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.
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 proejct seems no longer to be active.
Usage guide
Local installation
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 path. I have been told that if you do not do this, you
will spend a lot of time chasing dependency problems.
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
Docker
An easy way to hit the ground running and a good jumping off point depending on your use case.
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:
sh
1cd app
2conda activate tortoise
3time python tortoise/do_tts.py \
4 --output_path /results \
5 --preset ultra_fast \
6 --voice geralt \
7 --text "Time flies like an arrow; fruit flies like a bananna."
Apple Silicon
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:
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
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:
python
1reference_clips =[utils.audio.load_audio(p,22050)for p in clips_paths]2tts = api.TextToSpeech()3pcm_audio = tts.tts_with_preset("your text here", voice_samples=reference_clips, preset='fast')
To use deepspeed:
python
1reference_clips =[utils.audio.load_audio(p,22050)for p in clips_paths]2tts = api.TextToSpeech(use_deepspeed=True)3pcm_audio = tts.tts_with_preset("your text here", voice_samples=reference_clips, preset='fast')
To use kv cache:
python
1reference_clips =[utils.audio.load_audio(p,22050)for p in clips_paths]2tts = api.TextToSpeech(kv_cache=True)3pcm_audio = tts.tts_with_preset("your text here", voice_samples=reference_clips, preset='fast')
To run model in float16:
python
1reference_clips =[utils.audio.load_audio(p,22050)for p in clips_paths]2tts = api.TextToSpeech(half=True)3pcm_audio = tts.tts_with_preset("your text here", voice_samples=reference_clips, preset='fast')
for Faster runs use all three:
python
1reference_clips =[utils.audio.load_audio(p,22050)for p in clips_paths]2tts = api.TextToSpeech(use_deepspeed=True, kv_cache=True, half=True)3pcm_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.
