# VALL'E An unofficial PyTorch implementation of [VALL-E](https://vall-e-demo.ecker.tech/), utilizing the [EnCodec](https://github.com/facebookresearch/encodec) encoder/decoder. ## Requirements Besides a working PyTorch environment, the only hard requirement is [`espeak-ng`](https://github.com/espeak-ng/espeak-ng/) for phonemizing text: - Linux users can consult their package managers on installing `espeak`/`espeak-ng`. - Windows users are required to install [`espeak-ng`](https://github.com/espeak-ng/espeak-ng/releases/tag/1.51#Assets). + additionally, you may be required to set the `PHONEMIZER_ESPEAK_LIBRARY` environment variable to specify the path to `libespeak-ng.dll`. - In the future, an internal homebrew to replace this would be fantastic. ## Install 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`. I've tested this repo under Python versions `3.10.9`, `3.11.3`, and `3.12.3`. ## Pre-Trained Model My pre-trained weights can be acquired from [here](https://huggingface.co/ecker/vall-e). A script to setup a proper environment and download the weights can be invoked with `./scripts/setup.sh`. This will automatically create a `venv`, and download the `ar+nar-llama-8` weights and config file to the right place. * In the future, the model should be automatically downloaded. ## Train Training is very dependent on: * the quality of your dataset. * clean utterances and accurate transcriptions go a long way. * a diverse dataset in prosidy and speakers help a ton. * how much data you have. * training from scratch requires upwards of 15K hours. * training new languages from the base model simply requires maybe ~2K hours each. * the bandwidth you quantized your audio to, as this affects the how many tokens are processed per step. * the underlying model architecture used. * some models behave better than others for a unified approach, others do not. ### Try Me To quickly test if a configuration works, you can run `python -m vall_e.models.ar_nar --yaml="./data/config.yaml"`; a small trainer will overfit a provided utterance. ### Leverage Your Own Dataset If you already have a dataset you want, for example, your own large corpus or for finetuning, you can use your own dataset instead. 0. Set up a `venv` with `https://github.com/m-bain/whisperX/`. + At the moment only WhisperX is utilized. Using other variants like `faster-whisper` is an exercise left to the user at the moment. + It's recommended to use a dedicated virtualenv specifically for transcribing, as WhisperX will break a few dependencies. + The following command should work: ``` python3 -m venv venv-whisper source ./venv-whisper/bin/activate pip3 install torch torchvision torchaudio pip3 install git+https://github.com/m-bain/whisperX/ ``` 1. Populate your source voices under `./voices/{group name}/{speaker name}/`. 2. Run `python3 -m vall_e.emb.transcribe`. This will generate a transcription with timestamps for your dataset. + If you're interested in using a different model, edit the script's `model_name` and `batch_size` variables. 3. Run `python3 -m vall_e.emb.process`. This will phonemize the transcriptions and quantize the audio. + If you're using a Descript-Audio-Codec based model, ensure to set the sample rate and audio backend accordingly. 4. Run `python3 -m vall_e.emb.similar`. This will calculate the top-k most similar utterances for each utterance for use with sampling. + Doing this will help the model follow the input prompt stronger, at the possible "cost" of the model not learning how to "infer" the target speaker AND prosidy. 5. Copy `./data/config.yaml` to `./training/config.yaml`. Customize the training configuration and populate your `dataset.training` list with the values stored under `./training/dataset/list.json`. + Refer to `./vall_e/config.py` for additional configuration details. ### Dataset Formats Two dataset formats are supported: * the standard way: - data is stored under `./training/data/{group}/{speaker}/{id}.{enc|dac}` as a NumPy file, where `enc` is for the EnCodec/Vocos backend, and `dac` for the Descript-Audio-Codec backend. - it is *highly* recommended to generate metadata to speed up dataset pre-load with `python3 -m vall_e.data --yaml="./training/config.yaml" --action=metadata` * using an HDF5 dataset: - you can convert from the standard way with the following command: `python3 -m vall_e.data --yaml="./training/config.yaml"` (metadata for dataset pre-load is generated alongside HDF5 creation) - this will shove everything into a single HDF5 file and store some metadata alongside (for now, the symbol map generated, and text/audio lengths) - be sure to also define `use_hdf5` in your config YAML. ### Training For single GPUs, simply running `python3 -m vall_e.train --yaml="./training/config.yaml`. For multiple GPUs, or exotic distributed training: * with `deepspeed` backends, simply running `deepspeed --module vall_e.train --yaml="./training/config.yaml"` should handle the gory details. * with `local` backends, simply run `torchrun --nnodes=1 --nproc-per-node={NUMOFGPUS} -m vall_e.train --yaml="./training/config.yaml"` You can enter `save` to save the state at any time, or `quit` to save and quit training. The `lr` command will also let you adjust the learning rate on the fly. For example: `lr 1.0e-3` will set the learning rate to `0.001`. Some additional flags can be passed as well: * `--eval`: only run the evaluation / validation pass, then exit afterwards. * `--eval-random-text-prompts`: use random text prompts for the evaluation pass, rather than the provided text prompts in the dataset. ### Finetuning Finetuning can be done by training the full model, or using a LoRA. Finetuning the full model is done the same way as training a model, but be sure to have the weights in the correct spot, as if you're loading them for inferencing. For training a LoRA, add the following block to your `config.yaml`: ``` loras: - name : "arbitrary name" # whatever you want rank: 128 # dimensionality of the LoRA alpha: 128 # scaling factor of the LoRA training: True ``` And that's it. Training of the LoRA is done with the same command. Depending on the rank and alpha specified, the loss may be higher than it should, as the LoRA weights are initialized to appropriately random values. I found `rank` and `alpha` of 128 works fine. To export your LoRA weights, run `python3 -m vall_e.export --lora --yaml="./training/config.yaml"`. You *should* be able to have the LoRA weights loaded from a training checkpoint automagically for inferencing, but export them just to be safe. ### Plotting Metrics Included is a helper script to parse the training metrics. Simply invoke it with, for example: `python3 -m vall_e.plot --yaml="./training/config.yaml"` You can specify what X and Y labels you want to plot against by passing `--xs tokens_processed --ys loss.nll stats.acc` ### Notices #### Training Under Windows 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. Creature comforts like `float16`, `amp`, and multi-GPU training *should* work under the `local` backend, but extensive testing still needs to be done to ensure it all functions. #### Backend Architectures As the core of VALL-E makes use of a language model, various LLM architectures can be supported and slotted in. Currently supported LLM architectures: * `llama`: using HF transformer's LLaMa implementation for its attention-based transformer, boasting RoPE and other improvements. + I aim to utilize this for the foundational model, as I get to leverage a bunch of things tailored for LLaMA (and converting to them is rather easy). * `mixtral`: using HF transformer's Mixtral implementation for its attention-based transformer, also utilizing its MoE implementation. * `bitnet`: using [this](https://github.com/kyegomez/BitNet/) implementation of BitNet's transformer. - Setting `cfg.optimizers.bitnet=True` will make use of BitNet's linear implementation. * `transformer`: a basic attention-based transformer implementation, with attention heads + feed forwards. * `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. - Its implementation for MoE can also be utilized. * `retnet-hf`: using [syncdoth/RetNet](https://github.com/syncdoth/RetNet) with a HuggingFace-compatible RetNet model - has an inference penality, and MoE is not implemented. * `mamba`: using [state-spaces/mamba](https://github.com/state-spaces/mamba) (needs to mature) - ***really hard*** to have a unified AR and NAR model - inference penalty makes it a really hard sell, despite the loss already being a low 3 after a short amount of samples processed For audio backends: * [`encodec`](https://github.com/facebookresearch/encodec): a tried-and-tested EnCodec to encode/decode audio. * [`vocos`](https://huggingface.co/charactr/vocos-encodec-24khz): a higher quality EnCodec decoder. - encoding audio will use the `encodec` backend automagically, as there's no EnCodec encoder under `vocos` * [`descript-audio-codec`](https://github.com/descriptinc/descript-audio-codec): boasts better compression and quality, but has issues with model convergence. - models at 24KHz + 8kbps will NOT converge in any manner. - models at 44KHz + 8kbps seems harder to model its "language", and the NAR side of the model suffers greatly. `llama`-based models also support different attention backends: * `torch.nn.functional.scaled_dot_product_attention`-based attention: * `math`: torch's SDPA's `math` kernel * `mem_efficient`: torch's SDPA's memory efficient (`xformers` adjacent) kernel * `cudnn`: torch's SDPA's `cudnn` kernel * `flash`: torch's SDPA's flash attention kernel * internal implementations of external attention backends: * `xformers`: [facebookresearch/xformers](https://github.com/facebookresearch/xformers/)'s memory efficient attention * `flash_attn`: uses the available `flash_attn` package (including `flash_attn==1.0.9` through a funny wrapper) * `flash_attn_v100`: uses [ZRayZzz/flash-attention-v100](https://github.com/ZRayZzz/flash-attention-v100/)'s Flash Attention for Volta (but doesn't work currently) * `fused_attn`: uses an implementation using `triton` (tested on my 7900XTX and V100s), but seems to introduce errors when used to train after a while * `transformers` Llama\*Attention implementations: * `eager`: default `LlamaAttention` * `sdpa`: integrated `LlamaSdpaAttention` attention model * `flash_attention_2`: integrated `LlamaFlashAttetion2` attention model * `auto`: determine the best fit from the above The wide support for various backends is solely while I try and figure out which is the "best" for a core foundation model. ##### ROCm Flash Attention [ROCm/flash-attention](https://github.com/ROCm/flash-attention) currently does not support Navi3 cards (gfx11xx), so first-class support for Flash Attention is a bit of a mess on Navi3. Using the `howiejay/navi_support` branch can get inference support, but not training support (due to some error being thrown during the backwards pass) by: * edit `/opt/rocm/include/hip/amd_detail/amd_hip_bf16.h`: ``` #if defined(__HIPCC_RTC__) #define __HOST_DEVICE__ __device__ static #else #include #define __HOST_DEVICE__ __host__ __device__ static inline #endif ``` * install with `pip install -U git+https://github.com/ROCm/flash-attention@howiejay/navi_support --no-build-isolation` ## Export To export the models, run: `python -m vall_e.export --yaml=./training/config.yaml`. This will export the latest checkpoints, for example, under `./training/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. Desite being called `fp32.pth`, you can export it to a different precision type with `--dtype=float16|bfloat16|float32`. You can also export to `safetensors` with `--format=sft`, and `fp32.sft` will be exported instead. ## Synthesis To synthesize speech: `python -m vall_e --yaml=` Some additional flags you can pass are: * `--language`: specifies the language for phonemizing the text, and helps guide inferencing when the model is trained against that language. * `--task`: task to perform. Defaults to `tts`, but accepts `stt` for transcriptions. * `--max-ar-steps`: maximum steps for inferencing through the AR model. Each second is 75 steps. * `--device`: device to use (default: `cuda`, examples: `cuda:0`, `cuda:1`, `cpu`) * `--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. * `--nar-temp`: sampling temperature to use for the NAR pass. During experimentation, the lower value, the better. Set to `0` to enable greedy sampling. * `--input-prompt-length`: the maximum duration the input prompt can be (~6 seconds is fine, longer durations lead to slower generations for "better" accuracy, as long as the model was trained against such input prompt durations) And some experimental sampling flags you can use too (your mileage will ***definitely*** vary, but most of these are bandaids for a bad AR): * `--input-prompt-prefix`: (AR only) treats the input prompt as the initial response prefix, but... * the transcription of the prompt needs to be in the input text prompt. * doesn't perform all that well (I belive the model needs to be trained a bit on this, as `tts-c`). * `--min-ar-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)`. + This simply uplifts the [original implementation](https://github.com/kalomaze/koboldcpp/blob/dynamic-temp/llama.cpp#L5132) to perform it. + **!**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. * `--top-p`: limits the sampling pool to top sum of values that equal `P`% probability in the probability distribution. * `--top-k`: limits the sampling pool to the top `K` values in the probability distribution. * `--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. * `--repetition-penalty-decay`: modifies the above factor applied to scale based on how far away it is in the past sequence. * `--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. * `--beam-width`: (AR only) specifies the number of branches to search through for beam sampling. + This is a very naive implementation that's effectively just greedy sampling across `B` spaces. * `--mirostat-tau`: (AR only) the "surprise value" when performing mirostat sampling. + This simply uplifts the [original implementation](https://github.com/basusourya/mirostat/blob/master/mirostat.py) to perform it. + **!**NOTE**!**: This is incompatible with beam search sampling (for the meantime at least). * `--mirostat-eta`: (AR only) the "learning rate" during mirostat sampling applied to the maximum surprise. * `--dry-multiplier`: (AR only) performs DRY sampling, the scalar factor. * `--dry-base`: (AR only) for DRY sampling, the base of the exponent factor. * `--dry-allowed-length`: (AR only) for DRY sampling, the window to perform DRY sampling within. ### Speech-to-Text The `ar+nar-tts+stt-llama-8` model has received additional training for a speech-to-text task against EnCodec-encoded audio. Currently, the model only transcribes back into the IPA phonemes it was trained against, as an additional model or external program is required to translate the IPA phonemes back into text. * this does make a model that can phonemize text, and unphonemize text, more desirable in the future to replace espeak (having an additional task to handle this requires additional embeddings, output heads, and possible harm to the model as actual text is not a modality the model is trained on). ### Web UI A Gradio-based web UI is accessible by running `python3 -m vall_e.webui`. You can, optionally, pass: * `--yaml=./path/to/your/config.yaml`: will load the targeted YAML * `--listen 0.0.0.0:7860`: will set the web UI to listen to all IPs at port 7860. Replace the IP and Port to your preference. ### Emergent Behavior The model can be prompted in creative ways to yield some interesting behaviors: * prompting without an input audio prompt will have the model generate a random voice at the "cost" of some unintelligible utterance at the beginning of the output response (despite doing no promptless training). * finetunes / LoRAs can benefit from this by having input audio promptless synthesis, while opting to have an input audio prompt for guidance. * prompting with an input text prompt being the transcription of the input audio prompt will have the response follow very closely to the input prompt (despite not doing input=output training). * this should allow for easy transcription editing without much fuss. #### Inference Synthesizing speech is simple: * `Input Prompt`: The guiding text prompt. Each new line will be its own generated audio to be stitched together at the end. * `Audio Input`: The reference audio for the synthesis. Under Gradio, you can trim your clip accordingly, but leaving it as-is works fine. - A properly trained model can inference without a prompt to generate a random voice (without even needing to generate a random prompt itself). * `Output`: The resultant audio. * `Inference`: Button to start generating the audio. * `Basic Settings`: Basic sampler settings for most uses. * `Sampler Settings`: Advanced sampler settings that are common for most text LLMs, but needs experimentation. All the additional knobs have a description that can be correlated to the above CLI flags. Speech-To-Text phoneme transcriptions for models that support it can be done using the `Speech-to-Text` tab. #### Dataset This tab currently only features exploring a dataset already prepared and referenced in your `config.yaml`. You can select a registered voice, and have it randomly sample an utterance. In the future, this *should* contain the necessary niceties to process raw audio into a dataset to train/finetune through, without needing to invoke the above commands to prepare the dataset. #### Settings So far, this only allows you to load a different model without needing to restart. The previous model should seamlessly unload, and the new one will load in place. ## To-Do * [x] train and release a serviceable model for finetuning against. * [x] train and release a ***good*** zero-shot model. - for what it's worth it's decent enough for me to finally be happy with it. * [ ] well-integrated training through the Web UI (without the kludge from ai-voice-cloning) * [x] ~~explore alternative setups, like a NAR-only model or Descript-Audio-Codec~~ - the current experiment of an AR length-predictor + NAR for the rest seems to fall apart... - Descript-Audio-Codec 44KHz has NAR issues, but this *might* be user error. * [x] ~~explore better sampling techniques~~ - the AR doesn't *need* exotic sampling techniques, as they're bandaids for a bad AR. - the NAR benefits from greedy sampling, and anything else just harms output quality. * [ ] clean up the README, and document, document, document onto the wiki. * [x] extend to multiple languages ([VALL-E X](https://arxiv.org/abs/2303.03926)). - reference model is trained against English, Japanese, French, and German. * [ ] extend to addditional tasks ([SpeechX](https://arxiv.org/abs/2308.06873)). - `stt` (Speech-to-Text) seems to be working fine for the most part. - other tasks seem to require a ton of VRAM...... * [ ] extend using [VALL-E 2](https://arxiv.org/pdf/2406.05370)'s features (grouped code modeling + repetition aware sampling) - desu these don't seem to be worthwhile improvements, as inferencing is already rather fast, and RAS is just a fancy sampler. * [ ] audio streaming - this *technically* can work without any additional architecture changes, just clever tricks with sampling-then-decoding-to-audio. - something similar to HiFiGAN (or the one for TorToiSe) trained on the last hidden states of the AR *might* also enable an alternate way for streaming. * [ ] replace the phonemizer with something that doesn't depend on espeak * [ ] train the model to handle text => phoneme (without a hit to the rest of the model) * [ ] ...and phonemes => text * [ ] allow raw text as input instead - espeak is nice, but I can only really put my whole trust with phonemizing English. - a small model trained to handle converting text to phonemes might work, but has it's own problems (another model to carry around, as accurate as the dataset it was trained against, requires training for each language... etc). * [ ] explore exotic features like: * using a pure text vocab rather than IPA phonemes (as a transformer should be "smart" enough to map text tokens) * interleaving by using summed embedding tokens: * for example, `` => `` => `` (etc.) * however, I imagine the sequences to train for this are *too* exotic. * mixing multiple speakers through summing input prompt embeddings * I do not expect this to work, but you never know... ## Caveats Despite how lightweight it is in comparison to other TTS's I've meddled with, there are still some caveats, be it with the implementation or model weights: * the audio embeddings have some quirks to having the AR's RVQ level 0 separate from the NAR's RVQ level 0 (sharing them caused some problems in testing) * the trainer / dataloader assumes there are zero variations between a speaker's utterances, and thus it can extract the basics of a speaker's features rather than deeper features (like prosidy, tone, etc.) when performing inferences. + ~~however, trying to work around this would require training under `tts-c` (VALL-E continuous) mode or modifying an input prompt enough to where its quantized representation differs enough from the output response the prompt derives from.~~ + to remedy this, training benefits from calculating the most similar utterances for each utterance, and using that as the input prompt for training. * the trainer's default RVQ level distribution prioritizes lower RVQ levels over higher RVQ levels, as the lower levels contribute to the final waveform more; however, this leaves some minor artifacting that rises in the higher RVQ levels due to inaccuracy issues. + summing the audio embeddings for later RVQ levels seems to help? + `model.experimental.p_rvq_levels: [0,0,0,0,0,0,0,1,2,3,4,5,6,7]` seems to help? * speakers that aren't similar to an audiobook narrator voice has similarity issues due to the majority of training used `path`-based dataloader sampling instead of `speaker`-based (or `group`-based) dataloader sampling. + although LoRAs help a ton for fixing results for a single voice. + a diverse dataset in prosidy and speaker (such as a corpus sourced from dramatic media like video games) helps a ton. ## Notices and Citations Unless otherwise credited/noted in this README or within the designated Python file, this repository is [licensed](LICENSE) under AGPLv3. - [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. - This implementation was originally based on [enhuiz/vall-e](https://github.com/enhuiz/vall-e), but has been heavily, heavily modified over time. Without it I would not have had a good basis to muck around and learn. ```bibtex @article{wang2023neural, title={Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers}, 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}, journal={arXiv preprint arXiv:2301.02111}, year={2023} } ``` ```bibtex @article{defossez2022highfi, title={High Fidelity Neural Audio Compression}, author={Défossez, Alexandre and Copet, Jade and Synnaeve, Gabriel and Adi, Yossi}, journal={arXiv preprint arXiv:2210.13438}, year={2022} } ```