: https://github.com/declare-lab/tango

: https://huggingface.co/datasets/declare-lab/audio-alpaca

: https://huggingface.co/declare-lab/tango2

: https://tango2-web.github.io/

# Text-to-Audio Generation using Instruction-Guided Latent Diffusion Model

Navonil Majumder  Singapore University of Technology and Design, Singapore ,
Chia-Yu Hung  Singapore University of Technology and Design, Singapore ,
Deepanway Ghosal  Singapore University of Technology and Design, Singapore ,
Wei-Ning Hsu  Individual Contributor, USA ,  Rada Mihalcea  University of
Michigan, USA and  Soujanya Poria  Singapore University of Technology and
Design, Singapore

(2024)

# Tango 2: Improving Diffusion-based Text-to-Audio Generation using Direct
Preference Optimization

Navonil Majumder  Singapore University of Technology and Design, Singapore ,
Chia-Yu Hung  Singapore University of Technology and Design, Singapore ,
Deepanway Ghosal  Singapore University of Technology and Design, Singapore ,
Wei-Ning Hsu  Individual Contributor, USA ,  Rada Mihalcea  University of
Michigan, USA and  Soujanya Poria  Singapore University of Technology and
Design, Singapore

(2024)

# Tango 2: Improving Diffusion-based Text-to-Audio Generation using Direct
Preference Optimization based Alignment

Navonil Majumder  Singapore University of Technology and Design, Singapore ,
Chia-Yu Hung  Singapore University of Technology and Design, Singapore ,
Deepanway Ghosal  Singapore University of Technology and Design, Singapore ,
Wei-Ning Hsu  Individual Contributor, USA ,  Rada Mihalcea  University of
Michigan, USA and  Soujanya Poria  Singapore University of Technology and
Design, Singapore

(2024)

# Tango 2: Enhancing Diffusion-based Text-to-Audio Generation through Direct
Preference Optimization-based Alignment

Navonil Majumder  Singapore University of Technology and Design, Singapore ,
Chia-Yu Hung  Singapore University of Technology and Design, Singapore ,
Deepanway Ghosal  Singapore University of Technology and Design, Singapore ,
Wei-Ning Hsu  Individual Contributor, USA ,  Rada Mihalcea  University of
Michigan, USA and  Soujanya Poria  Singapore University of Technology and
Design, Singapore

(2024)

# Tango 2: Aligning Diffusion-based Text-to-Audio Generative Models through
Direct Preference Optimization

Navonil Majumder  Singapore University of Technology and Design, Singapore ,
Chia-Yu Hung  Singapore University of Technology and Design, Singapore ,
Deepanway Ghosal  Singapore University of Technology and Design, Singapore ,
Wei-Ning Hsu  Individual Contributor, USA ,  Rada Mihalcea  University of
Michigan, USA and  Soujanya Poria  Singapore University of Technology and
Design, Singapore

(2024)

# Tango 2: Aligning Diffusion-based Text-to-Audio Generations through Direct
Preference Optimization

Navonil Majumder  Singapore University of Technology and Design, Singapore ,
Chia-Yu Hung  Singapore University of Technology and Design, Singapore ,
Deepanway Ghosal  Singapore University of Technology and Design, Singapore ,
Wei-Ning Hsu  Individual Contributor, USA ,  Rada Mihalcea  University of
Michigan, USA and  Soujanya Poria  Singapore University of Technology and
Design, Singapore

(2024)

###### Abstract.

Generative multimodal content is increasingly prevalent in much of the content
creation arena, as it has the potential to allow artists and media personnel
to create pre-production mockups by quickly bringing their ideas to life. The
generation of audio from text prompts is an important aspect of such processes
in the music and film industry. Many of the recent diffusion-based text-to-
audio models focus on training increasingly sophisticated diffusion models on
a large set of datasets of prompt-audio pairs. These models do not explicitly
focus on the presence of concepts or events and their temporal ordering in the
output audio with respect to the input prompt. Our hypothesis is focusing on
how these aspects of audio generation could improve audio generation
performance in the presence of limited data. As such, in this work, using an
existing text-to-audio model Tango, we synthetically create a preference
dataset where each prompt has a winner audio output and some loser audio
outputs for the diffusion model to learn from. The loser outputs, in theory,
have some concepts from the prompt missing or in an incorrect order. We fine-
tune the publicly available Tango text-to-audio model using diffusion-DPO
(direct preference optimization) loss on our preference dataset and show that
it leads to improved audio output over Tango and AudioLDM2, in terms of both
automatic- and manual-evaluation metrics.

Multimodal AI, Text-to-Audio Generation, Diffusion Models, Large Language
Models, Preference Optimization

††journalyear: 2024††ccs: Computing methodologies Natural language
processing††ccs: Information systems Multimedia information systems

##  1\. Introduction

Generative AI is increasingly turning into a mainstay of our daily lives, be
it directly through using ChatGPT (OpenAI, 2023c), GPT-4 (OpenAI, 2023b) in an
assistive capacity, or indirectly by consuming AI-generated memes, generated
using models like StableDiffusion (Rombach et al., 2022), DALL-E 3 (OpenAI,
2023a; Betker et al., [n. d.]), on social media platforms. Nonetheless, there
is a massive demand for AI-generated content across industries, especially in
the multimedia sector. Quick creation of audio-visual content or prototypes
would require an effective text-to-audio model along with text-to-image and
-video models. Thus, improving the fidelity of such models with respect to the
input prompts is paramount.

Recently, supervised fine-tuning-based direct preference optimization
(Rafailov et al., 2023) (DPO) has emerged as a cheaper and more robust
alternative to reinforcement learning with human feedback (RLHF) to align LLM
responses with human preferences. This idea is subsequently adapted for
diffusion models by Wallace et al. (2023) to align the denoised outputs to
human preferences. In this work, we employ this DPO-diffusion approach to
improve the semantic alignment between input prompt and output audio of a
text-to-audio model. Particularly, we fine-tune the publicly available text-
to-audio latent diffusion model Tango (Ghosal et al., 2023a) on our
synthesized preference dataset with DPO-diffusion loss. This preference
dataset contains diverse audio descriptions (_prompts_) with their respective
preferred (_winner_) and undesirable (_loser_) audios. The preferred audios
are supposed to perfectly reflect their respective textual descriptions,
whereas the undesirable audios have some flaws, such as some missing concepts
from the prompt or in an incorrect temporal order or high noise level. To this
end, we perturbed the descriptions to remove or change the order of certain
concepts and passed them to Tango to generate undesirable audios. Another
strategy that we adopted for undesirable audio generation was adversarial
filtering: generate multiple audios from the original prompt and choose the
audio samples with CLAP-score below a certain threshold. We call this
preference dataset Audio-alpaca. To mitigate the effect of noisy preference
pairs stemming from automatic generation, we further choose a subset of
samples for DPO fine-tuning based on certain thresholds defined on the CLAP-
score differential between preferred and undesirable audios and the CLAP-score
of the undesirable audios. This likely ensures a minimal proximity to the
input prompt, while guaranteeing a minimum distance between the preference
pairs.

We experimentally show that fine-tuning Tango on the pruned Audio-alpaca
yields Tango 2 that significantly surpasses Tango and AudioLDM2 in both
objective and human evaluations. Moreover, exposure to the contrast between
good and bad audio outputs during DPO fine-tuning likely allows Tango 2 to
better map the semantics of the input prompt into the audio space, despite
relying on the same dataset as Tango for synthetic preference data-creation.

The broad contributions of this paper are the following:

  1. (1)

We develop a cheap and effective heuristics for semi automatically creating a
preference dataset for text-to-audio generation;

  2. (2)

On the same note, we also share the preference dataset Audio-alpaca for text-
to-audio generation that may aid in the future development of such models;

  3. (3)

Despite not sourcing additional out-of-distribution text-audio pairs over
Tango, our model Tango 2 outperforms both Tango and AudioLDM2 on both
objective and subjective metrics;

  4. (4)

Tango 2 demonstrates the applicability of diffusion-DPO in audio generation.

##  2\. Related Work

Text-to-audio generation has garnered serious attention lately thanks to
models like AudioLDM (Liu et al., 2023a), Make-an-Audio (Huang et al., 2023),
Tango (Ghosal et al., 2023a), and Audiogen (Kreuk et al., 2022). These models
rely on diffusion architectures for audio generation from textual prompts.
Recently, AudioLM (Borsos et al., 2023) was proposed which utilizes the state-
of-the-art semantic model w2v-Bert (Chung et al., 2021) to generate semantic
tokens from audio prompts. These tokens condition the generation of acoustic
tokens, which are decoded using the acoustic model SoundStream (Zeghidour et
al., 2022) to produce audio. The semantic tokens generated by w2v-Bert are
crucial for conditioning the generation of acoustic tokens, subsequently
decoded by SoundStream.

AudioLDM (Liu et al., 2023a) is a text-to-audio framework that employs CLAP
(Wu et al., 2023), a joint audio-text representation model, and a latent
diffusion model (LDM). Specifically, an LDM is trained to generate latent
representations of melspectrograms obtained using a Variational Autoencoder
(VAE). During diffusion, CLAP embeddings guide the generation process. Tango
(Ghosal et al., 2023b) utilizes the pre-trained VAE from AudioLDM and replaces
the CLAP model with a fine-tuned large language model: FLAN-T5. This
substitution aims to achieve comparable or superior results while training
with a significantly smaller dataset.

In the realm of aligning generated audio with human perception, Liao et al.
(2024) recently introduced BATON, a framework that initially gathers pairs of
audio and textual prompts, followed by annotating them based on human
preference. This dataset is subsequently employed to train a reward model. The
reward generated by this model is then integrated into the standard diffusion
loss to guide the network, leveraging feedback from the reward model. However,
our approach significantly diverges from this work in two key aspects: 1) we
automatically construct a _pairwise_ preference dataset, referred to as Audio-
alpaca, utilizing various techniques such as LLM-guided prompt perturbation
and re-ranking of generated audio from Tango using CLAP scores, and 2) we then
train Tango on Audio-alpaca using diffusion-DPO to generate audio samples
preferred by human perception.

##  3\. Background

###  3.1. Overview of Tango

Tango, proposed by Ghosal et al. (2023a), primarily relies on a latent
diffusion model (LDM) and an instruction-tuned LLM for text-to-audio
generation. It has three major components:

  1. (1)

Textual-prompt encoder

  2. (2)

Latent diffusion model (LDM)

  3. (3)

Audio VAE and Vocoder

The textual-prompt encoder encodes the input description of the audio.
Subsequently, the textual representation is used to construct a latent
representation of the audio or audio prior from standard Gaussian noise, using
reverse diffusion. Thereafter, the decoder of the mel-spectrogram VAE
constructs a mel-spectrogram from the latent audio representation. This mel-
spectrogram is fed to a vocoder to generate the final audio.

####  3.1.1. Textual Prompt Encoder

Tango utilizes the pre-trained LLM Flan-T5-Large (780M) (Chung et al., 2022)
as the text encoder ($E_{text}$) to acquire text encoding
$\tau\in\mathbb{R}^{L\times d_{text}}$, where $L$ and $d_{text}$ represent the
token count and token-embedding size, respectively.

####  3.1.2. Latent Diffusion Model

For ease of understanding, we briefly introduce the LDM of Tango in this
section. The latent diffusion model (LDM) (Rombach et al., 2022) in Tango is
derived from the work of Liu et al. (2023b), aiming to construct the audio
prior $x_{0}$ guided by text encoding $\tau$. This task essentially involves
approximating the true prior $q(x_{0}|\tau)$ using parameterized
$p_{\theta}(x_{0}|\tau)$.

LDM achieves this objective through forward and reverse diffusion processes.
The forward diffusion represents a Markov chain of Gaussian distributions with
scheduled noise parameters $0<\beta_{1}<\beta_{2}<\cdots<\beta_{N}<1$,
facilitating the sampling of noisier versions of $x_{0}$:

(1) $\displaystyle q(x_{n}|x_{n-1})$
$\displaystyle=\mathcal{N}(\sqrt{1-\beta_{n}}x_{n-1},\beta_{n}\mathbf{I}),$
(2) $\displaystyle q(x_{n}|x_{0})$
$\displaystyle=\mathcal{N}(\sqrt{\overline{\alpha}_{n}}x_{0},(1-\overline{\alpha}_{n})\mathbf{I}),$

where $N$ is the number of forward diffusion steps, $\alpha_{n}=1-\beta_{n}$,
and $\overline{\alpha}_{n}=\prod_{i=1}^{n}\alpha_{n}$. Song et al. (2020) show
that Eq. 2 conveniently follows from Eq. 1 through reparametrization trick
that allows direct sampling of any $x_{n}$ from $x_{0}$ via a non-Markovian
process:

(3)
$x_{n}=\sqrt{\overline{\alpha}_{n}}x_{0}+(1-\overline{\alpha}_{n})\epsilon,$

where the noise term $\epsilon\sim\mathcal{N}(\mathbf{0},\mathbf{I})$. The
final step of the forward process yields
$x_{N}\sim\mathcal{N}(\mathbf{0},\mathbf{I})$.

The reverse process denoises and reconstructs $x_{0}$ through text-guided
noise estimation ($\hat{\epsilon}_{\theta}$) using loss

(4)
$\displaystyle\mathcal{L}_{LDM}=\sum_{n=1}^{N}\gamma_{n}\mathbb{E}_{\epsilon_{n}\sim\mathcal{N}(\mathbf{0},\mathbf{I}),x_{0}}||\epsilon_{n}-\hat{\epsilon}_{\theta}^{(n)}(x_{n},\tau)||_{2}^{2},$

where $x_{n}$ is sampled according to Eq. 3 using standard normal noise
$\epsilon_{n}$, $\tau$ represents the text encoding for guidance, and
$\gamma_{n}$ denotes the weight of reverse step $n$ (Hang et al., 2023),
interpreted as a measure of signal-to-noise ratio (SNR) relative to
$\alpha_{1:N}$. The estimated noise is then employed for the reconstruction of
$x_{0}$:

(5) $\displaystyle p_{\theta}(x_{0:N}|\tau)$
$\displaystyle=p(x_{N})\prod_{n=1}^{N}p_{\theta}(x_{n-1}|x_{n},\tau),$ (6)
$\displaystyle p_{\theta}(x_{n-1}|x_{n},\tau)$
$\displaystyle=\mathcal{N}(\mu^{(n)}_{\theta}(x_{n},\tau),\tilde{\beta}^{(n)}),$
(7) $\displaystyle\mu_{\theta}^{(n)}(x_{n},\tau)$
$\displaystyle=\frac{1}{\sqrt{\alpha_{n}}}[x_{n}-\frac{1-\alpha_{n}}{\sqrt{1-\overline{\alpha}_{n}}}\hat{\epsilon}_{\theta}^{(n)}(x_{n},\tau)],$
(8) $\displaystyle\tilde{\beta}^{(n)}$
$\displaystyle=\frac{1-\bar{\alpha}_{n-1}}{1-\bar{\alpha}_{n}}\beta_{n}.$

The parameterization of noise estimation $\hat{\epsilon}_{\theta}$ involves
utilizing U-Net (Ronneberger et al., 2015), incorporating a cross-attention
component to integrate the textual guidance $\tau$.

####  3.1.3. Audio VAE and Vocoder

The audio variational auto-encoder (VAE) (Kingma and Welling, 2013) compresses
the mel-spectrogram of an audio sample, $m\in\mathbb{R}^{T\times F}$, into an
audio prior $x_{0}\in\mathbb{R}^{C\times T/r\times F/r}$, where $C$, $T$, $F$,
and $r$ denote the number of channels, time-slots, frequency-slots, and
compression level, respectively. The latent diffusion model (LDM) reconstructs
the audio prior $\hat{x}_{0}$ using input-text guidance $\tau$. Both the
encoder and decoder consist of ResUNet blocks (Kong et al., 2021) and are
trained by maximizing the evidence lower-bound (ELBO) (Kingma and Welling,
2013) and minimizing adversarial loss (Isola et al., 2016). Tango utilizes the
checkpoint of the audio VAE provided by Liu et al. (2023b).

As a vocoder to convert the audio-VAE decoder-generated mel-spectrogram into
audio, Tango employs HiFi-GAN (Kong et al., 2020) which is also utilized by
Liu et al. (2023b).

Finally, Tango utilizes a data augmentation method that merges two audio
signals while considering human auditory perception. This involves computing
the pressure level of each audio signal and adjusting the weights of the
signals to prevent the dominance of the signal with higher pressure level over
the one with lower pressure level. Specifically, when fusing two audio
signals, the relative pressure level is computed using the following equation:

(9) $\displaystyle p=(1+10^{\frac{G_{1}-G_{2}}{20}})^{-1},$

Here $G_{1}$ and $G_{2}$ are the pressure levels of signal $x_{1}$ and
$x_{2}$. Then the audio signals are mixed using the equation below:

(10)
$\displaystyle\text{mix}(x_{1},x_{2})=\frac{px_{1}+(1-p)x_{2}}{\sqrt{p^{2}+(1-p)^{2}}}.$

The denominator is to account for the fact that the energy of a sound wave is
proportional to the square of its amplitude as shown in Tokozume et al.
(2017). Note that in this augmentation, textual prompts are also concatenated.

###  3.2. Preference Optimization for Language Models

Tuning Large Language Models (LLMs) to generate responses according to human
preference has been a great interest to the ML community. The most popular
approach for aligning language models to human preference is reinforcement
learning with human feedback (RLHF). It comprises the following steps
(Rafailov et al., 2023):

##### Supervised Fine Tuning (SFT)

First, the pre-trained LLM undergoes supervised fine-tuning on high-quality
downstream tasks to obtain the fine-tuned model $\pi^{SFT}$.

Figure 1. An illustration of our pipeline for text-to-audio alignment. The top
part depicts the preference dataset creation where three strategies are
deployed to generate the undesirable audio outputs to the input prompts. These
samples are further filtered to form Audio-alpaca. This preference dataset is
finally used to align Tango using DPO-diffusion loss (Eq. 17), resulting Tango
2.

##### Reward Modeling

Next, $\pi^{SFT}$ is prompted with an input $\tau$ to generate multiple
responses. These responses are then shown to human labelers to rank. Once such
a rank is obtained, $x^{w}\succ x^{l}\mid\tau$ indicating $x^{w}$ is preferred
over $x^{l}$, the task is to model these preferences. Among several popular
choices of preference modeling, Bradley-Terry (BT) is the most popular one
which relies on the equation below:

(11) $\displaystyle p^{*}(x^{w}\succ
x^{l}\mid\tau)=\frac{\exp(r^{*}(\tau,x^{w}))}{\exp(r^{*}(\tau,x^{w}))+\exp(r^{*}(\tau,x^{l}))}$

The overall idea is to learn the human preference distribution $p^{*}$.
$r^{*}(\tau,x)$ is a latent reward function that generates the preferences.
With a static dataset created by human annotators,
$\mathcal{D}=\left\\{\left(\tau_{(i)},x^{w}_{(i)},x^{l}_{(i)}\right)\right\\}_{i=1}^{N}$,
one can train a reward model $r_{\phi}(\tau,x)$ using maximum likelihood
estimation. The negative log-likelihood loss of this training can be written
as follows:

(12)
$\displaystyle\mathcal{L}_{R}(r_{\phi},\mathcal{D})=-\mathbb{E}_{(\tau,x^{w},x^{l})\sim\mathcal{D}}\left[\log\sigma(r_{\phi}(\tau,x^{w})-r_{\phi}(\tau,x^{l}))\right]$

This formulation considers framing the problem as a binary classification
problem.

##### RL Optimization

The final step is to leverage $r_{\phi}(\tau,x)$ to feedback the language
model. As explained by Rafailov et al. (2023), this can be embedded into the
following learning objective:

(13)
$\displaystyle\max_{\pi_{\theta}}\mathbb{E}_{\tau\sim\mathcal{D},x\sim\pi_{\theta}(x|\tau)}\left[r_{\phi}(\tau,x)\right]-\beta
D_{KL}\left[\pi_{\theta}(x|\tau)\parallel\pi_{\text{ref}}(x|\tau)\right]$

Here, $\pi_{\text{ref}}$ represents the reference model, which in this context
is the supervised fine-tuned model denoted as $\pi^{\text{SFT}}$.
$\pi_{\theta}$ stands for the policy language model, intended for enhancement
based on feedback from $r_{\phi}(\tau,x)$. $\beta$ governs $\pi_{\theta}$ to
prevent significant divergence from $\pi_{\text{ref}}$. This control is
crucial as it ensures that the model stays close to the distributions upon
which $r_{\phi}(\tau,x)$ was trained. Since the outputs from LLM are discrete,
Eq. 13 becomes non-differentiable, necessitating reinforcement learning
methods like PPO to address this objective.

##  4\. Methodology

The two major parts of our approach (i) creation of preference dataset Audio-
alpaca and (ii) DPO for alignment are outlined in Fig. 1.

###  4.1. Creation of Audio-alpaca

####  4.1.1. Audio Generation from Text Prompts

Our first step is to create audio samples from various text prompts with the
pre-trained Tango model. We follow three different strategies as follows:

#####  Strategy 1: Multiple Inferences from the same Prompt

In the first setting, we start by selecting a subset of diverse captions from
the training split of the AudioCaps dataset. We use the sentence embedding
model gte-large111hf.co/thenlper/gte-large (Li et al., 2023) to compute dense
embedding vectors of all the captions in the training set. We then perform
K-Means clustering on the embedded vectors with 200 clusters. Finally, we
select 70 samples from each cluster to obtain a total of 14,000 captions. We
denote the selected caption set as $\mathcal{T}_{1}$.

The captions selected through the above process constitute the seed caption
set. Now, we follow two settings to generate audio samples from these
captions:

  1. (1)

Strategy 1.1: Prompt Tango-full-FT with the caption to generate four different
audio samples with 5, 25, 50, and 100 denoising steps. All samples are created
with a guidance scale of 3.

  2. (2)

Strategy 1.2: Prompt Tango-full-FT with the caption to generate four different
audio samples each with 50 denoising steps. All samples are created with a
guidance scale of 3.

In summary, we obtain $(\tau,x_{1},x_{2},x_{3},x_{4})$ from _Strategy 1_ ,
where $\tau$ denotes the caption from $\mathcal{T}_{1}$ and $x_{i}$ denotes
the audios generated from $\tau$.

Table 1. Prompts used in GPT-4 for creating the perturbations and the
corresponding output.

Strategy and Original Caption |  Prompt to GPT-4 |  Output from GPT-4
---|---|---
Strategy 2: Perturbed Prompts |  I have an audio clip for which the original caption is as follows: People cheering and race cars racing by. Can you generate five candidate captions that would satisfy the following requirements: |  1\. Crowd applauding and bicycles speeding past.
People cheering and race cars racing by. |  i) Would be closely related to the audio clip and the original caption. |  2\. Spectators clapping and trains rushing by.
|  ii) Would contain inaccuracies in terms of describing the audio clip i.e. they would be somewhat wrong captions for the audio clip. |  3\. Audience cheering and horses galloping past.
|  iii) However, the new captions should not be completely unrelated. Always keep some concepts from the original caption in the new one. |  4\. Fans shouting and airplanes flying by.
|  iv) Would be of similar length to the original caption. |  5\. Group celebrating and motorcycles revving past.
|  Generate only the captions in separate lines so that I can programmatically extract them later. |
Strategy 3: Temporally-Perturbed Prompts |  I have an audio clip for which the original caption is as follows: A man is speaking then a sewing machine briefly turns on and off. Can you generate five candidate captions that would satisfy the following requirements: |  1\. A sewing machine briefly starts, then a man begins speaking.
A man is speaking then a sewing machine briefly turns on and off. |  i) Would be closely related to the audio clip and the original caption. |  2\. The sound of a sewing machine is heard after a man’s voice.
|  ii) Would have a change of order of the events described in the original caption. |  3\. A man’s voice is followed by the noise of a blender.
|  iii) Would contain inaccuracies in terms of describing the audio clip i.e. they would be somewhat wrong captions for the audio clip. |  4\. A woman speaks and then a sewing machine is turned on.
|  iv) However, the new captions should not be completely unrelated. Always keep some concepts from the original caption in the new one. |  5\. The noise of a sewing machine is interrupted by a man talking.
|  v) Would be of similar length to the original caption. |
|  Generate only the captions in separate lines so that I can programmatically extract them later. |

#####  Strategy 2: Inferences from Perturbed Prompts

We start from the selected set $\mathcal{T}_{1}$ and make perturbations of the
captions using the GPT-4 language model (OpenAI, 2023b). For a caption $\tau$
from $\mathcal{T}_{1}$, we denote $\tau_{1}$ as the perturbed caption
generated from GPT-4. We add specific instructions in our input prompt to make
sure that $\tau_{1}$ is semantically or conceptually close to $\tau$. We show
an illustration of the process in Table 1. In practice, we create five
different perturbed $\tau_{1}$ for each $\tau$ from GPT-4, as shown in Table
1.

We then prompt Tango-full-FT with $\tau$ and $\tau_{1}$ to generate audio
samples $x_{\tau}$ and $x_{\tau_{1}}$. We use 50 denoising steps with a
guidance scale of 3 to generate these audio samples.

To summarize, we obtain $(\tau,x_{\tau},x_{\tau_{1}})$ from _Strategy 2_. Note
that, we considered $\tau_{1}$ only to generate the audio sample
$x_{\tau_{1}}$. We do not further consider $\tau_{1}$ while creating the
preference dataset.

#####  Strategy 3: Inferences from Temporally Perturbed Prompts

This strategy is aimed at prompts that describe some composition of sequence
and simultaneity of events. To identify such prompts in AudioCaps’ training
dataset, as a heuristics, we look for the following keywords in a prompt:
before, after, then, or followed. We denote the set of such prompts as
$\mathcal{T}_{2}$.

For each caption $\tau_{2}$ in $\mathcal{T}_{2}$, we then prompt GPT-4 to
create a set of temporal perturbations. The temporal perturbations include
changing the order of the events in the original caption, or introducing a new
event or removing an existing event, etc. We aim to create these temporal
perturbations by providing specific instructions to GPT-4, which we also
illustrate in Table 1.

We denote the temporally perturbed caption as $\tau_{2}$. We then follow the
same process as mentioned earlier in Strategy 2 to create the audio samples
$x_{\tau}$ and $x_{\tau_{2}}$. Finally, we pair the
$(\tau,x_{\tau},x_{\tau_{2}})$ samples from this strategy. Analogous to the
previous strategy, the $\tau_{2}$ is only used to create the $x_{\tau_{2}}$,
and is not used anywhere else for preference data creation.

We collect the paired text prompt and audio samples from the three strategies
and denote it overall as $(\tau,\langle x\rangle)$, where $\langle x\rangle$
indicates the set of 4 or 2 generated audio samples depending upon the
corresponding strategy.

####  4.1.2. Ranking and Preference-Data Selection

We first create a pool of candidate preference data for the three strategies
as follows:

##### For Strategy 1

Let’s assume we have an instance $(\tau,\langle x\rangle)$ from Strategy 1. We
first compute the CLAP matching score following Wu et al. (2023) between
$\tau$ and all the four audio samples in $\langle x\rangle$. We surmise that
the sample in $\langle x\rangle$ that has the highest matching score with
$\tau$ is most aligned with $\tau$, compared to the other three audio samples
that have a relatively lower matching score. We consider this audio with the
highest matching score as the winning sample $x^{w}$ and the other three audio
samples as the losing sample $x^{l}$. In this setting, we can thus create a
pool of three preference data points: $(\tau,x^{w},x^{l})$, for the three
losing audio samples $x^{l}$.

##### For Strategy 2 and 3

Let’s assume we have an instance $(\tau,\langle x\rangle)$ from Strategy 2 or
3. We compute the CLAP matching score between i) $\tau$ with $x_{\tau}$, and
ii) $\tau$ with the $x_{\tau_{1}}$ or $x_{\tau_{2}}$, corresponding to the
strategy. We consider only those instances where the CLAP score of i) is
higher than the CLAP score of ii). For these instances, we use $x_{\tau}$ as
the winning audio $x^{w}$ and $x_{\tau_{1}}$ or $x_{\tau_{2}}$ as the losing
audio $x^{l}$ to create the preference data point: $(\tau,x^{w},x^{l})$.

##### Final Selection

We want to ensure that the winning audio sample $x^{w}$ is strongly aligned
with the text prompt $\tau$. At the same time, the winning audio sample should
have a considerably higher alignment with the text prompt than the losing
audio sample. We use the CLAP score as a measurement to fulfill these
conditions. The CLAP score is measured using cosine similarity between the
text and audio embeddings, where higher scores indicate higher alignment
between the text and the audio. We thus use the following conditions to select
a subset of instances from the pool of preference data:

  1. (1)

The winning audio must have a minimum CLAP score of $\alpha$ with the text
prompt to ensure that the winning audio is strongly aligned with the text
prompt.

  2. (2)

The losing audio must have a minimum CLAP score of $\beta$ with the text
prompt to ensure that we have semantically close negatives that are useful for
preference modeling.

  3. (3)

The winning audio must have a higher CLAP score than the losing audio w.r.t to
the text prompt.

  4. (4)

We denote $\Delta$ to be the difference in CLAP score between the text prompt
with the winning audio222In our paper, we employ the terms ”winner” and
”preferred” interchangeably. Likewise, we use ”loser” and ”undesirable”
interchangeably throughout the text. and the text prompt with the losing
audio. The $\Delta$ should lie between certain thresholds, where the lower
bound will ensure that the losing audio is not too close to the winning audio,
and the upper bound will ensure that the losing audio is not too undesirable.

We use an _ensemble filtering_ strategy based on two different CLAP models:
630k-audioset-best and 630k-best (Wu et al., 2023). This can reduce the effect
of noise from individual CLAP checkpoints and increase the robustness of the
selection process. In this strategy, samples are included in our preference
dataset if and only if they satisfy all the above conditions based on CLAP
scores from both of the models. We denote the conditional scores mentioned
above as $\alpha_{1},\beta_{1},\Delta_{1}$, and
$\alpha_{2},\beta_{2},\Delta_{2}$ for the two CLAP models, respectively. Based
on our analysis of the distribution of the CLAP scores as shown in Figure 2,
we choose their values as follows: $\alpha_{1}=0.45,\alpha_{2}=0.60$,
$\beta_{1}=0.40,\beta_{2}=0.0$, $0.05\leq\Delta_{1}\leq 0.35$, and
$0.08\leq\Delta_{2}\leq 0.70$.

Finally, our preference dataset Audio-alpaca has a total of $\approx$ 15k
samples after this selection process. We report the distribution of Audio-
alpaca in Table 2.

Figure 2. The distribution of $\alpha_{1}$ and $\Delta_{1}$ scores in the
unfiltered dataset. We see that for an unfiltered dataset: i) the winner audio
sample is not always strongly aligned to the text prompt in the $\alpha_{1}$
plot; ii) winner and loser audio samples can be too close in the $\Delta_{1}$
plot. We thus choose the values of our $\alpha_{1}$, $\Delta_{1}$ and other
selection parameters to ensure the filtered dataset is less noisy with more
separation between the winner and loser audios.  Table 2. Statistic of Audio-
alpaca

Strategy | # Samples | Avg. Winner Score | Avg. Loser Score | Avg. Delta
---|---|---|---|---
Inference w/ Different Denoising Steps (Strategy 1.1) | 3004 | 0.645 | 0.447 | 0.198
Inference w/ Same Denoising Steps (Strategy 1.2) | 2725 | 0.647 | 0.494 | 0.153
GPT-4 Perturbed Prompts (Strategy 2) | 4544 | 0.641 | 0.425 | 0.216
GPT-4 Temporally Perturbed Prompts (Strategy 3) | 4752 | 0.649 | 0.458 | 0.191
Overall | 15025 | 0.645 | 0.452 | 0.193

###  4.2. DPO for Preference Modeling

As opposed to RLHF, recently DPO has emerged as a more robust and often more
practical and straightforward alternative for LLM alignment that is based on
the very same BT preference model (Eq. 11). In contrast with supervised fine-
tuning (SFT) that only optimizes for the desirable (_winner_) outputs, the DPO
objective also allows the model to learn from undesirable (_loser_) outputs,
which is key in the absence of a high-quality reward model, as required for
RLHF. To this end, the DPO objective is derived by substituting the globally
optimal reward—obtained by solving Eq. 13—in the negative log-likelihood (NLL)
loss (Eq. 12).

This success spurred on Wallace et al. (2023) to bring the same benefits of
DPO to diffusion networks. However, unlike DPO, the goal for diffusion
networks is to maximize the following learning objective (Eq. 14) with a
reward (Eq. 15) defined on the entire diffusion path $x_{0:N}$:

$\displaystyle\max_{\pi_{\theta}}\mathbb{E}_{\tau\sim\mathcal{D},x_{0:N}\sim\pi_{\theta}(x_{0:N}|\tau)}$
$\displaystyle[r(\tau,x_{0})]$ (14) $\displaystyle-\beta D_{\text{KL}}$
$\displaystyle[\pi_{\theta}(x_{0:N}|\tau)||\pi_{\text{ref}}(x_{0:N}|\tau)].$
(15) $\displaystyle
r(\tau,x_{0}):=\mathbb{E}_{\pi_{\theta}(x_{1:N}|x_{0},\tau)}$
$\displaystyle[R(\tau,x_{0:N})],$

Solving this objective and substituting the optimal reward in the NLL loss
(Eq. 12) yields the following DPO objective for diffusion:

$\displaystyle\mathcal{L}_{\text{DPO-
Diff}}=-\mathbb{E}_{(\tau,x_{0}^{w},x_{0}^{l})\sim\mathcal{D_{\text{pref}}}}\log\sigma($
(16)
$\displaystyle\beta\mathbb{E}_{x^{*}_{1:N}\sim\pi_{\theta}(x^{*}_{1:N}|x^{*}_{0},\tau)}[\log\frac{\pi_{\theta}(x^{w}_{0:N}|\tau)}{\pi_{\text{ref}}(x^{w}_{0:N}|\tau)}-\log\frac{\pi_{\theta}(x^{l}_{0:N}|\tau)}{\pi_{\text{ref}}(x^{l}_{0:N}|\tau)}]).$

Now, applying Jensen’s inequality by taking advantage of the convexity of
$-\log\sigma$ allows the inner expectation to be pushed outside. Subsequently,
approximating the denoising process with the forward process yields the
following final form in terms of the L2 noise-estimation losses from LDM (Eq.
4):

$\displaystyle\mathcal{L}_{\text{DPO-Diff}}:=$
$\displaystyle-\mathbb{E}_{n,\epsilon^{w},\epsilon^{l}}\log\sigma(-\beta
N\omega(\lambda_{n})(||\epsilon_{n}^{w}-\hat{\epsilon}_{\theta}^{(n)}(x_{n}^{w},\tau)||_{2}^{2}$
$\displaystyle-||\epsilon_{n}^{w}-\hat{\epsilon}_{\text{ref}}^{(n)}(x_{n}^{w},\tau)||_{2}^{2}$
(17)
$\displaystyle-(||\epsilon_{n}^{l}-\hat{\epsilon}_{\theta}^{(n)}(x_{n}^{l},\tau)||_{2}^{2}-||\epsilon_{n}^{l}-\hat{\epsilon}_{\text{ref}}^{(n)}(x_{n}^{l},\tau)||_{2}^{2})),$

where $\mathcal{D}_{\text{pref}}:=\\{(\tau,x_{0}^{w},x_{0}^{l})\\}$ is our
preference dataset Audio-alpaca, $\tau$, $x^{w}_{0}$, and $x^{l}_{0}$ being
the input prompt, preferred, and undesirable output, respectively.
Furthermore, $n\sim\mathcal{U}(0,N)$ is the diffusion step,
$\epsilon_{n}^{*}\sim\mathcal{N}(0,\mathbb{I})$ and $x_{n}^{*}$ are noise and
noisy posteriors, respectively, at some step $n$. $\lambda_{n}$ is the signal-
to-noise ratio (SNR) and $\omega(\lambda_{n})$ is a weighting function defined
on SNR. We use Tango-full-FT as our reference model through its noise
estimation $\hat{\epsilon}_{\text{ref}}$.

##  5\. Experiments

###  5.1. Datasets and Training Details

We fine-tuned our model starting from the Tango-full-FT checkpoint on our
preference dataset Audio-alpaca.

As mentioned earlier in Section 4.1.2, we have a total of 15,025 preference
pairs in Audio-alpaca, which we use for fine-tuning. We use AdamW (Loshchilov
and Hutter, 2017) with a learning rate of 9.6e-7 and a linear learning-rate
scheduler for fine-tuning. Following Wallace et al. (2023), we set the $\beta$
in DPO loss (Eq. 17) to 2000. We performed 1 epoch of supervised fine-tuning
on the prompt and the preferred audio as training samples, followed by 4
epochs of DPO. The entirety of the fine-tuning was executed on two A100 GPUs
which takes about 3.5 hours in total. We use a per GPU batch size of 4 and a
gradient accumulation step of 4, resulting in an effective batch size of 32.

###  5.2. Baselines

We primarily compare Tango 2 to three strong baselines:

  1. (1)

AudioLDM (Liu et al., 2023a): A text-to-audio model that uses CLAP (Wu et al.,
2023), a joint audio-text representation model, and a latent diffusion model
(LDM). Specifically, the LDM is trained to generate the latent representations
of melspectrograms obtained from a pre-trained Variational Autoencoder (VAE).
During diffusion, CLAP text-embeddings guide the generation process.

  2. (2)

AudioLDM2 (Liu et al., 2023c): An any-to-audio framework which uses language
of audio (LOA) as a joint encoding of audio, text, image, video, and other
modalities. Audio modality is encoded into LOA using a self-supervised masked
auto-encoder. The remaining modalities, including audio again, are mapped to
LOA through a composition of GPT-2 (Radford et al., 2019) and ImageBind
(Girdhar et al., 2023). This joint encoding is used as a conditioning in the
diffusion network for audio generation.

  3. (3)

Tango (Ghosal et al., 2023a): Utilizes the pre-trained VAE from AudioLDM but
replaces the CLAP text-encoder with an instruction-tuned large language model:
FLAN-T5. As compared to AudioLDM, its data-augmentation strategy is also
cognizant of the audio pressure levels of the source audios. These innovations
attain comparable or superior results while training on a significantly
smaller dataset.

Baton (Liao et al., 2024) represents another recent approach in human
preference based text-to-audio modeling. It trains a reward model to maximize
rewards through supervised fine-tuning, aiming to maximize the probability of
generating audio from a textual prompt. As discussed in Section 2, Baton’s
reward model is not trained using the pairwise preference objective presented
in Equation 12. In this approach, each text ($\tau$) and audio ($x$) pair is
classified as 1 or 0, indicating whether human annotators favored the text-
audio pair or not. Subsequently, this reward is incorporated into the
generative objective function of the diffusion. This methodology stands in
contrast to the prevailing approach in LLM alignment research. As of now,
neither the dataset nor the code has been made available for comparison.

Table 3. Text-to-audio generation results on AudioCaps evaluation set. Due to time and budget constraints, we could only subjectively evaluate AudioLDM 2-Full-Large and Tango-full-FT. Notably these two models are considered open-sourced SOTA models for text-to-audio generation as reported in (Vyas et al., 2023). Model | #Parameters | Objective | Subjective
---|---|---|---
FAD $\downarrow$ | KL $\downarrow$ | IS $\uparrow$ | CLAP $\uparrow$ | OVL $\uparrow$ | REL $\uparrow$
AudioLDM-M-Full-FT |  $416$M | $2.57$ | $1.26$ | $8.34$ | $0.43$ | $-$ | $-$
AudioLDM-L-Full |  $739$M | $4.18$ | $1.76$ | $7.76$ | $0.43$ | $-$ | $-$
AudioLDM 2-Full |  $346$M | $2.18$ | $1.62$ | $6.92$ | $0.43$ | $-$ | $-$
AudioLDM 2-Full-Large |  $712$M | 2.11 | $1.54$ | $8.29$ | $0.44$ | $3.56$ | $3.19$
Tango-full-FT |  $866$M | $2.51$ | $1.15$ | $7.87$ | $0.54$ | $3.81$ | $3.77$
Tango 2 |  $866$M | $2.69$ | 1.12 | 9.09 | 0.57 | 3.99 | 4.07
w/o Strategy 2 & 3 |  $866$M | $2.64$ | $1.13$ | $8.06$ | $0.54$ | $-$ | $-$
w/o Strategy 1 | $866M$ | $2.47$ | $1.13$ | $8.58$ | $0.56$ | $-$ | $-$
w/o Strategy 2 |  $866$M | $2.28$ | 1.12 | $8.38$ | $0.55$ | $-$ | $-$
w/o Strategy 3 |  $866$M | $2.46$ | $1.13$ | $8.63$ | $0.56$ | $-$ | $-$

###  5.3. Evaluation Metrics

##### Objective Metrics

We evaluated the text-to-audio generation using the standard Frechet Audio
Distance (FAD), KL divergence, Inception score (IS), and CLAP score (Liu et
al., 2023a). _FAD_ is adapted from Frechet Inception Distance (FID) and
measures the distribution-level gap between generated and reference audio
samples. _KL divergence_ is an instance-level reference-dependent metric that
measures the divergence between the acoustic event posteriors of the ground
truth and the generated audio sample. FAD and KL are computed using PANN, an
audio-event tagger. _IS_ evaluates the specificity and coverage of a set of
samples, not needing reference audios. IS is inversely proportional to the
entropy of the instance posteriors and directly proportional to the entropy of
the marginal posteriors. _CLAP score_ is defined as the cosine similarity
between the CLAP encodings of an audio and its textual description. We
borrowed the AudioLDM evaluation toolkit (Liu et al., 2023a) for the
computation of FAD, IS, and KL scores.

##### Subjective Metrics

Our subjective assessment examines two key aspects of the generated audio:
overall audio quality (OVL) and relevance to the text input (REL), mirroring
the approach outlined in the previous works, such as, (Ghosal et al., 2023a;
Vyas et al., 2023). The OVL metric primarily gauges the general sound quality,
clarity, and naturalness irrespective of its alignment with the input prompt.
Conversely, the REL metric assesses how well the generated audio corresponds
to the given text input. Annotators were tasked with rating each audio sample
on a scale from 1 (least) to 5 (highest) for both OVL and REL. This evaluation
was conducted on a subset of 50 randomly-selected prompts from the AudioCaps
test set, with each sample being independently reviewed by at least four
annotators. Please refer to the supplementary material for more details on the
evaluation instructions and evaluators.

Table 4. Objective evaluation results for audio generation in the presence of
multiple concepts or a single concept in the text prompt in the AudioCaps test
set.

Model | Multiple Events/Concepts | Single Event/Concept
---|---|---
Objective | Subjective | Objective | Subjective
FAD $\downarrow$ | KL $\downarrow$ | IS $\uparrow$ | CLAP $\uparrow$ | OVL $\uparrow$ | REL $\uparrow$ | FAD $\downarrow$ | KL $\downarrow$ | IS $\uparrow$ | CLAP $\uparrow$ | OVL $\uparrow$ | REL $\uparrow$
AudioLDM 2-Full | 2.03 | 1.64 | 7.88 | 0.43 | $-$ | $-$ | 7.93 | 1.24 | 4.50 | 0.47 | $-$ | $-$
AudioLDM 2-Full-Large | 2.33 | 1.58 | 8.14 | 0.44 | 3.54 | 3.16 | 5.82 | 1.09 | 6.60 | 0.49 | 3.65 | 3.41
Tango-full-FT | 2.69 | 1.16 | 7.85 | 0.54 | 3.83 | 3.80 | 7.52 | 1.01 | 6.41 | 0.51 | 3.67 | 3.49
Tango 2 | 2.60 | 1.11 | 8.98 | 0.57 | 3.99 | 4.07 | 5.48 | 1.00 | 4.95 | 0.52 | 3.95 | 4.10

Table 5. Objective evaluation results for audio generation in the presence of
temporal events or non-temporal events in the text prompt in the AudioCaps
test set.

Model | Temporal Events | Non Temporal Events
---|---|---
Objective | Subjective | Objective | Subjective
FAD $\downarrow$ | KL $\downarrow$ | IS $\uparrow$ | CLAP $\uparrow$ | OVL $\uparrow$ | REL $\uparrow$ | FAD $\downarrow$ | KL $\downarrow$ | IS $\uparrow$ | CLAP $\uparrow$ | OVL $\uparrow$ | REL $\uparrow$
AudioLDM 2-Full | 1.95 | 1.71 | 6.37 | 0.41 | $-$ | $-$ | 2.38 | 1.56 | 7.38 | 0.44 | $-$ | $-$
AudioLDM 2-Full-Large | 2.39 | 1.65 | 6.10 | 0.42 | 3.35 | 2.82 | 2.68 | 1.46 | 8.12 | 0.46 | 3.79 | 3.62
Tango-full-FT | 2.55 | 1.16 | 5.82 | 0.55 | 3.83 | 3.67 | 3.04 | 1.15 | 7.70 | 0.53 | 3.78 | 3.88
Tango 2 | 3.29 | 1.07 | 6.88 | 0.58 | 3.92 | 3.99 | 2.84 | 1.16 | 8.62 | 0.55 | 4.05 | 4.16

###  5.4. Main Results

We report the comparative evaluations of Tango 2 against the baselines Tango
(Ghosal et al., 2023a) and AudioLDM2 (Liu et al., 2023c) in Table 3. For a
fair comparison, we used exactly 200 inference steps in all our experiments.
Tango and Tango 2 were evaluated with a classifier-free guidance scale of 3
while AudioLDM2 uses a default guidance scale of 3.5. We generate only one
sample per text prompt.

##### Objective Evaluations

Tango 2 achieves notable improvements in objective metrics, with scores of
2.69 for FAD, 1.12 for KL, 9.09 for IS, and 0.57 for CLAP. While FAD, KL, and
IS assess general naturalness, diversity, and audio quality, CLAP measures the
semantic alignment between the input prompt and the generated audio. As
documented in Melechovsky et al. (2024), enhancing audio quality typically
relies on improving the pre-training process of the backbone, either through
architectural modifications or by leveraging larger or refined datasets.
However, in our experiments, we observe enhanced audio quality in two out of
three metrics, specifically KL and IS. Notably, Tango 2 also significantly
outperforms various versions of AudioLDM and AudioLDM2 on these two metrics.

On the other hand, we note a substantial enhancement in the CLAP score. CLAP
score is particularly crucial in our experimental setup as it directly
measures the semantic alignment between the textual prompt and the generated
audio. This outcome suggests that DPO-based fine-tuning on the preference data
from Audio-alpaca yields superior audio generation to Tango and AudioLDM2.

##### Subjective Evaluations

In our subjective evaluation, Tango 2 achieves high ratings of 3.99 in OVL
(overall quality) and 4.07 in REL (relevance), surpassing both Tango and
AudioLDM2. This suggests that Tango 2 significantly benefits from preference
modeling on Audio-alpaca. Interestingly, our subjective findings diverge from
those reported by Melechovsky et al. (2024). In their study, the authors
observed lower audio quality when Tango was fine-tuned on music data. However,
in our experiments, the objective of preference modeling enhances both overall
sound quality and the relevance of generated audio to the input prompts.
Notably, in our experiments, AudioLDM2 performed the worst, with the scores of
only 3.56 in OVL and 3.19 in REL, significantly lower than both Tango and
Tango 2.

###  5.5. Analyses

Table 6. GPT-4 prompt used to extract events or concepts from prompts. ”””
---
You are to extract all the indivisible events in the given text, labeled as
input. Imagine experiencing the events in the input as you are reading it and
write down the indivisible events one by one. After writing your experience,
list all the events in the sequence you observed them as a python list. Think
step-by-step. Do not directly give the answer. Please refer to these following
examples as refernce for input and output:
Example 1 - Input: An aircraft engine runs and vibrates, metal spinning and
grinding occur, and the engine accelerates and fades into the distance Output:
Firstly, an aircraft engine runs and vibrates. Then, I hear metal spinning and
grinding. Then, the aircraft engine accelerates. Finally, the aircraft fades
into the distance. So, here is the list of events that I observed: [”aircraft
engine runs”, ”aircraft engine vibrates”, ”metal spinning”, ”metal grinding”,
”aircraft engine acclerates”, ”aircraft fades into the distance”]
Example 2 - Input: Bubbles gurgling and water spraying as a man speaks softly
while crowd of people talk in the background Output: Firstly, I hear bubble
gurgling. Also, I hear water spraying. Simultaneously, a man is speaking
softly. Also, a crowd of people are talking in the background. So, here is the
list of events that I observed: [”bubble gurgling”, ”water spraying”, ”a man
is speaking softly”, ”crowd talking”]
Example 3 - Input: A man talking then meowing and hissing Output: Firstly, I
hear a man talking. Subsequently, I hear meowing. I also hear hissing. So,
here is the list of events that I observed: [”a man talking”, ”meowing”,
”hissing”]
*** Examples end here
Now, given the input text below extract all the indivisible events one by one
as explained above with examples. Also, remember to follow the exact format of
the examples.
Input: {PROMPT}
Output:
”””

##### Results on Temporal and Multi-concept Prompts.

We analyze the performance of Tango and AudioLDM2 models in audio generation
when text prompts contain multiple sequential events. For instance, “Two
gunshots followed by birds flying away then a boy laughing” consists of three
distinct events in a sequence. In contrast, “A man is snoring” lacks any
temporal sequence. We partition the AudioCaps test set based on temporal
identifiers such as ”while,” ”as,” ”before,” ”after,” ”then,” and ”followed,”
creating two subsets: one with multiple sequential events and the other with
no temporality. Our objective evaluation results for these subsets are shown
in Table 5. Tango 2 achieves the highest scores of KL, CLAP, and IS on both
temporal and non-temporal events, indicating a consistent trend of performance
improvement. Similar trends are observed in subjective evaluations. We
attribute this improvement to the addition of both temporal and non-temporal
samples to Audio-alpaca.

Additionally, we categorize prompts based on the presence of multiple concepts
or events, exemplified by phrases like “A woman speaks while cooking”. As
underlined, this prompt contains two distinct events i.e., _“sound of a woman
speaking”_ and _“sound of cooking”_. Through manual scrutiny, we discovered
that pinpointing prompts with such multi-concepts is challenging using basic
parts-of-speech or named entity-based rules. Consequently, we rely on GPT-4 to
extract various concepts or events from the prompts using in-context
exemplars. The specific prompt is displayed in Table 6. To evaluate GPT-4’s
performance on this task, we randomly selected outputs for 30 unique prompts
and manually verified GPT-4’s annotations. No errors attributable to GPT-4
were found. In general, Tango 2 outperforms AudioLDM2 and Tango across most
objective and subjective metrics, following Table 4. We proceed to visualize
the CLAP scores of the models in Figure 3. This visualization illustrates that
Tango 2 consistently outperforms the baselines as the number of events or
concepts per prompt increases. In particular, Specifically, Tango closely
matches the performance of Tango 2 only when the textual prompt contains a
single concept. However, the disparity between these two models widens as the
complexity of the prompt increases with multiple concepts.

Figure 3. CLAP score of the models vs the number of events or concepts in the
textual prompt.

The supremacy of Tango 2 over the baselines in both of these cases can be
strongly ascribed to DPO training the diffusion model the differences between
generating a preferred and an undesirable audio output. Particularly, the
undesirable audio outputs with missing concepts and wrong temporal orderings
of events are penalized. Conversely, the preferred audio samples with the
correct event orderings and presence are promoted by the noise-estimation
terms in the DPO-diffusion loss (Eq. 17).

#####  Ablations on Audio-alpaca.

We conducted an ablation study on Audio-alpaca to delve into the impact of
different negative data construction strategies. As depicted in Table 3,
excluding the data samples generated by Strategies 2 and 3 notably diminishes
the performance of Tango 2. This underscores the significance of event and
temporal prompt perturbations.

##### The Effect of Filtering.

In our experiments, we noticed that filtering to create different Audio-alpaca
can impact the performance (refer to Section 4.1). Figure 4 depicts the impact
of this filtering process. We found setting $\Delta_{2}\geq 0.08$, and
$\alpha_{2}\geq 0.6$ gives the best results.

Figure 4. The impact of filtering Audio-alpaca on performance observed through
$\Delta_{2}$, and $\alpha_{2}$. The CLAP score of the winning audio must be at
least $\alpha_{2}$ and $\Delta_{2}$ represents the difference in CLAP scores
between the winning audio $x^{w}$ and the losing audio $x^{l}$ given a prompt
$\tau$.

##  6\. Conclusion

In this work, we propose aligning text-to-audio generative models through
direct preference optimization. To the best of our knowledge, this represents
the first attempt to advance text-to-audio generation through preference
optimization. We achieve this by automatically generating a preference dataset
using a combination of Large Language Models (LLMs) and adversarial filtering.
Our preference dataset, Audio-alpaca, comprises diverse audio descriptions
(prompts) paired with their respective preferred (winner) and undesirable
(loser) audios. The preferred audios are expected to accurately reflect their
corresponding textual descriptions, while the undesirable audios exhibit flaws
such as missing concepts, incorrect temporal order, or high noise levels. To
generate undesirable audios, we perturb the descriptions by removing or
rearranging certain concepts and feeding them to Tango. Additionally, we
employ adversarial filtering, generating multiple audios from the original
prompt and selecting those with CLAP scores below a specified threshold.
Subsequently, we align a diffusion-based text-to-audio model, Tango, on Audio-
alpaca using DPO-diffusion loss. Our results demonstrate significant
performance leap over the previous models, both in terms of objective and
subjective metrics. We anticipate that our dataset, Audio-alpaca, and the
proposed model, Tango 2, will pave the way for further advancements in
alignment techniques for text-to-audio generation.

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