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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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56f25f7a9b
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more stuff for similar-speaker prompt sampling (to-do: actually test if this works...)
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2024-09-16 23:10:29 -05:00 |
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54203c059d
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validated rep pen for STT (sometimes needed to wrangle the model)
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2024-09-08 08:30:30 -05:00 |
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5d66a7db52
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webui cleanup, more tweaks, default to safetensors in config
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2024-09-07 21:45:05 -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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ed373957e2
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maybe not
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2024-08-09 11:38:08 -05:00 |
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c658a7b440
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make loss scaling opt-in rather than automatically determined (because it seems a DAC-based model really doesnt like loss scaling)
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2024-08-09 10:51:36 -05:00 |
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eac353cd0b
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busy work and cleanup while I wait for 1TB of audio to quantize... again.
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2024-08-06 20:23:33 -05:00 |
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2cb465018b
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implicitly load either normal pickled weights or safetensors on loading the model
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2024-08-03 23:34:18 -05:00 |
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