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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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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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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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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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