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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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1ecf2793f4
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(commented-out) support for facebookresearch/AudioDec, but support really didn't wow me (so I commented it out until I figure out why my output audio is super crusty with AudioDec)
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2024-07-04 15:40:51 -05:00 |
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db62e55a38
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oops, I forgot to use the new thing for audio_backend
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2024-07-04 14:54:11 -05:00 |
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f770467eb3
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stuff
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2024-07-01 18:13:29 -05:00 |
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312a8e3ead
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add shuffle to samplers that can support it
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2024-06-30 11:36:46 -05:00 |
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396af541c5
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ugh
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2024-06-30 11:11:58 -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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b21f74a5c5
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added summing of external embeddings (at this point i dont think any amount of cope bandaids will get DAC to train nicely, I think the RVQ levels the NAR tends add too much noise if they're not accurate)
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2024-06-29 23:42:30 -05:00 |
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793ccb16fb
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ugh
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2024-06-29 22:14:35 -05:00 |
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2808f881c8
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cleaned up subjugated audio embedding into a flag, flag can also have it include the original, underlying embedding as well (it seems to do better when set to inclusive)
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2024-06-29 21:46:35 -05:00 |
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ec5eaebcbc
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experimental method of using DACs quantizer ""embeddings"" to see if it helps with model quality
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2024-06-29 19:46:11 -05:00 |
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a8718d35a4
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nasty bandaid because some of my DAC dataset only has 8 RVQ levels instead of the full 9
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2024-06-29 10:16:37 -05:00 |
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c4dd523b6f
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change from chunk-slicing paths for distributed dataloader to instead interleave
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2024-06-29 10:10:35 -05:00 |
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dd40463803
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limit eval size because the training batch size seems to be used for the eval dataloader, somehow (bandaid)
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2024-06-29 09:11:28 -05:00 |
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591d3ac848
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have eval dataloader use eval batch size for batchedordersampler
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2024-06-28 22:44:00 -05:00 |
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1a392b69f6
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local training backend should be a bit more aware of variable batch sizes, maybe
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2024-06-28 22:39:05 -05:00 |
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83075c1505
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sort duration buckets to ensure that paths sorted-by-duration are actually sorted by duration (because i didnt know that python dicts can have non-strings as keys), added batching samples based on total duration to ensure best training throughput
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2024-06-28 22:28:54 -05:00 |
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5176ced35f
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readme tweaks
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2024-06-28 21:02:54 -05:00 |
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8fffb94964
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backport fix from tortoise_tts with local trainer + loading state when training lora
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2024-06-25 13:41:29 -05:00 |
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62a53eed64
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fixed deducing tokenizer path, added option to default to naive tokenizer (for old models, like ar+nar-retnet-8)
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2024-06-18 22:11:14 -05:00 |
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8a986eb480
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load exported LoRA weights if exists (to-do: make a better LoRA loading mechanism)
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2024-06-18 21:45:46 -05:00 |
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2bfe786ebd
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ban stop token for NAR levels (because sometimes it gets sampled and causes problems)
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2024-06-17 22:14:43 -05:00 |
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7cfb78fa64
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enable LoRA for targetted RVQ levels (to experiment with, seems to help)
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2024-06-17 21:45:03 -05:00 |
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7047fcc6e2
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actually make deepspeed work with LoRAs
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2024-06-17 13:55:37 -05:00 |
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1d159b1476
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updated export routine to split LoRA weights from the state dict (should work with deepspeed)
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2024-06-17 13:28:18 -05:00 |
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726a4b613f
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naive, rudimentary DeepSpeed support (just live with the LoRA weights living with the original weights, they can be split later)
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2024-06-17 13:17:24 -05:00 |
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bd0bc10ec0
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added LoRA policy to decide what layer of the model gets adapted based on simple inclusion/exclusion terms
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2024-06-17 13:05:06 -05:00 |
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be051d9544
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added other LoRA method using parametrization rather than linear injection
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2024-06-17 09:58:34 -05:00 |
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45a39fb79f
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very rudimentary lora support (no deepspeed support, tested training and saving but not loading yet)
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2024-06-17 00:09:16 -05:00 |
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19410a919e
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ugh
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2024-06-15 12:29:03 -05:00 |
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d343bde09b
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residual_in_fp32=False for mamba arch backends because it breaks the classifier (output projection / lm head / what-have-you) under AMP
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2024-06-15 12:08:03 -05:00 |
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ccb14c06ef
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mamba2-hf using vasqu/mamba2-torch because it lets me use mamba2 without triton ops (training with my 4xV100s are not happy with mamba2 because of triton)
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2024-06-14 19:42:17 -05:00 |
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31f71fa134
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sampler update (some brainworm just never actually had a sampler for sample_type=path)
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2024-06-14 16:55:40 -05:00 |
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b3b67f34ac
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added option to sort paths by durations to better group equally lengthed sequences together (and there was maybe a logic error from creating the samplers and then interleave-reordering paths, desyncing them, maybe)
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2024-06-13 22:37:34 -05:00 |
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83eab4fa59
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actually going for the suggested "2x layers, no intermediate scaling" is wrong for VALL-E, directly copying the normal transformer structure fixes mamba2 performance in the test trainer
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2024-06-13 20:08:22 -05:00 |
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ff97e7480d
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fixed pip shitting itself on setup
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2024-06-13 13:03:36 -05:00 |
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26da24fd8d
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mamba updated to fix that pesky NaN error during training
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2024-06-13 12:38:33 -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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65a8960305
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option to split classifier per-level instead of sharing one (at this point I'm just scrambling to try and cope with training a DAC model, the NAR is being a pain)
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2024-06-11 22:28:59 -05:00 |
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a7a6e0ac76
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validated that inferencing works, changed some defaults (NAR benefits from greedy sampling)
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2024-06-09 17:11:38 -05:00 |
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234f9efc6e
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ugh
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2024-06-09 11:39:43 -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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8d92dac829
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forgot I renamed this
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2024-06-09 11:12:30 -05:00 |
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80f9530840
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ugh
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2024-06-09 01:43:44 -05:00 |
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5c732b72ee
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ugh
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2024-06-08 20:34:00 -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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ead3e2f0cb
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ugh
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2024-06-08 16:14:57 -05:00 |
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