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230da8b559
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should be the final things to scramble around for, DAC's 24KHz model is unusable for this, but both encodec's 24KHz and DAC's 44KHz work
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2024-05-12 13:22:08 -05:00 |
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2437a86efa
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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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c4b696ebeb
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oops
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2024-05-09 22:33:40 -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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215800484d
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correcting my wrong of assuming I could just use raw 24Khz audio in the 44Khz DAC without too much of an issue (there are issues)
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2024-05-04 23:49:15 -05:00 |
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9f738fbd5b
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seems I actually don't need RVQ bins 9-32 with the 24Khz DAC model........ (time to requantize my audio...)
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2024-05-04 23:09:18 -05:00 |
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a8ffa88844
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it slipped my mind that technically DAC can be used at any sample rate, since it models waveforms; make it a config YAML option to allow this behavior
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2024-04-19 18:36:54 -05:00 |
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8214aa23d7
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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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2e9e6e68f7
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Forgot I need to use the DAC's 44K model because 24K model has 32 codebooks instead of 9.
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2024-04-17 20:59:25 -05:00 |
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5ff2b4aab5
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finally swallowing the Descript-Audio-Codec pill (I guess I'm going to have to regenerate my entire dataset)
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2024-04-17 20:39:35 -05:00 |
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545162195b
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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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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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2bc2d08b09
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(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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78378ed1ce
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overhauled dataloading code to be marginally faster, mostly cleaned up, and can leverage a metadata json to help things out
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2023-08-26 19:53:23 -05:00 |
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22904a8639
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more oversights fixed because I've been using a cached dataloader forever now and didn't catch these problems
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2023-08-24 10:25:33 -05:00 |
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4585824cd3
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tweaks, including exporting on save/quit
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2023-08-23 16:43:03 -05:00 |
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7b1b82e0e5
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inferencing cleanup
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2023-08-20 21:36:02 -05:00 |
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2d1a9f10c0
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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)
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2023-08-19 15:06:33 -05:00 |
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77292c42f9
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tested the training preparation for tasks ns, sr, and tse (I don't expect it to go well with only 2 RVQ bins)
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2023-08-18 23:55:40 -05:00 |
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bbb0563b3d
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pseudocode polyfill stub some other flavor of working on adding the tasks
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2023-08-18 22:22:13 -05:00 |
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fb4e816823
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oops
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2023-08-18 21:11:19 -05:00 |
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d7deaf6def
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distributed training works now (hopefully)
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2023-08-13 22:07:45 -05:00 |
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608c1970eb
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ops
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2023-08-03 20:36:19 -05:00 |
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f6597e2dfe
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adjustments
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2023-08-02 18:36:26 -05:00 |
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bf8cedc9dd
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Rewrite init
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2023-08-02 21:53:35 +00:00 |
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