tortoise-tts

TorToiSe Tortoise is a text-to-speech program built with the following priorities: Strong multi-voice capabilities. 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 pip 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 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 If you want to use this on your own computer, you must have an NVIDIA GPU. TipOn 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: create conda environment with minimal dependencies specified activate the environment install pytorch with the command provided here: https://pytorch.org/get-started/locally/ clone tortoise-tts change the current directory to tortoise-tts 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. NoteWhen 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. 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." 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: 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. 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_d