Dia is a 1.6B parameter text to speech model created by Nari Labs.

Dia directly generates highly realistic dialogue from a transcript. You can condition the output on audio, enabling emotion and tone control. The model can also produce nonverbal communications like laughter, coughing, clearing throat, etc.

To accelerate research, we are providing access to pretrained model checkpoints and inference code. The model weights are hosted on Hugging Face.

We also provide a demo page comparing our model to ElevenLabs Studio and Sesame CSM-1B.

• Join our discord server for community support and access to new features.
• Play with a larger version of Dia: generate fun conversations, remix content, and share with friends. 🔮 Join the waitlist for early access.

⚡️ Quickstart

This will open a Gradio UI that you can work on.

git clone https://github.com/nari-labs/dia.git
cd dia && uv run app.py

or if you do not have uv pre-installed:

git clone https://github.com/nari-labs/dia.git
cd dia
python -m venv .venv
source .venv/bin/activate
pip install uv
uv run app.py

⚙️ Usage

As a Python Library

import soundfile as sf

from dia.model import Dia


model = Dia.from_pretrained("nari-labs/Dia-1.6B")

text = "[S1] Dia is an open weights text to dialogue model. [S2] You get full control over scripts and voices. [S1] Wow. Amazing. (laughs) [S2] Try it now on Git hub or Hugging Face."

output = model.generate(text)

sf.write("simple.mp3", output, 44100)

A pypi package and a working CLI tool will be available soon.

💻 Hardware and Inference Speed

Dia has been tested on only GPUs (pytorch 2.0+, CUDA 12.6). CPU support is to be added soon. The initial run will take longer as the Descript Audio Codec also needs to be downloaded.

On enterprise GPUs, Dia can generate audio in real-time. On older GPUs, inference time will be slower. For reference, on a A4000 GPU, Dia rougly generates 40 tokens/s (86 tokens equals 1 second of audio). torch.compile will increase speeds for supported GPUs.

The full version of Dia requires around 10GB of VRAM to run. We will be adding a quantized version in the future.

If you don't have hardware available or if you want to play with bigger versions of our models, join the waitlist here.

🪪 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

⚠️ Disclaimer

This project offers a high-fidelity speech generation model intended solely for research and educational use. The following uses are strictly forbidden:

• Identity Misuse: Do not produce audio resembling real individuals without permission.
• Deceptive Content: Do not use this model to generate misleading content (e.g. fake news)
• Illegal or Malicious Use: Do not use this model for activities that are illegal or intended to cause harm.

By using this model, you agree to uphold relevant legal standards and ethical responsibilities. We are not responsible for any misuse and firmly oppose any unethical usage of this technology.

🔭 TODO / Future Work

• Docker support.
• Optimize inference speed.
• Add quantization for memory efficiency.

🤝 Contributing

We are a tiny team of 1 full-time and 1 part-time research-engineers. We are extra-welcome to any contributions! Join our Discord Server for discussions.

🤗 Acknowledgements

• We thank the Google TPU Research Cloud program for providing computation resources.
• Our work was heavily inspired by SoundStorm, Parakeet, and Descript Audio Codec.
• "Nari" is a pure Korean word for lily.
• We thank Jason Y. for providing help with data filtering.