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2053580838
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updated dataloader to hopefully reduce RAM usage
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2025-03-15 13:14:37 -05:00 |
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9cfbf94b1c
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config-ify the len_loss_factor
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2025-03-14 20:30:48 -05:00 |
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ca8cc15271
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more tweaks (vall_e.webui --yaml still breaks things, --model needs to deduce what audio backend now that im supporting other ones again // added easy top-sampler settings back for new implementation)
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2025-03-14 20:18:25 -05:00 |
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6ee505cffd
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fixed dac
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2025-03-12 23:17:27 -05:00 |
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ba5f3d19b4
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use the FSQ-targeted encoder/decodede whole-ly as it works for EnCodec too, as the RVQ-targeted encoder/decoder doesnt (and some notes)
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2025-03-12 22:47:19 -05:00 |
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2ccf1b5740
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actually do duration prediction
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2025-03-11 22:14:54 -05:00 |
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5c512717a6
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len prediction for new model (and remove logit normalization since it kills inferencing)
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2025-03-11 20:33:09 -05:00 |
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5f98543d4d
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ughh
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2025-03-10 21:18:57 -05:00 |
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8ac03aac8a
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ugh
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2025-03-10 21:14:56 -05:00 |
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5670fcb23f
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hopefully the final tweaks needed for this bastard of a model
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2025-03-10 20:59:11 -05:00 |
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00d1fed217
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another optimization (within the dataloader because the similar utterance sampler was mondo slow)
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2025-03-08 17:10:50 -06:00 |
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5e9d1a5302
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one more time one more time (this normalization isn't a spook)
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2025-03-07 19:32:42 -06:00 |
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93044829af
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one more time (could have sworn i tested it with batch size > 1)
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2025-03-07 19:14:33 -06:00 |
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6cea840710
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oops
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2025-03-07 18:57:25 -06:00 |
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dbd34b6430
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add specialized calc_loss because schizo
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2025-03-07 18:44:11 -06:00 |
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8d848ed549
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handle case of dropping cond for segment mask
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2025-03-07 14:11:58 -06:00 |
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89e52b9877
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ugh
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2025-03-07 13:55:57 -06:00 |
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6afc2b7526
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gut feeling to change the attention mask
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2025-03-07 13:51:59 -06:00 |
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91ede71cf0
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ugh
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2025-03-06 17:19:27 -06:00 |
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2dd80a03ff
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stuff for interfacing with the loss scaler value (because I want to cap it)
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2025-03-06 17:07:29 -06:00 |
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a30dffcca7
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wandb additions (to-do eventually, upload samples as artifacts)
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2025-03-06 15:44:40 -06:00 |
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ec87308d75
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final tweaks before training this meme 44khz model for the 3rd time
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2025-03-06 15:31:15 -06:00 |
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5cd71ef238
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QoL so I can stop having to manually inject different configs
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2025-03-06 14:48:14 -06:00 |
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0d809561c6
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accuracy k=1 and k=80 because im probably dumb for k=10 as the default since it does not represent any usecase
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2025-03-05 16:35:34 -06:00 |
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2fb2b732fc
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wow that was fast
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2025-03-04 23:17:18 -06:00 |
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462f71e2f7
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ugh
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2025-03-04 14:57:00 -06:00 |
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1cd24f3381
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a birdie tells me i should probably use a different optimizer (also preliminary support for native sparse attention but I don't know if I'll use it)
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2025-03-04 14:53:02 -06:00 |
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0451f75e33
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now that the new model seems a little more promising, i can re-document things non-cynically
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2025-03-03 13:21:41 -06:00 |
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3f1070f575
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tweaks
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2025-03-02 22:36:25 -06:00 |
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4afa4ccce5
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at wits end (parhaps the semantic token approach is the toughest pill to swallow)
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2025-03-01 21:03:25 -06:00 |
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1d3290b023
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could have sworn this worked before, might have broke it when i decoupled from omegaconf
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2025-03-01 19:30:26 -06:00 |
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17094b8002
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reticulating splines
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2025-03-01 17:48:51 -06:00 |
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56f8be4d62
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lol
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2025-02-28 22:15:37 -06:00 |
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ddc49c89c5
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the learning rate scheduler pill is a tough pill to swallow
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2025-02-28 22:12:19 -06:00 |
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b97faa8173
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fixes...
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2025-02-28 18:53:07 -06:00 |
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4e7d885542
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lol
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2025-02-28 18:06:41 -06:00 |
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a174c33db6
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a gorillionth time's the charm (aka: the encoder/decoder pill is a tough pill to swallow)
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2025-02-28 17:56:50 -06:00 |
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09d82a26fe
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ugh
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2025-02-28 01:06:38 -06:00 |
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93feb5660f
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do not like that
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2025-02-27 23:59:56 -06:00 |
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f4f435d7f5
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when you already had these ideas to stabilize training but you just ignored them
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2025-02-27 23:39:20 -06:00 |
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0a45c9c042
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fix attention backend not being used
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2025-02-27 21:38:38 -06:00 |
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b8e9f3d785
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maybe this will work
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2025-02-27 20:42:12 -06:00 |
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01e96bafc9
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ugh
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2025-02-27 19:05:32 -06:00 |
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eff180248c
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decoupled llama backend to avoid any funny changes from transformers, removed other backends since i dont think i'll ever bother using them
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2025-02-27 19:00:37 -06:00 |
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ceecac6ffe
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I think I made resp_parallel_training=True faster with loss factoring?
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2025-02-26 23:13:32 -06:00 |
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06ef3daf3c
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require minimum of 1 second durations for training because of my slop code auto-transposing that I don't wanna fix right now
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2025-02-26 22:00:33 -06:00 |
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cbd4d7d7f4
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ugh
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2025-02-26 21:31:10 -06:00 |
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2ea387c08a
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segregated experimental changes into its own streamlined file to avoid breaking the existing model, and it can pivot to the cleaned up code if it actually works (nothing is working)
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2025-02-26 21:26:13 -06:00 |
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7d2e64630c
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lol
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2025-02-26 10:49:06 -06:00 |
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95da4e9405
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made muon actually work by actually utilizing param groups (thanks APOLLO for reminding me this is the sane way to handle this split)
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2025-02-26 10:39:13 -06:00 |
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