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5d80a2d0d4
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fixed NAR-len issues with non-english maybe (langs weren't being passed), added interface to inference in batches through tts.batched_inference (no support for rolling context/prefixes because there's no way to do that), demo page uses batched inferencing now
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2024-12-07 19:21:05 -06:00 |
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1f54bf5b40
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revert sageattn back to optional dependency because it's not on windows, force resize_modules on by default because I broke something
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2024-12-07 17:09:39 -06:00 |
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f97e8b0c7f
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ACTUALLY do KD-loss because of an oversight with masked_select outputting 1D tensors that get softmax'd in total
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2024-12-07 09:52:51 -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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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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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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24d888c47c
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temporarily dropping support for xformers because it's breaking when using an attention mask (which i dont remember commenting it out when being passed), default to not use wandb because it's being a pain when doing tests and not actual sessionsS)
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2024-11-22 11:29:12 -06:00 |
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3fc0540f49
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m
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2024-11-21 15:07:46 -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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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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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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23fdba0c98
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tweaks and changes
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2024-11-16 15:49:06 -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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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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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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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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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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c6a38693a2
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This better work
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2024-11-09 18:04:59 -06:00 |
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bcabde3454
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more notes
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2024-11-06 13:51:28 -06:00 |
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c83670c38c
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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
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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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fb8faa295b
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actually float16(+AMP) and layerskip is bad and will kill the model......
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2024-11-01 18:36:44 -05:00 |
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edf1e66bf9
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layerskip_r=6 fries the model so hard the loss is sub-1...
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2024-11-01 17:06:07 -05:00 |
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9b6c57bc57
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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
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off-by-one...
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2024-10-31 13:24:48 -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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4049f51ba9
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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
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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
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2024-10-25 22:15:15 -05:00 |
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a96f5aee32
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adjusted how i want to pass eval kwargs
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2024-10-25 20:38:09 -05:00 |
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8920e5e86b
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actually have beam_width in the webUI work
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2024-10-22 22:06:22 -05:00 |
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8eb9a4056b
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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
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2024-10-22 18:12:39 -05:00 |
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c8f31db1de
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default to greedy sample AR (i should probably test this more but it seems to pass my harvard sentences and tongue twisters)
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2024-10-18 16:58:56 -05:00 |
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07f4935a75
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more tweaks
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2024-10-18 13:19:36 -05:00 |
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0dfab973e7
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oops
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2024-10-18 09:40:06 -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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f88097ccf6
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add config option to set the rate of sampling randomly vs similar speakers during training
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2024-10-16 14:27:58 -05:00 |
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04e983b86b
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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
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2024-10-12 11:27:55 -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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f24547ad4e
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add top_k sampling / offset for prompt similar utterance sampling
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2024-09-26 16:26:40 -05:00 |
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c5e9142863
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added option to retokenize phonemes for hdf5 (to save having to remake my hdf5 file)
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2024-09-21 13:08:01 -05:00 |
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fe241f6a99
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support for wildcard in training/validation/noise dataset array (to-do: a better way to query between metadata folder and data folder)
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2024-09-18 21:34:43 -05:00 |
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ebac1db16c
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maybe final tweaks, I really needed to unify my json read/write and orjson is proven to be fast enough for me to try and rely on it more
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2024-09-17 22:57:04 -05:00 |
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