license: openrail++
Democratizing access to LLMs, Multi-Modal Gen AI models for the open-source community.
Let's advance AI, together.
Tansen is a text-to-speech program built with the following priorities:
- Strong multi-voice capabilities.
- Highly realistic prosody and intonation.
- Speaking rate control
🎧 Demos
Demos
💻 Getting Started on GitHub
Ready to dive in? Here's how you can get started with our repo on GitHub.
1️⃣ : Clone our GitHub repository
First things first, you'll need to clone our repository. Open up your terminal, navigate to the directory where you want the repository to be cloned, and run the following command:
conda create --name Tansen python=3.9 numba inflect
conda activate Tansen
conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
conda install transformers=4.29.2
git clone https://github.com/BudEcosystem/Tansen.git
cd Tansen
2️⃣ : Install dependencies
python setup.py install
3️⃣ : Generate Audio
do_tts.py
This script allows you to speak a single phrase with one or more voices.
python do_tts.py --text "I'm going to speak this" --voice random --preset fast
read.py
This script provides tools for reading large amounts of text.
python Tansen/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 Tansen screws up an output. You can re-generate any bad clips by re-running read.py with the --regenerate
argument.
Intrested in running as as API ?
🐍 Usage in Python
Tansen can be used programmatically :
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')
Loss Curves
loss_mel_ce
loss_text_ce
Training Information
Device : A Single A100
Dataset : 876 hours