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

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

pip install tortoise-tts

If you would like to install the latest development version, you can also install it directly from the git repository:

pip install git+https://github.com/neonbjb/tortoise-tts

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 !!!

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.

If you want to use this on your own computer, you must have an NVIDIA GPU.

On Windows, I highly recommend using the Conda installation method. I have been told that if you do not do this, you will spend a lot of time chasing dependency problems.

First, install miniconda: https://docs.conda.io/en/latest/miniconda.html

Then run the following commands, using anaconda prompt as the terminal (or any other terminal configured to work with conda)

This will:

1. create conda environment with minimal dependencies specified
2. activate the environment
3. install pytorch with the command provided here: https://pytorch.org/get-started/locally/
4. clone tortoise-tts
5. change the current directory to tortoise-tts
6. run tortoise python setup install script

conda create --name tortoise python=3.9 numba inflect
conda activate tortoise
conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
conda install transformers=4.29.2
git clone https://github.com/neonbjb/tortoise-tts.git
cd tortoise-tts
python setup.py install

Optionally, pytorch can be installed in the base environment, so that other conda environments can use it too. To do this, simply send the conda install pytorch... line before activating the tortoise environment.

Note: When you want to use tortoise-tts, you will always have to ensure the tortoise conda environment is activated.

If you are on windows, you may also need to install pysoundfile: conda install -c conda-forge pysoundfile

An easy way to hit the ground running and a good jumping off point depending on your use case.

git clone https://github.com/neonbjb/tortoise-tts.git
cd tortoise-tts

docker build . -t tts

docker run --gpus all \
    -e TORTOISE_MODELS_DIR=/models \
    -v /mnt/user/data/tortoise_tts/models:/models \
    -v /mnt/user/data/tortoise_tts/results:/results \
    -v /mnt/user/data/.cache/huggingface:/root/.cache/huggingface \
    -v /root:/work \
    -it tts

This gives you an interactive terminal in an environment that's ready to do some tts. Now you can explore the different interfaces that tortoise exposes for tts.

For example:

cd app
conda activate tortoise
time python tortoise/do_tts.py \
    --output_path /results \
    --preset ultra_fast \
    --voice geralt \
    --text "Time flies like an arrow; fruit flies like a bananna."

On macOS 13+ with M1/M2 chips you need to install the nighly version of PyTorch, as stated in the official page you can do:

pip3 install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu

Be sure to do that after you activate the environment. If you don't use conda the commands would look like this:

python3.10 -m venv .venv
source .venv/bin/activate
pip install numba inflect psutil
pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu
pip install transformers
git clone https://github.com/neonbjb/tortoise-tts.git
cd tortoise-tts
pip install .

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.

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

This script provides tools for reading large amounts of text.

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

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.

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')

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.

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.

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.