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6845c447c9
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added more harvard sentences to load from a text file
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2024-11-21 13:18:11 -06:00 |
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2a084544e8
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moved duration padding for NAR-len to be a scalar instead (since it seems longer utterances need it much more so than shorter utterances)
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2024-11-21 13:04:07 -06:00 |
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6aee08f9c0
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moved stuff in the web UI around (un-experimented the max NAR-len steps because its kind of important to adjust this value for better sounding audio / quicker generated audio)
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2024-11-20 20:37:33 -06:00 |
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dfdba3f190
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oops
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2024-11-20 19:21:03 -06:00 |
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cd6e9ba2f2
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oops
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2024-11-20 16:27:51 -06:00 |
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1a73ac6a20
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I cannot believe it's not actually called Wand DB (added wandb logging support since I think it would have been a much better way to look at my metrics)
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2024-11-20 16:10:47 -06:00 |
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67f7bad168
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added mixed modality AR+NAR-len to generate a short prefix through the AR, then inference with said prefix through the NAR-len (need to experiment with it more to ensure that the masked off tokens are the only tokens getting updated)
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2024-11-20 14:22:12 -06:00 |
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db64e6cb59
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dependency updates (gradio 5.x now works on my machine)
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2024-11-20 12:33:01 -06:00 |
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b1369e7824
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better modality selection (pick AR+NAR by default for the ar+nar model, pick NAR-len by default for the nar-len model), lowered default CFG because it makes the AR+NAR output sped up (but can't be too low since it's required for the NAR-len)
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2024-11-19 18:51:17 -06:00 |
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190a917b3e
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I did it.
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2024-11-19 12:24:33 -06:00 |
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0e621354e7
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cleaned up classifier-free guidance logit processing (in order to try and cope with a bad nar-len model)
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2024-11-19 10:30:05 -06:00 |
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5ba80686e1
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two weeks of agony concludes
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2024-11-18 21:29:28 -06:00 |
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2b29790173
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oops
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2024-11-18 14:12:26 -06:00 |
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4a71981456
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normalize sampler index by batch size (if not using batched sampler), add option to cap out utterances for a speaker, some other things
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2024-11-18 12:46:50 -06:00 |
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6cfdf94bf9
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swap priority to use nar-len if available, added notes
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2024-11-18 09:40:04 -06:00 |
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069b27570f
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set option to set training masking ratio (I don't think for tts a fixed masking ratio is beneficial since the magic of the AR+NAR is being able to still reference the prior sequence of tokens for predicting things)
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2024-11-17 17:04:07 -06:00 |
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88d840218d
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default set cfg strength to 3.0 since the reference model is updated
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2024-11-17 10:23:40 -06:00 |
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a3e1fa3518
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ugh
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2024-11-17 09:28:33 -06:00 |
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23fdba0c98
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tweaks and changes
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2024-11-16 15:49:06 -06:00 |
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2fbeacfe92
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ugh
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2024-11-14 22:18:33 -06:00 |
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39096f8ff3
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redid loss calculation to be cleaner, and position ID generation, and other things (I might need to train the NAR-len from scratch and not resume from an existing checkpoint.........)
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2024-11-14 22:17:47 -06:00 |
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ef05c951ff
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adjust fp16 loss scaling since I fried a model overnight when it hit 8K scale
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2024-11-14 09:23:52 -06:00 |
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e412e98125
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ugh
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2024-11-14 07:34:22 -06:00 |
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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
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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
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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
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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
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lol
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2024-11-10 12:48:41 -06:00 |
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