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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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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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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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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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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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4ade2b60ee
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ugh
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2024-06-06 21:57:11 -05:00 |
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014e565c4b
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tweaks
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2024-06-04 20:41:13 -05:00 |
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6d5bd0156a
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fixes
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2024-06-04 18:50:48 -05:00 |
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ed3aeaf3a1
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copy pasted from test to actual trainer
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2024-06-04 18:40:30 -05:00 |
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0aa01ba31a
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forgot one crucial detail (you *need* the previous RVQ level to keep coherence between all RVQ levels) (experimental deinterleaved is a bit crusty though)
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2024-06-04 18:30:30 -05:00 |
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406ff7bbe1
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re-implemented config.model.interleave for the HF-compat experimental method
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2024-06-04 14:19:52 -05:00 |
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c93d5863fd
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fixes
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2024-06-04 00:07:00 -05:00 |
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934672252b
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feverish cleanup
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2024-06-03 21:28:49 -05:00 |
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8cf176ab46
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ugh
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2024-06-01 10:46:42 -05:00 |
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d0ebce6bac
|
ugh
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2024-06-01 10:30:13 -05:00 |
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74df2f5332
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split sampler dict by global_rank, also handle splitting dataset paths by global_rank if sampler_type == path (because I do not trust DistributedSampler) (need to test)
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2024-06-01 09:29:49 -05:00 |
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ddbacde0d1
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DAC just doesn't work well enough......
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2024-05-25 11:07:52 -05:00 |
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e3ef89f5aa
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100x better for subtrain/eval to be by group instead
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2024-05-19 16:40:14 -05:00 |
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4bc7e5a6d1
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fix loading without needing an hdf5 dataset already prepped (and some other incidental speedups during dataloader prep)
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2024-05-18 07:14:26 -05:00 |
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d88a5ca183
|
ugh
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2024-05-16 07:25:33 -05:00 |
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d9aabfa3ae
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final tweaks, hopefully, again
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2024-05-15 23:04:19 -05:00 |
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2437a86efa
|
ugh
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2024-05-12 13:02:15 -05:00 |
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4f1593c8db
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a bunch of shit to salvage my old encodec-quantized audio because dac-encoded audio just does not want to converge
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2024-05-12 10:17:29 -05:00 |
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14709ac67f
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ughh
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2024-05-12 07:30:59 -05:00 |
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3774fcbdee
|
ugh
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2024-05-11 22:58:38 -05:00 |
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4d93a16ef7
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might just be better to explicitly define prompt duration ranges, especially under a "train small contexts then increase it" training paradigm
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2024-05-11 09:50:54 -05:00 |
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0d5d545a40
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crammed in DAdaptation (doesn't seem worth it) and ScheduleFree (forgot I wanted to weeks ago, seems promising), optimization wrapper cleanup, test trainer changes, etc.
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2024-05-09 20:28:20 -05:00 |
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c6e0f905b5
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final tweaks (again) before training restarts
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2024-05-08 02:11:38 -05:00 |
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33b7f81b94
|
small cleanups
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2024-05-04 22:37:22 -05:00 |
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ffa200eec7
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added option to specify frames per second for the given audio representation (Encodec is 75Hz, DAC is 41Hz (at 24K sources))
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2024-05-04 12:05:41 -05:00 |
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b5d1456a09
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backwards compat for my shitty old weights (was testing if disabling AudioEmbedding summing magically made things better (it did not))
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2024-04-29 22:14:01 -05:00 |
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6a11bc9cb6
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update tokenizer because, for some reason, it had the wrong order for the special tokens to where eos = unk
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2024-04-29 09:09:26 -05:00 |
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57810e4ba4
|
metadata only path (might drop HDF5 since its giving file sizes twice as large as my actual unpacked dataset)
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2024-04-28 23:03:09 -05:00 |
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caad7ee3c9
|
final tweaks, hopefully
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2024-04-28 22:28:29 -05:00 |
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ffc334cf58
|
added dataset transcription helper script (now I don't ever have to touch ai-voice-cloning) (to-do: unify scripts into the module)
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2024-04-21 17:43:20 -05:00 |
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071fb97777
|
dataset preparation script updates, caved and am using HF tokenizer now
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2024-04-21 14:49:18 -05:00 |
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8214aa23d7
|
converting over to a different intermediary dataset format
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2024-04-18 21:24:06 -05:00 |
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4f5c9e518a
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actually use the passed-through sample rate from encode for DAC because it does its own resampling I guess
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2024-04-18 13:32:41 -05:00 |
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545162195b
|
deprecate sole AR/NAR model by only keeping the AR+NAR (the beauty of no one using this is that I can break compat as much as I want), add tone token for when I classify my dataset with tone/emotion in the future, some other things
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2024-04-15 19:54:32 -05:00 |
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9c198eb75a
|
added torchscale XMOE integration (because Mixtral 8x7B seems very promising and I want to see if it works)
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2023-12-20 18:45:58 -06:00 |
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0aa2a3cc07
|
evaluation/validation passes language ID during training (oops)
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2023-10-29 12:00:40 -05:00 |
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9a6040383e
|
make validation samplers ignore sampler type
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2023-10-22 09:01:47 -05:00 |
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3195026dba
|
fixed issue with the 'add another target audio to artificially create longer sequences' for HDF5 just duplicating the utterance initially sampled
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2023-10-18 20:38:33 -05:00 |
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09cda7d3f9
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added sampling by speaker group name (might be better to de-emphasize the LibriVox/Audiobooks that are in large numbers, and emphasize the smaller pools), log cleanup
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2023-10-16 19:30:38 -05:00 |
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65f500083d
|
tweaks to try and get deepspeed quantized inferencing, validating bitsandbytes and deepspeed quantization, nothing seems to work
|
2023-10-12 22:21:43 -05:00 |
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8740cdefc6
|
added initial support for languages (still testing, marked as model version 3), added experimental 'context extend by limiting the resp context' (untested)
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2023-10-11 20:38:40 -05:00 |
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6045cbce94
|
added experimental option to append utterances for training target (emphasis on experimental)
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2023-10-11 17:32:45 -05:00 |
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b4405c98ea
|
remove double spaces in the text phonemes (might have caused problems.........)
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2023-10-10 19:18:24 -05:00 |
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87db03dd93
|
trim the input prompt to 3 seconds when training NAR tasks (marked as experimental; the paper mentions doing so, but I don't know how much this would harm the retention heads)
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2023-10-09 22:03:58 -05:00 |
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893a610fad
|
cleanup, use deepspeed inferencing pathway if requested
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2023-10-09 15:24:04 -05:00 |
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27483e56f0
|
disabled preparing of SpeechX tasks, added dynamic temperature testing (to-do: test it, credited in the function)
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2023-10-09 13:01:40 -05:00 |
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82f02ae9b1
|
oops
|
2023-10-06 09:26:52 -05:00 |
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d12877ee09
|
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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a6bfe43590
|
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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d07c63b9d8
|
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
|
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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8a6c203277
|
added per-speaker samplers
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2023-09-03 21:27:13 -05:00 |
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922404285c
|
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
|
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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71e68a8528
|
tweaked tts-continuous task
|
2023-09-02 13:39:17 -05:00 |
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57db3ccfa8
|
shuffled VALL-E continuous as a task tts-c instead, logic fixes for it
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2023-09-02 12:23:40 -05:00 |
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2bc2d08b09
|
(need to verify) added modifying model size and config bool to align with VALL-E continuous' methodology
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2023-09-01 17:19:34 -05:00 |
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5c8694db8e
|
nasty bandaid if there's no validation dataset specified during training (for example, during finetunes)
|
2023-08-30 18:23:05 -05:00 |
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7f4388e591
|
added total samples processed and tokens processed (len of text tokens + len of target response tokens)
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2023-08-28 11:02:45 -05:00 |
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87c4bfedba
|
added ability to mark models as disabled for training, and hotloading them for eval/validation (useful if training only one model, or training a model per GPU)
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2023-08-27 12:26:12 -05:00 |
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165a1154e0
|
Undo naive=False test flag, this shouldn't have made its way in
|
2023-08-26 22:00:43 -05:00 |
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78378ed1ce
|
overhauled dataloading code to be marginally faster, mostly cleaned up, and can leverage a metadata json to help things out
|
2023-08-26 19:53:23 -05:00 |
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0517d620b8
|
fixes with the local backend
|
2023-08-24 17:05:56 -05:00 |
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22904a8639
|
more oversights fixed because I've been using a cached dataloader forever now and didn't catch these problems
|
2023-08-24 10:25:33 -05:00 |
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5873c27f1a
|
ops
|
2023-08-24 09:20:47 -05:00 |
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4585824cd3
|
tweaks, including exporting on save/quit
|
2023-08-23 16:43:03 -05:00 |
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9c5a33bfd2
|
added repo with my weights so far
|
2023-08-22 13:09:44 -05:00 |
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7b1b82e0e5
|
inferencing cleanup
|
2023-08-20 21:36:02 -05:00 |
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a47029065b
|
I don't know if the lack of start/stop tokens being added was causing my inference tests to fail, but it seems better now
|
2023-08-20 19:21:54 -05:00 |
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fc576010ce
|
wrapped saving the checkpoint in a try/catch so I can stop waking up to the damn trainer crashing because it ran out of disk space; I'd much rather it keep training to give me time to eventually clear up disk space rather than it silently restarting on its own
|
2023-08-20 06:29:17 -05:00 |
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2d1a9f10c0
|
nightmare of spaghetti that might break compat; mechanism to increase RVQ bins of an existing model without retraining, keeps sampled proms/resps at max RVQ level and trim off excess levels according to what model receives them, some other things I already forgot (I really hope no one else has weights being baked right now)
|
2023-08-19 15:06:33 -05:00 |
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f7f6d3bf6d
|
validated that SpeechX tasks cse and nse works, added a method to test each task by invoking python3 -m vall_e.data --action=tasks --tasks='sr,se,cse,nse'
|
2023-08-19 09:50:07 -05:00 |
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6ca347e1e1
|
literally had a urethra moment before going to bed with a way to implement cse/nse tasks
|
2023-08-19 01:16:46 -05:00 |
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8f42c578c9
|
setting up for allowing training for a partial amount of the speechx tasks (do NOT try this at home yet without a proper model, as performance is predecated on having a solid base vall-e model for the tasks
|
2023-08-19 00:16:08 -05:00 |
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ae9d38aa31
|
forgot to have it pull from specified noise to the hdf5 dataset
|
2023-08-18 23:57:07 -05:00 |
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77292c42f9
|
tested the training preparation for tasks ns, sr, and tse (I don't expect it to go well with only 2 RVQ bins)
|
2023-08-18 23:55:40 -05:00 |
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bbb0563b3d
|
pseudocode polyfill stub some other flavor of working on adding the tasks
|
2023-08-18 22:22:13 -05:00 |
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|
2a71486cb6
|
preparing for SpeechX extensions
|
2023-08-18 20:58:07 -05:00 |
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|
ced31fd9b7
|
removed the sampler as it's very misleading
|
2023-08-18 14:47:48 -05:00 |
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|
8e7f900210
|
forgot the =
|
2023-08-17 19:07:59 -05:00 |
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|
3ff7cf8341
|
maybe fix evaluation dataset not being capped to cfg.evaluation.size
|
2023-08-17 18:56:37 -05:00 |
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|
ee58db746f
|
actually make the evaluation dataset shuffled for sample_type=speaker
|
2023-08-17 15:04:45 -05:00 |
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|
18403a3523
|
maybe fixes eval dataloader not shuffling under distributed
|
2023-08-17 13:41:53 -05:00 |
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|
b5f247aa11
|
just nuked about 9 hours of progress because I didn't make sure it pruned only on the global leader
|
2023-08-16 23:37:52 -05:00 |
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44c08d828e
|
added sample_type that samples from speakers to truly balance an epoch by speakers rather than the entire dataset and a sampler that tries to balance by speakers
|
2023-08-16 19:39:21 -05:00 |
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277c759ab1
|
fixed issue with non-distributed training, oops
|
2023-08-14 21:42:35 -05:00 |
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|
5fa86182b5
|
oops
|
2023-08-14 10:50:40 -05:00 |
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|
d7deaf6def
|
distributed training works now (hopefully)
|
2023-08-13 22:07:45 -05:00 |
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|
bf8cedc9dd
|
Rewrite init
|
2023-08-02 21:53:35 +00:00 |
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