Tango: LLM-guided Diffusion-based Text-to-Audio Generation and DPO-based Alignment

🎵 🔥 🎉 🎉 We are releasing Tango 2 built upon Tango for text-to-audio generation. Tango 2 was initialized with the Tango-full-ft checkpoint and underwent alignment training using DPO on audio-alpaca, a pairwise text-to-audio preference dataset. Download the model, Access the demo. Trainer is available in the tango2 directory🎶

Quickstart on Google Colab

Colab	Info
	Tango_2_Google_Colab_demo.ipynb

Tango Model Family

Model Name	Model Path
Tango	https://huggingface.co/declare-lab/tango
Tango-Full-FT-Audiocaps	https://huggingface.co/declare-lab/tango-full-ft-audiocaps
Tango-Full-FT-Audio-Music-Caps	https://huggingface.co/declare-lab/tango-full-ft-audio-music-caps
Mustango	https://huggingface.co/declare-lab/mustango
Tango-Full	https://huggingface.co/declare-lab/tango-full
Tango-2	https://huggingface.co/declare-lab/tango2
Tango-2-full	https://huggingface.co/declare-lab/tango2-full

Description

TANGO is a latent diffusion model (LDM) for text-to-audio (TTA) 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 perform comparably to current state-of-the-art models for TTA across both objective and subjective metrics, despite training the LDM on a 63 times smaller dataset. We release our model, training, inference code, and pre-trained checkpoints for the research community.

🎵 🔥 We are releasing Tango 2 built upon Tango for text-to-audio generation. Tango 2 was initialized with the Tango-full-ft checkpoint and underwent alignment training using DPO on audio-alpaca, a pairwise text-to-audio preference dataset. 🎶

🎵 🔥 We are also making Audio-alpaca available. Audio-alpaca is a pairwise preference dataset containing about 15k (prompt,audio_w, audio_l) triplets where given a textual prompt, audio_w is the preferred generated audio and audio_l is the undesirable audio. Download Audio-alpaca. Tango 2 was trained on Audio-alpaca.

Quickstart Guide

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

import IPython
import soundfile as sf
from tango import Tango

tango = Tango("declare-lab/tango2")

prompt = "An audience cheering and clapping"
audio = tango.generate(prompt)
sf.write(f"{prompt}.wav", audio, samplerate=16000)
IPython.display.Audio(data=audio, rate=16000)

CheerClap.webm

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.

prompt = "Rolling thunder with lightning strikes"
audio = tango.generate(prompt, steps=200)
IPython.display.Audio(data=audio, rate=16000)

Thunder.webm

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

prompts = [
    "A car engine revving",
    "A dog barks and rustles with some clicking",
    "Water flowing and trickling"
]
audios = tango.generate_for_batch(prompts, samples=2)

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

More generated samples are shown here.

Prerequisites

Our code is built on pytorch version 1.13.1+cu117. We mention torch==1.13.1 in the requirements file but you might need to install a specific cuda version of torch depending on your GPU device type.

Install requirements.txt.

git clone https://github.com/declare-lab/tango/
cd tango
pip install -r requirements.txt

You might also need to install libsndfile1 for soundfile to work properly in linux:

(sudo) apt-get install libsndfile1

Datasets

Follow the instructions given in the AudioCaps repository for downloading the data. The audio locations and corresponding captions are provided in our data directory. The *.json files are used for training and evaluation. Once you have downloaded your version of the data you should be able to map it using the file ids to the file locations provided in our data/*.json files.

Note that we cannot distribute the data because of copyright issues.

How to train?

We use the accelerate package from Hugging Face for multi-gpu training. Run accelerate config from terminal and set up your run configuration by the answering the questions asked.

You can now train TANGO on the AudioCaps dataset using:

accelerate launch train.py \
--text_encoder_name="google/flan-t5-large" \
--scheduler_name="stabilityai/stable-diffusion-2-1" \
--unet_model_config="configs/diffusion_model_config.json" \
--freeze_text_encoder --augment --snr_gamma 5 \

The argument --augment uses augmented data for training as reported in our paper. We recommend training for at-least 40 epochs, which is the default in train.py.

To start training from our released checkpoint use the --hf_model argument.

accelerate launch train.py \
--hf_model "declare-lab/tango" \
--unet_model_config="configs/diffusion_model_config.json" \
--freeze_text_encoder --augment --snr_gamma 5 \

Check train.py and train.sh for the full list of arguments and how to use them.

The training script should automatically download the AudioLDM weights from here. However if the download is slow or if you face any other issues then you can: i) download the audioldm-s-full file from here, ii) rename it to audioldm-s-full.ckpt, and iii) keep it in /home/user/.cache/audioldm/ direcrtory.

To train TANGO 2 on the Audio-alpaca dataset from TANGO checkpoint using: The training script will download audio_alpaca wav files and save it in {PATH_TO_DOWNLOAD_WAV_FILE}/audio_alpaca. Default location will be ~/.cache/huggingface/datasets.

accelerate launch  tango2/tango2-train.py --hf_model "declare-lab/tango-full-ft-audiocaps" \
--unet_model_config="configs/diffusion_model_config.json" \
--freeze_text_encoder  \
--learning_rate=9.6e-7 \
--num_train_epochs=5  \
--num_warmup_steps=200 \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4  \
--gradient_accumulation_steps=4 \
--beta_dpo=2000  \
--sft_first_epochs=1 \
--dataset_dir={PATH_TO_DOWNLOAD_WAV_FILE}

How to make inferences?

From your trained checkpoints

Checkpoints from training will be saved in the saved/*/ directory.

To perform audio generation and objective evaluation in AudioCaps test set from your trained checkpoint:

CUDA_VISIBLE_DEVICES=0 python inference.py \
--original_args="saved/*/summary.jsonl" \
--model="saved/*/best/pytorch_model_2.bin" \

Check inference.py and inference.sh for the full list of arguments and how to use them.

To perform audio generation and objective evaluation in AudioCaps test set for TANGO 2 :

CUDA_VISIBLE_DEVICES=0 python tango2/inference.py \
--original_args="saved/*/summary.jsonl" \
--model="saved/*/best/pytorch_model_2.bin" \

Note that TANGO 2 inference.py script is different from TANGO .

From our released checkpoints in Hugging Face Hub

To perform audio generation and objective evaluation in AudioCaps test set from our huggingface checkpoints:

python inference_hf.py --checkpoint="declare-lab/tango"

Note

We use functionalities from audioldm_eval for objective evalution in inference.py. It requires the gold reference audio files and generated audio files to have the same name. You need to create the directory data/audiocaps_test_references/subset and keep the reference audio files there. The files should have names as following: output_0.wav, output_1.wav and so on. The indices should correspond to the corresponding line indices in data/test_audiocaps_subset.json.

We use the term subset as some data instances originally released in AudioCaps have since been removed from YouTube and are no longer available. We thus evaluated our models on all the instances which were available as of 8th April, 2023.

We use wandb to log training and infernce results.

Experimental Results

Tango

Model	Datasets	Text	#Params	FD ↓	KL ↓	FAD ↓	OVL ↑	REL ↑
Ground truth	−	−	−	−	−	−	91.61	86.78

DiffSound	AS+AC	✓	400M	47.68	2.52	7.75	−	−
AudioGen	AS+AC+8 others	✗	285M	−	2.09	3.13	−	−
AudioLDM-S	AC	✗	181M	29.48	1.97	2.43	−	−
AudioLDM-L	AC	✗	739M	27.12	1.86	2.08	−	−

AudioLDM-M-Full-FT^‡	AS+AC+2 others	✗	416M	26.12	1.26	2.57	79.85	76.84
AudioLDM-L-Full^‡	AS+AC+2 others	✗	739M	32.46	1.76	4.18	78.63	62.69
AudioLDM-L-Full-FT	AS+AC+2 others	✗	739M	23.31	1.59	1.96	−	−

TANGO	AC	✓	866M	24.52	1.37	1.59	85.94	80.36

Tango 2

Model	Parameters	FAD ↓	KL ↓	IS ↑	CLAP ↑	OVL ↑	REL ↑
AudioLDM-M-Full-FT	416M	2.57	1.26	8.34	0.43	-	-
AudioLDM-L-Full	739M	4.18	1.76	7.76	0.43	-	-
AudioLDM 2-Full	346M	2.18	1.62	6.92	0.43	-	-
AudioLDM 2-Full-Large	712M	2.11	1.54	8.29	0.44	3.56	3.19
Tango-full-FT	866M	2.51	1.15	7.87	0.54	3.81	3.77
Tango 2	866M	2.69	1.12	9.09	0.57	3.99	4.07

Citation

Please consider citing the following articles if you found our work useful:

@misc{majumder2024tango,
      title={Tango 2: Aligning Diffusion-based Text-to-Audio Generations through Direct Preference Optimization}, 
      author={Navonil Majumder and Chia-Yu Hung and Deepanway Ghosal and Wei-Ning Hsu and Rada Mihalcea and Soujanya Poria},
      year={2024},
      eprint={2404.09956},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}
@article{ghosal2023tango,
  title={Text-to-Audio Generation using Instruction Tuned LLM and Latent Diffusion Model},
  author={Ghosal, Deepanway and Majumder, Navonil and Mehrish, Ambuj and Poria, Soujanya},
  journal={arXiv preprint arXiv:2304.13731},
  year={2023}
}

Acknowledgement

We borrow the code in audioldm and audioldm_eval from the AudioLDM repositories. We thank the AudioLDM team for open-sourcing their code.

khushbubijawat/tango