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ceecac6ffe
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I think I made resp_parallel_training=True faster with loss factoring?
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2025-02-26 23:13:32 -06:00 |
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06ef3daf3c
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require minimum of 1 second durations for training because of my slop code auto-transposing that I don't wanna fix right now
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2025-02-26 22:00:33 -06:00 |
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cbd4d7d7f4
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ugh
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2025-02-26 21:31:10 -06:00 |
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2ea387c08a
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segregated experimental changes into its own streamlined file to avoid breaking the existing model, and it can pivot to the cleaned up code if it actually works (nothing is working)
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2025-02-26 21:26:13 -06:00 |
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7d2e64630c
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lol
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2025-02-26 10:49:06 -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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de27115bb7
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there's something wrong with it on my 4xV100 rig......
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2025-02-25 15:14:08 -06:00 |
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db181f8e88
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only do auto=equal for nemo as its an FSQ
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2025-02-24 21:07:44 -06:00 |
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a5a04c39ef
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when the
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2025-02-24 21:03:23 -06:00 |
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918e0dbac1
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small slop cleanup
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2025-02-24 19:03:53 -06:00 |
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3330b5bb00
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maybe fix NaNs being thrown for immature models at fp16 for training evals
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2025-02-24 18:25:54 -06:00 |
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0f39f4d7a1
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lol
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2025-02-24 17:51:35 -06:00 |
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33d5a7109a
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its a miracle i was able to get a semblance of audio with the naive AudioEncoder (now it interleaves properly)
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2025-02-24 14:39:12 -06:00 |
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6e7b269147
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ugh
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2025-02-24 13:54:21 -06:00 |
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8f5a3997bd
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another experimental flag
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2025-02-24 13:50:41 -06:00 |
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f593ee98fc
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ugh
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2025-02-23 21:20:36 -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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d33ccd188a
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ugh
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2025-02-23 12:31:07 -06:00 |
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8f3c3e01ee
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oops
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2025-02-23 12:09:56 -06:00 |
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b39aaacd77
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oops
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2025-02-23 11:55:43 -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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67a6009555
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(finally) added parallel AR for cfg.model.version >= 7 (nvidia/audio-codec-44khz is being a pain and it might require training purely AR first......)
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2025-02-23 08:31:03 -06:00 |
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15b3c20e19
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also throw exception for zero'd out tensor during training (I am very paranoid now)
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2025-02-22 14:09:41 -06:00 |
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ab0abd2b12
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fixes fixes fixes (a quarter of my recently processed audio returned zero'd tensors......)
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2025-02-22 09:07:33 -06:00 |
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50506e5ebc
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oops
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2025-02-20 20:55:58 -06:00 |
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fc1ec2019d
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added option to buffer process jobs across multiple speakers to maybe squeeze out some throughput speeds for vall_e.emb.process (in the event of lots of speakers with low file counts, such as Emilia)
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2025-02-20 14:56:32 -06:00 |
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ce1ca0124a
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lol...
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2025-02-20 13:40:36 -06:00 |
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92139b6da9
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additional cruft, added a note in documentation to be aware of NUMA node topology when running vall_e.emb.process with more than one process
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2025-02-18 19:56:30 -06:00 |
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596c2df11c
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added arg to skip processing speakers with not enough utterances for whenever I get around to processing my subest of Emilia for nvidia/audio-codec-44khz (because Emilia has a ton of low-utternace speaker counts and right now my focus with the nemo model is on getting it to actually speak without much problems rather than feed it a gorillion speakers)
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2025-02-18 10:49:21 -06:00 |
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8331eee6fa
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added arg to limit vall_e.emb.process batch size since there's some speaker groups in LibriLight/Speech/whatever that have 10K utterances and I'm going impatient
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2025-02-18 10:19:17 -06:00 |
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8f86cf0e4e
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possible logic optimization so I don't spend another 15 minutes simply iterating back to the point I was at in vall_e.emb.process
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2025-02-16 11:34:05 -06:00 |
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0dc49ef4d5
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documentation update while I wait for more audio (between 4 and 8 seconds per utterance) quantize for nvidia/audio-codec-44khz (I was foolish to think I can get something servicable with just 4 seconds max for an utterance)
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2025-02-15 17:42:06 -06:00 |
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13c3a08853
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nevermind thats slow
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2025-02-14 16:35:17 -06:00 |
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285e493b12
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ugh..........
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2025-02-14 16:24:34 -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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e3becec0e8
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more better-er loss calc I suppose
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2025-02-13 12:49:53 -06:00 |
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e8f182b634
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cleaned up loss calc code (it REALLY hates ignore_loss_for_inputs, but is fine with splitting with loss factors)
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2025-02-13 09:35:27 -06:00 |
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319ca09a4f
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cleanup
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2025-02-12 23:36:32 -06:00 |
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b52c5c5d80
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this seems to work in testing
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2025-02-12 16:16:04 -06:00 |
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e029a8804d
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ironically none of this cruft gets the loss lower than the original way
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2025-02-12 11:17:00 -06:00 |
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4b31f5c808
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this seems preferable
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2025-02-12 00:36:50 -06:00 |
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04fef5dad5
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agony
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2025-02-12 00:18:24 -06:00 |
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1c0ed6abac
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added notes on this unfruitful experiment
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2025-02-11 16:21:43 -06:00 |
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e5916ea519
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for my sanity it seems having extraneous tokens in the embedding/classifier has the loss/acc a little higher than it should
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2025-02-11 14:47:35 -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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c0b46b82eb
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tweaks
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2025-02-10 21:48:29 -06:00 |
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d6a679ca5c
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tweaks
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2025-02-10 20:53:08 -06:00 |
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276a2342a4
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tweaks to processing script
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2025-02-10 19:18:13 -06:00 |
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