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TorToiSe

Tortoise is a text-to-speech program built with the following priorities:

  1. Strong multi-voice capabilities.
  2. Highly realistic prosody and intonation.

This repo contains all the code needed to run Tortoise TTS in inference mode.

Manuscript: https://arxiv.org/abs/2305.07243

Hugging Face space

A live demo is hosted on Hugging Face Spaces. If you'd like to avoid a queue, please duplicate the Space and add a GPU. Please note that CPU-only spaces do not work for this demo.

https://huggingface.co/spaces/Manmay/tortoise-tts

Install via Nix

nix develop --impure .

What's in a name?

I'm naming my speech-related repos after Mojave desert flora and fauna. Tortoise is a bit tongue in cheek: this model is insanely slow. It leverages both an autoregressive decoder and a diffusion decoder; both known for their low sampling rates. On a K80, expect to generate a medium sized sentence every 2 minutes.

well..... not so slow anymore now we can get a 0.25-0.3 RTF on 4GB vram and with streaming we can get < 500 ms latency !!!

Demos

See this page for a large list of example outputs.

A cool application of Tortoise + GPT-3 (not affiliated with this repository): https://twitter.com/lexman_ai. Unfortunately, this project seems no longer to be active.

Usage guide

Local installation

This flake was built with NixOS. If the flake.nix and flake.lock are in the tortoise-tts github repo, running enter_nix_development_shell.sh should set everything up If you want to use this on your own computer, you must have an NVIDIA GPU.

Be aware that DeepSpeed is disabled on Apple Silicon since it does not work. The flag --use_deepspeed is ignored. You may need to prepend PYTORCH_ENABLE_MPS_FALLBACK=1 to the commands below to make them work since MPS does not support all the operations in Pytorch.

do_tts.py

This script allows you to speak a single phrase with one or more voices.

python tortoise/do_tts.py --text "I'm going to speak this" --voice random --preset fast

do socket streaming

python tortoise/socket_server.py

will listen at port 5000

faster inference read.py

This script provides tools for reading large amounts of text.

python tortoise/read_fast.py --textfile <your text to be read> --voice random

read.py

This script provides tools for reading large amounts of text.

python tortoise/read.py --textfile <your text to be read> --voice random

This will break up the textfile into sentences, and then convert them to speech one at a time. It will output a series of spoken clips as they are generated. Once all the clips are generated, it will combine them into a single file and output that as well.

Sometimes Tortoise screws up an output. You can re-generate any bad clips by re-running read.py with the --regenerate argument.

API

Tortoise can be used programmatically, like so:

reference_clips = [utils.audio.load_audio(p, 22050) for p in clips_paths]
tts = api.TextToSpeech()
pcm_audio = tts.tts_with_preset("your text here", voice_samples=reference_clips, preset='fast')

To use deepspeed:

reference_clips = [utils.audio.load_audio(p, 22050) for p in clips_paths]
tts = api.TextToSpeech(use_deepspeed=True)
pcm_audio = tts.tts_with_preset("your text here", voice_samples=reference_clips, preset='fast')

To use kv cache:

reference_clips = [utils.audio.load_audio(p, 22050) for p in clips_paths]
tts = api.TextToSpeech(kv_cache=True)
pcm_audio = tts.tts_with_preset("your text here", voice_samples=reference_clips, preset='fast')

To run model in float16:

reference_clips = [utils.audio.load_audio(p, 22050) for p in clips_paths]
tts = api.TextToSpeech(half=True)
pcm_audio = tts.tts_with_preset("your text here", voice_samples=reference_clips, preset='fast')

for Faster runs use all three:

reference_clips = [utils.audio.load_audio(p, 22050) for p in clips_paths]
tts = api.TextToSpeech(use_deepspeed=True, kv_cache=True, half=True)
pcm_audio = tts.tts_with_preset("your text here", voice_samples=reference_clips, preset='fast')

Acknowledgements

This project has garnered more praise than I expected. I am standing on the shoulders of giants, though, and I want to credit a few of the amazing folks in the community that have helped make this happen:

  • Hugging Face, who wrote the GPT model and the generate API used by Tortoise, and who hosts the model weights.
  • Ramesh et al who authored the DALLE paper, which is the inspiration behind Tortoise.
  • Nichol and Dhariwal who authored the (revision of) the code that drives the diffusion model.
  • Jang et al who developed and open-sourced univnet, the vocoder this repo uses.
  • Kim and Jung who implemented univnet pytorch model.
  • lucidrains who writes awesome open source pytorch models, many of which are used here.
  • Patrick von Platen whose guides on setting up wav2vec were invaluable to building my dataset.

Notice

Tortoise was built entirely by the author (James Betker) using their own hardware. Their employer was not involved in any facet of Tortoise's development.

License

Tortoise TTS is licensed under the Apache 2.0 license.

If you use this repo or the ideas therein for your research, please cite it! A bibtex entree can be found in the right pane on GitHub.