| > loader_time: 2.54590 (1.72876)
 --> STEP: 381/406 -- GLOBAL_STEP: 10125
| > loss: 0.33595 (0.34776)
| > log_mle: -0.08653 (-0.07751)
| > loss_dur: 0.42248 (0.42527)
| > amp_scaler: 32768.00000 (40594.47769)
| > grad_norm: 2.27133 (3.04745)
| > current_lr: 0.00001
| > step_time: 0.84760 (0.64428)
| > loader_time: 2.09060 (1.76689)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 0.93304 (+0.00473)
| > avg_loss: 0.29703 (-0.03130)
| > avg_log_mle: -0.09486 (-0.01230)
| > avg_loss_dur: 0.39189 (-0.01900)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_10150.pth
 > EPOCH: 25/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 14:21:01) 
 --> STEP: 0/406 -- GLOBAL_STEP: 10150
| > loss: 0.36791 (0.36791)
| > log_mle: -0.04815 (-0.04815)
| > loss_dur: 0.41606 (0.41606)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 1.83520 (1.83520)
| > current_lr: 0.00001
| > step_time: 0.95990 (0.95987)
| > loader_time: 2.10810 (2.10811)
 --> STEP: 25/406 -- GLOBAL_STEP: 10175
| > loss: 0.34707 (0.32220)
| > log_mle: -0.06176 (-0.06302)
| > loss_dur: 0.40883 (0.38522)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 1.86322 (2.12800)
| > current_lr: 0.00001
| > step_time: 0.25680 (0.34955)
| > loader_time: 1.43490 (1.04003)
 --> STEP: 50/406 -- GLOBAL_STEP: 10200
| > loss: 0.36008 (0.32337)
| > log_mle: -0.08598 (-0.06481)
| > loss_dur: 0.44606 (0.38818)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.66169 (2.15511)
| > current_lr: 0.00001
| > step_time: 0.42250 (0.36954)
| > loader_time: 1.38110 (1.24011)
 --> STEP: 75/406 -- GLOBAL_STEP: 10225
| > loss: 0.32980 (0.32397)
| > log_mle: -0.07962 (-0.06847)
| > loss_dur: 0.40942 (0.39243)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.62078 (2.63454)
| > current_lr: 0.00001
| > step_time: 0.52770 (0.39641)
| > loader_time: 1.69910 (1.32480)
 --> STEP: 100/406 -- GLOBAL_STEP: 10250
| > loss: 0.31785 (0.32260)
| > log_mle: -0.07358 (-0.07141)
| > loss_dur: 0.39143 (0.39401)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.21757 (3.12863)
| > current_lr: 0.00001
| > step_time: 0.96130 (0.42854)
| > loader_time: 1.77380 (1.37966)
 --> STEP: 125/406 -- GLOBAL_STEP: 10275
| > loss: 0.29476 (0.32113)
| > log_mle: -0.10105 (-0.07454)
| > loss_dur: 0.39582 (0.39567)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.03556 (3.13855)
| > current_lr: 0.00001
| > step_time: 0.55550 (0.44597)
| > loader_time: 1.45420 (1.41426)
 --> STEP: 150/406 -- GLOBAL_STEP: 10300
| > loss: 0.31675 (0.32077)
| > log_mle: -0.08453 (-0.07681)
| > loss_dur: 0.40128 (0.39758)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 1.66820 (3.14097)
| > current_lr: 0.00001
| > step_time: 0.67580 (0.46705)
| > loader_time: 1.66530 (1.46104)
 --> STEP: 175/406 -- GLOBAL_STEP: 10325
| > loss: 0.31513 (0.32040)
| > log_mle: -0.08665 (-0.07857)
| > loss_dur: 0.40178 (0.39896)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 5.97584 (3.26017)
| > current_lr: 0.00001
| > step_time: 0.60720 (0.48876)
| > loader_time: 1.88050 (1.54341)
 --> STEP: 200/406 -- GLOBAL_STEP: 10350
| > loss: 0.31346 (0.32003)
| > log_mle: -0.09315 (-0.08039)
| > loss_dur: 0.40661 (0.40043)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 5.46800 (3.32630)
| > current_lr: 0.00001
| > step_time: 0.59530 (0.51058)
| > loader_time: 2.14180 (1.60371)
 --> STEP: 225/406 -- GLOBAL_STEP: 10375
| > loss: 0.33735 (0.31950)
| > log_mle: -0.09175 (-0.08200)
| > loss_dur: 0.42911 (0.40149)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.14477 (3.42000)
| > current_lr: 0.00001
| > step_time: 0.75030 (0.52950)
| > loader_time: 2.30530 (1.66011)
 --> STEP: 250/406 -- GLOBAL_STEP: 10400
| > loss: 0.31736 (0.31887)
| > log_mle: -0.11224 (-0.08354)
| > loss_dur: 0.42959 (0.40241)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.76417 (3.42508)
| > current_lr: 0.00001
| > step_time: 0.75690 (0.54962)
| > loader_time: 2.30640 (1.72168)
 --> STEP: 275/406 -- GLOBAL_STEP: 10425
| > loss: 0.31471 (0.31801)
| > log_mle: -0.08803 (-0.08492)
| > loss_dur: 0.40274 (0.40292)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.78913 (3.43352)
| > current_lr: 0.00001
| > step_time: 0.82710 (0.56858)
| > loader_time: 2.43750 (1.78036)
 --> STEP: 300/406 -- GLOBAL_STEP: 10450
| > loss: 0.32317 (0.31742)
| > log_mle: -0.08734 (-0.08599)
| > loss_dur: 0.41051 (0.40341)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.55488 (3.42827)
| > current_lr: 0.00001
| > step_time: 0.83550 (0.59015)
| > loader_time: 2.48410 (1.83080)
 --> STEP: 325/406 -- GLOBAL_STEP: 10475
| > loss: 0.32069 (0.31708)
| > log_mle: -0.09187 (-0.08703)
| > loss_dur: 0.41256 (0.40412)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.13710 (3.35333)
| > current_lr: 0.00001
| > step_time: 0.90860 (0.61018)
| > loader_time: 2.82340 (1.88937)
 --> STEP: 350/406 -- GLOBAL_STEP: 10500
| > loss: 0.28665 (0.31654)
| > log_mle: -0.10594 (-0.08809)
| > loss_dur: 0.39259 (0.40463)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 4.03045 (3.40664)
| > current_lr: 0.00001
| > step_time: 0.85680 (0.63554)
| > loader_time: 2.75960 (1.94982)
 --> STEP: 375/406 -- GLOBAL_STEP: 10525
| > loss: 0.29872 (0.31579)
| > log_mle: -0.11507 (-0.08921)
| > loss_dur: 0.41380 (0.40500)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 4.25818 (3.43185)
| > current_lr: 0.00001
| > step_time: 0.83920 (0.65555)
| > loader_time: 2.67370 (2.01398)
 --> STEP: 400/406 -- GLOBAL_STEP: 10550
| > loss: 0.31888 (0.31510)
| > log_mle: -0.11022 (-0.09023)
| > loss_dur: 0.42910 (0.40533)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.88292 (3.41859)
| > current_lr: 0.00001
| > step_time: 1.05960 (0.67872)
| > loader_time: 2.67980 (2.07001)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.13241 (+0.19938)
| > avg_loss: 0.27015 (-0.02688)
| > avg_log_mle: -0.10556 (-0.01069)
| > avg_loss_dur: 0.37570 (-0.01619)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_10556.pth
 > EPOCH: 26/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 14:40:18) 
 --> STEP: 19/406 -- GLOBAL_STEP: 10575
| > loss: 0.31905 (0.29570)
| > log_mle: -0.06453 (-0.07463)
| > loss_dur: 0.38358 (0.37033)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 1.95285 (2.08358)
| > current_lr: 0.00001
| > step_time: 0.38230 (0.32271)
| > loader_time: 1.72870 (1.41896)
 --> STEP: 44/406 -- GLOBAL_STEP: 10600
| > loss: 0.29485 (0.29653)
| > log_mle: -0.08148 (-0.07508)
| > loss_dur: 0.37633 (0.37162)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.60284 (1.97887)
| > current_lr: 0.00001
| > step_time: 0.35380 (0.38022)
| > loader_time: 1.95720 (1.66818)
 --> STEP: 69/406 -- GLOBAL_STEP: 10625
| > loss: 0.29900 (0.29633)
| > log_mle: -0.08687 (-0.07837)
| > loss_dur: 0.38587 (0.37470)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.68065 (2.36066)
| > current_lr: 0.00001
| > step_time: 0.51550 (0.41248)
| > loader_time: 1.84280 (1.78511)
 --> STEP: 94/406 -- GLOBAL_STEP: 10650
| > loss: 0.28810 (0.29502)
| > log_mle: -0.09557 (-0.08195)
| > loss_dur: 0.38367 (0.37697)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 5.68335 (2.65214)
| > current_lr: 0.00001
| > step_time: 0.55950 (0.44215)
| > loader_time: 1.71670 (1.82218)
 --> STEP: 119/406 -- GLOBAL_STEP: 10675
| > loss: 0.28052 (0.29354)
| > log_mle: -0.10613 (-0.08468)
| > loss_dur: 0.38665 (0.37822)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.36627 (2.79194)
| > current_lr: 0.00001
| > step_time: 0.60470 (0.46592)
| > loader_time: 2.22370 (1.84523)
 --> STEP: 144/406 -- GLOBAL_STEP: 10700
| > loss: 0.29097 (0.29301)
| > log_mle: -0.09276 (-0.08713)
| > loss_dur: 0.38373 (0.38014)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.84522 (3.20076)
| > current_lr: 0.00001
| > step_time: 1.14280 (0.49092)
| > loader_time: 2.36260 (1.88266)
 --> STEP: 169/406 -- GLOBAL_STEP: 10725
| > loss: 0.30650 (0.29271)
| > log_mle: -0.09801 (-0.08890)
| > loss_dur: 0.40451 (0.38161)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.34032 (3.12170)
| > current_lr: 0.00001
| > step_time: 0.65830 (0.50896)
| > loader_time: 2.50200 (1.94883)
 --> STEP: 194/406 -- GLOBAL_STEP: 10750
| > loss: 0.28301 (0.29239)
| > log_mle: -0.10233 (-0.09078)
| > loss_dur: 0.38534 (0.38317)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.88190 (3.12262)
| > current_lr: 0.00001
| > step_time: 0.63190 (0.52616)
| > loader_time: 2.28460 (2.01504)
 --> STEP: 219/406 -- GLOBAL_STEP: 10775
| > loss: 0.28306 (0.29158)
| > log_mle: -0.10657 (-0.09227)
| > loss_dur: 0.38963 (0.38384)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.65077 (3.17638)
| > current_lr: 0.00001
| > step_time: 0.80500 (0.54407)
| > loader_time: 2.55860 (2.06934)
 --> STEP: 244/406 -- GLOBAL_STEP: 10800
| > loss: 0.26848 (0.29107)
| > log_mle: -0.10769 (-0.09393)
| > loss_dur: 0.37617 (0.38500)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 1.69881 (3.24113)
| > current_lr: 0.00001
| > step_time: 0.63010 (0.56236)
| > loader_time: 2.40450 (2.11080)
 --> STEP: 269/406 -- GLOBAL_STEP: 10825
| > loss: 0.27135 (0.29015)
| > log_mle: -0.11310 (-0.09524)
| > loss_dur: 0.38444 (0.38538)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 4.41463 (3.31364)
| > current_lr: 0.00001
| > step_time: 0.70470 (0.58189)
| > loader_time: 2.31720 (2.14751)
 --> STEP: 294/406 -- GLOBAL_STEP: 10850
| > loss: 0.27688 (0.28927)
| > log_mle: -0.11561 (-0.09644)
| > loss_dur: 0.39249 (0.38571)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.25505 (3.32884)
| > current_lr: 0.00001
| > step_time: 0.73150 (0.60137)
| > loader_time: 2.52920 (2.17966)
 --> STEP: 319/406 -- GLOBAL_STEP: 10875
| > loss: 0.27698 (0.28899)
| > log_mle: -0.11379 (-0.09755)
| > loss_dur: 0.39076 (0.38654)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.22433 (3.33609)
| > current_lr: 0.00001
| > step_time: 0.89220 (0.61948)
| > loader_time: 2.86190 (2.21529)
 --> STEP: 344/406 -- GLOBAL_STEP: 10900
| > loss: 0.29553 (0.28841)
| > log_mle: -0.10609 (-0.09856)
| > loss_dur: 0.40162 (0.38697)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 1.94456 (3.32810)
| > current_lr: 0.00001
| > step_time: 1.06930 (0.63793)
| > loader_time: 2.72140 (2.26242)
 --> STEP: 369/406 -- GLOBAL_STEP: 10925
| > loss: 0.27682 (0.28770)
| > log_mle: -0.10608 (-0.09964)
| > loss_dur: 0.38290 (0.38734)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.09411 (3.39881)
| > current_lr: 0.00001
| > step_time: 1.06060 (0.65995)
| > loader_time: 2.55610 (2.30859)
 --> STEP: 394/406 -- GLOBAL_STEP: 10950
| > loss: 0.27825 (0.28705)
| > log_mle: -0.11287 (-0.10064)
| > loss_dur: 0.39112 (0.38769)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.23576 (3.45716)
| > current_lr: 0.00001
| > step_time: 0.99690 (0.67680)
| > loader_time: 2.93270 (2.34434)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.15634 (+0.02392)
| > avg_loss: 0.24522 (-0.02492)
| > avg_log_mle: -0.11552 (-0.00996)
| > avg_loss_dur: 0.36074 (-0.01496)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_10962.pth
 > EPOCH: 27/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 15:01:26) 
 --> STEP: 13/406 -- GLOBAL_STEP: 10975
| > loss: 0.25450 (0.27252)
| > log_mle: -0.09341 (-0.08467)
| > loss_dur: 0.34792 (0.35720)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.12503 (2.20860)
| > current_lr: 0.00001
| > step_time: 0.40790 (0.33176)
| > loader_time: 1.89600 (1.43379)
 --> STEP: 38/406 -- GLOBAL_STEP: 11000
| > loss: 0.28087 (0.27329)
| > log_mle: -0.09488 (-0.08562)
| > loss_dur: 0.37575 (0.35891)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.06309 (2.19596)
| > current_lr: 0.00001
| > step_time: 0.38650 (0.35656)
| > loader_time: 1.62400 (1.69670)
 --> STEP: 63/406 -- GLOBAL_STEP: 11025
| > loss: 0.28427 (0.27141)
| > log_mle: -0.09951 (-0.08805)
| > loss_dur: 0.38377 (0.35946)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 4.59792 (2.63140)
| > current_lr: 0.00001
| > step_time: 0.39550 (0.39487)
| > loader_time: 1.85290 (1.79351)
 --> STEP: 88/406 -- GLOBAL_STEP: 11050
| > loss: 0.28895 (0.27024)
| > log_mle: -0.08771 (-0.09121)
| > loss_dur: 0.37666 (0.36146)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 1.26100 (2.64407)
| > current_lr: 0.00001
| > step_time: 0.53570 (0.42663)
| > loader_time: 1.82170 (1.83881)
 --> STEP: 113/406 -- GLOBAL_STEP: 11075
| > loss: 0.28028 (0.26794)
| > log_mle: -0.10793 (-0.09433)
| > loss_dur: 0.38821 (0.36227)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 7.54213 (2.81994)
| > current_lr: 0.00001
| > step_time: 0.41110 (0.45591)
| > loader_time: 1.69300 (1.86757)
 --> STEP: 138/406 -- GLOBAL_STEP: 11100
| > loss: 0.28809 (0.26816)
| > log_mle: -0.11248 (-0.09672)
| > loss_dur: 0.40058 (0.36488)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 5.01520 (2.89930)
| > current_lr: 0.00001
| > step_time: 0.64380 (0.47816)
| > loader_time: 1.99430 (1.89708)
 --> STEP: 163/406 -- GLOBAL_STEP: 11125
| > loss: 0.29171 (0.26742)
| > log_mle: -0.11642 (-0.09858)
| > loss_dur: 0.40813 (0.36600)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 4.59138 (2.94171)
| > current_lr: 0.00001
| > step_time: 0.53680 (0.50101)
| > loader_time: 2.35170 (1.94871)
 --> STEP: 188/406 -- GLOBAL_STEP: 11150
| > loss: 0.27765 (0.26724)
| > log_mle: -0.12752 (-0.10039)
| > loss_dur: 0.40516 (0.36763)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 13.96418 (3.35335)
| > current_lr: 0.00001
| > step_time: 0.59420 (0.51827)
| > loader_time: 2.63030 (2.02814)
 --> STEP: 213/406 -- GLOBAL_STEP: 11175
| > loss: 0.25370 (0.26697)
| > log_mle: -0.11926 (-0.10183)
| > loss_dur: 0.37295 (0.36880)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 7.61691 (3.91675)
| > current_lr: 0.00001
| > step_time: 0.62910 (0.53636)
| > loader_time: 2.42150 (2.08316)
 --> STEP: 238/406 -- GLOBAL_STEP: 11200
| > loss: 0.25704 (0.26656)
| > log_mle: -0.11957 (-0.10335)
| > loss_dur: 0.37660 (0.36991)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 4.41119 (4.23871)
| > current_lr: 0.00001
| > step_time: 0.61440 (0.55636)
| > loader_time: 2.43040 (2.13226)
 --> STEP: 263/406 -- GLOBAL_STEP: 11225
| > loss: 0.25484 (0.26568)
| > log_mle: -0.12070 (-0.10471)
| > loss_dur: 0.37554 (0.37039)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 1.84671 (4.28862)
| > current_lr: 0.00001
| > step_time: 0.77020 (0.57541)
| > loader_time: 2.55390 (2.17513)
 --> STEP: 288/406 -- GLOBAL_STEP: 11250
| > loss: 0.24486 (0.26486)
| > log_mle: -0.11440 (-0.10589)
| > loss_dur: 0.35926 (0.37075)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.05309 (4.20038)
| > current_lr: 0.00001
| > step_time: 1.04440 (0.59586)
| > loader_time: 2.44920 (2.21128)
 --> STEP: 313/406 -- GLOBAL_STEP: 11275
| > loss: 0.27056 (0.26453)
| > log_mle: -0.11867 (-0.10692)
| > loss_dur: 0.38923 (0.37145)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.36317 (4.16680)
| > current_lr: 0.00001
| > step_time: 0.86780 (0.61630)
| > loader_time: 2.60630 (2.23402)
 --> STEP: 338/406 -- GLOBAL_STEP: 11300
| > loss: 0.24359 (0.26398)
| > log_mle: -0.12069 (-0.10788)
| > loss_dur: 0.36428 (0.37186)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 8.10564 (4.07323)
| > current_lr: 0.00001
| > step_time: 0.79310 (0.63234)
| > loader_time: 2.93840 (2.28031)
 --> STEP: 363/406 -- GLOBAL_STEP: 11325
| > loss: 0.26628 (0.26342)
| > log_mle: -0.13004 (-0.10894)
| > loss_dur: 0.39632 (0.37236)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 4.56009 (4.12299)
| > current_lr: 0.00001
| > step_time: 0.83040 (0.65106)
| > loader_time: 3.07310 (2.33037)
 --> STEP: 388/406 -- GLOBAL_STEP: 11350
| > loss: 0.25700 (0.26289)
| > log_mle: -0.12098 (-0.10985)
| > loss_dur: 0.37798 (0.37274)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 5.16298 (4.16924)
| > current_lr: 0.00001
| > step_time: 0.96230 (0.67047)
| > loader_time: 3.19480 (2.37481)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.11481 (-0.04153)
| > avg_loss: 0.22513 (-0.02009)
| > avg_log_mle: -0.12299 (-0.00748)
| > avg_loss_dur: 0.34813 (-0.01261)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_11368.pth
 > EPOCH: 28/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 15:22:52) 
 --> STEP: 7/406 -- GLOBAL_STEP: 11375
| > loss: 0.24754 (0.24895)
| > log_mle: -0.08381 (-0.08936)
| > loss_dur: 0.33136 (0.33830)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 1.85162 (2.00496)
| > current_lr: 0.00001
| > step_time: 0.25760 (0.40619)
| > loader_time: 1.72620 (1.44538)
 --> STEP: 32/406 -- GLOBAL_STEP: 11400
| > loss: 0.26184 (0.25077)
| > log_mle: -0.09838 (-0.09410)
| > loss_dur: 0.36022 (0.34487)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.66730 (2.14867)
| > current_lr: 0.00001
| > step_time: 0.31660 (0.36407)
| > loader_time: 1.85530 (1.65188)
 --> STEP: 57/406 -- GLOBAL_STEP: 11425
| > loss: 0.26797 (0.24835)
| > log_mle: -0.09775 (-0.09613)
| > loss_dur: 0.36572 (0.34448)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.30505 (2.52244)
| > current_lr: 0.00001
| > step_time: 0.49980 (0.40266)
| > loader_time: 2.03750 (1.77590)
 --> STEP: 82/406 -- GLOBAL_STEP: 11450
| > loss: 0.26705 (0.24775)
| > log_mle: -0.11287 (-0.09912)
| > loss_dur: 0.37992 (0.34687)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.62870 (2.48962)
| > current_lr: 0.00001
| > step_time: 0.66840 (0.43656)
| > loader_time: 2.31600 (1.86147)
 --> STEP: 107/406 -- GLOBAL_STEP: 11475
| > loss: 0.24044 (0.24708)
| > log_mle: -0.11161 (-0.10220)
| > loss_dur: 0.35205 (0.34928)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.18449 (2.92253)
| > current_lr: 0.00001
| > step_time: 0.47190 (0.46425)
| > loader_time: 1.90730 (1.89461)
 --> STEP: 132/406 -- GLOBAL_STEP: 11500
| > loss: 0.23223 (0.24616)
| > log_mle: -0.12862 (-0.10499)
| > loss_dur: 0.36085 (0.35115)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 6.69415 (3.05425)
| > current_lr: 0.00001
| > step_time: 0.48160 (0.48247)
| > loader_time: 2.16880 (1.91470)
 --> STEP: 157/406 -- GLOBAL_STEP: 11525
| > loss: 0.24312 (0.24625)
| > log_mle: -0.11761 (-0.10686)
| > loss_dur: 0.36073 (0.35311)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.22078 (3.22693)
| > current_lr: 0.00001
| > step_time: 0.64330 (0.50350)
| > loader_time: 2.64580 (1.96773)
 --> STEP: 182/406 -- GLOBAL_STEP: 11550
| > loss: 0.23910 (0.24580)
| > log_mle: -0.12347 (-0.10862)
| > loss_dur: 0.36256 (0.35442)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.97826 (3.30809)
| > current_lr: 0.00001
| > step_time: 0.57360 (0.52134)
| > loader_time: 2.44190 (2.05219)
 --> STEP: 207/406 -- GLOBAL_STEP: 11575
| > loss: 0.24730 (0.24568)
| > log_mle: -0.11261 (-0.11016)
| > loss_dur: 0.35991 (0.35584)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 4.36016 (3.86091)
| > current_lr: 0.00001
| > step_time: 0.71770 (0.53819)
| > loader_time: 2.85260 (2.11001)
 --> STEP: 232/406 -- GLOBAL_STEP: 11600
| > loss: 0.23810 (0.24515)
| > log_mle: -0.13104 (-0.11178)
| > loss_dur: 0.36914 (0.35693)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.31728 (4.17088)
| > current_lr: 0.00001
| > step_time: 0.67090 (0.55551)
| > loader_time: 2.18010 (2.16037)
 --> STEP: 257/406 -- GLOBAL_STEP: 11625
| > loss: 0.23360 (0.24449)
| > log_mle: -0.12605 (-0.11315)
| > loss_dur: 0.35964 (0.35764)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.53223 (4.15985)
| > current_lr: 0.00001
| > step_time: 0.71480 (0.57525)
| > loader_time: 2.11940 (2.20111)
 --> STEP: 282/406 -- GLOBAL_STEP: 11650
| > loss: 0.22450 (0.24364)
| > log_mle: -0.13085 (-0.11437)
| > loss_dur: 0.35535 (0.35801)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 9.06947 (4.32209)
| > current_lr: 0.00001
| > step_time: 0.91790 (0.59604)
| > loader_time: 2.63340 (2.23064)
 --> STEP: 307/406 -- GLOBAL_STEP: 11675
| > loss: 0.23451 (0.24330)
| > log_mle: -0.12334 (-0.11538)
| > loss_dur: 0.35786 (0.35868)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 4.04611 (4.32227)
| > current_lr: 0.00001
| > step_time: 0.88530 (0.61691)
| > loader_time: 2.89010 (2.25881)
 --> STEP: 332/406 -- GLOBAL_STEP: 11700
| > loss: 0.23318 (0.24298)
| > log_mle: -0.12302 (-0.11628)
| > loss_dur: 0.35620 (0.35926)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 4.00700 (4.29802)
| > current_lr: 0.00001
| > step_time: 0.79380 (0.63422)
| > loader_time: 2.90880 (2.30298)
 --> STEP: 357/406 -- GLOBAL_STEP: 11725
| > loss: 0.24122 (0.24251)
| > log_mle: -0.12114 (-0.11729)
| > loss_dur: 0.36236 (0.35981)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 1.77495 (4.30505)
| > current_lr: 0.00001
| > step_time: 0.95580 (0.65304)
| > loader_time: 3.09350 (2.35214)
 --> STEP: 382/406 -- GLOBAL_STEP: 11750
| > loss: 0.23372 (0.24182)
| > log_mle: -0.12874 (-0.11829)
| > loss_dur: 0.36246 (0.36010)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 4.99866 (4.32335)
| > current_lr: 0.00001
| > step_time: 0.89600 (0.67464)
| > loader_time: 3.28690 (2.40182)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.16440 (+0.04959)
| > avg_loss: 0.20483 (-0.02031)
| > avg_log_mle: -0.13320 (-0.01021)
| > avg_loss_dur: 0.33803 (-0.01010)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_11774.pth
 > EPOCH: 29/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 15:44:39) 
 --> STEP: 1/406 -- GLOBAL_STEP: 11775
| > loss: 0.21087 (0.21087)
| > log_mle: -0.10073 (-0.10073)
| > loss_dur: 0.31160 (0.31160)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.08018 (3.08018)
| > current_lr: 0.00001
| > step_time: 0.35140 (0.35143)
| > loader_time: 1.32570 (1.32574)
 --> STEP: 26/406 -- GLOBAL_STEP: 11800
| > loss: 0.24168 (0.22689)
| > log_mle: -0.10708 (-0.10202)
| > loss_dur: 0.34876 (0.32891)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.10323 (2.84690)
| > current_lr: 0.00001
| > step_time: 0.42380 (0.37359)
| > loader_time: 1.92050 (1.71067)
 --> STEP: 51/406 -- GLOBAL_STEP: 11825
| > loss: 0.22614 (0.22983)
| > log_mle: -0.12822 (-0.10369)
| > loss_dur: 0.35437 (0.33352)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 11.64828 (3.19642)
| > current_lr: 0.00001
| > step_time: 0.43750 (0.40118)
| > loader_time: 1.48960 (1.81637)
 --> STEP: 76/406 -- GLOBAL_STEP: 11850
| > loss: 0.22871 (0.22930)
| > log_mle: -0.10567 (-0.10646)
| > loss_dur: 0.33438 (0.33576)
| > amp_scaler: 65536.00000 (39235.36842)
| > grad_norm: 4.86194 (3.66272)
| > current_lr: 0.00001
| > step_time: 0.55780 (0.43194)
| > loader_time: 2.12800 (1.86057)
 --> STEP: 101/406 -- GLOBAL_STEP: 11875
| > loss: 0.25688 (0.22926)
| > log_mle: -0.12246 (-0.10931)
| > loss_dur: 0.37934 (0.33857)
| > amp_scaler: 32768.00000 (39256.71287)
| > grad_norm: 6.65345 (3.72442)
| > current_lr: 0.00001
| > step_time: 0.57660 (0.45852)
| > loader_time: 1.88840 (1.92446)
 --> STEP: 126/406 -- GLOBAL_STEP: 11900
| > loss: 0.24779 (0.22742)
| > log_mle: -0.11488 (-0.11218)
| > loss_dur: 0.36268 (0.33960)
| > amp_scaler: 32768.00000 (37969.26984)
| > grad_norm: 3.86605 (3.65566)
| > current_lr: 0.00001
| > step_time: 0.61210 (0.47867)
| > loader_time: 1.82150 (1.96366)
 --> STEP: 151/406 -- GLOBAL_STEP: 11925
| > loss: 0.23473 (0.22738)
| > log_mle: -0.12094 (-0.11434)
| > loss_dur: 0.35567 (0.34172)
| > amp_scaler: 32768.00000 (37108.13245)
| > grad_norm: 3.45270 (3.78111)
| > current_lr: 0.00001
| > step_time: 0.75090 (0.50099)
| > loader_time: 2.12960 (2.00384)
 --> STEP: 176/406 -- GLOBAL_STEP: 11950
| > loss: 0.21988 (0.22693)
| > log_mle: -0.12217 (-0.11596)
| > loss_dur: 0.34204 (0.34289)
| > amp_scaler: 32768.00000 (36491.63636)
| > grad_norm: 2.73817 (3.76767)
| > current_lr: 0.00001
| > step_time: 0.60160 (0.51757)
| > loader_time: 2.55870 (2.10309)
 --> STEP: 201/406 -- GLOBAL_STEP: 11975
| > loss: 0.21297 (0.22650)
| > log_mle: -0.12407 (-0.11764)
| > loss_dur: 0.33704 (0.34414)
| > amp_scaler: 32768.00000 (36028.49751)
| > grad_norm: 3.07779 (3.86291)
| > current_lr: 0.00001
| > step_time: 0.70480 (0.53324)
| > loader_time: 2.74380 (2.17137)
 --> STEP: 226/406 -- GLOBAL_STEP: 12000
| > loss: 0.21680 (0.22619)
| > log_mle: -0.13852 (-0.11920)
| > loss_dur: 0.35533 (0.34539)
| > amp_scaler: 32768.00000 (35667.82301)
| > grad_norm: 7.90049 (3.83669)
| > current_lr: 0.00001
| > step_time: 0.62560 (0.54991)
| > loader_time: 2.74420 (2.23400)
 --> STEP: 251/406 -- GLOBAL_STEP: 12025
| > loss: 0.22428 (0.22589)
| > log_mle: -0.12953 (-0.12061)
| > loss_dur: 0.35381 (0.34651)
| > amp_scaler: 32768.00000 (35378.99602)
| > grad_norm: 5.24565 (3.93303)
| > current_lr: 0.00001
| > step_time: 0.65520 (0.56497)
| > loader_time: 2.67890 (2.28620)
 --> STEP: 276/406 -- GLOBAL_STEP: 12050
| > loss: 0.21249 (0.22504)
| > log_mle: -0.13507 (-0.12193)
| > loss_dur: 0.34755 (0.34697)
| > amp_scaler: 32768.00000 (35142.49275)
| > grad_norm: 6.33063 (3.91549)
| > current_lr: 0.00001
| > step_time: 0.79560 (0.58351)
| > loader_time: 2.40900 (2.32888)
 --> STEP: 301/406 -- GLOBAL_STEP: 12075
| > loss: 0.22373 (0.22454)
| > log_mle: -0.14812 (-0.12299)
| > loss_dur: 0.37185 (0.34753)
| > amp_scaler: 32768.00000 (34945.27575)
| > grad_norm: 2.45454 (3.95192)
| > current_lr: 0.00001
| > step_time: 0.86970 (0.60159)
| > loader_time: 2.42700 (2.36487)
 --> STEP: 326/406 -- GLOBAL_STEP: 12100
| > loss: 0.20375 (0.22416)
| > log_mle: -0.13071 (-0.12393)
| > loss_dur: 0.33447 (0.34810)
| > amp_scaler: 32768.00000 (34778.30675)
| > grad_norm: 5.85059 (3.98087)
| > current_lr: 0.00001
| > step_time: 0.81990 (0.62018)
| > loader_time: 3.19840 (2.41327)
 --> STEP: 351/406 -- GLOBAL_STEP: 12125
| > loss: 0.23237 (0.22380)
| > log_mle: -0.13275 (-0.12494)
| > loss_dur: 0.36512 (0.34873)
| > amp_scaler: 32768.00000 (34635.12251)
| > grad_norm: 4.26761 (4.19877)
| > current_lr: 0.00001
| > step_time: 0.89280 (0.64048)
| > loader_time: 2.72040 (2.46228)
 --> STEP: 376/406 -- GLOBAL_STEP: 12150
| > loss: 0.22911 (0.22313)
| > log_mle: -0.13616 (-0.12599)
| > loss_dur: 0.36528 (0.34912)
| > amp_scaler: 32768.00000 (34510.97872)
| > grad_norm: 3.21636 (4.15879)
| > current_lr: 0.00001
| > step_time: 0.92120 (0.65994)
| > loader_time: 3.39680 (2.50673)
 --> STEP: 401/406 -- GLOBAL_STEP: 12175
| > loss: 0.20776 (0.22261)
| > log_mle: -0.14106 (-0.12691)
| > loss_dur: 0.34882 (0.34952)
| > amp_scaler: 16384.00000 (33993.73566)
| > grad_norm: 7.96923 (4.46150)
| > current_lr: 0.00001
| > step_time: 1.30740 (0.68263)
| > loader_time: 3.02960 (2.55268)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.19300 (+0.02860)
| > avg_loss: 0.19561 (-0.00921)
| > avg_log_mle: -0.13555 (-0.00235)
| > avg_loss_dur: 0.33117 (-0.00686)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_12180.pth
 > EPOCH: 30/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 16:07:17) 
 --> STEP: 20/406 -- GLOBAL_STEP: 12200
| > loss: 0.24063 (0.21234)
| > log_mle: -0.09650 (-0.10937)
| > loss_dur: 0.33713 (0.32170)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 1.53536 (2.56651)
| > current_lr: 0.00001
| > step_time: 0.37070 (0.33811)
| > loader_time: 1.59070 (1.63577)
 --> STEP: 45/406 -- GLOBAL_STEP: 12225
| > loss: 0.21135 (0.21420)
| > log_mle: -0.10661 (-0.11005)
| > loss_dur: 0.31796 (0.32425)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 2.00610 (2.88577)
| > current_lr: 0.00001
| > step_time: 0.85630 (0.38509)
| > loader_time: 2.18210 (1.93081)
 --> STEP: 70/406 -- GLOBAL_STEP: 12250
| > loss: 0.19239 (0.21423)
| > log_mle: -0.12921 (-0.11348)
| > loss_dur: 0.32160 (0.32771)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 3.45864 (3.11269)
| > current_lr: 0.00001
| > step_time: 0.36440 (0.41884)
| > loader_time: 1.70100 (2.00526)
 --> STEP: 95/406 -- GLOBAL_STEP: 12275
| > loss: 0.21566 (0.21326)
| > log_mle: -0.12279 (-0.11679)
| > loss_dur: 0.33845 (0.33005)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 2.90941 (3.48385)
| > current_lr: 0.00001
| > step_time: 0.44470 (0.44729)
| > loader_time: 1.86720 (2.02928)
 --> STEP: 120/406 -- GLOBAL_STEP: 12300
| > loss: 0.20166 (0.21191)
| > log_mle: -0.13519 (-0.11944)
| > loss_dur: 0.33686 (0.33135)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 16.41538 (4.38916)
| > current_lr: 0.00001
| > step_time: 0.60850 (0.47586)
| > loader_time: 2.16030 (2.05834)
 --> STEP: 145/406 -- GLOBAL_STEP: 12325
| > loss: 0.21107 (0.21131)
| > log_mle: -0.12597 (-0.12161)
| > loss_dur: 0.33704 (0.33292)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 2.42298 (5.20432)
| > current_lr: 0.00001
| > step_time: 0.57510 (0.49503)
| > loader_time: 2.08790 (2.10371)
 --> STEP: 170/406 -- GLOBAL_STEP: 12350
| > loss: 0.20886 (0.21106)
| > log_mle: -0.13400 (-0.12331)
| > loss_dur: 0.34286 (0.33437)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 5.15673 (4.89153)
| > current_lr: 0.00001
| > step_time: 0.83930 (0.51361)
| > loader_time: 3.16450 (2.20842)
 --> STEP: 195/406 -- GLOBAL_STEP: 12375
| > loss: 0.21318 (0.21076)
| > log_mle: -0.12141 (-0.12500)
| > loss_dur: 0.33459 (0.33575)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 3.08684 (4.86627)
| > current_lr: 0.00001
| > step_time: 0.71680 (0.53065)
| > loader_time: 2.78090 (2.28370)
 --> STEP: 220/406 -- GLOBAL_STEP: 12400
| > loss: 0.20313 (0.20995)
| > log_mle: -0.15888 (-0.12655)
| > loss_dur: 0.36201 (0.33649)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 7.51397 (4.93426)
| > current_lr: 0.00001
| > step_time: 0.60100 (0.54462)
| > loader_time: 2.91230 (2.34998)
 --> STEP: 245/406 -- GLOBAL_STEP: 12425
| > loss: 0.19267 (0.20958)
| > log_mle: -0.13610 (-0.12798)
| > loss_dur: 0.32877 (0.33755)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 5.36821 (5.10768)
| > current_lr: 0.00001
| > step_time: 0.76250 (0.56158)
| > loader_time: 3.14360 (2.40010)
 --> STEP: 270/406 -- GLOBAL_STEP: 12450
| > loss: 0.20557 (0.20877)
| > log_mle: -0.14316 (-0.12917)
| > loss_dur: 0.34873 (0.33794)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 7.03006 (5.22299)
| > current_lr: 0.00001
| > step_time: 0.85610 (0.57932)
| > loader_time: 3.17680 (2.44365)
 --> STEP: 295/406 -- GLOBAL_STEP: 12475
| > loss: 0.20959 (0.20806)
| > log_mle: -0.14400 (-0.13027)
| > loss_dur: 0.35359 (0.33833)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 11.46003 (5.27169)
| > current_lr: 0.00001
| > step_time: 0.84390 (0.59489)
| > loader_time: 2.83510 (2.48626)
 --> STEP: 320/406 -- GLOBAL_STEP: 12500
| > loss: 0.21836 (0.20771)
| > log_mle: -0.13369 (-0.13124)
| > loss_dur: 0.35205 (0.33895)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 5.00327 (5.29213)
| > current_lr: 0.00001
| > step_time: 0.79130 (0.61103)
| > loader_time: 3.28960 (2.52698)
 --> STEP: 345/406 -- GLOBAL_STEP: 12525
| > loss: 0.19616 (0.20733)
| > log_mle: -0.15363 (-0.13221)
| > loss_dur: 0.34978 (0.33954)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 2.91110 (5.24789)
| > current_lr: 0.00001
| > step_time: 0.90970 (0.62863)
| > loader_time: 3.17490 (2.58442)
 --> STEP: 370/406 -- GLOBAL_STEP: 12550
| > loss: 0.17943 (0.20682)
| > log_mle: -0.16094 (-0.13321)
| > loss_dur: 0.34037 (0.34003)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 3.45980 (5.18804)
| > current_lr: 0.00001
| > step_time: 0.99500 (0.65249)
| > loader_time: 2.56270 (2.62560)
 --> STEP: 395/406 -- GLOBAL_STEP: 12575
| > loss: 0.20235 (0.20628)
| > log_mle: -0.15379 (-0.13410)
| > loss_dur: 0.35614 (0.34038)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 6.14794 (5.12375)
| > current_lr: 0.00001
| > step_time: 1.04700 (0.67220)
| > loader_time: 3.66350 (2.66844)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.24956 (+0.05656)
| > avg_loss: 0.16998 (-0.02563)
| > avg_log_mle: -0.14871 (-0.01316)
| > avg_loss_dur: 0.31869 (-0.01248)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_12586.pth
 > EPOCH: 31/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 16:30:39) 
 --> STEP: 14/406 -- GLOBAL_STEP: 12600
| > loss: 0.18341 (0.19296)
| > log_mle: -0.13178 (-0.11798)
| > loss_dur: 0.31518 (0.31094)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.08665 (3.14351)
| > current_lr: 0.00001
| > step_time: 0.27500 (0.37941)
| > loader_time: 1.91290 (1.74267)
 --> STEP: 39/406 -- GLOBAL_STEP: 12625
| > loss: 0.20587 (0.19332)
| > log_mle: -0.11557 (-0.11720)
| > loss_dur: 0.32144 (0.31052)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 2.52464 (3.21819)
| > current_lr: 0.00001
| > step_time: 0.43290 (0.39147)
| > loader_time: 2.37910 (2.02480)
 --> STEP: 64/406 -- GLOBAL_STEP: 12650
| > loss: 0.19400 (0.19558)
| > log_mle: -0.11872 (-0.11958)
| > loss_dur: 0.31272 (0.31516)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 1.90432 (3.89284)
| > current_lr: 0.00001
| > step_time: 0.53140 (0.42303)
| > loader_time: 2.61750 (2.14506)
 --> STEP: 89/406 -- GLOBAL_STEP: 12675
| > loss: 0.21451 (0.19522)
| > log_mle: -0.12472 (-0.12275)
| > loss_dur: 0.33923 (0.31797)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 3.00377 (4.36636)
| > current_lr: 0.00001
| > step_time: 0.57270 (0.45128)
| > loader_time: 2.34230 (2.21652)
 --> STEP: 114/406 -- GLOBAL_STEP: 12700
| > loss: 0.17691 (0.19339)
| > log_mle: -0.14972 (-0.12608)
| > loss_dur: 0.32662 (0.31947)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 12.02988 (4.52387)
| > current_lr: 0.00001
| > step_time: 0.58680 (0.47520)
| > loader_time: 2.67840 (2.26995)
 --> STEP: 139/406 -- GLOBAL_STEP: 12725
| > loss: 0.19898 (0.19324)
| > log_mle: -0.13935 (-0.12832)
| > loss_dur: 0.33833 (0.32156)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 5.36887 (4.62356)
| > current_lr: 0.00001
| > step_time: 0.50750 (0.49324)
| > loader_time: 2.86310 (2.36551)
 --> STEP: 164/406 -- GLOBAL_STEP: 12750
| > loss: 0.19809 (0.19288)
| > log_mle: -0.13143 (-0.13011)
| > loss_dur: 0.32953 (0.32299)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 9.52580 (4.77355)
| > current_lr: 0.00001
| > step_time: 0.53630 (0.50859)
| > loader_time: 2.92090 (2.47831)
 --> STEP: 189/406 -- GLOBAL_STEP: 12775
| > loss: 0.18926 (0.19298)
| > log_mle: -0.14329 (-0.13188)
| > loss_dur: 0.33255 (0.32486)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.93592 (4.89201)
| > current_lr: 0.00001
| > step_time: 0.64060 (0.52547)
| > loader_time: 3.07120 (2.58146)
 --> STEP: 214/406 -- GLOBAL_STEP: 12800
| > loss: 0.17481 (0.19256)
| > log_mle: -0.14668 (-0.13326)
| > loss_dur: 0.32149 (0.32583)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.04469 (4.93098)
| > current_lr: 0.00001
| > step_time: 0.71570 (0.54136)
| > loader_time: 2.95970 (2.64904)
 --> STEP: 239/406 -- GLOBAL_STEP: 12825
| > loss: 0.18783 (0.19266)
| > log_mle: -0.15175 (-0.13470)
| > loss_dur: 0.33958 (0.32737)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 6.71076 (5.08928)
| > current_lr: 0.00001
| > step_time: 0.74230 (0.55798)
| > loader_time: 3.77300 (2.69542)
 --> STEP: 264/406 -- GLOBAL_STEP: 12850
| > loss: 0.19316 (0.19209)
| > log_mle: -0.14092 (-0.13590)
| > loss_dur: 0.33407 (0.32798)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 20.52954 (5.60037)
| > current_lr: 0.00001
| > step_time: 0.81990 (0.57727)
| > loader_time: 2.91720 (2.73508)
 --> STEP: 289/406 -- GLOBAL_STEP: 12875
| > loss: 0.17969 (0.19143)
| > log_mle: -0.14235 (-0.13694)
| > loss_dur: 0.32203 (0.32837)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 6.95224 (6.18036)
| > current_lr: 0.00001
| > step_time: 0.76200 (0.59683)
| > loader_time: 3.15210 (2.76290)
 --> STEP: 314/406 -- GLOBAL_STEP: 12900
| > loss: 0.19715 (0.19142)
| > log_mle: -0.15424 (-0.13792)
| > loss_dur: 0.35139 (0.32933)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 7.31879 (6.21103)
| > current_lr: 0.00001
| > step_time: 0.71110 (0.61926)
| > loader_time: 3.36470 (2.77901)
 --> STEP: 339/406 -- GLOBAL_STEP: 12925
| > loss: 0.18654 (0.19112)
| > log_mle: -0.15159 (-0.13877)
| > loss_dur: 0.33814 (0.32989)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 1.99726 (6.25118)
| > current_lr: 0.00001
| > step_time: 0.90410 (0.63671)
| > loader_time: 3.48850 (2.81899)
 --> STEP: 364/406 -- GLOBAL_STEP: 12950
| > loss: 0.18875 (0.19084)
| > log_mle: -0.15247 (-0.13973)
| > loss_dur: 0.34122 (0.33057)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 9.87665 (6.32742)
| > current_lr: 0.00001
| > step_time: 1.26810 (0.65419)
| > loader_time: 3.58740 (2.85707)
 --> STEP: 389/406 -- GLOBAL_STEP: 12975
| > loss: 0.17468 (0.19052)
| > log_mle: -0.16490 (-0.14060)
| > loss_dur: 0.33958 (0.33112)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 2.26780 (6.36125)
| > current_lr: 0.00001
| > step_time: 0.98850 (0.67255)
| > loader_time: 3.48030 (2.88126)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.33579 (+0.08622)
| > avg_loss: 0.15445 (-0.01553)
| > avg_log_mle: -0.15587 (-0.00716)
| > avg_loss_dur: 0.31032 (-0.00837)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_12992.pth
 > EPOCH: 32/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 16:55:28) 
 --> STEP: 8/406 -- GLOBAL_STEP: 13000
| > loss: 0.13669 (0.17554)
| > log_mle: -0.13630 (-0.12161)
| > loss_dur: 0.27299 (0.29715)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 2.33196 (2.19341)
| > current_lr: 0.00001
| > step_time: 0.36660 (0.34471)
| > loader_time: 2.02150 (1.54843)
 --> STEP: 33/406 -- GLOBAL_STEP: 13025
| > loss: 0.15852 (0.18029)
| > log_mle: -0.12109 (-0.12371)
| > loss_dur: 0.27960 (0.30399)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 2.34610 (2.63329)
| > current_lr: 0.00001
| > step_time: 0.44380 (0.36456)
| > loader_time: 2.14720 (1.79350)
 --> STEP: 58/406 -- GLOBAL_STEP: 13050
| > loss: 0.19404 (0.18227)
| > log_mle: -0.12847 (-0.12564)
| > loss_dur: 0.32251 (0.30791)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 7.16764 (3.92413)
| > current_lr: 0.00001
| > step_time: 0.36270 (0.40367)
| > loader_time: 1.80330 (1.86608)
 --> STEP: 83/406 -- GLOBAL_STEP: 13075
| > loss: 0.19554 (0.18215)
| > log_mle: -0.13713 (-0.12838)
| > loss_dur: 0.33267 (0.31053)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 3.74251 (4.96493)
| > current_lr: 0.00001
| > step_time: 0.56990 (0.44110)
| > loader_time: 2.19750 (1.93517)
 --> STEP: 108/406 -- GLOBAL_STEP: 13100
| > loss: 0.16166 (0.18118)
| > log_mle: -0.13998 (-0.13121)
| > loss_dur: 0.30164 (0.31239)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 3.52586 (5.47559)
| > current_lr: 0.00001
| > step_time: 0.48100 (0.46757)
| > loader_time: 2.27380 (1.99692)
 --> STEP: 133/406 -- GLOBAL_STEP: 13125
| > loss: 0.18084 (0.18046)
| > log_mle: -0.14585 (-0.13386)
| > loss_dur: 0.32669 (0.31432)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 7.36860 (5.50774)
| > current_lr: 0.00001
| > step_time: 0.51810 (0.48794)
| > loader_time: 2.54380 (2.03052)
 --> STEP: 158/406 -- GLOBAL_STEP: 13150
| > loss: 0.15650 (0.18070)
| > log_mle: -0.15374 (-0.13569)
| > loss_dur: 0.31024 (0.31639)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 3.40974 (5.41481)
| > current_lr: 0.00001
| > step_time: 0.50940 (0.50662)
| > loader_time: 2.71780 (2.08883)
 --> STEP: 183/406 -- GLOBAL_STEP: 13175
| > loss: 0.16200 (0.18055)
| > log_mle: -0.15306 (-0.13733)
| > loss_dur: 0.31506 (0.31788)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.97983 (5.36274)
| > current_lr: 0.00001
| > step_time: 0.67620 (0.52355)
| > loader_time: 2.69980 (2.18702)
 --> STEP: 208/406 -- GLOBAL_STEP: 13200
| > loss: 0.15741 (0.18067)
| > log_mle: -0.15755 (-0.13880)
| > loss_dur: 0.31496 (0.31947)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 7.94445 (5.70781)
| > current_lr: 0.00001
| > step_time: 0.61200 (0.53967)
| > loader_time: 2.96060 (2.26265)
 --> STEP: 233/406 -- GLOBAL_STEP: 13225
| > loss: 0.18234 (0.18011)
| > log_mle: -0.15554 (-0.14036)
| > loss_dur: 0.33787 (0.32047)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.29415 (6.22818)
| > current_lr: 0.00001
| > step_time: 0.66840 (0.55409)
| > loader_time: 3.08410 (2.32579)
 --> STEP: 258/406 -- GLOBAL_STEP: 13250
| > loss: 0.17168 (0.17956)
| > log_mle: -0.15204 (-0.14165)
| > loss_dur: 0.32372 (0.32121)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 3.45658 (6.33978)
| > current_lr: 0.00001
| > step_time: 0.77690 (0.57168)
| > loader_time: 2.91780 (2.38969)
 --> STEP: 283/406 -- GLOBAL_STEP: 13275
| > loss: 0.16943 (0.17874)
| > log_mle: -0.15390 (-0.14280)
| > loss_dur: 0.32332 (0.32154)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 7.06266 (6.39208)
| > current_lr: 0.00001
| > step_time: 0.69750 (0.58710)
| > loader_time: 2.74610 (2.44262)
 --> STEP: 308/406 -- GLOBAL_STEP: 13300
| > loss: 0.16864 (0.17839)
| > log_mle: -0.15691 (-0.14375)
| > loss_dur: 0.32555 (0.32215)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 8.31416 (6.55689)
| > current_lr: 0.00001
| > step_time: 0.76240 (0.60437)
| > loader_time: 2.96400 (2.48552)
 --> STEP: 333/406 -- GLOBAL_STEP: 13325
| > loss: 0.16390 (0.17816)
| > log_mle: -0.16132 (-0.14456)
| > loss_dur: 0.32522 (0.32272)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 17.79581 (6.65337)
| > current_lr: 0.00001
| > step_time: 0.76350 (0.62224)
| > loader_time: 3.64910 (2.54031)
 --> STEP: 358/406 -- GLOBAL_STEP: 13350
| > loss: 0.14139 (0.17800)
| > log_mle: -0.17371 (-0.14543)
| > loss_dur: 0.31509 (0.32343)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 6.99503 (7.03956)
| > current_lr: 0.00001
| > step_time: 0.83560 (0.64021)
| > loader_time: 3.22550 (2.59841)
 --> STEP: 383/406 -- GLOBAL_STEP: 13375
| > loss: 0.18562 (0.17774)
| > log_mle: -0.15762 (-0.14631)
| > loss_dur: 0.34324 (0.32405)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 6.54304 (7.06729)
| > current_lr: 0.00001
| > step_time: 0.92400 (0.65858)
| > loader_time: 3.74200 (2.65277)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.09921 (-0.23658)
| > avg_loss: 0.14342 (-0.01104)
| > avg_log_mle: -0.16242 (-0.00655)
| > avg_loss_dur: 0.30584 (-0.00449)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_13398.pth
 > EPOCH: 33/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 17:18:51) 
 --> STEP: 2/406 -- GLOBAL_STEP: 13400
| > loss: 0.16169 (0.15551)
| > log_mle: -0.13966 (-0.13418)
| > loss_dur: 0.30135 (0.28969)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 2.44563 (2.21555)
| > current_lr: 0.00001
| > step_time: 0.36110 (0.36334)
| > loader_time: 1.67240 (1.67648)
 --> STEP: 27/406 -- GLOBAL_STEP: 13425
| > loss: 0.16092 (0.16693)
| > log_mle: -0.12969 (-0.12955)
| > loss_dur: 0.29061 (0.29648)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 2.50272 (3.53519)
| > current_lr: 0.00001
| > step_time: 0.29620 (0.38705)
| > loader_time: 2.13220 (1.94045)
 --> STEP: 52/406 -- GLOBAL_STEP: 13450
| > loss: 0.18015 (0.17065)
| > log_mle: -0.13214 (-0.13107)
| > loss_dur: 0.31229 (0.30172)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 2.49070 (4.01541)
| > current_lr: 0.00001
| > step_time: 0.76520 (0.42149)
| > loader_time: 2.53700 (2.08032)
 --> STEP: 77/406 -- GLOBAL_STEP: 13475
| > loss: 0.17626 (0.17042)
| > log_mle: -0.13364 (-0.13363)
| > loss_dur: 0.30990 (0.30405)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 3.52486 (4.37503)
| > current_lr: 0.00001
| > step_time: 0.57750 (0.45824)
| > loader_time: 2.21720 (2.13191)
 --> STEP: 102/406 -- GLOBAL_STEP: 13500
| > loss: 0.14250 (0.17001)
| > log_mle: -0.15620 (-0.13653)
| > loss_dur: 0.29870 (0.30654)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 13.33667 (4.93345)
| > current_lr: 0.00001
| > step_time: 0.60210 (0.48203)
| > loader_time: 2.31240 (2.16000)
 --> STEP: 127/406 -- GLOBAL_STEP: 13525
| > loss: 0.17377 (0.16872)
| > log_mle: -0.14956 (-0.13922)
| > loss_dur: 0.32332 (0.30794)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 3.96259 (5.44303)
| > current_lr: 0.00001
| > step_time: 0.50420 (0.50068)
| > loader_time: 2.28780 (2.19667)
 --> STEP: 152/406 -- GLOBAL_STEP: 13550
| > loss: 0.17253 (0.16863)
| > log_mle: -0.14854 (-0.14127)
| > loss_dur: 0.32107 (0.30990)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.85801 (5.41579)
| > current_lr: 0.00001
| > step_time: 0.53910 (0.51692)
| > loader_time: 2.66870 (2.23398)
 --> STEP: 177/406 -- GLOBAL_STEP: 13575
| > loss: 0.15706 (0.16827)
| > log_mle: -0.16459 (-0.14290)
| > loss_dur: 0.32165 (0.31117)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 9.01996 (5.69098)
| > current_lr: 0.00001
| > step_time: 0.75390 (0.53589)
| > loader_time: 2.94600 (2.32174)
 --> STEP: 202/406 -- GLOBAL_STEP: 13600
| > loss: 0.20279 (0.16804)
| > log_mle: -0.14700 (-0.14439)
| > loss_dur: 0.34978 (0.31243)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 7.82672 (6.31438)
| > current_lr: 0.00001
| > step_time: 0.61350 (0.55236)
| > loader_time: 2.72140 (2.39036)
 --> STEP: 227/406 -- GLOBAL_STEP: 13625
| > loss: 0.18066 (0.16741)
| > log_mle: -0.14746 (-0.14589)
| > loss_dur: 0.32812 (0.31330)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 7.04587 (6.51060)
| > current_lr: 0.00001
| > step_time: 0.64710 (0.57158)
| > loader_time: 3.15280 (2.45068)
 --> STEP: 252/406 -- GLOBAL_STEP: 13650
| > loss: 0.15714 (0.16690)
| > log_mle: -0.16079 (-0.14730)
| > loss_dur: 0.31793 (0.31420)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 12.56037 (6.66382)
| > current_lr: 0.00001
| > step_time: 0.77420 (0.58992)
| > loader_time: 3.22410 (2.49066)
 --> STEP: 277/406 -- GLOBAL_STEP: 13675
| > loss: 0.16076 (0.16617)
| > log_mle: -0.15792 (-0.14851)
| > loss_dur: 0.31868 (0.31468)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.86737 (6.98557)
| > current_lr: 0.00001
| > step_time: 0.90010 (0.60710)
| > loader_time: 2.64220 (2.52904)
 --> STEP: 302/406 -- GLOBAL_STEP: 13700
| > loss: 0.16814 (0.16570)
| > log_mle: -0.16713 (-0.14952)
| > loss_dur: 0.33527 (0.31522)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.11344 (7.03789)
| > current_lr: 0.00001
| > step_time: 1.15160 (0.62551)
| > loader_time: 3.13720 (2.57642)
 --> STEP: 327/406 -- GLOBAL_STEP: 13725
| > loss: 0.15923 (0.16539)
| > log_mle: -0.16283 (-0.15037)
| > loss_dur: 0.32207 (0.31575)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 7.17010 (7.06292)
| > current_lr: 0.00001
| > step_time: 0.78610 (0.64057)
| > loader_time: 4.05280 (2.64352)
 --> STEP: 352/406 -- GLOBAL_STEP: 13750
| > loss: 0.16208 (0.16513)
| > log_mle: -0.15863 (-0.15127)
| > loss_dur: 0.32071 (0.31640)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 5.20759 (7.15633)
| > current_lr: 0.00001
| > step_time: 0.93270 (0.65839)
| > loader_time: 3.83200 (2.72378)
 --> STEP: 377/406 -- GLOBAL_STEP: 13775
| > loss: 0.15815 (0.16478)
| > log_mle: -0.16205 (-0.15226)
| > loss_dur: 0.32020 (0.31704)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 6.60328 (7.26080)
| > current_lr: 0.00001
| > step_time: 0.99030 (0.67495)
| > loader_time: 3.68860 (2.79143)
 --> STEP: 402/406 -- GLOBAL_STEP: 13800
| > loss: 0.14873 (0.16439)
| > log_mle: -0.17112 (-0.15314)
| > loss_dur: 0.31985 (0.31753)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.18298 (7.34873)
| > current_lr: 0.00001
| > step_time: 0.87800 (0.69529)
| > loader_time: 3.94870 (2.84973)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.29380 (+0.19459)
| > avg_loss: 0.12892 (-0.01450)
| > avg_log_mle: -0.16840 (-0.00599)
| > avg_loss_dur: 0.29732 (-0.00852)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_13804.pth
 > EPOCH: 34/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 17:43:28) 
 --> STEP: 21/406 -- GLOBAL_STEP: 13825
| > loss: 0.12717 (0.15391)
| > log_mle: -0.14020 (-0.13489)
| > loss_dur: 0.26737 (0.28879)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 3.24051 (3.06028)
| > current_lr: 0.00001
| > step_time: 0.29210 (0.36585)
| > loader_time: 2.46680 (1.87932)
 --> STEP: 46/406 -- GLOBAL_STEP: 13850
| > loss: 0.14422 (0.15637)
| > log_mle: -0.14046 (-0.13522)
| > loss_dur: 0.28468 (0.29159)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.12352 (3.52957)
| > current_lr: 0.00001
| > step_time: 0.45190 (0.39905)
| > loader_time: 2.14770 (2.11392)
 --> STEP: 71/406 -- GLOBAL_STEP: 13875
| > loss: 0.15777 (0.15683)
| > log_mle: -0.14567 (-0.13863)
| > loss_dur: 0.30344 (0.29546)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.82487 (3.87185)
| > current_lr: 0.00001
| > step_time: 0.53900 (0.42897)
| > loader_time: 2.46570 (2.22887)
 --> STEP: 96/406 -- GLOBAL_STEP: 13900
| > loss: 0.14410 (0.15646)
| > log_mle: -0.15256 (-0.14199)
| > loss_dur: 0.29665 (0.29845)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.85645 (4.16965)
| > current_lr: 0.00001
| > step_time: 0.57060 (0.45522)
| > loader_time: 2.58080 (2.28142)
 --> STEP: 121/406 -- GLOBAL_STEP: 13925
| > loss: 0.15738 (0.15580)
| > log_mle: -0.15288 (-0.14465)
| > loss_dur: 0.31026 (0.30045)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 9.57585 (4.49256)
| > current_lr: 0.00001
| > step_time: 0.61800 (0.48038)
| > loader_time: 2.58500 (2.31909)
 --> STEP: 146/406 -- GLOBAL_STEP: 13950
| > loss: 0.15027 (0.15578)
| > log_mle: -0.14814 (-0.14675)
| > loss_dur: 0.29841 (0.30254)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.47496 (4.96594)
| > current_lr: 0.00001
| > step_time: 0.65780 (0.49801)
| > loader_time: 2.34270 (2.36360)
 --> STEP: 171/406 -- GLOBAL_STEP: 13975
| > loss: 0.14249 (0.15583)
| > log_mle: -0.15985 (-0.14842)
| > loss_dur: 0.30234 (0.30425)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 6.45747 (5.38958)
| > current_lr: 0.00001
| > step_time: 1.03940 (0.51962)
| > loader_time: 3.14800 (2.47346)
 --> STEP: 196/406 -- GLOBAL_STEP: 14000
| > loss: 0.15437 (0.15557)
| > log_mle: -0.16447 (-0.15003)
| > loss_dur: 0.31884 (0.30560)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 9.90697 (5.75309)
| > current_lr: 0.00001
| > step_time: 0.74740 (0.53745)
| > loader_time: 2.90710 (2.53994)
 --> STEP: 221/406 -- GLOBAL_STEP: 14025
| > loss: 0.16028 (0.15506)
| > log_mle: -0.15434 (-0.15144)
| > loss_dur: 0.31462 (0.30650)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.40505 (6.13303)
| > current_lr: 0.00001
| > step_time: 0.65480 (0.55637)
| > loader_time: 3.25050 (2.58732)
 --> STEP: 246/406 -- GLOBAL_STEP: 14050
| > loss: 0.13726 (0.15468)
| > log_mle: -0.16193 (-0.15283)
| > loss_dur: 0.29920 (0.30752)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 8.56177 (6.35898)
| > current_lr: 0.00001
| > step_time: 0.82440 (0.57529)
| > loader_time: 2.71280 (2.61289)
 --> STEP: 271/406 -- GLOBAL_STEP: 14075
| > loss: 0.12639 (0.15407)
| > log_mle: -0.17662 (-0.15399)
| > loss_dur: 0.30301 (0.30806)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 9.62354 (6.66081)
| > current_lr: 0.00001
| > step_time: 0.74390 (0.59503)
| > loader_time: 2.62210 (2.63650)
 --> STEP: 296/406 -- GLOBAL_STEP: 14100
| > loss: 0.16659 (0.15373)
| > log_mle: -0.16463 (-0.15491)
| > loss_dur: 0.33121 (0.30864)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 5.71441 (6.74830)
| > current_lr: 0.00001
| > step_time: 0.79760 (0.61414)
| > loader_time: 2.72390 (2.65603)
 --> STEP: 321/406 -- GLOBAL_STEP: 14125
| > loss: 0.14048 (0.15362)
| > log_mle: -0.16084 (-0.15574)
| > loss_dur: 0.30132 (0.30936)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 16.19803 (7.01508)
| > current_lr: 0.00001
| > step_time: 0.90950 (0.63884)
| > loader_time: 3.66990 (2.68446)
 --> STEP: 346/406 -- GLOBAL_STEP: 14150
| > loss: 0.15569 (0.15351)
| > log_mle: -0.16347 (-0.15650)
| > loss_dur: 0.31916 (0.31001)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 9.53304 (7.54974)
| > current_lr: 0.00001
| > step_time: 1.00190 (0.65855)
| > loader_time: 2.91510 (2.73274)
 --> STEP: 371/406 -- GLOBAL_STEP: 14175
| > loss: 0.14090 (0.15296)
| > log_mle: -0.17311 (-0.15743)
| > loss_dur: 0.31401 (0.31039)
| > amp_scaler: 32768.00000 (16825.61725)
| > grad_norm: 15.07241 (7.71799)
| > current_lr: 0.00001
| > step_time: 0.93380 (0.67658)
| > loader_time: 3.31350 (2.77497)
 --> STEP: 396/406 -- GLOBAL_STEP: 14200
| > loss: 0.13467 (0.15270)
| > log_mle: -0.18215 (-0.15824)
| > loss_dur: 0.31682 (0.31094)
| > amp_scaler: 32768.00000 (17832.08081)
| > grad_norm: 9.69981 (7.80128)
| > current_lr: 0.00001
| > step_time: 0.94770 (0.69710)
| > loader_time: 3.09220 (2.81994)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.19214 (-0.10166)
| > avg_loss: 0.11754 (-0.01138)
| > avg_log_mle: -0.17346 (-0.00506)
| > avg_loss_dur: 0.29100 (-0.00632)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_14210.pth
 > EPOCH: 35/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 18:08:00) 
 --> STEP: 15/406 -- GLOBAL_STEP: 14225
| > loss: 0.12265 (0.13595)
| > log_mle: -0.14356 (-0.14145)
| > loss_dur: 0.26621 (0.27740)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 6.26809 (2.80936)
| > current_lr: 0.00001
| > step_time: 0.40590 (0.35811)
| > loader_time: 1.83620 (1.74523)
 --> STEP: 40/406 -- GLOBAL_STEP: 14250
| > loss: 0.14248 (0.14412)
| > log_mle: -0.13346 (-0.14024)
| > loss_dur: 0.27594 (0.28436)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 3.98937 (3.77495)
| > current_lr: 0.00001
| > step_time: 0.47340 (0.38989)
| > loader_time: 2.32480 (2.04181)
 --> STEP: 65/406 -- GLOBAL_STEP: 14275
| > loss: 0.14575 (0.14496)
| > log_mle: -0.15200 (-0.14283)
| > loss_dur: 0.29776 (0.28779)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 13.38681 (3.93266)
| > current_lr: 0.00001
| > step_time: 0.53960 (0.42450)
| > loader_time: 2.59750 (2.15437)
 --> STEP: 90/406 -- GLOBAL_STEP: 14300
| > loss: 0.15150 (0.14503)
| > log_mle: -0.15545 (-0.14590)
| > loss_dur: 0.30695 (0.29093)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.28900 (4.57765)
| > current_lr: 0.00001
| > step_time: 0.99720 (0.45935)
| > loader_time: 2.82000 (2.20186)
 --> STEP: 115/406 -- GLOBAL_STEP: 14325
| > loss: 0.15731 (0.14387)
| > log_mle: -0.14788 (-0.14902)
| > loss_dur: 0.30519 (0.29288)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 6.74098 (5.22979)
| > current_lr: 0.00001
| > step_time: 0.59480 (0.48168)
| > loader_time: 2.48760 (2.22738)
 --> STEP: 140/406 -- GLOBAL_STEP: 14350
| > loss: 0.13695 (0.14428)
| > log_mle: -0.16437 (-0.15134)
| > loss_dur: 0.30131 (0.29562)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 18.38086 (5.74387)
| > current_lr: 0.00001
| > step_time: 0.65990 (0.50057)
| > loader_time: 2.25640 (2.25424)
 --> STEP: 165/406 -- GLOBAL_STEP: 14375
| > loss: 0.13732 (0.14420)
| > log_mle: -0.17291 (-0.15302)
| > loss_dur: 0.31022 (0.29722)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 17.74237 (5.93860)
| > current_lr: 0.00001
| > step_time: 0.51570 (0.51820)
| > loader_time: 2.90730 (2.32249)
 --> STEP: 190/406 -- GLOBAL_STEP: 14400
| > loss: 0.13703 (0.14410)
| > log_mle: -0.17047 (-0.15474)
| > loss_dur: 0.30750 (0.29884)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 11.83859 (6.40987)
| > current_lr: 0.00001
| > step_time: 0.73140 (0.53403)
| > loader_time: 2.68630 (2.42397)
 --> STEP: 215/406 -- GLOBAL_STEP: 14425
| > loss: 0.13395 (0.14381)
| > log_mle: -0.17088 (-0.15613)
| > loss_dur: 0.30483 (0.29994)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 6.68230 (6.67241)
| > current_lr: 0.00001
| > step_time: 0.85690 (0.55074)
| > loader_time: 3.01690 (2.50097)
 --> STEP: 240/406 -- GLOBAL_STEP: 14450
| > loss: 0.11912 (0.14360)
| > log_mle: -0.17802 (-0.15761)
| > loss_dur: 0.29714 (0.30121)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 10.02797 (6.97138)
| > current_lr: 0.00001
| > step_time: 0.67520 (0.56577)
| > loader_time: 3.45940 (2.56741)
 --> STEP: 265/406 -- GLOBAL_STEP: 14475
| > loss: 0.14162 (0.14304)
| > log_mle: -0.17358 (-0.15884)
| > loss_dur: 0.31520 (0.30187)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 10.98019 (7.18588)
| > current_lr: 0.00001
| > step_time: 0.67540 (0.58317)
| > loader_time: 3.30310 (2.61260)
 --> STEP: 290/406 -- GLOBAL_STEP: 14500
| > loss: 0.13207 (0.14248)
| > log_mle: -0.18224 (-0.15997)
| > loss_dur: 0.31431 (0.30245)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 8.04802 (7.34394)
| > current_lr: 0.00001
| > step_time: 0.83550 (0.59963)
| > loader_time: 2.83120 (2.64148)
 --> STEP: 315/406 -- GLOBAL_STEP: 14525
| > loss: 0.12122 (0.14225)
| > log_mle: -0.18349 (-0.16094)
| > loss_dur: 0.30471 (0.30319)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 4.74674 (7.47730)
| > current_lr: 0.00001
| > step_time: 0.79310 (0.61959)
| > loader_time: 3.32070 (2.67425)
 --> STEP: 340/406 -- GLOBAL_STEP: 14550
| > loss: 0.15332 (0.14218)
| > log_mle: -0.16101 (-0.16168)
| > loss_dur: 0.31433 (0.30386)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 4.79514 (7.44385)
| > current_lr: 0.00001
| > step_time: 0.97970 (0.64097)
| > loader_time: 3.05620 (2.71911)
 --> STEP: 365/406 -- GLOBAL_STEP: 14575
| > loss: 0.13448 (0.14177)
| > log_mle: -0.17529 (-0.16265)
| > loss_dur: 0.30978 (0.30442)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 9.08377 (7.58155)
| > current_lr: 0.00001
| > step_time: 0.87850 (0.66034)
| > loader_time: 3.32860 (2.75724)
 --> STEP: 390/406 -- GLOBAL_STEP: 14600
| > loss: 0.13414 (0.14147)
| > log_mle: -0.18415 (-0.16351)
| > loss_dur: 0.31829 (0.30498)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 10.24231 (7.77287)
| > current_lr: 0.00001
| > step_time: 1.00900 (0.68333)
| > loader_time: 3.55670 (2.79647)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.25816 (+0.06601)
| > avg_loss: 0.10521 (-0.01233)
| > avg_log_mle: -0.17872 (-0.00526)
| > avg_loss_dur: 0.28393 (-0.00707)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_14616.pth
 > EPOCH: 36/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 18:32:23) 
 --> STEP: 9/406 -- GLOBAL_STEP: 14625
| > loss: 0.16649 (0.13133)
| > log_mle: -0.14451 (-0.14423)
| > loss_dur: 0.31100 (0.27556)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 2.43564 (2.68859)
| > current_lr: 0.00001
| > step_time: 0.39200 (0.36422)
| > loader_time: 1.92070 (1.75801)
 --> STEP: 34/406 -- GLOBAL_STEP: 14650
| > loss: 0.16049 (0.13352)
| > log_mle: -0.13527 (-0.14545)
| > loss_dur: 0.29576 (0.27898)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 5.73710 (3.94991)
| > current_lr: 0.00001
| > step_time: 0.45360 (0.38615)
| > loader_time: 1.84600 (1.98232)
 --> STEP: 59/406 -- GLOBAL_STEP: 14675
| > loss: 0.14232 (0.13438)
| > log_mle: -0.16165 (-0.14788)
| > loss_dur: 0.30397 (0.28225)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 7.61499 (3.93101)
| > current_lr: 0.00001
| > step_time: 0.53620 (0.42132)
| > loader_time: 2.43530 (2.05056)
 --> STEP: 84/406 -- GLOBAL_STEP: 14700
| > loss: 0.14084 (0.13436)
| > log_mle: -0.16429 (-0.15061)
| > loss_dur: 0.30513 (0.28497)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 6.17457 (4.80597)
| > current_lr: 0.00001
| > step_time: 0.92830 (0.45753)
| > loader_time: 2.48550 (2.12086)
 --> STEP: 109/406 -- GLOBAL_STEP: 14725
| > loss: 0.13461 (0.13334)
| > log_mle: -0.17156 (-0.15346)
| > loss_dur: 0.30617 (0.28680)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 8.59391 (5.39283)
| > current_lr: 0.00001
| > step_time: 0.60560 (0.48183)
| > loader_time: 2.36830 (2.15147)
 --> STEP: 134/406 -- GLOBAL_STEP: 14750
| > loss: 0.13511 (0.13257)
| > log_mle: -0.16346 (-0.15604)
| > loss_dur: 0.29857 (0.28861)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 7.07278 (5.96386)
| > current_lr: 0.00001
| > step_time: 0.67750 (0.50240)
| > loader_time: 2.50840 (2.17662)
 --> STEP: 159/406 -- GLOBAL_STEP: 14775
| > loss: 0.13380 (0.13332)
| > log_mle: -0.16575 (-0.15778)
| > loss_dur: 0.29955 (0.29110)
| > amp_scaler: 32768.00000 (32768.00000)
| > grad_norm: 13.41621 (6.47404)
| > current_lr: 0.00001
| > step_time: 0.52820 (0.52261)
| > loader_time: 3.30750 (2.24350)
 --> STEP: 184/406 -- GLOBAL_STEP: 14800
| > loss: 0.14589 (0.13370)
| > log_mle: -0.18302 (-0.15931)
| > loss_dur: 0.32891 (0.29301)
| > amp_scaler: 16384.00000 (30720.00000)
| > grad_norm: 16.99070 (7.42676)
| > current_lr: 0.00001
| > step_time: 0.69880 (0.54204)
| > loader_time: 2.90750 (2.32989)
 --> STEP: 209/406 -- GLOBAL_STEP: 14825
| > loss: 0.11795 (0.13339)
| > log_mle: -0.17955 (-0.16065)
| > loss_dur: 0.29750 (0.29404)
| > amp_scaler: 16384.00000 (29005.16746)
| > grad_norm: 5.06593 (7.53055)
| > current_lr: 0.00001
| > step_time: 0.58160 (0.55841)
| > loader_time: 3.25720 (2.40968)
 --> STEP: 234/406 -- GLOBAL_STEP: 14850
| > loss: 0.11928 (0.13314)
| > log_mle: -0.18296 (-0.16218)
| > loss_dur: 0.30224 (0.29532)
| > amp_scaler: 16384.00000 (27656.75214)
| > grad_norm: 11.44762 (7.80780)
| > current_lr: 0.00001
| > step_time: 0.73180 (0.57576)
| > loader_time: 3.11540 (2.47430)
 --> STEP: 259/406 -- GLOBAL_STEP: 14875
| > loss: 0.13620 (0.13269)
| > log_mle: -0.18627 (-0.16347)
| > loss_dur: 0.32247 (0.29616)
| > amp_scaler: 16384.00000 (26568.64865)
| > grad_norm: 10.60219 (7.87879)
| > current_lr: 0.00001
| > step_time: 0.79890 (0.59127)
| > loader_time: 3.03590 (2.53834)
 --> STEP: 284/406 -- GLOBAL_STEP: 14900
| > loss: 0.14784 (0.13221)
| > log_mle: -0.18183 (-0.16457)
| > loss_dur: 0.32968 (0.29678)
| > amp_scaler: 16384.00000 (25672.11268)
| > grad_norm: 7.67395 (7.83206)
| > current_lr: 0.00001
| > step_time: 0.87530 (0.60871)
| > loader_time: 3.35710 (2.59128)
 --> STEP: 309/406 -- GLOBAL_STEP: 14925
| > loss: 0.12172 (0.13200)
| > log_mle: -0.17826 (-0.16548)
| > loss_dur: 0.29997 (0.29748)
| > amp_scaler: 16384.00000 (24920.64725)
| > grad_norm: 11.20879 (7.85180)
| > current_lr: 0.00001
| > step_time: 0.90920 (0.62632)
| > loader_time: 3.19560 (2.63049)
 --> STEP: 334/406 -- GLOBAL_STEP: 14950
| > loss: 0.11909 (0.13184)
| > log_mle: -0.18602 (-0.16632)
| > loss_dur: 0.30510 (0.29816)
| > amp_scaler: 16384.00000 (24281.67665)
| > grad_norm: 15.70514 (8.14293)
| > current_lr: 0.00001
| > step_time: 1.58510 (0.64555)
| > loader_time: 3.72820 (2.69193)
 --> STEP: 359/406 -- GLOBAL_STEP: 14975
| > loss: 0.11910 (0.13149)
| > log_mle: -0.17578 (-0.16725)
| > loss_dur: 0.29488 (0.29874)
| > amp_scaler: 16384.00000 (23731.69916)
| > grad_norm: 9.81887 (8.30485)
| > current_lr: 0.00001
| > step_time: 0.87080 (0.66354)
| > loader_time: 3.69300 (2.75291)
 --> STEP: 384/406 -- GLOBAL_STEP: 15000
| > loss: 0.12119 (0.13117)
| > log_mle: -0.17607 (-0.16813)
| > loss_dur: 0.29726 (0.29930)
| > amp_scaler: 16384.00000 (23253.33333)
| > grad_norm: 8.90840 (8.49163)
| > current_lr: 0.00001
| > step_time: 0.97770 (0.68203)
| > loader_time: 3.90920 (2.80259)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.37821 (+0.12005)
| > avg_loss: 0.09218 (-0.01303)
| > avg_log_mle: -0.18295 (-0.00423)
| > avg_loss_dur: 0.27513 (-0.00880)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_15022.pth
 > EPOCH: 37/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 18:57:02) 
 --> STEP: 3/406 -- GLOBAL_STEP: 15025
| > loss: 0.15197 (0.12544)
| > log_mle: -0.15510 (-0.15613)
| > loss_dur: 0.30706 (0.28157)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 3.61063 (3.96927)
| > current_lr: 0.00001
| > step_time: 0.37550 (0.37620)
| > loader_time: 2.27450 (2.19049)
 --> STEP: 28/406 -- GLOBAL_STEP: 15050
| > loss: 0.15990 (0.12323)
| > log_mle: -0.14996 (-0.15081)
| > loss_dur: 0.30986 (0.27403)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.84760 (3.90552)
| > current_lr: 0.00001
| > step_time: 0.44130 (0.38089)
| > loader_time: 2.50360 (2.31033)
 --> STEP: 53/406 -- GLOBAL_STEP: 15075
| > loss: 0.13820 (0.12534)
| > log_mle: -0.15004 (-0.15216)
| > loss_dur: 0.28824 (0.27750)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 3.87013 (5.12030)
| > current_lr: 0.00001
| > step_time: 0.53280 (0.41772)
| > loader_time: 2.33320 (2.35288)
 --> STEP: 78/406 -- GLOBAL_STEP: 15100
| > loss: 0.10577 (0.12440)
| > log_mle: -0.15965 (-0.15487)
| > loss_dur: 0.26542 (0.27927)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 3.97347 (5.39695)
| > current_lr: 0.00001
| > step_time: 0.42940 (0.44610)
| > loader_time: 2.12140 (2.33319)
 --> STEP: 103/406 -- GLOBAL_STEP: 15125
| > loss: 0.09619 (0.12421)
| > log_mle: -0.18638 (-0.15799)
| > loss_dur: 0.28257 (0.28219)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 24.76799 (6.08791)
| > current_lr: 0.00001
| > step_time: 0.46100 (0.47058)
| > loader_time: 2.24990 (2.31683)
 --> STEP: 128/406 -- GLOBAL_STEP: 15150
| > loss: 0.13764 (0.12347)
| > log_mle: -0.17667 (-0.16046)
| > loss_dur: 0.31432 (0.28393)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 9.99037 (6.79325)
| > current_lr: 0.00001
| > step_time: 0.63440 (0.49736)
| > loader_time: 1.82440 (2.30666)
 --> STEP: 153/406 -- GLOBAL_STEP: 15175
| > loss: 0.13703 (0.12390)
| > log_mle: -0.16727 (-0.16243)
| > loss_dur: 0.30430 (0.28633)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 8.46086 (7.14615)
| > current_lr: 0.00001
| > step_time: 0.52990 (0.51656)
| > loader_time: 3.19790 (2.32082)
 --> STEP: 178/406 -- GLOBAL_STEP: 15200
| > loss: 0.13427 (0.12363)
| > log_mle: -0.16159 (-0.16399)
| > loss_dur: 0.29586 (0.28761)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.47292 (7.27390)
| > current_lr: 0.00001
| > step_time: 0.55220 (0.53392)
| > loader_time: 2.83190 (2.39306)
 --> STEP: 203/406 -- GLOBAL_STEP: 15225
| > loss: 0.14076 (0.12364)
| > log_mle: -0.16551 (-0.16548)
| > loss_dur: 0.30627 (0.28912)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 8.00383 (7.62051)
| > current_lr: 0.00001
| > step_time: 0.64830 (0.55639)
| > loader_time: 2.80190 (2.44052)
 --> STEP: 228/406 -- GLOBAL_STEP: 15250
| > loss: 0.11629 (0.12331)
| > log_mle: -0.18520 (-0.16698)
| > loss_dur: 0.30149 (0.29029)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.31268 (8.09164)
| > current_lr: 0.00001
| > step_time: 0.75640 (0.57254)
| > loader_time: 2.49710 (2.47699)
 --> STEP: 253/406 -- GLOBAL_STEP: 15275
| > loss: 0.13417 (0.12334)
| > log_mle: -0.18877 (-0.16831)
| > loss_dur: 0.32294 (0.29166)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 20.71135 (8.40731)
| > current_lr: 0.00001
| > step_time: 0.67940 (0.59015)
| > loader_time: 2.90130 (2.51037)
 --> STEP: 278/406 -- GLOBAL_STEP: 15300
| > loss: 0.11284 (0.12260)
| > log_mle: -0.16676 (-0.16933)
| > loss_dur: 0.27960 (0.29192)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 6.49358 (8.62222)
| > current_lr: 0.00001
| > step_time: 0.73280 (0.60694)
| > loader_time: 2.92480 (2.53675)
 --> STEP: 303/406 -- GLOBAL_STEP: 15325
| > loss: 0.14625 (0.12229)
| > log_mle: -0.17829 (-0.17034)
| > loss_dur: 0.32454 (0.29262)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 11.56898 (8.80498)
| > current_lr: 0.00001
| > step_time: 0.71350 (0.62399)
| > loader_time: 2.91400 (2.57200)
 --> STEP: 328/406 -- GLOBAL_STEP: 15350
| > loss: 0.14260 (0.12219)
| > log_mle: -0.18416 (-0.17113)
| > loss_dur: 0.32676 (0.29333)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 9.97766 (8.91714)
| > current_lr: 0.00001
| > step_time: 0.89270 (0.64078)
| > loader_time: 3.95100 (2.64332)
 --> STEP: 353/406 -- GLOBAL_STEP: 15375
| > loss: 0.10931 (0.12200)
| > log_mle: -0.19313 (-0.17200)
| > loss_dur: 0.30244 (0.29401)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 15.21723 (9.04221)
| > current_lr: 0.00001
| > step_time: 0.95870 (0.65822)
| > loader_time: 3.51450 (2.70893)
 --> STEP: 378/406 -- GLOBAL_STEP: 15400
| > loss: 0.11962 (0.12154)
| > log_mle: -0.18013 (-0.17291)
| > loss_dur: 0.29975 (0.29446)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 12.15584 (9.19546)
| > current_lr: 0.00001
| > step_time: 0.91080 (0.67923)
| > loader_time: 3.52370 (2.75553)
 --> STEP: 403/406 -- GLOBAL_STEP: 15425
| > loss: 0.09559 (0.12114)
| > log_mle: -0.18133 (-0.17377)
| > loss_dur: 0.27692 (0.29491)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.23599 (9.33006)
| > current_lr: 0.00001
| > step_time: 1.18870 (0.70184)
| > loader_time: 3.69220 (2.80048)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.32717 (-0.05104)
| > avg_loss: 0.07936 (-0.01282)
| > avg_log_mle: -0.18939 (-0.00644)
| > avg_loss_dur: 0.26875 (-0.00638)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_15428.pth
 > EPOCH: 38/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 19:21:23) 
 --> STEP: 22/406 -- GLOBAL_STEP: 15450
| > loss: 0.10927 (0.11232)
| > log_mle: -0.15090 (-0.15499)
| > loss_dur: 0.26017 (0.26731)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 3.29305 (4.88956)
| > current_lr: 0.00001
| > step_time: 0.43060 (0.38723)
| > loader_time: 3.09490 (2.05922)
 --> STEP: 47/406 -- GLOBAL_STEP: 15475
| > loss: 0.12521 (0.11666)
| > log_mle: -0.16709 (-0.15546)
| > loss_dur: 0.29230 (0.27212)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 9.56020 (4.52774)
| > current_lr: 0.00001
| > step_time: 0.33800 (0.41200)
| > loader_time: 2.54580 (2.27425)
 --> STEP: 72/406 -- GLOBAL_STEP: 15500
| > loss: 0.12259 (0.11686)
| > log_mle: -0.15524 (-0.15840)
| > loss_dur: 0.27783 (0.27526)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.20997 (5.32188)
| > current_lr: 0.00001
| > step_time: 0.57500 (0.44853)
| > loader_time: 2.33560 (2.34219)
 --> STEP: 97/406 -- GLOBAL_STEP: 15525
| > loss: 0.12234 (0.11620)
| > log_mle: -0.16575 (-0.16172)
| > loss_dur: 0.28809 (0.27792)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 3.51257 (6.18131)
| > current_lr: 0.00001
| > step_time: 1.02200 (0.48117)
| > loader_time: 2.52020 (2.37099)
 --> STEP: 122/406 -- GLOBAL_STEP: 15550
| > loss: 0.09708 (0.11540)
| > log_mle: -0.16338 (-0.16415)
| > loss_dur: 0.26046 (0.27955)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 6.05086 (6.75713)
| > current_lr: 0.00001
| > step_time: 0.50350 (0.50353)
| > loader_time: 2.22090 (2.38090)
 --> STEP: 147/406 -- GLOBAL_STEP: 15575
| > loss: 0.12527 (0.11553)
| > log_mle: -0.17606 (-0.16629)
| > loss_dur: 0.30133 (0.28182)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.47762 (7.21610)
| > current_lr: 0.00001
| > step_time: 0.55940 (0.52298)
| > loader_time: 2.26910 (2.38510)
 --> STEP: 172/406 -- GLOBAL_STEP: 15600
| > loss: 0.10754 (0.11537)
| > log_mle: -0.18012 (-0.16796)
| > loss_dur: 0.28766 (0.28332)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 9.50214 (7.57399)
| > current_lr: 0.00001
| > step_time: 0.54830 (0.54330)
| > loader_time: 2.82760 (2.47063)
 --> STEP: 197/406 -- GLOBAL_STEP: 15625
| > loss: 0.09436 (0.11492)
| > log_mle: -0.17875 (-0.16956)
| > loss_dur: 0.27311 (0.28448)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.62914 (7.90230)
| > current_lr: 0.00001
| > step_time: 0.59150 (0.55927)
| > loader_time: 3.25130 (2.56771)
 --> STEP: 222/406 -- GLOBAL_STEP: 15650
| > loss: 0.10549 (0.11451)
| > log_mle: -0.18595 (-0.17104)
| > loss_dur: 0.29144 (0.28556)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 8.94097 (8.20647)
| > current_lr: 0.00001
| > step_time: 0.77190 (0.57340)
| > loader_time: 3.54540 (2.65642)
 --> STEP: 247/406 -- GLOBAL_STEP: 15675
| > loss: 0.11813 (0.11420)
| > log_mle: -0.17057 (-0.17238)
| > loss_dur: 0.28870 (0.28658)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 6.40159 (8.43353)
| > current_lr: 0.00001
| > step_time: 1.48270 (0.59173)
| > loader_time: 3.41120 (2.70599)
 --> STEP: 272/406 -- GLOBAL_STEP: 15700
| > loss: 0.09501 (0.11342)
| > log_mle: -0.19000 (-0.17361)
| > loss_dur: 0.28500 (0.28703)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.84535 (8.62859)
| > current_lr: 0.00001
| > step_time: 0.88380 (0.60847)
| > loader_time: 2.96760 (2.73327)
 --> STEP: 297/406 -- GLOBAL_STEP: 15725
| > loss: 0.10477 (0.11300)
| > log_mle: -0.18295 (-0.17452)
| > loss_dur: 0.28772 (0.28752)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 8.53754 (8.77253)
| > current_lr: 0.00001
| > step_time: 0.75840 (0.62441)
| > loader_time: 2.99090 (2.76734)
 --> STEP: 322/406 -- GLOBAL_STEP: 15750
| > loss: 0.11243 (0.11282)
| > log_mle: -0.19002 (-0.17541)
| > loss_dur: 0.30245 (0.28823)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 11.41290 (8.88195)
| > current_lr: 0.00001
| > step_time: 1.47120 (0.64267)
| > loader_time: 4.39220 (2.82056)
 --> STEP: 347/406 -- GLOBAL_STEP: 15775
| > loss: 0.09181 (0.11257)
| > log_mle: -0.19676 (-0.17628)
| > loss_dur: 0.28857 (0.28885)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 15.15130 (9.07154)
| > current_lr: 0.00001
| > step_time: 0.85660 (0.66199)
| > loader_time: 3.57660 (2.87634)
 --> STEP: 372/406 -- GLOBAL_STEP: 15800
| > loss: 0.12307 (0.11228)
| > log_mle: -0.17959 (-0.17713)
| > loss_dur: 0.30266 (0.28941)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 4.50627 (9.18688)
| > current_lr: 0.00001
| > step_time: 1.00200 (0.68314)
| > loader_time: 3.55520 (2.91659)
 --> STEP: 397/406 -- GLOBAL_STEP: 15825
| > loss: 0.09786 (0.11188)
| > log_mle: -0.19069 (-0.17794)
| > loss_dur: 0.28855 (0.28982)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 11.37954 (9.44082)
| > current_lr: 0.00001
| > step_time: 1.08760 (0.71191)
| > loader_time: 3.66470 (2.94222)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.44630 (+0.11913)
| > avg_loss: 0.07102 (-0.00834)
| > avg_log_mle: -0.19164 (-0.00225)
| > avg_loss_dur: 0.26266 (-0.00609)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_15834.pth
 > EPOCH: 39/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 19:46:58) 
 --> STEP: 16/406 -- GLOBAL_STEP: 15850
| > loss: 0.10503 (0.09667)
| > log_mle: -0.16476 (-0.16174)
| > loss_dur: 0.26978 (0.25840)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 2.08885 (4.49030)
| > current_lr: 0.00001
| > step_time: 0.25090 (0.38562)
| > loader_time: 1.59710 (1.86109)
 --> STEP: 41/406 -- GLOBAL_STEP: 15875
| > loss: 0.10712 (0.10572)
| > log_mle: -0.15210 (-0.15956)
| > loss_dur: 0.25921 (0.26528)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 8.39793 (5.75471)
| > current_lr: 0.00001
| > step_time: 0.36220 (0.40293)
| > loader_time: 2.46650 (2.12042)
 --> STEP: 66/406 -- GLOBAL_STEP: 15900
| > loss: 0.13577 (0.10750)
| > log_mle: -0.16784 (-0.16222)
| > loss_dur: 0.30361 (0.26972)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 5.67607 (6.35388)
| > current_lr: 0.00001
| > step_time: 0.39350 (0.43282)
| > loader_time: 2.68490 (2.21250)
 --> STEP: 91/406 -- GLOBAL_STEP: 15925
| > loss: 0.10957 (0.10707)
| > log_mle: -0.18952 (-0.16552)
| > loss_dur: 0.29909 (0.27259)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 15.04740 (6.89232)
| > current_lr: 0.00001
| > step_time: 0.42850 (0.47177)
| > loader_time: 2.33420 (2.23743)
 --> STEP: 116/406 -- GLOBAL_STEP: 15950
| > loss: 0.12826 (0.10601)
| > log_mle: -0.16750 (-0.16842)
| > loss_dur: 0.29576 (0.27442)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 7.99428 (7.65427)
| > current_lr: 0.00001
| > step_time: 0.49590 (0.49661)
| > loader_time: 2.79400 (2.24574)
 --> STEP: 141/406 -- GLOBAL_STEP: 15975
| > loss: 0.10047 (0.10578)
| > log_mle: -0.18467 (-0.17077)
| > loss_dur: 0.28514 (0.27655)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 16.05843 (8.00487)
| > current_lr: 0.00001
| > step_time: 0.65910 (0.51471)
| > loader_time: 2.53980 (2.25984)
 --> STEP: 166/406 -- GLOBAL_STEP: 16000
| > loss: 0.10159 (0.10561)
| > log_mle: -0.19322 (-0.17249)
| > loss_dur: 0.29481 (0.27810)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 8.09475 (8.25569)
| > current_lr: 0.00001
| > step_time: 0.67400 (0.52971)
| > loader_time: 3.21300 (2.31534)
 --> STEP: 191/406 -- GLOBAL_STEP: 16025
| > loss: 0.08485 (0.10515)
| > log_mle: -0.18837 (-0.17415)
| > loss_dur: 0.27323 (0.27930)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 11.02240 (8.58935)
| > current_lr: 0.00001
| > step_time: 0.74950 (0.54844)
| > loader_time: 2.50530 (2.37541)
 --> STEP: 216/406 -- GLOBAL_STEP: 16050
| > loss: 0.09161 (0.10458)
| > log_mle: -0.19340 (-0.17550)
| > loss_dur: 0.28501 (0.28008)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 14.37985 (8.92875)
| > current_lr: 0.00001
| > step_time: 0.78280 (0.56772)
| > loader_time: 2.99930 (2.42387)
 --> STEP: 241/406 -- GLOBAL_STEP: 16075
| > loss: 0.11023 (0.10446)
| > log_mle: -0.18041 (-0.17688)
| > loss_dur: 0.29063 (0.28134)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 11.57902 (9.15183)
| > current_lr: 0.00001
| > step_time: 0.78150 (0.58829)
| > loader_time: 2.69570 (2.47618)
 --> STEP: 266/406 -- GLOBAL_STEP: 16100
| > loss: 0.11085 (0.10403)
| > log_mle: -0.18469 (-0.17805)
| > loss_dur: 0.29554 (0.28207)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 9.72145 (9.28998)
| > current_lr: 0.00001
| > step_time: 1.15860 (0.66741)
| > loader_time: 2.92390 (2.56491)
 --> STEP: 291/406 -- GLOBAL_STEP: 16125
| > loss: 0.11540 (0.10326)
| > log_mle: -0.17754 (-0.17908)
| > loss_dur: 0.29293 (0.28234)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 6.16918 (9.46266)
| > current_lr: 0.00001
| > step_time: 1.06540 (0.69442)
| > loader_time: 2.86100 (2.59221)
 --> STEP: 316/406 -- GLOBAL_STEP: 16150
| > loss: 0.10089 (0.10311)
| > log_mle: -0.19050 (-0.18005)
| > loss_dur: 0.29139 (0.28316)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 7.97837 (9.60224)
| > current_lr: 0.00001
| > step_time: 1.28320 (0.72292)
| > loader_time: 3.45010 (2.62391)
 --> STEP: 341/406 -- GLOBAL_STEP: 16175
| > loss: 0.08478 (0.10303)
| > log_mle: -0.20796 (-0.18082)
| > loss_dur: 0.29274 (0.28385)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 12.77769 (9.69013)
| > current_lr: 0.00001
| > step_time: 1.03250 (0.74768)
| > loader_time: 3.20780 (2.67925)
 --> STEP: 366/406 -- GLOBAL_STEP: 16200
| > loss: 0.09378 (0.10260)
| > log_mle: -0.19970 (-0.18175)
| > loss_dur: 0.29348 (0.28435)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 16.19172 (9.85402)
| > current_lr: 0.00001
| > step_time: 0.81550 (0.76589)
| > loader_time: 3.59540 (2.72768)
 --> STEP: 391/406 -- GLOBAL_STEP: 16225
| > loss: 0.08415 (0.10228)
| > log_mle: -0.20295 (-0.18257)
| > loss_dur: 0.28710 (0.28486)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 19.04012 (9.98774)
| > current_lr: 0.00001
| > step_time: 0.85280 (0.78284)
| > loader_time: 2.92280 (2.76881)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.13499 (-0.31132)
| > avg_loss: 0.06085 (-0.01017)
| > avg_log_mle: -0.19705 (-0.00541)
| > avg_loss_dur: 0.25790 (-0.00475)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_16240.pth
 > EPOCH: 40/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 20:12:10) 
 --> STEP: 10/406 -- GLOBAL_STEP: 16250
| > loss: 0.09178 (0.08895)
| > log_mle: -0.16327 (-0.16353)
| > loss_dur: 0.25505 (0.25247)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 2.53258 (2.70416)
| > current_lr: 0.00001
| > step_time: 0.71430 (0.61828)
| > loader_time: 1.55040 (1.53884)
 --> STEP: 35/406 -- GLOBAL_STEP: 16275
| > loss: 0.08183 (0.09314)
| > log_mle: -0.18129 (-0.16460)
| > loss_dur: 0.26312 (0.25774)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 6.47728 (5.84182)
| > current_lr: 0.00001
| > step_time: 0.73160 (0.65208)
| > loader_time: 2.01460 (1.64849)
 --> STEP: 60/406 -- GLOBAL_STEP: 16300
| > loss: 0.08587 (0.09622)
| > log_mle: -0.16950 (-0.16608)
| > loss_dur: 0.25538 (0.26230)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 8.14916 (7.61407)
| > current_lr: 0.00001
| > step_time: 1.04010 (0.72273)
| > loader_time: 2.16760 (1.74275)
 --> STEP: 85/406 -- GLOBAL_STEP: 16325
| > loss: 0.08031 (0.09680)
| > log_mle: -0.19077 (-0.16888)
| > loss_dur: 0.27108 (0.26568)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 5.95813 (7.56177)
| > current_lr: 0.00001
| > step_time: 0.62350 (0.74553)
| > loader_time: 1.65040 (1.78914)
 --> STEP: 110/406 -- GLOBAL_STEP: 16350
| > loss: 0.10078 (0.09584)
| > log_mle: -0.19607 (-0.17177)
| > loss_dur: 0.29685 (0.26762)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 14.15659 (8.05553)
| > current_lr: 0.00001
| > step_time: 0.79740 (0.76031)
| > loader_time: 2.13230 (1.83350)
 --> STEP: 135/406 -- GLOBAL_STEP: 16375
| > loss: 0.10650 (0.09535)
| > log_mle: -0.18865 (-0.17430)
| > loss_dur: 0.29515 (0.26965)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 7.08076 (8.32791)
| > current_lr: 0.00001
| > step_time: 0.75340 (0.75011)
| > loader_time: 2.43970 (1.88851)
 --> STEP: 160/406 -- GLOBAL_STEP: 16400
| > loss: 0.09813 (0.09567)
| > log_mle: -0.18793 (-0.17605)
| > loss_dur: 0.28605 (0.27173)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 7.23996 (8.60956)
| > current_lr: 0.00001
| > step_time: 0.83590 (0.75196)
| > loader_time: 2.85880 (1.99355)
 --> STEP: 185/406 -- GLOBAL_STEP: 16425
| > loss: 0.11812 (0.09578)
| > log_mle: -0.18866 (-0.17768)
| > loss_dur: 0.30678 (0.27346)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 3.45463 (8.63014)
| > current_lr: 0.00001
| > step_time: 0.60290 (0.74782)
| > loader_time: 3.22560 (2.11532)
 --> STEP: 210/406 -- GLOBAL_STEP: 16450
| > loss: 0.08163 (0.09541)
| > log_mle: -0.18669 (-0.17902)
| > loss_dur: 0.26832 (0.27443)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 12.79550 (8.87239)
| > current_lr: 0.00001
| > step_time: 0.60810 (0.75266)
| > loader_time: 2.88840 (2.20153)
 --> STEP: 235/406 -- GLOBAL_STEP: 16475
| > loss: 0.08805 (0.09521)
| > log_mle: -0.19096 (-0.18054)
| > loss_dur: 0.27901 (0.27575)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 12.27228 (9.37461)
| > current_lr: 0.00001
| > step_time: 0.84780 (0.76565)
| > loader_time: 2.85490 (2.27941)
 --> STEP: 260/406 -- GLOBAL_STEP: 16500
| > loss: 0.08022 (0.09471)
| > log_mle: -0.19887 (-0.18183)
| > loss_dur: 0.27909 (0.27654)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 13.37421 (9.70845)
| > current_lr: 0.00001
| > step_time: 0.67360 (0.76490)
| > loader_time: 2.91000 (2.34208)
 --> STEP: 285/406 -- GLOBAL_STEP: 16525
| > loss: 0.09566 (0.09407)
| > log_mle: -0.18904 (-0.18290)
| > loss_dur: 0.28471 (0.27697)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 11.36318 (9.92837)
| > current_lr: 0.00001
| > step_time: 0.70390 (0.76731)
| > loader_time: 3.56050 (2.41242)
 --> STEP: 310/406 -- GLOBAL_STEP: 16550
| > loss: 0.08919 (0.09393)
| > log_mle: -0.18590 (-0.18377)
| > loss_dur: 0.27510 (0.27770)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 10.47760 (10.11414)
| > current_lr: 0.00001
| > step_time: 0.74930 (0.77009)
| > loader_time: 3.14000 (2.46686)
 --> STEP: 335/406 -- GLOBAL_STEP: 16575
| > loss: 0.11346 (0.09390)
| > log_mle: -0.19725 (-0.18453)
| > loss_dur: 0.31071 (0.27844)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 14.00020 (10.20950)
| > current_lr: 0.00001
| > step_time: 0.89710 (0.77668)
| > loader_time: 3.41880 (2.54396)
 --> STEP: 360/406 -- GLOBAL_STEP: 16600
| > loss: 0.08793 (0.09356)
| > log_mle: -0.20019 (-0.18542)
| > loss_dur: 0.28811 (0.27898)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 8.18499 (10.42254)
| > current_lr: 0.00001
| > step_time: 0.83020 (0.78781)
| > loader_time: 3.69610 (2.62321)
 --> STEP: 385/406 -- GLOBAL_STEP: 16625
| > loss: 0.09869 (0.09319)
| > log_mle: -0.19444 (-0.18623)
| > loss_dur: 0.29313 (0.27942)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 15.89072 (10.82015)
| > current_lr: 0.00001
| > step_time: 1.02070 (0.79918)
| > loader_time: 3.19100 (2.67896)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.16428 (+0.02929)
| > avg_loss: 0.04992 (-0.01093)
| > avg_log_mle: -0.20285 (-0.00580)
| > avg_loss_dur: 0.25278 (-0.00513)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_16646.pth
 > EPOCH: 41/1000
--> /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000
 > TRAINING (2023-06-26 20:36:32) 
 --> STEP: 4/406 -- GLOBAL_STEP: 16650
| > loss: 0.13945 (0.09262)
| > log_mle: -0.15013 (-0.16871)
| > loss_dur: 0.28958 (0.26133)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 2.40418 (4.10325)
| > current_lr: 0.00001
| > step_time: 0.39830 (0.40094)
| > loader_time: 1.28740 (1.39615)
 --> STEP: 29/406 -- GLOBAL_STEP: 16675
| > loss: 0.10748 (0.08463)
| > log_mle: -0.16943 (-0.16870)
| > loss_dur: 0.27691 (0.25333)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 3.37976 (4.95611)
| > current_lr: 0.00001
| > step_time: 0.47330 (0.39918)
| > loader_time: 1.76320 (1.73037)
 --> STEP: 54/406 -- GLOBAL_STEP: 16700
| > loss: 0.09280 (0.08909)
| > log_mle: -0.17373 (-0.16972)
| > loss_dur: 0.26653 (0.25881)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 7.23956 (5.42100)
| > current_lr: 0.00001
| > step_time: 0.39300 (0.43575)
| > loader_time: 2.12180 (1.94563)
 --> STEP: 79/406 -- GLOBAL_STEP: 16725
| > loss: 0.08361 (0.08873)
| > log_mle: -0.18492 (-0.17244)
| > loss_dur: 0.26853 (0.26117)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 11.65090 (6.64751)
| > current_lr: 0.00001
| > step_time: 0.58030 (0.46831)
| > loader_time: 2.47510 (2.02826)
 --> STEP: 104/406 -- GLOBAL_STEP: 16750
| > loss: 0.10629 (0.08893)
| > log_mle: -0.17745 (-0.17541)
| > loss_dur: 0.28374 (0.26434)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 8.04149 (7.56235)
| > current_lr: 0.00001
| > step_time: 0.66870 (0.49701)
| > loader_time: 2.29580 (2.09893)
 --> STEP: 129/406 -- GLOBAL_STEP: 16775
| > loss: 0.08635 (0.08821)
| > log_mle: -0.18047 (-0.17796)
| > loss_dur: 0.26682 (0.26616)
| > amp_scaler: 16384.00000 (16384.00000)
| > grad_norm: 9.52951 (8.10047)
| > current_lr: 0.00001
| > step_time: 0.52650 (0.51767)
| > loader_time: 1.99570 (2.13108)
 --> STEP: 154/406 -- GLOBAL_STEP: 16800
| > loss: 0.08854 (0.08808)
| > log_mle: -0.18379 (-0.17991)
| > loss_dur: 0.27233 (0.26799)
| > amp_scaler: 16384.00000 (18192.62338)
| > grad_norm: 10.93746 (8.50206)
| > current_lr: 0.00001
| > step_time: 0.71720 (0.53639)
| > loader_time: 3.01690 (2.15897)
 --> STEP: 179/406 -- GLOBAL_STEP: 16825
| > loss: 0.09546 (0.08792)
| > log_mle: -0.19532 (-0.18146)
| > loss_dur: 0.29078 (0.26938)
| > amp_scaler: 16384.00000 (17940.02235)
| > grad_norm: 20.21898 (9.10620)
| > current_lr: 0.00001
| > step_time: 0.77660 (0.55009)
| > loader_time: 3.16390 (2.29214)
 --> STEP: 204/406 -- GLOBAL_STEP: 16850
| > loss: 0.09146 (0.08802)
| > log_mle: -0.18955 (-0.18276)
| > loss_dur: 0.28102 (0.27078)
| > amp_scaler: 16384.00000 (17749.33333)
| > grad_norm: 34.91249 (9.78030)
| > current_lr: 0.00001
| > step_time: 0.61180 (0.56546)
| > loader_time: 2.97970 (2.36939)
 --> STEP: 229/406 -- GLOBAL_STEP: 16875
| > loss: 0.07900 (0.08745)
| > log_mle: -0.18914 (-0.18422)
| > loss_dur: 0.26814 (0.27167)
| > amp_scaler: 16384.00000 (17600.27948)
| > grad_norm: 15.85990 (10.28329)
| > current_lr: 0.00001
| > step_time: 1.31500 (0.58497)
| > loader_time: 3.12020 (2.41538)
 --> STEP: 254/406 -- GLOBAL_STEP: 16900
| > loss: 0.08431 (0.08752)
| > log_mle: -0.18849 (-0.18559)
| > loss_dur: 0.27280 (0.27311)
| > amp_scaler: 16384.00000 (17480.56693)
| > grad_norm: 13.44719 (10.72456)
| > current_lr: 0.00001
| > step_time: 0.81680 (0.60200)
| > loader_time: 2.85410 (2.44882)
 --> STEP: 279/406 -- GLOBAL_STEP: 16925
| > loss: 0.09877 (0.08679)
| > log_mle: -0.19039 (-0.18670)
| > loss_dur: 0.28916 (0.27349)
| > amp_scaler: 16384.00000 (17382.30824)
| > grad_norm: 9.18960 (10.98046)
| > current_lr: 0.00001
| > step_time: 1.05230 (0.61940)
| > loader_time: 3.17810 (2.48499)
 --> STEP: 304/406 -- GLOBAL_STEP: 16950
| > loss: 0.07333 (0.08670)
| > log_mle: -0.20255 (-0.18768)
| > loss_dur: 0.27587 (0.27438)
| > amp_scaler: 16384.00000 (17300.21053)
| > grad_norm: 12.33768 (11.39028)
| > current_lr: 0.00001
| > step_time: 0.90890 (0.63667)
| > loader_time: 2.86150 (2.51235)
 --> STEP: 329/406 -- GLOBAL_STEP: 16975
| > loss: 0.08461 (0.08662)
| > log_mle: -0.19008 (-0.18839)
| > loss_dur: 0.27469 (0.27502)
| > amp_scaler: 16384.00000 (17230.58967)
| > grad_norm: 9.55473 (11.31876)
| > current_lr: 0.00001
| > step_time: 0.94700 (0.65462)
| > loader_time: 3.00190 (2.56082)
 --> STEP: 354/406 -- GLOBAL_STEP: 17000
| > loss: 0.08634 (0.08648)
| > log_mle: -0.19731 (-0.18927)
| > loss_dur: 0.28365 (0.27575)
| > amp_scaler: 16384.00000 (17170.80226)
| > grad_norm: 18.33617 (11.36661)
| > current_lr: 0.00001
| > step_time: 1.00170 (0.72011)
| > loader_time: 4.91030 (2.60755)
 --> STEP: 379/406 -- GLOBAL_STEP: 17025
| > loss: 0.07347 (0.08591)
| > log_mle: -0.20559 (-0.19016)
| > loss_dur: 0.27906 (0.27606)
| > amp_scaler: 16384.00000 (17118.90237)
| > grad_norm: 8.27077 (11.30550)
| > current_lr: 0.00001
| > step_time: 1.31270 (0.76448)
| > loader_time: 3.62220 (2.67447)
 --> STEP: 404/406 -- GLOBAL_STEP: 17050
| > loss: 0.09158 (0.08553)
| > log_mle: -0.20016 (-0.19093)
| > loss_dur: 0.29174 (0.27646)
| > amp_scaler: 16384.00000 (17073.42574)
| > grad_norm: 20.25908 (11.46174)
| > current_lr: 0.00001
| > step_time: 2.02400 (0.81222)
| > loader_time: 3.15630 (2.75480)
 > EVALUATION 
--> EVAL PERFORMANCE
| > avg_loader_time: 1.46697 (+0.30269)
| > avg_loss: 0.04040 (-0.00952)
| > avg_log_mle: -0.20743 (-0.00458)
| > avg_loss_dur: 0.24783 (-0.00494)
> BEST MODEL : /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000/best_model_17052.pth
! Run is kept in /home/cryptogoth/src/tts-training/tacotron/run-June-26-2023_05+28AM-0000000