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c5e9142863
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added option to retokenize phonemes for hdf5 (to save having to remake my hdf5 file)
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2024-09-21 13:08:01 -05:00 |
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fe241f6a99
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support for wildcard in training/validation/noise dataset array (to-do: a better way to query between metadata folder and data folder)
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2024-09-18 21:34:43 -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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56f25f7a9b
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more stuff for similar-speaker prompt sampling (to-do: actually test if this works...)
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2024-09-16 23:10:29 -05:00 |
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54203c059d
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validated rep pen for STT (sometimes needed to wrangle the model)
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2024-09-08 08:30:30 -05:00 |
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5d66a7db52
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webui cleanup, more tweaks, default to safetensors in config
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2024-09-07 21:45:05 -05:00 |
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54547b74d8
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experimental implementation of STT (need to actually test on a model, test trainer seems to work)
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2024-09-05 20:43:20 -05:00 |
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32287710a2
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moved prints to use logger, edited readme (fused_attn doesnt seem stable for training)
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2024-08-29 13:27:16 -05:00 |
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ed373957e2
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maybe not
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2024-08-09 11:38:08 -05:00 |
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c658a7b440
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make loss scaling opt-in rather than automatically determined (because it seems a DAC-based model really doesnt like loss scaling)
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2024-08-09 10:51:36 -05:00 |
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eac353cd0b
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busy work and cleanup while I wait for 1TB of audio to quantize... again.
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2024-08-06 20:23:33 -05:00 |
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2cb465018b
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implicitly load either normal pickled weights or safetensors on loading the model
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2024-08-03 23:34:18 -05:00 |
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c09133d00f
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added safetensors support (with metadata) and feed whatever torch.load/torch.save into it
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2024-08-03 23:15:20 -05:00 |
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6a733eb2ed
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changed torch.Tensor().to(device, dtype) to just torch.tensor(..., device, dtype) because it's been bothering my autism that I'm creating tensors then converting rather than creating with the right device/dtype, some 'optimization' to compile the model but it doesnt seem to do anything useful
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2024-08-03 22:10:21 -05:00 |
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11fa3da665
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some cleanup, fixed the wrapper attention to explicitly use other sdpa backends
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2024-08-03 19:51:00 -05:00 |
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c9ec6b28ef
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it actually wasn't working because Engines.__init__() automatically moves the entire module to the requested device, which was being called after offloading the model in the test trainer (and it seems I cant do it without injecting a bunch of shit in modeling_llama.py)
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2024-08-01 20:56:28 -05:00 |
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b4c895114c
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naive model offloading support (handles automatically splitting parts of the model to requested device per memory constraints, either inferred or requested in the yaml, input tensors are automatically migrated to the right device, it SEEMS to work for training under the test trainer when split between GPU and CPU) (this was specifically only because that Flux imagegen model released so I can test it there)
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2024-08-01 20:12:06 -05:00 |
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387358bc8a
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fixes for the NAR-len model, and documentation some config options, and a better way to handle resizing modules on state_dict load
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2024-07-31 20:35:09 -05:00 |
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52d13b321f
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I rather have it default to non-strict loading instead so I can clean up YAMLs
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2024-07-30 22:24:38 -05:00 |
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07f8e2ad06
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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)
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2024-07-30 20:53:51 -05:00 |
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682e4387dc
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oops (fixed proms being erased from a config oversight)
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2024-07-25 12:39:57 -05:00 |
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1acb0e9c84
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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)
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2024-07-24 19:35:17 -05:00 |
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75b04686f8
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added prom-less training / inferencing, some other things
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2024-07-22 19:36:07 -05:00 |
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e19aa643a6
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cleaned up demo page creation, added option to pass in RVQ level sampling distribution for training
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2024-07-21 19:12:03 -05:00 |
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d53038a9e4
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actually have split classifiers working
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2024-07-19 15:33:31 -05:00 |
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83a0954f85
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fixes for re-introducing SpeechX tasks (need to actually validate if these all do the right things)
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2024-07-18 17:16:32 -05:00 |
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bccbb77a1a
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added option to either naively concat codes to concat audio waveforms (prior behavior) or to decode => concat => encode instead (although this only currently happens for prom sampling if an utternace is too small)
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2024-07-18 16:48:41 -05:00 |
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97e768601c
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re-introducing SpeechX tasks (need to validate them all, everything works with base tts anyways)
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2024-07-18 16:16:14 -05:00 |
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22fe53508c
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added experimental disjointed position IDs (because I *think* this might help because technically a sequence is made up of several parts, and the position embeddings shouldn't be unified)
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2024-07-16 19:52:41 -05:00 |
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fe0f235335
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mechanism to store the model config inside the weights and load them, some other things to allow LoRA training on the RetNet (gradient checkpointing will gripe about inputs not having require_grad and nothing seems to remedy it)
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2024-07-16 18:23:13 -05:00 |
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3acc54df22
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allow loading a different model within the web ui (apparently I did not have the web UI in the documentation)
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2024-07-15 19:59:48 -05:00 |
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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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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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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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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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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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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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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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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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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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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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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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