Commit Graph

108 Commits

Author SHA1 Message Date
mrq
07f8e2ad06 added option to set the causal size (how many tokens to sample per AR step), but requires the model to be trained for this (which explains why recurrent chunk sampling just doesn't work for the retnet tests, obvious in hindsight) 2024-07-30 20:53:51 -05:00
mrq
c2f5b916fc added what I think is DRY sampling 2024-07-29 19:15:07 -05:00
mrq
ce8bb1e4f7 sanity cleanups with weird off-by-one-ness, cleaned up and validated vall_e.models.experimental works again 2024-07-27 15:36:05 -05:00
mrq
06e948aec1 suppress warning on exit about distributed not being cleaned up (because I updated my system) 2024-07-25 16:50:47 -05:00
mrq
1acb0e9c84 added experimental training setting to perform token dropout to MAYBE compensate for errors from the preceding RVQ level (two types: token error offset, token dropout embedding replace) 2024-07-24 19:35:17 -05:00
mrq
75b04686f8 added prom-less training / inferencing, some other things 2024-07-22 19:36:07 -05:00
mrq
e19aa643a6 cleaned up demo page creation, added option to pass in RVQ level sampling distribution for training 2024-07-21 19:12:03 -05:00
mrq
d87b492295 added rudimentary demo page creator (currently just embeds base64 wavs into the page, need to test not doing that) 2024-07-19 20:49:40 -05:00
mrq
39f961abcd test trainer (vall_e.models.ar_nar) tests some SpeechX features 2024-07-18 18:46:45 -05:00
mrq
97e768601c re-introducing SpeechX tasks (need to validate them all, everything works with base tts anyways) 2024-07-18 16:16:14 -05:00
mrq
c2b8035e74 oops, kept forgetting to actually pass in lang/tone tokens (despite not really using these at the moment) 2024-07-18 14:18:34 -05:00
mrq
3acc54df22 allow loading a different model within the web ui (apparently I did not have the web UI in the documentation) 2024-07-15 19:59:48 -05:00
mrq
7b210d9738 sanity cleanup 2024-07-04 15:58:08 -05:00
mrq
dced595391 more cleanup 2024-06-30 11:00:12 -05:00
mrq
bc2a6fa756 sanity cleanup: moved experimental features under its own thing 2024-06-30 10:37:33 -05:00
mrq
b21f74a5c5 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) 2024-06-29 23:42:30 -05:00
mrq
2808f881c8 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) 2024-06-29 21:46:35 -05:00
mrq
ec5eaebcbc experimental method of using DACs quantizer ""embeddings"" to see if it helps with model quality 2024-06-29 19:46:11 -05:00
mrq
a8718d35a4 nasty bandaid because some of my DAC dataset only has 8 RVQ levels instead of the full 9 2024-06-29 10:16:37 -05:00
mrq
591d3ac848 have eval dataloader use eval batch size for batchedordersampler 2024-06-28 22:44:00 -05:00
mrq
83075c1505 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 2024-06-28 22:28:54 -05:00
mrq
2bfe786ebd ban stop token for NAR levels (because sometimes it gets sampled and causes problems) 2024-06-17 22:14:43 -05:00
mrq
7cfb78fa64 enable LoRA for targetted RVQ levels (to experiment with, seems to help) 2024-06-17 21:45:03 -05:00
mrq
19410a919e ugh 2024-06-15 12:29:03 -05:00
mrq
83eab4fa59 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 2024-06-13 20:08:22 -05:00
mrq
26da24fd8d mamba updated to fix that pesky NaN error during training 2024-06-13 12:38:33 -05:00
mrq
65a8960305 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) 2024-06-11 22:28:59 -05:00
mrq
132a02c48b sanity cleanup, backup config yaml for each log file 2024-06-09 11:22:52 -05:00
mrq
8d068fa3f9 reticulating splines 2024-06-08 20:30:15 -05:00
mrq
b072f9b96b fixes 2024-06-08 16:01:34 -05:00
mrq
58fb0a84db added experimental NAR only model (inferences text length, need more experimenting), AudioEmbedding logic cleanup (I still think it's being done wrong) 2024-06-08 15:42:02 -05:00
mrq
7d6fff24f9 un-tensor'd quant_level marker since it doesn't need to be one (I forgot why I had it as one but nothing seems to need it as a tensor that didn't already make it one) 2024-06-07 20:46:22 -05:00
mrq
f9f309281a ugh 2024-06-06 20:55:27 -05:00
mrq
a5c90348d9 head hurt 2024-06-06 20:51:31 -05:00
mrq
ee25d2e62e removed the need to supply targ_list + different AudioEmbedding + other things 2024-06-06 18:52:41 -05:00
mrq
fcac9503e2 cleanup 2024-06-06 13:08:02 -05:00
mrq
b2194b859a re-added loading multiple models because I'm now entertaining having split AR/NAR models again (and need a way to load both at once) 2024-06-06 09:48:43 -05:00
mrq
ff6fe6f1bc cleanup 2024-06-05 20:30:43 -05:00
mrq
880b4ecd1b cleanup, putting some thoughts in comments before I forget about them 2024-06-05 19:50:06 -05:00
mrq
48cd1054f9 madness 2024-06-04 23:48:51 -05:00
mrq
9e3f2e300f experimental "just have a token for what rvq level we're on" that seems to help all models (mamba almost works, but it might just have to be relegated as a pure AR model) 2024-06-04 23:23:31 -05:00
mrq
e0886c5a78 re-added mamba as a possible non-experimental arch backend (test trainer will set it as AR only, doing any NAR tasks lobotomizes it) 2024-06-04 22:41:22 -05:00
mrq
c93d5863fd fixes 2024-06-04 00:07:00 -05:00
mrq
7feeb944a0 probably insane with even entertaining going this route 2024-06-03 20:26:27 -05:00
mrq
e15c6c74c3 correctness 2024-05-30 20:50:45 -05:00
mrq
ddbacde0d1 DAC just doesn't work well enough...... 2024-05-25 11:07:52 -05:00
mrq
e3ef89f5aa 100x better for subtrain/eval to be by group instead 2024-05-19 16:40:14 -05:00
mrq
458b95d196 added option to split between text loss and audio loss (to-do: document this better), because it may or may not be a problem with LLaMA-backed models because my loss hovers around 3.9 / 56% accuracy despite sounding decent at the moment 2024-05-19 11:23:56 -05:00
mrq
3337c69e5a leverage between xformers and torch.backends.cuda.sdp_kernel for attention 2024-05-11 17:14:05 -05:00
mrq
2109712e5b resolve deprecation warning that doesn't show on my old training rig but does on my new one 2024-05-09 23:25:44 -05:00