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d12877ee09
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added option to set probability of selecting the AR during training under a monolithic AR+NAR, added some more to-dos while I have them in mind
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2023-10-02 16:52:42 -05:00 |
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e85b798fbf
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set default NAR levels to max for the web UI
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2023-09-29 19:14:16 -05:00 |
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c7fb740d41
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do not specify a default dtype for the web UI, let it implicitly load from the yaml instead
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2023-09-24 17:54:03 -05:00 |
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4abd6564d1
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fixed training stats not loading from exported weights, a bit of a readme cleanup, updated example training yaml
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2023-09-23 19:59:00 -05:00 |
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9384900ce6
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revert the frankensteined "train one model but hotload the other" since it kept loading the last exported weights and I'm not supporting this usecase anymore anyways
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2023-09-22 13:04:17 -05:00 |
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e7da1eb90d
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edge case
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2023-09-20 19:20:17 -05:00 |
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c0b25541e3
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restructured some things with the model to remove dead weights
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2023-09-20 19:10:59 -05:00 |
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a6bfe43590
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added mirostat sampling (given a partially trained model, it got far decent output than I expected, need to test on a better trained model)
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2023-09-18 18:55:41 -05:00 |
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2567e082b5
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UGH
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2023-09-16 00:26:13 -05:00 |
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22ffaf3a33
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have loss for the NAR not-ignore the text prompt, I imagine this should help the NAR and explain why it's always had a bit of an issue with training
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2023-09-15 19:08:44 -05:00 |
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4aef798135
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added picking final candidate based on sum of score instead of first candidate (this changes nothing).
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2023-09-13 13:19:11 -05:00 |
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23a5fdd645
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implemented a naive beam search (I really should be taking a break)
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2023-09-12 21:28:07 -05:00 |
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a6ae344e5b
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some comments
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2023-09-12 16:04:45 -05:00 |
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d07c63b9d8
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unified more things with training the AR+NAR monolothic model
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2023-09-12 15:54:41 -05:00 |
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40ef34e1ca
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this embedding class definitely works, and migrating from the previous embedding weights seems to work.
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2023-09-11 14:13:42 -05:00 |
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a1f250ffac
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set default max_levels for NAR to 0 and implicitly set it to max resps levels because the previous way was implicitly assuming all models were outputting at 1+7 RVQ bins.
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2023-09-10 20:33:33 -05:00 |
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671dca88ee
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throw error when no reference audio is provided in the web UI because someone keeps doing that in the HF space
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2023-09-10 15:50:50 -05:00 |
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ba71020318
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added option to limit (or exceed) inferenced RVQ-bin levels through the NAR
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2023-09-10 13:50:13 -05:00 |
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c74fe2f718
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tweaks to web UI
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2023-09-09 22:27:20 -05:00 |
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7f8bd2b936
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added printing elasped inference time
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2023-09-09 20:05:03 -05:00 |
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4f61f5c889
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added option to set the trim length for an input prompt
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2023-09-09 18:04:44 -05:00 |
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d10053d11f
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render README.md markdown for huggingface space
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2023-09-09 17:04:51 -05:00 |
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bc30026377
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added advanced sampler parameters to the web UI
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2023-09-09 16:51:36 -05:00 |
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5ac119a6e7
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added light web UI (need to port the telemetry disabling bandaids from aivc)
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2023-09-09 16:17:20 -05:00 |
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10c34c5b98
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added a length-based decay factor for repetition penalty
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2023-09-08 21:02:00 -05:00 |
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b922f35b6b
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added documentation on how these new sampling parameters are very iffy and you really need to know what you are doing to use them because this is audio generation and not text generation
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2023-09-08 20:43:36 -05:00 |
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14c78bae39
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added lots of sampling options (top-k/top-p, repetition penalty, length penalty)
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2023-09-08 20:30:54 -05:00 |
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f69aad9c65
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some day I'll get it right
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2023-09-08 15:36:26 -05:00 |
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b2907ae7e0
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seems that my PromEmbedding/RespEmbedding doesn't actually work all that well, naively using dedicated MultiEmbeddings for AR/NAR in the monolithic model is the best way to go
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2023-09-08 01:03:24 -05:00 |
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67617d7d69
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also cull frozen_params in the params optimizer receives to reduce VRAM it consumes
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2023-09-07 18:27:02 -05:00 |
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8837bc34d7
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added option to specify parameters to freeze per-model in YAML (because I need to see about committing atrocities with convering an AR into an AR+NAR)
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2023-09-07 18:19:51 -05:00 |
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c47fc3274e
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added backwards compat flag
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2023-09-07 17:12:17 -05:00 |
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ab5134f385
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tweaks and fixes
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2023-09-07 17:08:38 -05:00 |
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b2c2dec291
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added homebrewed per-RVQ-bin embedding solutions
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2023-09-07 16:48:02 -05:00 |
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e7a67410d1
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oops
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2023-09-07 09:14:03 -05:00 |
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712808494f
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added support for optional prodigy optimizer (https://github.com/konstmish/prodigy) although it consumes a lot more VRAM per parameter
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2023-09-06 20:33:16 -05:00 |
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7ce06432fd
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fixed the AR+NAR dual model, the resp_emb has to be split up (classifier might too)
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2023-09-06 19:33:39 -05:00 |
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100ca6b7d0
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added option to use SGD optimizer through the YAML, added option to pass in additional optimizer parameters through the YAML, added experimental unified AR+NAR model (does not seem fruitful in testing)
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2023-09-06 18:58:35 -05:00 |
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451726fdd5
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added ability to disable activation checkpointing through the YAML (it is very VRAM intensive at double layer size)
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2023-09-05 15:38:21 -05:00 |
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143aee7526
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removed dedicated interleaved AR code
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2023-09-03 22:47:03 -05:00 |
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2f9cd0842f
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merged dedicated interleaved AR code with the normal AR code
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2023-09-03 22:46:08 -05:00 |
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3a6bd50322
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haha
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2023-09-03 21:36:58 -05:00 |
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c56ce033d9
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work on an interleaved AR (spoiler: it does not work)
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2023-09-03 21:27:58 -05:00 |
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8a6c203277
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added per-speaker samplers
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2023-09-03 21:27:13 -05:00 |
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81b05dabb9
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accurate epoch metric is now reported (based on samples processed / length of dataset's paths, rather than naive assumptions)
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2023-09-03 08:03:36 -05:00 |
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922404285c
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fixed segfault from tts-c task token exceeding being too big (inserted it in the hypothetical svc task token because in reality that is never ever going to be a feasible task to train against)
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2023-09-02 19:25:43 -05:00 |
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4613781e23
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integrated plot script, added tts-c task token to help the model be able to mix between normal VALL-E and VALL-E continuous
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2023-09-02 16:29:53 -05:00 |
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f7e942ec99
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modified plotting script to be more agnostic to X
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2023-09-02 13:59:43 -05:00 |
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71e68a8528
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tweaked tts-continuous task
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2023-09-02 13:39:17 -05:00 |
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21e5d250cc
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fixed up plot script that I forgot about
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2023-09-02 13:31:04 -05:00 |
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