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c00fc18b62
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actually use the right embedding for nar-len
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2024-11-13 18:04:04 -06:00 |
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3ea8a610d6
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fix STT
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2024-11-13 14:27:15 -06:00 |
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910033343c
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overhauled how the right resp level / classifier gets picked to avoid cringemath
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2024-11-13 13:31:17 -06:00 |
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269648605e
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move NAR-len rvq level 0 to separate embedding
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2024-11-13 11:38:58 -06:00 |
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29e45be0b4
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tweaks to bucket sampling
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2024-11-13 11:09:24 -06:00 |
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b2eca271a8
|
ugh
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2024-11-13 10:35:44 -06:00 |
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be83ddabaa
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better causal-ness for split loss calc, and also do masking for NAR-len for it
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2024-11-13 10:17:52 -06:00 |
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6b76419123
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ugh
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2024-11-13 09:54:20 -06:00 |
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ad7cfffc00
|
NAR-len RVQ-0 was being trained causally.............
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2024-11-13 09:43:50 -06:00 |
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976ee87f6f
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resume iteration step in tqdm trainer, warn to logger if the sampler state dict was invalidated
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2024-11-13 09:09:28 -06:00 |
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8286aa54c8
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do not pass timestep token/embedding since it doesn't seem to matter at all after all, fixed training masking rate to 80% because a paper said so
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2024-11-13 09:07:10 -06:00 |
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caf721c67b
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set it to zero because it'll make the stop token hide more often than not
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2024-11-12 22:30:50 -06:00 |
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0f2584eba7
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new meme sampler PogChamp new meme sampler PogChamp (it sort of helps?)
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2024-11-12 22:30:09 -06:00 |
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663f07038d
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haha... (do not create a token dropout/noise mask when not training (this sadly didnt fix NAR-len output))
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2024-11-12 16:41:58 -06:00 |
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b09328069e
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actually do CFG sampling for base AR+NAR tasks
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2024-11-12 13:42:39 -06:00 |
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2495a7ef67
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Fixed STT in the web UI
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2024-11-12 12:49:53 -06:00 |
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8927bad7bc
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actually fixed rep pen (for ar and nar, it seems to help with nar unmasking)
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2024-11-11 21:40:19 -06:00 |
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ec92613847
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actually pass input prompt length size to inference
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2024-11-11 20:39:48 -06:00 |
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b1df6a7bed
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reverted rep pen sampler due to a regression
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2024-11-11 20:35:08 -06:00 |
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b1f4db39c8
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threw in CFG sampling for normal model as well to experiment with
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2024-11-11 20:27:38 -06:00 |
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2f56696506
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overhauled inference/sampler kwargs to stop being a bloated mess
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2024-11-11 20:21:16 -06:00 |
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354f8e059d
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store dataset hash alongside state dict so it can be ignored if mismatched
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2024-11-11 18:16:56 -06:00 |
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f7b8b1e825
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dropped subtrain dataloader since its useless to duplicate
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2024-11-11 17:00:49 -06:00 |
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cf9df71f2c
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use homwbrewed caching system for dataloader paths / durations (I'm pretty sure I am now triggering OOM killers with my entire dataset used)
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2024-11-11 16:32:08 -06:00 |
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a748e223ce
|
tweaks
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2024-11-11 12:40:41 -06:00 |
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48490757da
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fixes
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2024-11-10 20:37:50 -06:00 |
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9def34cd66
|
lol
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2024-11-10 12:48:41 -06:00 |
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9cb0b6901b
|
unified nar.py into ar_nar.py
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2024-11-10 12:19:48 -06:00 |
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a9d2faf2d7
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all I can do now until I wait for the model to (re)train for pure NAR
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2024-11-09 22:57:34 -06:00 |
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ad7e290a5e
|
ugh (ROCm seems to silently clamp any token value >= logits.shape[-1] for loss calculation, while cuda will throw an assert, making it hard to find this dumb fuckup)
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2024-11-09 19:40:02 -06:00 |
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943fe70c10
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I don't know why this fixes an assert thrown but it does
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2024-11-09 19:04:13 -06:00 |
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f50d92ba6c
|
Almost made a mistake
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2024-11-09 18:12:54 -06:00 |
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c6a38693a2
|
This better work
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2024-11-09 18:04:59 -06:00 |
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8b3d1cf70a
|
Something's Wrong
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2024-11-09 15:07:43 -06:00 |
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dcd5fecff3
|
some cleanup while I wait for the NAR-len to train to an acceptable state (currently it performs okay, but only on audo after 3 seconds or so)
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2024-11-09 12:12:46 -06:00 |
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69b0b3b854
|
set timestep tensor to whatever the time embedding's dtype is because it'll gripe under amp
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2024-11-09 00:11:16 -06:00 |
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5a09a5f6e9
|
I forgot about the time embedding...
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2024-11-08 22:46:26 -06:00 |
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811b15d280
|
I suppose I just have a shit training method since the sampler is as solid as I can get it...............
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2024-11-08 22:05:41 -06:00 |
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13b54953bd
|
agony
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2024-11-08 13:34:39 -06:00 |
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c127c4e488
|
'borrowed' a sampling scheduler for NAR-len's RVQ level 0 (better than before, but still not good enough)
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2024-11-07 21:19:14 -06:00 |
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e108c54daf
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new NAR-len training paradigm......
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2024-11-07 11:32:11 -06:00 |
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ed174c589e
|
ugh
|
2024-11-07 09:19:21 -06:00 |
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d13ab00ad8
|
one more note
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2024-11-07 09:11:21 -06:00 |
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5698188824
|
あたしって、ほんとバカ
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2024-11-07 09:10:18 -06:00 |
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77ff23e319
|
repeat extend the prom to fill the initial tokens for nar-len (it somewhat works, the model just needs to train more)
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2024-11-06 23:29:53 -06:00 |
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a3bc26f7ec
|
ugh
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2024-11-06 23:16:28 -06:00 |
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d606a693ff
|
eval fix for nar-len
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2024-11-06 23:14:16 -06:00 |
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105ed51159
|
I guess I'll fall for the NAR-len meme again (I don't know where my previous weights are, so I need to train it again to test something)
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2024-11-06 19:17:12 -06:00 |
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bcabde3454
|
more notes
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2024-11-06 13:51:28 -06:00 |
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bfc5e1d723
|
agony
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2024-11-05 22:30:49 -06:00 |
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aefe8fcdad
|
UGH
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2024-11-05 22:13:58 -06:00 |
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556d9db0d5
|
web UI support for HF ZeroGPU
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2024-11-05 21:38:02 -06:00 |
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e58a9469a3
|
move layerskip to experimental settings.......
|
2024-11-05 20:37:06 -06:00 |
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bbc2de3713
|
ugh
|
2024-11-05 11:50:05 -06:00 |
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9e65e05e83
|
more windows specific fixes, limit gradio to <5.0.0 on linux (it works on windows, but not on my linux machine tm)
|
2024-11-04 18:00:33 -06:00 |
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c83670c38c
|
Windows specific fixes (to-do: find libespeak-ng.dll automatically because it cannot be trusted to do it by default)
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2024-11-03 19:19:15 -06:00 |
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d229725c76
|
more adjustments (adjustments of early-exit entropy/varentropy thresholds, default rep pen being 1.5, experimental refine-on-stop, etc.)
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2024-11-03 18:31:28 -06:00 |
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aee08b7307
|
changed layerskip float16 training warning (since it didnt seem to fry on my 4xV100 system)
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2024-11-03 09:58:29 -06:00 |
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3826f9bae4
|
saner mask creation? (it doesnt matter, kv cache wont work)
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2024-11-02 21:00:21 -05:00 |
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ded746e157
|
very, very naive layerskip speculative sampling (it just checks if the current layer's state is good enough)
|
2024-11-02 11:49:05 -05:00 |
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62fe5b0943
|
ughh
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2024-11-01 22:36:48 -05:00 |
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ec79230965
|
shuffled web UI options hidden by cfg.experimental to its own tab, expose early exit selection to inferencing (it kinda works naively, still need to implement self-speculation)
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2024-11-01 21:30:06 -05:00 |
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ef1c17430f
|
skip step on nan loss (ironically I have not had a nan loss after adding this), throw exception with invalid cfg.dataset.sample_type and sample_order combination (because I was tricked by this in my yaml and had inconsistent vram usage)
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2024-11-01 20:54:53 -05:00 |
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fb8faa295b
|
actually float16(+AMP) and layerskip is bad and will kill the model......
|
2024-11-01 18:36:44 -05:00 |
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edf1e66bf9
|
layerskip_r=6 fries the model so hard the loss is sub-1...
|
2024-11-01 17:06:07 -05:00 |
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9b6c57bc57
|
third time's the charm (for some reason it escaped me that I should treat early exit loss as an aux_loss to be used with the normal loss, as if I was training a MoE's router)
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2024-11-01 12:50:37 -05:00 |
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76ebef45dc
|
off-by-one...
|
2024-10-31 13:24:48 -05:00 |
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b63293cbbe
|
ugh
|
2024-10-30 22:49:11 -05:00 |
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a22534e8f4
|
layer skip training implemented (need to gut the inferencing from the repo, and to actually see if the model can benefit from this)
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2024-10-30 20:05:45 -05:00 |
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4049f51ba9
|
added option to load lora directly from the model file itself with --lora
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2024-10-26 00:13:10 -05:00 |
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ccf71dc1b6
|
added option to load from a model state dict directly instead of a yaml (to-do: do this for LoRAs too), automatically download the default model if none is provided
|
2024-10-25 22:15:15 -05:00 |
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a96f5aee32
|
adjusted how i want to pass eval kwargs
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2024-10-25 20:38:09 -05:00 |
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92e6bff6dc
|
actually ar temp 0.5 with rep pen 1.125 seems to have the benefits of better outputs without it degrading some of the time but not all the time
|
2024-10-23 00:03:35 -05:00 |
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8920e5e86b
|
actually have beam_width in the webUI work
|
2024-10-22 22:06:22 -05:00 |
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910571ad34
|
too brainlet to diagnose why low temp / greedy sampling is randomly unstable some of the time
|
2024-10-22 20:13:54 -05:00 |
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8eb9a4056b
|
modified default arguments (ar temp = 0 and rep pen = 1.125 seems to be stable, at least given the few things i tested), do not pass top k/top p/min p to NAR even though technically none of those things should matter when greedy sampling
|
2024-10-22 18:12:39 -05:00 |
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1a02cd5bce
|
modify demo template to say F5 instead of YourTTS, swap LoRA comparison around to make the lora'd the base file, and the no-lora the suffix'd file
|
2024-10-21 19:52:02 -05:00 |
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02dfc60ac3
|
ugh
|
2024-10-18 17:23:22 -05:00 |
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71731ed785
|
added prefixing with silence (was to test something, currently hidden under cfg.experimental=True)
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2024-10-18 17:19:52 -05:00 |
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6b04c13c56
|
print warning if audio promtpless inferencing with low AR temp (it really doesn't like low temps / greedy sampling)
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2024-10-18 17:01:40 -05:00 |
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c8f31db1de
|
default to greedy sample AR (i should probably test this more but it seems to pass my harvard sentences and tongue twisters)
|
2024-10-18 16:58:56 -05:00 |
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fc8dfd8617
|
made greedy AR sampling viable (and preferable), with caveats (per comment in vall_e.models.ar_nar)
|
2024-10-18 16:55:00 -05:00 |
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07f4935a75
|
more tweaks
|
2024-10-18 13:19:36 -05:00 |
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|
0dfab973e7
|
oops
|
2024-10-18 09:40:06 -05:00 |
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75b90be325
|
cleaned up unused config flags, allow less strict yaml by pruning missing keys, renamed some dataset configs to be more unified
|
2024-10-17 17:06:48 -05:00 |
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8b6095f681
|
saner defaults, maybe
|
2024-10-17 14:37:21 -05:00 |
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f88097ccf6
|
add config option to set the rate of sampling randomly vs similar speakers during training
|
2024-10-16 14:27:58 -05:00 |
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|
|
48461833c2
|
ugh
|
2024-10-15 19:30:43 -05:00 |
|
|
|
eea70f5698
|
kludge fix for an oversight in the model when trying to train for longer input prompt durations......
|
2024-10-15 19:25:03 -05:00 |
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|
84005c5b00
|
entropix apparently processes the entire sequence of logits but it falls apart when doing that
|
2024-10-13 12:01:12 -05:00 |
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|
c800d28bb8
|
respect attention defined in the yaml for web UI (which might explain why theres been a discrepancy in outputs for me)
|
2024-10-13 11:02:24 -05:00 |
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|
ed6b7a690f
|
ugh.........
|
2024-10-13 00:26:46 -05:00 |
|
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|
d405f243d4
|
at wits end in trying to output the right attention scores
|
2024-10-12 23:53:13 -05:00 |
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70cf694cfd
|
output attention scores for SDPA/flash, since naive attention seems broken
|
2024-10-12 12:09:17 -05:00 |
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|
541e45263c
|
ugh
|
2024-10-12 11:29:16 -05:00 |
|
|
|
04e983b86b
|
modified demo page to be more modular with demoing comparisons, actually provide a path to use modified naive attention, entropix sampling is not tied to an experimental yaml flag now
|
2024-10-12 11:27:55 -05:00 |
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|
666e8038fb
|
ugh
|
2024-10-12 10:41:35 -05:00 |
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|
3d6ef9666b
|
overridden naive llama attention to get the right score values that entropix needs
|
2024-10-12 10:05:47 -05:00 |
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40b089daf3
|
lol
|
2024-10-12 09:57:34 -05:00 |
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d6f7c86a5c
|
entropix tweaks (it doesn't output garbage but it loves to go for silence)
|
2024-10-12 09:46:18 -05:00 |
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