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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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dcd5fecff3
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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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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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811b15d280
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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
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agony
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2024-11-08 13:34:39 -06:00 |
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c127c4e488
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'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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d13ab00ad8
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one more note
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2024-11-07 09:11:21 -06:00 |
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5698188824
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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
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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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105ed51159
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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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ec79230965
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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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a22534e8f4
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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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75b90be325
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cleaned up unused config flags, allow less strict yaml by pruning missing keys, renamed some dataset configs to be more unified
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2024-10-17 17:06:48 -05:00 |
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d0ab7d755a
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added min-p (really does not seem useful since it's very sensitive), more tweaks to entropix
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2024-10-11 22:36:06 -05:00 |
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bef43a0c18
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added experimental entropix sampling support
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2024-10-11 21:18:26 -05:00 |
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54547b74d8
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experimental implementation of STT (need to actually test on a model, test trainer seems to work)
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2024-09-05 20:43:20 -05:00 |
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32287710a2
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moved prints to use logger, edited readme (fused_attn doesnt seem stable for training)
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2024-08-29 13:27:16 -05:00 |
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6a733eb2ed
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changed torch.Tensor().to(device, dtype) to just torch.tensor(..., device, dtype) because it's been bothering my autism that I'm creating tensors then converting rather than creating with the right device/dtype, some 'optimization' to compile the model but it doesnt seem to do anything useful
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2024-08-03 22:10:21 -05:00 |
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11fa3da665
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some cleanup, fixed the wrapper attention to explicitly use other sdpa backends
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2024-08-03 19:51:00 -05:00 |
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66407e5bdb
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tweaks for the NAR-len model, maybe
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2024-08-03 08:40:39 -05:00 |
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97c5241bef
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fixes, throw an exception when using NAR only model with non-unified position IDs, since for some reason it outputs garbage for the NAR
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2024-08-02 22:25:49 -05:00 |
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387358bc8a
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fixes for the NAR-len model, and documentation some config options, and a better way to handle resizing modules on state_dict load
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2024-07-31 20:35:09 -05:00 |
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ce8bb1e4f7
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sanity cleanups with weird off-by-one-ness, cleaned up and validated vall_e.models.experimental works again
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2024-07-27 15:36:05 -05:00 |
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e19aa643a6
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cleaned up demo page creation, added option to pass in RVQ level sampling distribution for training
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2024-07-21 19:12:03 -05:00 |
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d87b492295
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added rudimentary demo page creator (currently just embeds base64 wavs into the page, need to test not doing that)
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2024-07-19 20:49:40 -05:00 |
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97e768601c
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re-introducing SpeechX tasks (need to validate them all, everything works with base tts anyways)
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2024-07-18 16:16:14 -05:00 |
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3acc54df22
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allow loading a different model within the web ui (apparently I did not have the web UI in the documentation)
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2024-07-15 19:59:48 -05:00 |
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7b210d9738
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sanity cleanup
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2024-07-04 15:58:08 -05:00 |
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dced595391
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more cleanup
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2024-06-30 11:00:12 -05:00 |
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bc2a6fa756
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sanity cleanup: moved experimental features under its own thing
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2024-06-30 10:37:33 -05:00 |
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bcf3910a17
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the NAR only dream is dead (it just won't work)
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2024-06-12 19:49:47 -05:00 |
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a9353cf9fa
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ugh
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2024-06-12 00:14:29 -05:00 |
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cca542a4c0
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ugh
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2024-06-11 23:59:28 -05:00 |
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132a02c48b
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sanity cleanup, backup config yaml for each log file
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2024-06-09 11:22:52 -05:00 |
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8d068fa3f9
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reticulating splines
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2024-06-08 20:30:15 -05:00 |
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545162195b
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deprecate sole AR/NAR model by only keeping the AR+NAR (the beauty of no one using this is that I can break compat as much as I want), add tone token for when I classify my dataset with tone/emotion in the future, some other things
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2024-04-15 19:54:32 -05:00 |
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08bae355eb
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actually use langs from the dataloader
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2023-10-11 21:21:50 -05:00 |
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8740cdefc6
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added initial support for languages (still testing, marked as model version 3), added experimental 'context extend by limiting the resp context' (untested)
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2023-10-11 20:38:40 -05:00 |
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7facacf7c9
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separated samplers into its own file, don't bother copying the logits back to the GPU after sampling, it's not necessary
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2023-10-11 12:25:31 -05:00 |
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e727b6e5c1
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changed dynamic temperature trigger to be a min-(n)ar-temp value between [0,(n)ar-temp), flags to set min temp, checkbox in web UI to request it
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2023-10-10 17:02:33 -05:00 |
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87db03dd93
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trim the input prompt to 3 seconds when training NAR tasks (marked as experimental; the paper mentions doing so, but I don't know how much this would harm the retention heads)
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2023-10-09 22:03:58 -05:00 |
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c0b25541e3
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restructured some things with the model to remove dead weights
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2023-09-20 19:10:59 -05:00 |
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a6bfe43590
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added mirostat sampling (given a partially trained model, it got far decent output than I expected, need to test on a better trained model)
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2023-09-18 18:55:41 -05:00 |
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2567e082b5
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UGH
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2023-09-16 00:26:13 -05:00 |
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23a5fdd645
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implemented a naive beam search (I really should be taking a break)
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2023-09-12 21:28:07 -05:00 |
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40ef34e1ca
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this embedding class definitely works, and migrating from the previous embedding weights seems to work.
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2023-09-11 14:13:42 -05:00 |
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a1f250ffac
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set default max_levels for NAR to 0 and implicitly set it to max resps levels because the previous way was implicitly assuming all models were outputting at 1+7 RVQ bins.
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2023-09-10 20:33:33 -05:00 |
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ba71020318
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added option to limit (or exceed) inferenced RVQ-bin levels through the NAR
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2023-09-10 13:50:13 -05:00 |
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