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59f56ad099
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cleaup
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2024-12-24 23:14:32 -06:00 |
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82e8592f2a
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working vall_e.cpp
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2024-12-24 17:54:48 -06:00 |
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497bdfc67b
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more work (the wall is non-causal decoding......)
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2024-12-22 20:11:31 -06:00 |
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5f289db275
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ugh
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2024-12-22 16:15:24 -06:00 |
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0d4329d2e3
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sanity cleanup
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2024-12-22 15:05:45 -06:00 |
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353e478e68
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agony
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2024-12-21 22:52:10 -06:00 |
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91caf00212
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ugh
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2024-12-20 17:13:37 -06:00 |
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59bf6b8b33
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exposed additional task (ns, sr, vc) (vc is experimental)
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2024-12-20 11:15:29 -06:00 |
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e7e7f48043
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livid
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2024-12-19 19:25:27 -06:00 |
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cddf8ca814
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sort batches to try and reduce number of padded tokens in batched inference (also commented out F5 samples getting added to the demo page because I would have to regenerate them)
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2024-12-11 22:45:38 -06:00 |
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61ed662856
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ACTUALLY actually fix KD-loss (the -inf in the logits was caused by cringecode)
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2024-12-07 12:31:54 -06:00 |
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34a66e1052
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agnostified KD
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2024-12-06 23:53:46 -06:00 |
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953d3eb030
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ugh
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2024-12-06 22:35:30 -06:00 |
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42fafbaaca
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actually fixed knowledge distillation because of errant -inf logits causing problems and needed to be filtered (and splitting text language / output audio language because it helps)
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2024-12-06 21:55:20 -06:00 |
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23d402bf01
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added knowledge distillation in the trainer (sadly it is not agnostic because of the grave mistake of further processing the batch within the forward pass, so subsequent calls do not match......)
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2024-12-05 23:05:52 -06:00 |
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84a05acb6d
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touch ups in docs
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2024-12-02 19:10:42 -06:00 |
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4aa685e749
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what has science done
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2024-11-22 16:45:40 -06:00 |
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147219a5e0
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huge oversight in the attention masking......... (i realized I have not been providing a non-causal mask to non-causal tasks)
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2024-11-22 13:44:43 -06:00 |
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8aafae91fd
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dont use timeembedding
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2024-11-21 23:14:52 -06:00 |
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2cef97e43f
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cleanup
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2024-11-21 23:08:43 -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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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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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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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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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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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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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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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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9cb0b6901b
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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
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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
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Almost made a mistake
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2024-11-09 18:12:54 -06:00 |
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c6a38693a2
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This better work
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2024-11-09 18:04:59 -06:00 |
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8b3d1cf70a
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Something's Wrong
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2024-11-09 15:07:43 -06:00 |
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69b0b3b854
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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
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I forgot about the time embedding...
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2024-11-08 22:46:26 -06:00 |
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