194 lines
12 KiB
Markdown
Executable File
194 lines
12 KiB
Markdown
Executable File
<p align="center">
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<img src="./vall-e.png" width="500px"></img>
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</p>
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# VALL'E
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An unofficial PyTorch implementation of [VALL-E](https://valle-demo.github.io/), utilizing the [EnCodec](https://github.com/facebookresearch/encodec) encoder/decoder.
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> **Note** Development on this is very sporadic. Gomen.
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## Requirements
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* [`espeak-ng`](https://github.com/espeak-ng/espeak-ng/):
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- For phonemizing text, this repo requires `espeak`/`espeak-ng` installed.
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- Linux users can consult their package managers on installing `espeak`/`espeak-ng`.
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- Windows users are required to install [`espeak-ng`](https://github.com/espeak-ng/espeak-ng/releases/tag/1.51#Assets).
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+ additionally, you may be required to set the `PHONEMIZER_ESPEAK_LIBRARY` environment variable to specify the path to `libespeak-ng.dll`.
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## Install
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Simply run `pip install git+https://git.ecker.tech/mrq/vall-e` or `pip install git+https://github.com/e-c-k-e-r/vall-e`.
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I've tested this repo under Python versions `3.10.9` and `3.11.3`.
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## Try Me
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To quickly try it out, you can run `python -m vall_e.models.ar_nar yaml="./data/config.yaml"`
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Each model file has a barebones trainer and inference routine.
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## Pre-Trained Model
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My pre-trained weights can be acquired from [here](https://huggingface.co/ecker/vall-e).
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A script to setup a proper environment and download the weights can be invoked with `./scripts/setup.sh`
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## Train
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Training is very dependent on:
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* the quality of your dataset.
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* how much data you have.
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* the bandwidth you quantized your audio to.
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* the underlying model architecture used.
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### Pre-Processed Dataset
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A "libre" dataset can be found [here](https://huggingface.co/ecker/vall-e) under `data.tar.gz`.
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A script to setup a proper environment and train can be invoked with `./scripts/setup-training.sh`
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### Leverage Your Own Dataset
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> **Note** It is highly recommended to utilize [mrq/ai-voice-cloning](https://git.ecker.tech/mrq/ai-voice-cloning) with `--tts-backend="vall-e"` to handle transcription and dataset preparations.
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1. Put your data into a folder, e.g. `./data/custom`. Audio files should be named with the suffix `.wav` and text files with `.txt`.
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2. Quantize the data: `python -m vall_e.emb.qnt ./data/custom`
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3. Generate phonemes based on the text: `python -m vall_e.emb.g2p ./data/custom`
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4. Customize your configuration and define the dataset by modifying `./data/config.yaml`. Refer to `./vall_e/config.py` for details. If you want to choose between different model presets, check `./vall_e/models/__init__.py`.
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If you're interested in creating an HDF5 copy of your dataset, simply invoke: `python -m vall_e.data --action='hdf5' yaml='./data/config.yaml'`
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5. Train the model using the following scripts: `python -m vall_e.train yaml=./data/config.yaml`
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* If distributing your training (for example, multi-GPU), use `deepspeed --module vall_e.train yaml="./data/config.yaml"`
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You may quit your training any time by just entering `quit` in your CLI. The latest checkpoint will be automatically saved.
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### Dataset Formats
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Two dataset formats are supported:
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* the standard way:
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- data is stored under `${speaker}/${id}.phn.txt` and `${speaker}/${id}.qnt.pt`
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* using an HDF5 dataset:
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- you can convert from the standard way with the following command: `python3 -m vall_e.data yaml="./path/to/your/config.yaml"`
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- this will shove everything into a single HDF5 file and store some metadata alongside (for now, the symbol map generated, and text/audio lengths)
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- be sure to also define `use_hdf5` in your config YAML.
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### Plotting Metrics
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Included is a helper script to parse the training metrics. Simply invoke it with, for example: `python3 -m vall_e.plot yaml="./training/valle/config.yaml"`
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You can specify what X and Y labels you want to plot against by passing `--xs tokens_processed --ys loss stats.acc`
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### Notices
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#### Training Under Windows
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As training under `deepspeed` and Windows is not (easily) supported, under your `config.yaml`, simply change `trainer.backend` to `local` to use the local training backend.
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Keep in mind that creature comforts like distributed training or `float16` training cannot be verified as working at the moment with the local trainer.
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#### Training on Low-VRAM Cards
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During experimentation, I've found I can comfortably train on a 4070Ti (12GiB VRAM). Howver, VRAM use is predicated on your dataset; a mix of large and small utterances will cause VRAM usage to spike and can trigger OOM conditions during the backwards pass if you are not careful.
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Additionally, under Windows, I managed to finetune the AR on my 2060 (6GiB VRAM) with a batch size of 8 (although, with the card as a secondary GPU).
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#### Training Caveats
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Unfortunately, efforts to train a *good* foundational model seems entirely predicated on a good dataset. My dataset might be too fouled with:
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* too short utterances: trying to extrapolate longer contexts seems to utterly fall apart from just the `text` being too long.
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* too tightly trimmed utterances: there being little to no space at the start and end might harm associating `<s>` and `</s>` tokens with empty utterances.
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* a poorly mapped phoneme mapping: I naively crafted my own phoneme mapping, where a HuggingFace tokenizer might supply a better token mapping.
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#### Backend Architectures
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As the core of VALL-E makes use of a language model, various LLM architectures can be supported and slotted in. Currently supported:
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* `transformer`: a basic attention-based transformer implementation, with attention heads + feed forwards.
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* `retnet`: using [TorchScale's RetNet](https://github.com/microsoft/torchscale/blob/main/torchscale/architecture/retnet.py) implementation, a retention-based approach can be used instead.
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- Its implementation for MoE can also be utilized.
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* `retnet-hf`: using [syncdoth/RetNet/](https://github.com/syncdoth/RetNet/) with a HuggingFace-compatible RetNet model
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- inferencing cost is about 0.5x, and MoE is not implemented.
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* `llama`: using HF transformer's LLaMa implementation for its attention-based transformer, boasting RoPE and other improvements.
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* `mixtral`: using HF transformer's Mixtral implementation for its attention-based transformer, also utilizing its MoE implementation.
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* `bitnet`: using [this](https://github.com/kyegomez/BitNet/) implementation of BitNet's transformer.
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- Setting `bitsandbytes.bitnet=True` will make use of BitNet's linear implementation.
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## Export
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To export the models, run: `python -m vall_e.export yaml=./data/config.yaml`.
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This will export the latest checkpoints, for example, under `./data/ckpt/ar+nar-retnet-8/fp32.pth`, to be loaded on any system with PyTorch, and will include additional metadata, such as the symmap used, and training stats.
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## Synthesis
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To synthesize speech, invoke either (if exported the models): `python -m vall_e <text> <ref_path> <out_path> --model-ckpt ./data/ckpt/ar+nar-retnet-8/fp32.pth` or `python -m vall_e <text> <ref_path> <out_path> yaml=<yaml_path>`
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Some additional flags you can pass are:
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* `--language`: specifies the language for phonemizing the text, and helps guide inferencing when the model is trained against that language.
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* `--max-ar-steps`: maximum steps for inferencing through the AR model. Each second is 75 steps.
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* `--device`: device to use (default: `cuda`, examples: `cuda:0`, `cuda:1`, `cpu`)
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* `--ar-temp`: sampling temperature to use for the AR pass. During experimentation, `0.95` provides the most consistent output, but values close to it works fine.
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* `--nar-temp`: sampling temperature to use for the NAR pass. During experimentation, `0.2` provides clean output, but values upward of `0.6` seems fine too.
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And some experimental sampling flags you can use too (your mileage will ***definitely*** vary):
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* `--max-ar-context`: Number of `resp` tokens to keep in the context when inferencing. This is akin to "rolling context" in an effort to try and curb any context limitations, but currently does not seem fruitful.
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* `--min-ar-temp` / `--min-nar-temp`: triggers the dynamic temperature pathway, adjusting the temperature based on the confidence of the best token. Acceptable values are between `[0.0, (n)ar-temp)`.
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+ This simply uplifts the [original implementation](https://github.com/kalomaze/koboldcpp/blob/dynamic-temp/llama.cpp#L5132) to perform it.
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+ **!**NOTE**!**: This does not seem to resolve any issues with setting too high/low of a temperature. The right values are yet to be found.
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* `--top-p`: limits the sampling pool to top sum of values that equal `P`% probability in the probability distribution.
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* `--top-k`: limits the sampling pool to the top `K` values in the probability distribution.
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* `--repetition-penalty`: modifies the probability of tokens if they have appeared before. In the context of audio generation, this is a very iffy parameter to use.
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* `--repetition-penalty-decay`: modifies the above factor applied to scale based on how far away it is in the past sequence.
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* `--length-penalty`: (AR only) modifies the probability of the stop token based on the current sequence length. This is ***very*** finnicky due to the AR already being well correlated with the length.
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* `--beam-width`: (AR only) specifies the number of branches to search through for beam sampling.
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+ This is a very naive implementation that's effectively just greedy sampling across `B` spaces.
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* `--mirostat-tau`: (AR only) the "surprise value" when performing mirostat sampling.
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+ This simply uplifts the [original implementation](https://github.com/basusourya/mirostat/blob/master/mirostat.py) to perform it.
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+ **!**NOTE**!**: This is incompatible with beam search sampling (for the meantime at least).
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* `--mirostat-eta`: (AR only) the "learning rate" during mirostat sampling applied to the maximum surprise.
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## To-Do
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* train and release a ***good*** model.
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* clean up the README, and document, document, document onto the wiki.
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* extend to ~~multiple languages ([VALL-E X](https://arxiv.org/abs/2303.03926)) and~~ addditional tasks ([SpeechX](https://arxiv.org/abs/2308.06873)).
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- training additional tasks needs the SpeechX implementation to be reworked.
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* improve throughput (despite peaking at 120it/s):
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- properly utilize RetNet's recurrent forward / chunkwise forward passes (does not seem to want to work no matter how the model is trained).
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- utilize an approach similar to [FasterDecoding/Medusa](https://github.com/FasterDecoding/Medusa/) with additional heads for decoding N+1, N+2, N+3 AR tokens
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+ this requires a properly trained AR, however.
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* work around issues with extending context past what's trained (despite RetNet's retention allegedly being able to defeat this):
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- "sliding" AR input, such as have the context a fixed length.
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+ the model may need to be trained for this with a fancy positional embedding injected OR already trained with a sliding context window in mind. Naively sliding the context window while making use of the RetNet implementation's positional embedding doesn't seem fruitful.
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## Notices and Citations
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Unless otherwise credited/noted in this README or within the designated Python file, this repository is [licensed](LICENSE) under AGPLv3.
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- [EnCodec](https://github.com/facebookresearch/encodec) is licensed under CC-BY-NC 4.0. If you use the code to generate audio quantization or perform decoding, it is important to adhere to the terms of their license.
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- This implementation was originally based on [enhuiz/vall-e](https://github.com/enhuiz/vall-e), but has been heavily, heavily modified over time.
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```bibtex
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@article{wang2023neural,
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title={Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers},
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author={Wang, Chengyi and Chen, Sanyuan and Wu, Yu and Zhang, Ziqiang and Zhou, Long and Liu, Shujie and Chen, Zhuo and Liu, Yanqing and Wang, Huaming and Li, Jinyu and others},
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journal={arXiv preprint arXiv:2301.02111},
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year={2023}
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}
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```
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```bibtex
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@article{defossez2022highfi,
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title={High Fidelity Neural Audio Compression},
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author={Défossez, Alexandre and Copet, Jade and Synnaeve, Gabriel and Adi, Yossi},
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journal={arXiv preprint arXiv:2210.13438},
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year={2022}
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}
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```
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