vall-e/vall_e.cpp/README.md
2024-12-25 00:28:34 -06:00

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# vall_e.cpp
This is an implementation that makes use of [llama.cpp](https://github.com/ggerganov/llama.cpp/) and [encodec.cpp](https://github.com/PABannier/encodec.cpp).
At the moment it's ***very*** work in progress.
Model weights can be found at [`ecker/vall-e@gguf`](https://huggingface.co/ecker/vall-e/tree/gguf).
## Build
Populate `./include/` with the `ggml`, `llama.cpp`, and `encodec.cpp` headers.
Populate `./libs/` with the compiled libraries of `llama.cpp`, `encodec.cpp`, and `espeak-ng`.
Run `make`.
### Required Modifications
[`encodec.cpp`](https://github.com/PABannier/encodec.cpp) requires updating its GGML copy to the latest version, which requires a few lines to get the CPU backend working (per my [fork](https://github.com/e-c-k-e-r/encodec.cpp)).
[`llama.cpp`](https://github.com/ggerganov/llama.cpp) only possible modification needs to ensure that a non-causal attention mask is used; everything necessary can be hacked together with clever tricks.
## To-Do
* [x] converted model to GGUF
* [ ] convert it without modifying any of the existing code, as the tokenizer requires some care
* [x] basic framework
* [x] load the quantized model
* [x] orchestrate the required embeddings
* [x] juggle the output head / classifier properly
* [x] phonemize text
* with the help of espeak-ng
* [x] tokenize phonemes
* tokenize with `llama_tokenize` instead of a homebrewed method because the tokenizer is being a huge thorn
* [x] load audio from disk
* [x] encode audio
* [x] sum embeddings for the `prom` and prior `resp`s
* [x] working `AR` output
* [x] `AR` sampling
* [x] working `NAR-len` output
* [x] `NAR-len` sampling
* [x] working `NAR` output
* [x] `NAR` sampling
* [x] decode audio to disk
* [x] a functional CLI
* [x] actually make it work
* [x] clean up to make the code usable elsewhere
* [ ] feature parity with the PyTorch version
* [ ] vocos
* [ ] additional tasks (`stt`, `ns`, `sr`, samplers)