TANGO is a latent diffusion model for text-to-audio generation. TANGO can generate realistic audios including human sounds, animal sounds, natural and artificial sounds and sound effects from textual prompts. We use the frozen instruction-tuned LLM Flan-T5 as the text encoder and train a UNet based diffusion model for audio generation. We outperform current state-of-the-art models for audio generation across both objective and subjective metrics. We release our model, training, inference code and pre-trained checkpoints for the research community.

📣 We are releasing Tango-Full-FT-Audiocaps which was first pre-trained on TangoPromptBank and later fine tuned on AudioCaps. This checkpoint obtained state-of-the-art results for text-to-audio generation on AudioCaps.

Our code is released here: https://github.com/declare-lab/tango

We uploaded several TANGO generated samples here: https://tango-web.github.io/

Please follow the instructions in the repository for installation, usage and experiments.

Download the TANGO model and generate audio from a text prompt:

The model will be automatically downloaded and saved in cache. Subsequent runs will load the model directly from cache.

The generate function uses 100 steps by default to sample from the latent diffusion model. We recommend using 200 steps for generating better quality audios. This comes at the cost of increased run-time.

Use the generate_for_batch function to generate multiple audio samples for a batch of text prompts:

This will generate two samples for each of the three text prompts.