e-c-k-e-r/tortoise-tts

An unofficial re-implementation of the audio LM TorToiSe

TorToiSe TTS

An unofficial PyTorch re-implementation of TorToise TTS.

Almost all of the documentation and usage are carried over from my VALL-E implementation, as documentation is lacking for this implementation, as I whipped it up over the course of two days using knowledge I haven't touched in a year.

Requirements

A working PyTorch environment.

• python3 -m venv venv && source ./venv/bin/activate is sufficient.

Install

Simply run pip install git+https://git.ecker.tech/mrq/tortoise-tts@new or pip install git+https://github.com/e-c-k-e-r/tortoise-tts.

Usage

Inferencing

Using the default settings: python3 -m tortoise_tts --yaml="./data/config.yaml" "Read verse out loud for pleasure." "./path/to/a.wav"

To inference using the included Web UI: python3 -m tortoise_tts.webui --yaml="./data/config.yaml"

• Pass --listen 0.0.0.0:7860 if you're accessing the web UI from outside of localhost (or pass the host machine's local IP instead)

Training / Finetuning

Training is as simple as copying the reference YAML from ./data/config.yaml to any training directory of your choice (for examples: ./training/ or ./training/lora-finetune/).

A pre-processed dataset is required. Refer to the VALL-E implementation for more details.

To start the trainer, run python3 -m tortoise_tts.train --yaml="./path/to/your/training/config.yaml.

• Type save to save whenever. Type quit to quit and save whenever. Type eval to run evaluation / validation of the model.

For training a LoRA, uncomment the loras block in your training YAML.

For loading an existing finetuned model, create a folder with this structure, and load its accompanying YAML:

./some/arbitrary/path/:
    ckpt:
        autoregressive:
            fp32.pth # finetuned weights
    config.yaml

For LoRAs, replace the above fp32.pth with lora.pth.

To-Do

• Reimplement original inferencing through TorToiSe (as done with api.py)
  • Reimplement candidate selection with the CLVP
  • Reimplement redaction with the Wav2Vec2
• Implement training support (without DLAS)
  • Feature parity with the VALL-E training setup with preparing a dataset ahead of time
• Automagic offloading to CPU for unused models (for training and inferencing)
• Automagic handling of the original weights into compatible weights
• Reimplement added features from my original fork:
  • "Better" conditioning latents calculating
  • Use of KV-cache for the AR
  • Re-enable DDIM sampler
• Extend the original inference routine with additional features:
  • non-float32 / mixed precision for the entire stack
    • Parts of the stack will whine about mismatching dtypes...
  • BitsAndBytes support
    • Provided Linears technically aren't used because GPT2 uses Conv1D instead...
  • LoRAs
  • Web UI
    • Feature parity with ai-voice-cloning
      • Although I feel a lot of its features are the wrong way to go about it.
  • Additional samplers for the autoregressive model (such as mirostat / dynamic temperature)
  • Additional samplers for the diffusion model (beyond the already included DDIM)
  • BigVGAN in place of the original vocoder
    • HiFiGAN integration as well
  • XFormers / flash_attention_2 for the autoregressive model
    • Beyond HF's internal implementation of handling alternative attention
    • Both the AR and diffusion models also do their own attention...
  • Saner way of loading finetuned models / LoRAs
  • Some vector embedding store to find the "best" utterance to pick
• Documentation
  • this also includes a correct explanation of the entire stack (rather than the poor one I left in ai-voice-cloning)

Why?

To:

• atone for the mess I've made with forking TorToiSe TTS originally with a bunch of slopcode, and the nightmare that ai-voice-cloning turned out.
• unify the trainer and the inference-er.
• implement additional features with much ease, as I'm very well familiar with my framework.
• disillusion myself that it won't get better than TorToiSe TTS:
  • while it's faster than VALL-E, the quality leaves a lot to be desired (although this is simply due to the overall architecture).

License

Unless otherwise credited/noted in this README or within the designated Python file, this repository is licensed under AGPLv3.