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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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de27115bb7
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there's something wrong with it on my 4xV100 rig......
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2025-02-25 15:14:08 -06:00 |
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db181f8e88
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only do auto=equal for nemo as its an FSQ
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2025-02-24 21:07:44 -06:00 |
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a5a04c39ef
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when the
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2025-02-24 21:03:23 -06:00 |
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918e0dbac1
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small slop cleanup
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2025-02-24 19:03:53 -06:00 |
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3330b5bb00
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maybe fix NaNs being thrown for immature models at fp16 for training evals
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2025-02-24 18:25:54 -06:00 |
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0f39f4d7a1
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lol
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2025-02-24 17:51:35 -06:00 |
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33d5a7109a
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its a miracle i was able to get a semblance of audio with the naive AudioEncoder (now it interleaves properly)
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2025-02-24 14:39:12 -06:00 |
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6e7b269147
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ugh
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2025-02-24 13:54:21 -06:00 |
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8f5a3997bd
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another experimental flag
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2025-02-24 13:50:41 -06:00 |
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f593ee98fc
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ugh
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2025-02-23 21:20:36 -06:00 |
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cbf6b84e27
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fixed grad norm and loss scale not reporting for local trainer
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2025-02-23 19:08:26 -06:00 |
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b640fabab5
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borrowed muon since it might better work under deepspeed and not require cruft (even though it really does not like the masked-NAR, also make the masked-NAR faux-causal since it might better help out for cfg.model.version >= 7
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2025-02-23 17:23:24 -06:00 |
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d33ccd188a
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ugh
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2025-02-23 12:31:07 -06:00 |
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8f3c3e01ee
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oops
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2025-02-23 12:09:56 -06:00 |
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b39aaacd77
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oops
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2025-02-23 11:55:43 -06:00 |
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3019c88799
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separate mask token and stop token because this might cause issues
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2025-02-23 11:36:32 -06:00 |
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6634d07576
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added muon optimizer through kludge hacks because it necessitates a second optimizer in tandum that seems to only sometimes work with deepspeed
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2025-02-23 11:22:13 -06:00 |
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67a6009555
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(finally) added parallel AR for cfg.model.version >= 7 (nvidia/audio-codec-44khz is being a pain and it might require training purely AR first......)
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2025-02-23 08:31:03 -06:00 |
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15b3c20e19
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also throw exception for zero'd out tensor during training (I am very paranoid now)
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2025-02-22 14:09:41 -06:00 |
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ab0abd2b12
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fixes fixes fixes (a quarter of my recently processed audio returned zero'd tensors......)
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2025-02-22 09:07:33 -06:00 |
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50506e5ebc
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oops
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2025-02-20 20:55:58 -06:00 |
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fc1ec2019d
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added option to buffer process jobs across multiple speakers to maybe squeeze out some throughput speeds for vall_e.emb.process (in the event of lots of speakers with low file counts, such as Emilia)
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2025-02-20 14:56:32 -06:00 |
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ce1ca0124a
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lol...
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2025-02-20 13:40:36 -06:00 |
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