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2dd80a03ff
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stuff for interfacing with the loss scaler value (because I want to cap it)
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2025-03-06 17:07:29 -06:00 |
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1cd24f3381
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a birdie tells me i should probably use a different optimizer (also preliminary support for native sparse attention but I don't know if I'll use it)
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2025-03-04 14:53:02 -06:00 |
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3f1070f575
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tweaks
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2025-03-02 22:36:25 -06:00 |
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ddc49c89c5
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the learning rate scheduler pill is a tough pill to swallow
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2025-02-28 22:12:19 -06:00 |
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a174c33db6
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a gorillionth time's the charm (aka: the encoder/decoder pill is a tough pill to swallow)
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2025-02-28 17:56:50 -06:00 |
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09d82a26fe
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ugh
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2025-02-28 01:06:38 -06:00 |
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f4f435d7f5
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when you already had these ideas to stabilize training but you just ignored them
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2025-02-27 23:39:20 -06:00 |
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95da4e9405
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made muon actually work by actually utilizing param groups (thanks APOLLO for reminding me this is the sane way to handle this split)
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2025-02-26 10:39:13 -06:00 |
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cbf6b84e27
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fixed grad norm and loss scale not reporting for local trainer
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2025-02-23 19:08:26 -06:00 |
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b640fabab5
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borrowed muon since it might better work under deepspeed and not require cruft (even though it really does not like the masked-NAR, also make the masked-NAR faux-causal since it might better help out for cfg.model.version >= 7
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2025-02-23 17:23:24 -06:00 |
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3019c88799
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separate mask token and stop token because this might cause issues
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2025-02-23 11:36:32 -06:00 |
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6634d07576
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added muon optimizer through kludge hacks because it necessitates a second optimizer in tandum that seems to only sometimes work with deepspeed
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2025-02-23 11:22:13 -06:00 |
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a65c8144f4
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with the amount of tweaks I keep making I could have probably had the nvidia/audio-codec-44khz model realized already......
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2025-02-13 18:38:40 -06:00 |
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d4a6709fb4
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stopgap cringe to get this training session working (it does not seem fruitful)
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2025-02-11 13:45:09 -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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d85273609e
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corrected export.py's --hf
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2024-12-20 15:17:13 -06:00 |
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c2c6d912ac
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actually do speaker verification
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2024-12-17 10:11:14 -06:00 |
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8515038968
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imagine my disappointment when the epoch finished just for it to throw an exception
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2024-12-16 18:28:01 -06:00 |
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2ba6b483dc
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ugh
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2024-12-14 22:43:51 -06:00 |
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3dd31e74d1
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finally figured out a clean way to handle "resuming" the tqdm bar
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2024-12-14 18:44:43 -06:00 |
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35389481ee
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move lazy-stored ortho matrix to the grad device for apollo because agony
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2024-12-13 23:22:26 -06:00 |
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09804ecc16
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APOLLO tweaks to make it work with deepspeed
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2024-12-13 23:03:52 -06:00 |
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64c67160a3
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tweaks
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2024-12-13 19:00:35 -06:00 |
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f41251f648
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more fixes for local engine backend
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2024-12-12 14:38:42 -06:00 |
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9a62e3b824
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APOLLO cringe (doesn't want to work with deepspeed)
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2024-12-12 00:31:58 -06:00 |
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b81a98799b
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uplifting transformer's WavLM stuff to do speaker verification instead
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2024-12-11 19:30:05 -06:00 |
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6f1ee0c6fa
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Added CER, transcription/similarity model args in demo
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2024-12-10 21:00:51 -06:00 |
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8568a93dad
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added WER/SIM-O metrics, added APOLLO but I need to test it
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2024-12-10 20:13:21 -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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dcaf38b359
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fixed training tqdm being stubborn
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2024-11-23 09:45:23 -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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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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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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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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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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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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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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fc8dfd8617
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made greedy AR sampling viable (and preferable), with caveats (per comment in vall_e.models.ar_nar)
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2024-10-18 16:55:00 -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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a507b769a1
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sped up inferencing by not doing .tolist() for rep pen / length pen (and a bug fix in the web UI from prev commit)
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2024-10-04 22:18:20 -05:00 |
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769f67dcfe
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actually fix validation of phonemes in the symmap
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2024-09-21 12:19:34 -05:00 |
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b5bec0c9ce
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oops, turns out these are not split by speaker names already........ (also added sampling the dataset in the webui for easy viewing)
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2024-09-18 20:19:46 -05:00 |
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84647f588a
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more tweaks
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2024-09-18 16:43:57 -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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