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license: agpl-3.0 |
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This repo catalogs my weights for use with my [VALL-E](https://github.com/e-c-k-e-r/vall-e) implementation as I try and iron out the kinks. |
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The model currently is in a *semi-usable* state, and I'm releasing them now in hopes that it also helps jumpstart anyone else that wants to use them. |
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To reiterate, this is ***by no means*** complete. I am not passing this off as competitive. |
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## Models |
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This repo contains the following configurations: |
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* `config.retnet.yaml` / `ar+nar-retnet-8`: The previously released weights. |
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+ This configuration utilizes a RetNet (retention based "transformer") as the underlying architecture due to a number of misleading interpretations with comparisons, for better or for worse. |
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+ Prompt and response embeddings are summed (further RVQ levels gets the previous RVQ levels' embeddings factored in). |
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+ Tokenizer is a homebrewed "naive" implementation. |
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+ This model received the most training time between my 4070Ti, 7900XTX, and a few rental rigs to training further progress, entirely at `bfloat16` with `prodigyopt` (and a few optimizer restarts). |
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+ The later part of training aimed to shuffle between speakers rather than the global pool of utterances to better focus on zero-shot performance. Due to this, I feel it achieved *decent* zero-shot performance. |
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+ However, due to the dataset being aggressively trimmed under 12 seconds for memory savings during training, it suffers trying to inference non-short utterances. Additional training may fix this, the following models seemed to adapt well to longer utterances. |
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+ From the `ar+nar-llama-8` experiment, I believe this can be "fixed" with additional training on the currently processed dataset. |
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+ Prior testing showed that longer prompt durations results in better utterances. |
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+ *Can* benefit from additional training, but I recall the average loss being around `1.9` to `2.1`. |
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+ However, due to regressions (or bias from working under `llama`), I don't think I can optimially train with a RetNet again (both in terms of VRAM consumption and throughput). |
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* `config.llama.yaml` / `ar+nar-llama-8`: The most recent-ishly trained weights after learning from my mistakes. |
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+ This configuration utilizes Llama's attention-based transformer as the underlying architecture, making use of creature comforts like RoPE, GQA, and memory-efficient attention (trained under `xformers`, shouldn't really affect things). |
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+ Prompt and response embeddings are NOT summed (each RVQ level only attends to the current RVQ level). |
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+ Utilizes a HF tokenizer for "optimal" vocab. |
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+ The current RVQ level is included as a token as well to help guide NAR tasks better. |
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+ This model received a few days of training on my 4xV100s, stepping up the duration window to *try* and better make the model inference for longer utterances. |
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+ Some sessions end up training the current duration window for a few epochs, but I don't know how much it affected things. |
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+ ~~However, it seems to *only* do well with long utterances. Short utterances fumble. I believe further training with a variety of durations should allow the AR to handle a variety of durations.~~ |
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- ~~I believe the "slowly stepping up the context length" only works for text, and not audio.~~ |
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- Addendum: Additional brief training for a variety of duration lengths seemed to have mostly fixed this issue. |
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+ Zero-shot performance leaves a bit to be desired, as it did not receive the special training prioritizing shuffling between speakers rather than the global pool of utterances. |
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- Addendum: Additional brief training for sampling based on speaker per "epoch" (per dataloader, not dataset) seemed to slightly improve it. |
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+ Testing showed that, despite also stepping up the prompt duration, it *really* likes three second prompts. |
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+ Definitely needs additional training, but the next way to go is unknown. |
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+ Naturally, training it on a "next RVQ level is half as likely" distribution introduces some crust as the later RVQ levels are less accurate, introducing noise and artifacts. |
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+ Naively training it on equally distributed RVQ levels *does* lobotomize the AR. |
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+ Additional training on the AR will see huge diminishing returns, so I don't know if it's worth doing so. |
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+ Seems to be a decent foundation for "distillation", at the very least for LoRA training. |
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* `config.llama.split.yaml` / `ar-llama-1` + `nar-llama-8`: The above model, but split and trained a little bit more. |
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+ This experiment is to see whether the AR and NAR benefitted from being split up after enough pretraining, to un-"lobotomize" any penalties from attending to two different tasks (as the AR predicts the next token, and the NAR predicts the same token but a different level). |
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+ I believe I trained each separate model an additional extra day for another additional audio-duration window for similar training lengths. |
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+ ~~I don't think audio quality differs a non-trivial amount to warrant splitting the model.~~ |
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- From recent experiments, it does seem a NAR-only model is beneficial. |
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Some additional configurations have been explored with, but experiments have not been fruitful: |
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* Exotic wrappers like `BitNet` seemed to yield little gains in inferencing, somehow. |
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* Mamba / Mamba2-based models have shown that it's ***really*** hard to have an AR+NAR model. |
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* A NAR only model has been experimented with, but seemed utterly useless in practice. |
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+ The underlying architecture will query the model for the duration, and then inference *all* RVQ levels in parallel (one level at a time). |
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+ Despite working in the overfitting test trainer and decent training metrics, inferencing will have the model fall completely flat. |
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+ I have zero ideas for which path to go with for further experimentation. |
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* A [Descript-Audio-Codec](https://github.com/descriptinc/descript-audio-codec/) based model has been experimented with, but has not seem fruitful. |
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+ This model would make use of 16 layers instead of the default 12 layers. I feel the performance hit is negligible, even with the additional tokens-per-frame increase with DAC. |
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+ This utilizes DAC's 44Khz model (erroniously at an actual 44KHz instead of 44.1KHz), as audio quantized through the 24KHz model will *always* diverge. |
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+ I imagine due to the nature of DAC leaving very little room for errors (a testament to how "optimized" the codes are), it's ***really*** hard to model an LM with it. |
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+ Output audio is rather crunchy and crusty from the later RVQ levels being inaccurate enough. |
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+ I'm not sure which path to go with it for further experimentation: |
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+ Utilizing the original model for embeddings or last hidden state as the input embeddings for the prompt/response. |
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+ I don't think this is the way to go. It seems negligible for additional complexity. |
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+ Training a dedicated NAR model in hopes to bolster the later RVQ levels' performance, as the issues come from the later RVQ levels. |
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+ Utilizing an interleaved pattern instead to make better use of attending to past tokens for all levels. |
