LeVo: High-Quality Song Generation with Multi-Preference Alignment
Authors: Shun Lei
Yaoxun Xu
ZhiweiLin
Huaicheng Zhang
Wei tan
Hangting Chen
Yixuan Zhang
Chenyu Yang
Haina Zhu
Shuai Wang
Zhiyong Wu
Dong Yu
Venue: NeurIPS 2025 poster ()
Keywords: Song generation
Music generation
Language Model
Direct Preference Optimization
Multi-Preference Alignment
Primary area: applications
TL;DR: LeVo generates high-quality songs that closely follow instruction by pairing paralled mixed and dual-track token prediction with three-stage training and DPO-based multi-preference alignment.

Abstract: Recent advances in large language models (LLMs) and audio language models have significantly improved music generation, particularly in lyrics-to-song generation.
However, existing approaches still struggle with the complex composition of songs and the scarcity of high-quality data, leading to limitations in audio quality, musicality, instruction following, and vocal-instrument harmony.
To address these challenges, we introduce LeVo, a language model based framework consisting of LeLM and Music Codec.
LeLM is capable of parallel modeling of two types of tokens: mixed tokens, which represent the combined audio of vocals and accompaniment to achieve better vocal-instrument harmony, and dual-track tokens, which separately encode vocals and accompaniment for high-quality song generation.
It employs two decoder-only transformers and a modular extension training strategy to prevent interference between different token types.
To further enhance musicality and instruction following ability, we introduce a multi-preference alignment method based on Direct Preference Optimization (DPO).
This method handles diverse human preferences through a semi-automatic data construction process and post-training.
Experimental results demonstrate that LeVo significantly outperforms existing open-source methods in both objective and subjective metrics, while performing competitively with industry systems.
Ablation studies further justify the effectiveness of our designs.
Audio examples and source code are available at https://levo-demo.github.io and https://github.com/tencent-ailab/songgeneration.

Le High-Quality Song Generation with Multi-Preference Alignment
Shun Lei!t, Yaoxun Xu 17 Zhiwei Lin', Huaicheng Zhang', Wei Tan Hangting Chen
Yixuan Zhang", Chenyu Yang', Haina Zhu", Shuai Wang", Zhiyong Wu Dong Yu²*
Shenzhen International Graduate School, Tsinghua University, Shenzhen Tencent Al Lab Wuhan University
The Chinese University of Hong Kong, Shenzhen X-LANCE Lab, Shanghai Jiao Tong University, Shanghai
Shenzhen, China
School of Intelligence Science and Technology. Nanjing University. Suzhou, China tsinghua. edu. cn, erichtchenstencent com,
fleis21, xuyx22) 2aaiis.mmmaa...... aammaal.ttiinnuaaa..aaaaaaaaaaaa zywu tsinghua. edw..a.dd.cc.o
Abstract
Recent advances in large language models (LLMs) and audio language models have significantly improved music generation, particularly lyrics-to-song genera- tion. However, existing approaches still struggle with the complex composition of songs and the scarcity of high-quality data, leading limitations in audio quality, musicality, instruction following. and vocal rocal-instrumen harmony. To address these challenges, we introduce Lo a language model based framework consisting of LeLM and Music Codec. LeLM is capable of parallel modeling of two types of tokens: mixed tokens, which represent the combined audio of vocals and accompa- niment to achieve better vocal vosal-instrument harmony, and dual-track tokens, which separately encode vocals and accompaniment for high-quality song generation. It employs two decoder-only transformers and modular extension training strategy to prevent interference between different token types. To further enhance musical- ity and instruction following ability, we introduce method based on Direct Preference Optimization (DPO). This method handles diverse human preferences through semi-automatic data construction process and post-trane post-training. Experimental results demonstrate that LeVo significantly outper- forms existing open-source methods in both objective and subjective metrics, while performing competitively with industry systems. Ablation studies further justify the effectiveness of our designs. Audio examples and source code available https://levo-demo.giiie htt.iii.ioouiioooooooooo and http:///gggiiiiioooooooaeoooeooa
alignment
1 Introduction
Songs represent one of the most significant forms of musical expression, encapsulating human creativity and intelligence. By blending expressive vocals with rich tapestry of instruments, songs convey the emotions and thoughts of their creators, offering artistic appeal and broad cultural influence. Rapid advances in Artificial Intelligence Generated Content (AIGC) have already transformed creative domains from text generation domains such as image generation 10 and instrumental music generation [1] Nevertheless, creating songs remains complex challenge for AIGC systems: they must generate high-quality vocal and instrumental tracks and integrate them seamlessly while maintaining musicality and
[ and speech synthesis 7 to more sophisticated
"Corresponding author, Equul contribution.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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following instructions. These challenges are amplified for long-form song generation, where modeling complexity and computational cost increase significantly.
Early song generation approaches treat the combined audio of the vocals and accompaniment as a single prediction target. Jukehox [15) introduced the idea of using language models (LMs) predict discrete codes ("mixed tokens") extracted from the song audio. Later studies, such as SongCreator [16] and MusiCot [77. have further optimized the language model improve musicality. Despite their success, mixed token-based approaches exhibit a critical limitation: the restricted vocabulary cannot fully capture the intricate combination of vocals and accompaniment, which leads to lower sound quality. YuE [18] tackles this limitation by introducing a dual-track token prediction strategy that generates separate token sequences for the vocal and instrumental tracks. SongGen [99] extends this idea and shows that interleaving the two streams along the temporal dimension, instead of predicting them in parallel, reduces interference between token types and achieves better performance. While these methods produce high-quality vocals and accompaniments, they struggle to maintain meell-nstruueen harmony due to the independent prediction. The interleaved prediction mode dramatically increases the length of the token sequence, which limits scalability and makes it difficult to maintain musicality and follow instructions.
Moreover, available datasets in the song generation community have long been constrained by highly uneven quality and unreliable music annotations, which exhibit several challenges. On the one hand, models trained on such noisy data are generic enough to model song signals, but they lack prior knowledge of musicality hand, unreliable annotations limit the model's 's ability to follow instructions such as lyrics and prompts. Similar issues in text generation are typically circumvented by using the reinforcement learning (RL) method (e.g., RLHF), but music annotation requires specialized domain knowledge and evaluation along multiple perceptual dimensions.
music. On the other bias the generated songs toward listener-preferee
This paper presents Le Vo, language model based song generation framework consisting of LeLM and Music Codec. LeLM is used to model mixed tokens and dual-track tokens in parallel. Mixed tokens guide the overall arrangement of melody, rhythm, and tempo, ensuring vocal-instrumer harmony, while dual-track tokens capture finer nuances to improve the sound quality and musicality. The Music Codec then reconstructs the dual-track tokens into high-fidelity music audio. To prevent interference between these two token types, the proposed LeIM utilizes a language model with an autoregressive (AR) decoder and a modular extension training strategy. This framework allows flexible control over musical elements, enabling text-based descriptions influence instrumental, genre, and emotion, and audio prompts achieve zero-shot style transfer. Additionally, to address the challenges of musicality and instruction following, we further introduce a DPO-based [20] multi- preference alignment method to improve the model's performance across multiple dimensions. The main contributions of this paper are summarized as follows:
LeVo presents a new paradigm to optimize LM-based music generation by three-stage training: pre-training, modular extension training, and multi-preferrnce alignment. Pre- training brings generation diversity and vocal-instrument harmony, modular extension training improves sound quality and musicality without disrupting the pre-trained knowledge, and multi-preference alignment further enhances instruction following and musicality.
The architecture of LeLM carefully designed using a language model with an AR decoder to predict mixed and dual-track tokens in parallel. Compared to single token prediction (only using mixed or dual-track tokens) or straightforward joint training, our method simul- taneously optimizes musicality, vocal-instrument harmony, and sound quality.
To balance the multi-dimensional demands of song generation, we propose multi- preference alignment method based on DPO, which is the first attempt to apply multi- preference DPO song generation. Our strategy handles diverse human preferences by corresponding semi-automatic data construction processes and DPO fine-tuning. The final combined model achieves higher musicality and better instruction following.
In terms of musicality, sound quality, vocal-instrument harmony, and instruction following, the proposed LeVo model significantly improves over the open-source music generation models and performs competitively with current -fate-of-the-att industry systems.

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2
Related Work
Music Generation Early efforts [21 [2 in music generation primarily focused on symbolic music, which is limited by fixed instruments and lacks expressiveness. Recently LLMs have demonstrated strong reasoning capabilities and scaling behaviors MH3. Several works [T] 14 23 have achieved
the end-to-end music generation using language models. These works encode music as discrete token sequences by the Vector Quantised- Variational Autoencoder (VQ-VAE) [24] or the Residual Vector Quantization (RVQ) 25 26]. and then process them with language models. However, their performance was constrained by quantization loss. To address this, some studies adopt diffusion models, which have been proven effective modeling continuous representations (27 28 However, these methods face challenges in long-context generation tasks due to the significantly increased computational demands as the sequence lengthens and the model size grows. Recently, MeLoDy [12] and AudioLDM 2 [29] combined language models and diffusion models, achieving ofthe-ttt (SOTA) performances with high-fidelity and musicality. We extend this idea by using a diffusion model the decoder of the codec and LeLM, which considers the complex components of songs, to better achieve song generation.
Furthermore, several intriguing studies have focused on applying RL in music generation. BATON 30 integrates reward model, which is trained based on human preferences, into the standard diffusion loss to guide the training of diffusion models. MusicRL. [31 fine-tunes MusicLM [14] using reward functions derived from large-scale user feedback through Reinforcement Learning from Human Feedback (RLHF) to learn human preferences. Tango2 [32] semi-automatically generates preference datasets for DPO [20 training, providing a cheaper and more robust alternative. Different from these works that only focus on one specific dimension in non-vocal music generation, to our knowledge, we are the first attempt to align multiple preferences song generation.
Song Generation Recently, several studies have explored the challenging task of song generation, which involves generating vocals with rich accompaniment. Jukebox [15 is one of the pioneering efforts, which utilizes a multi-scale VQ-VAE to compress songs into discrete codes and models them with a cascaded transformer model. To improve the musicality of generated songs, SongCreator [16] introduced a dual-sequence language model to capture the relationship between vocals and accompaniment. MusiCot [17 predicts coarse-grained style representations to first sketch an overall musical structure before generating audio tokens, thereby improving the coherence and creativity of the generated songs. However, these approaches are affected by limited vocabulary and interference between vocals and accompaniment. To address these issues, Melodist [33] and MelodyLM [34] utilize multi-stage process to sequentially produce vocals and accompaniment. YUE 18 and
SongGen [99 attempt to operate on sequences consisting of vocal tokens and accompaniment tokens. Different from these, DiffRhythm 355 uses a diffusion-basees approach to generate full-length songs. Despite the advancements in these methods, they still struggle with the complex composition of songs and the uneven quality of data, which makes it difficult to maintain musicality, lyrics alignment, and vocal-instrument harmony. Industry tools such as Meruka [36), Seed-Muse (37,, Suno (38), and Udio (39] have demonstrated promising results for song generation, but none have fully disclosed their technical details. Our proposed Ve outperforms previous academic methods by predicting mixed and dual-track tokens in parallel while explicitly preventing their mutual interference. Furthermore, by leveraging a DPO-based multi pelliiireeerence alignment method, Vo achieves additional improvements, yielding human-perceptur perceptual quality that is competitive with leading industry tools.
3 Method
3.1
Overview
Let x X' represent song audio. A song generation process can be defined as f: C X, where C is the set of conditions. However, end-to-end generating a high-fidelity song x from C with a neural network ff remains challenging to date. In the same spirit as previous works [16] 18) introduce a language-alike token sequence, denoted as = (S)...., Sx), to capture significant structural information within the song and to embody language models as the "brain" for creating songs.
Our proposed LeVo generates songs from lyrics and can be supplemented with text descriptions and audio prompts. As illustrated in Figure it consists of LeLM and Music Codec. The encoder
3

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Lyries
[Optinal]
[Optimal]
[[verse]
Audio Prompt
Text Deseription female dark hip pop: melancholic;
don't love you no more
you crying the Roor
don't
Codes Decoder
[chorus]
Dual-Tack Tokens
Like fool begs supper
find myself waiting for her
piano and drum
Matd Tokers
RPF Tokenizer
Codec Encoder
BPE Tokenizer
LeL.M
The overview of LeVo, song generation framework based on lyries, optional text descriptions, and optional audio prompts. consists of LeLM and Music Codec.
Figure 1:
Delay Step-
Lyties
Prompe Audio Token
Genre Token
Mixed Tokan
AR Decodar
Vonal Token
Accompaninnei Tokes
1M Hilden
Decoder Iaput
Projection
Language Model
Token Limbedding
BOS tokes
Concstensted
Figure 2: The architecture of LeLM, which consists of language model and an AR decoder.
of Music Codec utilized to extract mixed tokens (i.e., Sn E Sm), vocal tokens (i.e., S. E S.),, and accompaniment tokens (i.e., S. E S.)) from the song audio prediction targets for LeLM. To parallelly predict the mixed tokens and dual-track tokens given C, we propose language model with an AR decoder named LcLM, illustrated in Figure The lyrics, optional text descriptions, and optional audio prompts are concatenated and fed into the LeLM as prefix context. Then, the decoder of Music Codec is used to generate high-quality and high-fidelity song audio based dual-track tokens that contain more detailed information about the vocal and accompaniment tracks.
Furthermore, address the challenges posed by interference between different types of tokens and issues such as musicality and instruction following, we propose a three-stage training strategy and DPO-based multi- multi-preference alignment method. In the remainder of this section, we present the LeVo architecture details and the training process described above.
3.2 Language Modeling of LeVo
Although modeling dual-track tokens struggles to maintain vocal-instrument harmony, its significant advantages in sound quality have led to widespread attempts [I8 19). The interleaving pattern, in particular, has shown improved performance over the parallel pattern by reducing interference between different token types. However, limited by the substantial increase in sequence length, the interleaving pattern struggles to scale to long-contex: song generation. To address this, LeVo introduces novel parallel modeling approach that avoids cross-token interference without significantly lengthening the sequence, thereby improving both
harmony and sound quality.
As shown Figure 2 this approach consists a language model and an autoregressive (AR) decoder. The language model utilizes decoder-only Transformer architecture, which has been widely adopted in previous works on music generation 122 [8] focuses on the next-token prediction task for mixed tokens to capture the high-level structural information about the song, such as melody, rhythm,

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Encoder
Codec Decoder
RVQ Mised
Mood Inkens
Diflusion Transfommer
YAE Decoder
MuEncoder
Reconered Finbediing
1|lll Vocal
Music Separation
Vocal
Diffusion Lraasdontrr
Decnder
-
Accompaniieent
Tries
Recoverel Extrvidling
Accomparmment
Figure 3: The framework of the Music Codec in Ve
and tempo, ensuring vocal-instrument harmony. This task can be formulated as:
p(Sm II (.Smm/Smmcce C;0)
where C' denotes conditions, including lyrics, optional text descriptions, and optional audio prompts.
To facilitate high-qualit song generation, we use an AR decoder that predicts dual-track tokens in parallel based on the outputs of the language model. It also utilizes decoder-only Transformer architecture with significantly fewer layers and parameters than the language model and is designed to refine the information generated by the language model through modeling of finer acoustic details. As the hidden states of the language model contain richer semantic and acoustic information than the mixed tokens, we pass these hidden states to the AR decoder. Specifically, at each time step, the embeddings of the predicted vocal and accompaniment tokens from the previous time step, along with the hidden states of the language model's last layer, are concatenated to form the input of the AR decoder. Two groups of linear heads are then employed to predict the audio tokens for each track based on the output of the AR decoder. Moreover, to provide rich contextual information, we introduce delay pattern between the dual-track tokens and mixed tokens. This means that when predicting the dual-track tokens the t-th time step, the model can consider not only the language model's 's output at time steps less than or equal to t, but also the output for the next & time steps, where k represents the number of time steps for the delay. This process can be simply represented as:
p(S., S,CC;0 = II p(S..c S.. Sa,ce Sm,ce C)
(2)
II T=+-
p(S...
C, C)
3.3 Music Codec in LeVo
Building upon MuCodec an an efficient 48kHz music codec capable of operating at low bitrates we have developed the Music Codec in Vo, as shown in Figure 3 The codec consists of an encoder and decoder. The encoder is composed of the MuEncoder 40 and an RVQ: the MuEncoder extracts music-relevant representations, while the RVQ discretizes these representations into tokens. As the central intermediary in Vo,o, these tokens serve not only as the prediction targets for LeLM but also play pivotal role in achieving high-fidelity music reconstruction by acting inputs for the decoder. The codec decoder comprises a diffusion transformer and Variational Autoencoder (VAE) decoder. The diffusion transformer reconstructs VAE features from the token-derived embeddings, which the VAE decoder then converts dircctly into audio. This approach is significantly faster than methods that rely on -ee-stpccss as intermediate steps for audio reconstruction.
Music inherently exhibits rich hierarchical structure, particularly in the complex interplay between vocals and accompaniment. To generate expressive vocals alongside clear and diverse accompaniment, we propose two strategies: the mixed token approach and the dual-track token approach. In the mixed token approach, music is processed by the encoder to extract mixed tokens. These tokens encapsulate information from both the vocals and the accompaniment. The decoder then uses the recovered embeddings from these mixed tokens to reconstruct the VAE latent, ultimately restoring the original music. In the dual-track token approach, a pretrained music separation model is employed to separate the vocal and accompaniment tracks. Each track is then processed by its respective encoder to produce
5

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vocal tokens and accompaniment tokens. The decoder conditions on the recovered embeddings from both sets of tokens to reconstruct the VAE latent of the original music, and finally restores the music.
3.4 DPO-based Mulfi-Preference Alignment
The scarcity of high-quality data has severely constrained the effectiveness of previous works in academic research. On the one hand, uneven data quality prevents models from learning prior knowledge about musicality. On the other hand, the lack of accurate music annotations, such as lyrics and descriptive text, undermines the model's ability to perform lyrics alignment and prompt-driven control. To address these challenges, we first adopt a data construction method to generate large-scale preference data at low cost for DPO fine-tuning.
multi-dimensional preference
First, we generate 20,000 lyrics by using large language model. For each lyric, we randomly select an audio prompt from a pre-defined dataset that includes text descriptions. Then, we generate multiple samples under three different conditions: using only the text description, using only the audio prompt, and using both the text description and the audio prompt. For the three dimensions of lyrics alignment, prompt consistency, and musicality, we then introduce three distinct strategies for constructing preference data corresponding to each dimension.
Strategy 1: Lyric Alignment Preferences The lyrics of the generated audio might exhibit misalign- ment with the input text, for example, word substitution, insertion, and deletion. To eliminate such a misalignment, we construct win-lose pairs using phoneme errors calculated by automatic speech recognition (ASR). Two audio samples will form win-lose pair if their phoneme error numbers larger than 40, We pick out all pairs satisfying the phoneme error gap condition.
Strategy 2: Prompt Consistency Preferences Considering the noise observed in the annotation of the training data, the style of the generated audio might be inconsistent with the input prompt. To improve the ability to prompt-driven control, we construct preference pairs using similarity scores calculated by the MuQ-Mul .n [4]] model. We aim to ensure that the winning audio sample is both strongly aligned with the prompt and substantially better aligned than the losing sample. For text descriptions, the winning sample must achieve score of at least 0.3, exceeding the losing sample's 's score by at least 0.1. For audio prompts, the winning sample must reach a score of at least 0.75, again with score difference greater than 0.1 compared to the losing sample.
Strategy 3: Musicality Preferences To enhance musicality, we employ a three-stage procedure to sift through win-lose pairs. Initially, we carry out crowdsoureing rank subset of the generated samples, which included 4,000 different sets of lyrics. We select those with high levels of agreement among human evaluators, forming a dataset of win-lose pairs. In the second stage, we train a reward model based on the datasets curated from the first stage. Finally, in the third stage, we deploy the reward model across all generated samples. We establish a threshold to filter out data that exhibits large gap in reward scores and tune the threshold to ensure that the selected win-lose pairs have an accuracy above 80%. Through this three-stage procedure, we ultimately collect roughly 60,000 win- pairs. The rationale behind this procedure is that while the size of the human-labeled dataset is limited, the use of a reward model enables us to identify all potential pairs.
The above paired data are then used for DPO fine-tuning to address the challenges posed by scarce high-quality data in the dimensions of lyrics alignment, prompt consistency, and musicality. To balance different aspects of music generation, we introduce an interpolation- based multi-preferent alignment method inspired by DNI (Deep Network Interpolation) (42). In this mode, we fine-tune the model separately on the preference data curated for each strategy to obtain the network parameters dedicated to each preference. Then, we apply linear interpolation across all parameters of these three specialized networks to produce the final model. Moreover, this approach also supports smooth transitions in performance according to specific demands by the controllable interpolation coefficient.
3.5 Three-Stage Training Paradigm
To facilitate the performance of L.M-based song generation approaches, we introduce a novel three- stage training paradigm that progressively enhances the capabilities of LeLM:

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Table Objective results of comparison and ablation systems for song generation. The asterisk (*) denotes that we reproduce SongGen using our training data. The overall first and second results are marked with bold and underline, respectively.
Content Scores 1
Models
FAD 4
MuQ-T
MuQ-At
PER L
CE
CU
PC
PQ
Suno-V4.5
2.59
0.34
0.84
21.6
7.65
7.86
5.91
8.35
Haimian
2.97
0.22
11.8
7.56
7.85
5.89
8.27
Mureka-O1
2.50
0.33
0.87
7.2
7.71
7.83
6.39
8.44
YuE
2.65
0.27
0.74
36.4
7.13
7.39
5.90
7.77
DiffRhythm
4.86
0.26
0.51
12.3
6.65
7.32
5.71
7.77
ACF-Step
2.69
0.28
37.1
7-37
7.52
6.26
7.85
SongGen
2.68
0.25
0.80
27.5
7.63
7.79
5.94
8.37
LeVo
2.68
0.34
0.83
7.2
7.78
7.90
6.03
8. 3.46
w/o Train stage 2
2.71
0.28
0.82
17.5
7.76
7.81
5.69
8.44
w/o AR decoder
2.83
0.27
0.80
26.0
7.54
7.71
5.61
8.32
w/o Dual-track
2.83
0.33
0.83
11.0
7.88
5.82
8.43
w/o DPO
2.60
0.31
0.82
10.6
7.70
7.86
5.89
8.39
Stage 1: Pre-training The language model in LeLM is first trained on large-scale music data to align the modalities between the various conditioning inputs and the mixed tokens. To ensure the model functions under different input conditions, both audio prompts and text descriptions undergo 50% random drop. At this stage, the AR decoder is frozen to facilitate the language model to focus solely on mixed tokens, bringing generation diversity and incallinstrument harmony.
Stage 2: Modular Extension Training Building Stage 1, we further train the AR decoder in LeLM to model dual-track tokens in parallel. This captures fine-grained acoustic details and nuances, thereby improving sound quality and musicality. To preserve the pre-trained knowledge, all modules trained Stage are frozen.
Stage 3: Multi- preference alignment Finally, we employ the semi-automatic data construction processes to create multi-dimensional preference dataset and fine-tune the entire Lel.M using the DPO loss. This stage significantly enhances musicality and the ability to follow instructions.
4 Experiments
4.1 Experimental setup
Dataset LeVo is trained on a large-scale music dataset comprising 2 million songs (approximately 1 110,000 hours). We adopt an automatic data processing pipeline similar to AutoPrep 4K] the challenge of the lack of accurate annotations. Initially, we use the Demucs 44 45
to address to extract the
vocals from the songs. Whisper [46] and wav2vec 2.0
47 48 are then leveraged for lyric recognition
and to provide timestamps for the lyrics. To enable the model to learn the structure of the songs, adopt the All-In-One model 49 to automatically extract music segments (c.g., verse, chorus, etc.). Additionally, we annotate all tracks utilizing Qwen2-Audio [50 to obtain open-vocabulary tags.
Model setup Our proposed LeLM has approximately 2B parameters. For the MuEncoder model that generates mixed tokens and dual-track tokens, we utilize the pre-trained weights from Mucodec 40 with 300M parameters and a frame rate of 25H7. Our diffusion model features model size of about 700M parameters convert tokens into high-quality waveforms. We replicate the VAE used in Stable Audio with 150M parameters. Additionally, for lyrics and text description, Vo employs a byte pair encoding (BPE)-based Qwen2 tokenizer [5] to process raw text. Detailed configurations and training setups can be found in the Appendix
Evaluations For objective evaluation, both the Fréchet Audio Distance (FAD) 52 and the Phoneme Error Rate (PER) are employed. calculate PER, the vocal track first extracted by Demucs, and then 2 is utilized for lyric recognition. We employ MuQ-MuLan 41 contrastive
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Table Subjective results of comparison and ablation systems for song generation. The asterisk (*) denotes that we reproduce SongGen using our training data. The overall first and second results are marked with bold and underline, respectively.
MOS
Models
OVL
MEL
HAM
SSC
AQ
LYC
Su.5-V4.5
3.59
4.10
3.93
4.19
4.00
3.17
Haimian
3.05
3.51
3.55
3.62
3.87
3.32
Mureka-O1
3.42
3.88
3.89
4.14
3.87
3.32
YuE
2.45
3.04
2.94
3.53
3.08
2.41
DiffRhythm
2.60
3.18
3.22
3.55
3.09
2.69
ACE-Step
2.26
3.02
3.30
3.21
2.36
2.22
SongGen"
2.91
3.43
3.44
3.66
3.69
2.84
LeVo
3.42
3.93
3.90
4.09
3.96
3.38
w/o Train stage 2
3.29
3.76
3.77
3.80
3.96
2.91
w/o AR decoder
2.93
3.44
3.34
3.59
3.71
2.74
w/o Dual-track
3.25
3.82
3.84
3.96
3.86
3.18
w/o DPO
3.18
3.71
3.76
3.97
3.93
3.18
mnusiclanguage pre-training model, to measure the similarity between the generated song and its text description (MuQ-T) audio prompt (MuQ-A). Additionally, the Meta Audiobox-Aesthetic [53] is used to capture perceived musical aesthetics, including content enjoyment (CE), content usefulness (CU), production complexity (PC), and production quality (PQ). For subjective evaluation, conduct a mean opinion score (MOS) listening test. Specifically, we enlist ten music professionals to rate each generated song scale from 1 to 5 across six aspects: overall quality (OVL), focusing on musicality and naturalness; vocal melodic attractiveness (MEL); vocal-instrument harmony (HAM);; song structure clarity (SSC); audio sound quality (AQ): lyrics following accuracy (LYC). None of the evaluators participated in model training to ensure objectivity. During the experiments, we asked participants to listen the entire music generated by different models with the same input before scoring, thereby obtaining scores that reflect the relative differences between models. The Appendix D shows details.
Comparison systems We conducted a comprehensive comparison between LeVo and multiple systems. We selected three leading industry systems for benchmarking: Suno V4.5 [38], Mureka-O1 (36), and Haimian (54). is important note that due the black-box nature of these closed-source models, our evaluation conducted in May 2025 reflects the performance of these systems at that specific time. Furthermore, we selected four open-source academic systems for benchmarking: YuH (18), DiffRhythm [35, ACE-Step
[55) and SongGen [19]. Notably, the official SongGen model can only generate songs up to 30 seconds with sampling rate of 16 kHz. For fair comparison, we reproduced its Interleaving (A-V) mode based on our dataset and codec.
4.2 Comparison with the State-of-The-Are Systems
Objective results Table summarizes the objective results. LeVo achieves either superior or competitive performance across all metrics. It obtains the highest MuQ-T scores (0.34) and lowers PER (7.2%) among all systems, and the highest MuQ-A scores (0.83) after closed-source systems, which demonstrates
powerful instruction following ability, especially in text description and lyries. LeVo also records the highest scores the Audiobox-Aestheti dimensions of CE (7.78), CU (7.90), and PQ (8.46), indicating its superior perceived musicality. For FAD and PC, LeVo achieves comparable results other open-source approaches that focus on dual-track tokens modeling or diffusion-based generation, reflecting comparable sound quality, whereas industry model Mureka-O1 records the lowest FAD and higher PC, attributed to its exceptional sound quality.
YYuE is tested using the model relcased at https//ggthh..commmmmmmmaaa//ppp DiffRhythm tested using DiffRhythm-fun released at http://ggitaaccomm//A//PPaaatttr
AAep-ttep is tested using the text2music demo space wtlipss//wugginggte...ssspppseep Stepse///////ESeeeeAAEEESeeeee

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Table Comparison of various DPO methods across multiple objective metrics. The overall first and second results are marked with bold and underline, respectively.
Content Scores
Models
FAD /
MuQ-T'
MuQ-A1
PER
CE
CU
PC
PQs
w/o DPO
2.60
0.31
0.82
10.6
7.70
7.86
5.89
8.39
with Strategy
2.85
0.30
0,81
6.5
7.72
7.86
5.97
8.42
with Strategy
2.89
0.34
0.83
10.3
7.75
7.87
5.96
8.43
with Strategy
2.63
0.32
0.82
11.2
7.78
7.93
6.16
8.45
Mixed Training
2.75
0.33
0.83
7.5
7.76
7.89
6.04
8.43
LeVo (Interpolation)
2.68
0.34
0.83
7.2
7.78
7.90
6.03
8.46
Subjective results As shown in Table LeVo outperforms all open-source academic approaches on all dimensions, highlighting the musicality, audio quality, aligmment of the generated song. It is important to note that our proposed parallel prediction method surpasses the interleaved pattern of SongGen, whose performance degrades long-context song generation. Although the industry system Suno-V4. excels in most metrics, LeVo exceeds by 0.21 points LYC, which demonstrates Vo's excellent lyric-alignment capability. Furthermore, Vo achieves MOS scores close to Suno-V4.5 on OVL, MEL, HAM, and AQ, and outperforms all other systems, indicating its superior overall quality-especiin and sound quality. However, Suno-V4.5 and Mureka-O1 show slightly better song structure clarity scores, which suggests room for improvement in music structural modeling.
harmony, and lyrics
in musicality, vocal-instrument harmony,
4.3 Ablation study
Framework As presented in the lower part of Table we demonstrate the effectiveness of several techniques used in LeVo, including the modular extension training strategy, the AR decoder, and the prediction of dual-track tokens. It is observed that jointly training the language model and AR decoder ("w/o train stage 2") decreases all metrics, particularly in PER, LYC, SSC, and MEL. Removing the AR decoder further amplifies this decrease. The results indicate that the proposed AR decoder and modular extension training strategy crucial for preventing interference between mixed and dual-track tokens, thereby improving musicality and intelligibility while maintaining vocal-instrument harmony. Moreover, we find that removing the prediction of dual-track tokens causes a significant decline in FAD, PER, and PC (objective) together with OVL. and LYC (subjective), whereas MEL, SSC, and AQ decline more modestly. It confirms that the prediction of dual-track tokens is essential for sound quality and intelligibility, and also has few impact on musicality. We further explore the reconstruction performance of dual-track tokens in Appendix
DPO-based Multi-Preference Alignment As shown in the lower part of Table and Table 2 removing DPO leads to significant decreases in PER, OVL, MEL, and LYC, indicating that DPO- based multi-prefie multi-preference alignment effectively enhances both musicality and lyric alignment. We also conducted a MOS listening test to evaluate the alignment between the given prompt and the generated music. In this experiment, "LeVo" achieved score of 3.175, while "LeVo w/o DPO" scored 3.055, suggesting that DPO-based multi-preference alignment also improves prompt consistency.
Table further details how different preferences affect objective performance. When the model fine-tuned with a single preference, each strategy selectively enhances its targeted capability: Lyric Alignment Preference (Strategy 1) reduces PER from 10.6% to 7. 7.0%: Prompt Consistency Preference (Strategy 2) yields the highest MuQ-T (0.34) and MuQ-A (0.83) scores; and Musicality Preference (Strategy 3) achieves the best Audiobox-Aesthete results. We then compare our proposed interpolation-basse multi- culti--referencee alignment method with simple mixed training approach, where preference data from all strategies are combined during post-training. While mixed training improves both instruction following and musicality compared to the no-DPO baseline-alba slight increase in FAD proposed interpolation method outperforms mixed training across most metrics, demonstrating better balance among the multi-dimensional demands of music generation. We further analyze the smooth performance transitions enabled by the controllable interpolation coefficient in Appendix
with

---

Conclusion and Discussion
Conclusion In this paper, we present Ve,o, song generation model that parallelly predicts mixed tokens for vocal-instrument harmony and dual-track tokens for high-fidelity vocals and accompaniment. By leveraging the LeLM and its training strategy, Le eliminates the mutual influence between different types of tokens. In addition, we propose a multi- multi-preeerence alignment method to improve the performance in terms lyries alignment, prompt consistency, and musicality. Experimental results demonstrate the excellent song generation capabilities of LeVo.
Limitations Despite Ve outperforming all academic approaches, its audio quality remains constrained by the variable quality of training data and the use of discrete tokens, leaving gap from state-of-the-art industry models. Moreover, there is still a disparity between LeVo and Suno across several subjective metrics, suggesting that further alignment with human preference is necessary. In addition, the scarcity and high cost of annotated music data have led us to rely heavily on pre-trained models for pseudo-labeling, which inevitably impacts LeVo's ' instruction following capability. On one hand, since text descriptions are generated using Qwen2-Audis the diversity and richness of effective prompts are limited. On the other hand, errors accumulated in lyric extraction, structure recognition, and music captioning processes introduce noise into supervision, reducing the fidelity of instruction alignment. These factors collectively contribute the remaining gap between Le Vo and top-tier proprietary systems in both controllability and overall quality.
Broader Impact The proposed work has the potential empower both content creators and novices to express their musical creativity with a low entry barrier, while also streamlining the workflow of experienced music producers. However, given LeVo's ability perform style transfer and end-to-end song generation, it could be misused for creating misinformation, deepfake audio, or other harmful content. To promote responsible deployment, we plan establish appropriate safeguards and usage guidelines when releasing the open-source code and model checkpoints. In addition, we examine the risks of training data memorization and content leakage, finding evidence of direct reproduction from the training set under identical inputs (see Appendix H for detailed analysis).
Acknowledgements
This work is supported by National Natural Science Foundation of China (62076144) and Shenzhen Science and Technology Program (JCY120220818100) 18101014030).
10

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A Project Contributors
Project Sponsors: Dong Yu, Zhiyong Wu
Core Contributors:
Data Collection & Washing: Wei Tan, Hangting Chen, Jianwei Yu
Music Encoder: Shun Lei, Hangting Chen, Haina Zhu
Diffusion & VAB: Yaoxun Xu, Zhiwei Lin, Hangting Chen
LLM Pre-train & Post-train: Shun Lei, Wei Tan, Huaicheng Zhang, Chenyu Yang,
Hangting Chen, Jianwei Yu
Music Caption: Yixuan Zhang, Wei Tan
Other Contributors: Chuan Lin, Rongzhi Gu", Ruoning Hong, Shang Gao, Shulan Zhang, Yi Luo", Yizhi Zhou" (* contributed while working in Tencent Al Lab)
B Training and Implementation Details
B.1
LeLM
Our LeLM consists of a language model and an AR decoder. The language model is 28-layer Transformer with 1536 hidden size. The AR decoder is 12-layer Transformer with 1536 hidden size. We provide detailed hyperparameter settings about this model configuration in Table 4 We collected approximately 10,000 hours of songs from the internet for model training, comprising part of the DISCO-10M [56]. Million Song Dataset [57]. and some copyrighted in-house data.
During training, used 32 NVIDIA H20 GPUs, with batch size of for each GPU train the LeLM for 265K steps. Of these, 200k steps are required for the pre-training, with 60k steps for modular extension training, then 5k steps for multi multi-preefrence aligmment. Adam optimizer used with 31 = 0.9, B2 = 0.98, c = 10 9 and follow the same learning rate schedule in [58]. Consistently, top-k sampling adopted for inference, in which l and temperature are set 50 and 0.9, respectively.
Table Hyper-parameters of LeL.M
Plyper-parametee
Value
Encoder Layers
28
Hidden Size
1536
Language model
Attention Head
12
Feed-Forward Dim
8960
Max Context Length (in #tokens)
8196
Decoder Layers
12
Hidden Size
1536
AR Decoder
Attention Head
12
Dim
8960
Max Context Length (in #tokens)
10000
Total Number of Parameters
2136M
B.2 Music Codec
The Music Codec consists of four main components. First, the MuEncoder [40] extracts represen- tations specifically designed for music. Next, the RVQ module discretizes these representations. The Diffusion Transformer (DiT) then transforms the discretized representations into Variational AutoEncoder (VAE) representations, which finally decoded back into waveforms by the VAE. In the following parts, we will provide a detailed description of the architecture and training parameters of both the DiT and the VAE.
B.2.1 DiT
Building upon the GPT-2 (3 architecture, we developed the core framework of DIT [59], incorporating rotary positional encoding enhance the model's capacity for sequence modeling. In addition,
15

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introduced a timestep feature injection mechanism, which deeply integrates temporal information into the inference process, thereby aligning more effectively with the denoising dynamics of diffusion models.
To address the challenge of limited GPU memory during long audio generation, adopted chunk-wise cascaded inference strategy. Specifically, inspired by MuCodec's approach [40] to maintaining coherence, we use the tail features of the previous audio chunk as a prompt to guide the generation of the current chunk, ensuring smooth transitions between musical segments. Furthermore, we implemented a masking mechanism to distinguish between prompt regions and regions to be generated. By concatenating the prompt features, mask indicators, and the audio codec representations as the model's 's imput, we significantly enhance the model's ability generate long, coherent sequences.
Recognizing the distinct characteristics of mixed token and dual-track token audio representations, designed differentiated network structures for each. In the mixed token mode, audio is first processed by a MuEncoder [40] and then discretized via RVQ to obtain a unified representation. In contrast, the dual-track mode involves separating vocals and accompaniment, each of which is independently encoded and discretized, resulting in two distinct representations. To accommodate these differing requirements, we tailored the model configurations accordingly: for the mixed token mode, we employed 1000 positional encodings, 39 transformer layers, 30 attention heads, and an embedding dimension of 1200; for the dual dual-track mode, we used 1000 positional encodings, 16 transformer layers, 20 attention heads, an embedding dimension of 2200, and set the inner layer dimension to 4400.
We trained our DiT model using large-scale dataset of 300,000 music samples sourced from the internet, incorporating subsets of DISCO- [56), the Million Song Dataset [57), and our in-house collections. From this dataset, we randomly selected 10,000 music samples to serve as our test set. For each training epoch, we randomly sampled 100,000 audio clips from the full training set, while 200 samples were drawn from the test set for evaluation during that epoch. For optimization, we employed the AdamW optimizer, setting the learning rate to 3 x 10-5 and the weight decay to 1e-8. The optimizer's ' beta parameters were configured as 31 0.9 and B2 = 0.999, with an epsilon value of 10 to ensure numerical stability. Training was conducted with a batch size of 3 and gradient accumulation over 3 steps to effectively manage memory usage. For both the Music Codec (Mixed) and Music Codec (Dual-Track) models, we utilized 8 40GB NVIDIA A 100 GPUs for each configuration, training each model's DiT for 300,000 steps.
B.2.2 VAE
We retrained a 48kHz stereo Wave VAE based on Stable Audio [60), with a latent representation dimension of 64. Specifically, we set the strides of the encoder and decoder to 4, 4, 6. and 10, resulting in downsampling rate of 1920. Consequently, the original 48kHz audio is downsampled to final frame rate of 25Hz, which is consistent with the frame rate of the mueneoder. Apart from these adjustments, all other configurations -inclutiionnnnluuinnnn model parameters for both the generator and discriminator, as well as training hyperparameters remain identical those used in Stable Audio
The Wave VAE was trained for 1,000k steps on the same music dataset as LeLM on 8 40GB NVIDIA A 100 GPUs During DiT training, the parameters of the Wave VAE were kept frozen, and its latent representations of music were used as the training targets for DiT.
C Results of the Music Reconstruction
C.1 Reconstruction quality
To comprehensively and thoroughly evaluate the performance of Music Codec, we selected several representative and high- performff audio codecs for comparison, including SemantiCodec [6]]. Wav Tokenizze [62), XCodec [63], and MuCodec [40]. Following the experimental setup of MuCodec, we focused on two key aspects: vocals (measured by speaker embedding similarity, SPK SIM, and word error rate, WER) and accompaniment
(eaaaa (measured by the VISQOL metric). This allows
Cottlgg//oeellloanngg/////oooood __e__ae jssonn
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for a more detailed assessment of Music Codec's performance across different dimensions. The
corresponding experimental results are presented Table S
Table 5: Comprehensive comparison Codec reconstruction quality across bitrates.
Tokenrate (tps)
Bitrate (kbps)
WER
Method
CodeBook
VISQOL 1
SPK_SIM f
(%)
Original music
10.92
1 x 32768
0.375
1.92/1.92
0.52
120.17
SemantiCodec
1 16384
100
1.40
1.96/1.96
0.68
55.17
x 4096
40
0.48
2.93/2.93
0.49
101.49
WaTTokenizer
x 4096
75
0.90
3.05/3.05
0.56
86.19
1 x 1024
50
0.50
3.04/3.04
0.53
85.10
x 1024
50
1.00
3.30/3.30
0.79
55.37
XCodec
x 1024
50
2.00
3.38/3.38
0.63
36.32
x 1024
50
4.00
3.58/3.58
0.72
26.42
1 x 16384
25
0.35
3.17/3.18
0.75
36.21
MuCodec
4 x 10000
25
1.33
3.45/3.46
0.87
24.26
1 x 16384
0.35
3.27/3.27
0.78
38.22
Music Codec
2 x 16384
25
0.70
3.34/3.34
0.82
33.43
(Mixed)
x 16384
25
1.40
3.52/3.53
0.84
28.92
Music Codec
16384+16384
25
0.70
3.43/3.44
0.82
31.54
(Dual-Track)
In low-bitrate scenarios (bitrate < 0.5 kbps), both MuCodec and Music Codec demonstrate clear advantages over WayTokennize SemantiCodec, and XCodec. Whether in terms of preserving vocal characteristics reconstructing background quality, MuCodec and Music Codec consistently achieve superior scores. For example, at bitrate of 0.35 kbps, both Music Codec (Mixed) and MuCodec significantly outperform other methods in terms of VISQOL, SPK SIM, and WER, fully showcasing their reconstruction capabilities at extremely low bitrates.
As the bitrate increases, the reconstruction performance of XCodec, WayTokenizer, and SemantiCodec improves in both vocal and background dimensions. However, they still lag behind MuCodec and Music Codec comparable bitrates. For instance, around 0.9 kbps, WayTokenizer shows improvements in VISQOL and SPK_SIM, but its WER remains higher than that of MuCodec and Music Codec. Similarly, XCodec achieves a VISQOL of 3.58 at 4.0 kbps, yet its WER is still higher than that of Music Codec at much lower bitrates. These results indicate that MuCodec and Music Codec only enhance audio quality and intelligibility but also maintain better bitrate efficiency.
closer comparison between MuCodec and Music Codec (Mixed) reveals that both employ a hybrid representation model vocals and background, and their overall performance is similar at the same bitrate (e.g., 0.35 kbps). However, Music Codec (Mixed) is slightly inferior to MuCodec in terms of the VISQOL metric. This be attributed to MuCodec's intermediate representation. Compared to directly mapping latent features to waveforms, using the Mel-spectrogram bridge provides stronger expressive power for audio quality reconstruction.
of the Mel-spectrogram
Furthermore, it is noteworthy that Music Codec (Dual-Track) achieves even better performance than Music Codec (Mixed) at the same bitrate of 0.70 kbps across all metrics. Although both operate at the same bitrate, the dual-Track approach explicitly models vocals and background separately, which allows for more accurate reconstruction of audio details and structure compared to the hybrid approach. This also one of the main reasons why we ultimately adopt dual-track tokens as the prediction target in the LeVo system.
C.2 Reconstruction elliciency
In this experiment, we conducted a comparative analysis of the inference speed between MuCodec and Music Codec (Dual-Track) with detailed results presented in Table 6 We adopted the Real-
17

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Time Factor (RTF) as the evaluation metric and divided the audio reconstruction process into three stages: first, Wav2Code, where the Codec Encoder transforms audio into discrete codes: second, Code21 atent, in which the DiT model performs multi-step denoising to convert the codes into latent representations; and finally, Latent2Wav, where the VAE decoder reconstructs the audio from the latent representations. To ensure a fair comparison, we set the number of inference steps for both models to 50.
Table Comparison of Inference Efficiency between MuCodec and Music Codec TrackTrack)..
DiT
Real-Time Factor
Method
Param
Wav2Code
Code2Latent
Latent2Wav
Total
MuCodec
743M
0.0201
0.5576
0.1001
0.6778
Music Codec
770M
0.0183
0.0680
0.0039
0.0902
(Dual-Track)
As shown in Table 6 MuCodec and Music Codec "Dull-rrack have very similar DiT parameter sizes, 743M and 770M, respectively. In the Wav2Code stage, their RTFs are 0.0201 and 0.0183, indicating almost identical efficiency in the encoder component. However, in the Code2Latent stage, MuCodec's RTF reaches 0.5576, while Music Codec (Dual- achieves a much lower 0.0680-an an almost eightfold difference demonstrating a significant advantage for Music Codec (Dual-Track) in generating latent representations through denoising. Furthermore, the Late stage, MuCodec's ' RTF is 0.1001, compared to just 0.0039 for Music Codec TrackTr similarly striking gap. This improvement can be largely attributed the fact that Music Codec (Dual-Track) establishes direct mapping from latent representations to audio, bypassing the intermediate mel- spectrogram representation and thus greatly reducing inference time.
Overall, Music Codec (ual--rack) significantly outperforms MuCodec in terms of total inference speed, with a total RTF of only 0.0902 compared to MuCodec's s 0.6778 nearly sevenfold difference. This demonstrates that, given similar parameter sizes, Music Codec (Dual-Track) offers a clear advantage in inference efficiency. Moreover, as shown in the results of Table its reconstruction performance is highly comparable to that of MuCodec. This combination of speed and quality is the primary reason we have chosen Music Codec (Dual- (hal-)rack) as the core decoding module in the Lo system.
D Detailed Experimental Settings
For evaluation, we generate 20 distinct lyrics and accompanying text descriptions with a large language model, and select 20 unseen music clips as audio prompts. For every lyric we produced up to three song variants, depending on system capabilities: (i) lyric-only, (ii) lyric + text description, and (iii) lyric + audio prompt. All metrics were computed over the entire set of generated songs.
D.1 Details in objective evaluations
Here, we provide details of the objective evaluations.
FAD Fréchet Audio Distance (FAD) is used to evaluate the generation fidelity of music. We calculate FAD based on the distribution distance between the feature of the generated audio and MusicCaps [14] test set using audioldm eva
PER Phoneme Error Rate (PER) is used to evaluate the lyrics alignment and intelligibility of songs. To calculate PER, the vocal track is first extracted by Demucs, and then Whisperlarge-2. 47 is utilized for lyric recognition. Using the clean vocal track rather than the song audio improves recognition accuracy. Whisperlarge-v2 one of the oftte--f-theaar automatic speech recognition (ASR) models. It not only achieves high performance for speech but is also robust in recognizing singing voice. Considering singing often involves homophones or elongated vowels that not alter
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phonetic content. Word-level (WER) or character-level (CER) metrics, therefore, penalize many errors that are inaudible to listeners. By operating the phoneme level, PER captures intelligibility more faithfully and correlates better with human ratings of lyric clarity.
MuQ-T and MuQ-A
To quantify how faithfully Le follows user prompts, we report two embedding-basee based similarity scores derived from MuQ-MuLan [1]] contrastive music- pre-training model.
MuQ-T: similarity between the song and its text description.
MMQ-A: similarity between the song and its audio prompt.
It is important to note that while the CLAP 664 is commonly used for similarity calculations, the training set of CLAP is dominated by non-vocal audio events, leading to inaccurate similarity comparisons. For clean evaluation, MuQ-T is calculated on songs generated only from the text description, and MuQ-A on songs generated only from the audio prompt, avoiding conflicts between mismatched prompt modalities.
Meta Audiobox-Aesthetic Measures the perceived musical aesthetics by leveraging advanced neural networks, including content enjoyment (CE), content usefulness (CU), production complexity (PC), and production quality (PQ).
D.2 Details in subjective evaluations
Table 7: Subjective results of comparison and ablation systems for song generation on OVL, MEL, and HAM with 95% Confidence Intervals. The overall first and second results are marked with bold and underline, respectively.
MOS 1
Models
OVL
MEL
HAM
Suno-V4.5
3.59 + 0.054
4.10 +: 0.042
3.93 + 0.033
Haimian
3.05 = 0.044
3.51 0.046
3 0.049
Mureka-O1
3.42 0.047
3.88
3.89 + 0.030
YuE
2,45 = 0.053
3.04
2.94 0.058
DiflRhythm
2.60 = 0.054
3.05
3.22 0.066
ACE-Step
2.26 = 0.065
3.02 0.070
3.30 + 0.073
SongGen"
2.91 0.049
3.43 0.053
3.44 0.055
LeVo
3.42 0.051
3.93 0.044
3.90 + 0.032
w/o Train stage 2
3.29 = 0.044
0.052
3.77 0.044
w/o AR decoder
2.93 0.053
0.05
0.073
+ 0.073
w/o Dual- -rack
3.25 = 0.049
3.82 0.048
3.84 0.040 0.040
w/o DPO
3.18 0.050
3.71 0.061
3.76 + 0.039
For the subjective evaluations, we focus on the performance of generated songs in the following six dimensions:
Overall Quality (OVL). The overall musicality and naturalness of the generated song.
Vocal Melodic Attractiveness (MEL). The beauty, smoothness, and appeal of the vocal melody.
IostumEnst Harmony (HAM), The coherence and integration between vocals and accompaniment.
Song Structure Clarity (SSC). The clarity and distinction of song sections (e.g., verse, chorus, bridge)
Audio Sound Quality (AQ). The clarity, absence of noise, dynamic range, and technical quality of the audio.
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Table 8: Subjective results of comparison and ablation systems for song generation on SSC, AQ, and LYC with 95% Confidence Intervals. The overall first and second results are marked with bold and underline, respectively.
MOS 1
Models
SSC
AQ
LYC
S4.5
4.19 + 0.077
4.00 + 0.068
3.17 + 0.067
Haimian
3.62 = 0.060
3.87 0.040
3.32 + 0.062
Mureka-O1
4.14 = 0.077
3.87 + 0.068
3.32 + 0.056
YuE
3.53 0.062
3.08 = 0.055
2.41 + 0.060
DiffRhythm
3.55 = 0.060
3.09 0.054
2.69 - 0.050
ACE-Step
3.21 0.092
2.36 + 0.064
2.22 + 0.060
SongGen"
3.66 = 0.061
3.69 + 0.054
2.84 + 0.063
LeVo
4.09 = 0.060
3.96 + 0.038
3.38 + 0.071
w/o Train stage
3.80 0.052
3.96 0.037
2.91 + 0.072
w/o AR decoder
3.59 0.044
3.71 + 0.051
2.74 + 0.058
w/o Dual-Track
3.96 = 0.058
6.860.000
3.18 + 0.073
w/o DPO
3.97 0.068
3.93 1.93 0.041
3.18 + 0.078
0.078
Lyries Following Accuracy (LYC). The accuracy and clarity with which the vocals follow the intended lyrics.
We conducted MOS (Mean Opinion Score) tests for all aspects. providing subjects with detailed descriptions, and reported both mean and CI95 scores of our MOS tests. The subjects present and rate the samples, and each subject is asked evaluate by 10 music professionals. All the screenshots of instructions for subjects have been shown in Figure We paid at least the minimum wage in the country of these subjects. We tell the subjects that the data will be used in scientific research.
scale of 1-5. Our MOS tests were conducted
4
E Detailed Results with 95 % Confidence Intervals
To support the main claims of the paper, report in Tables and 8 the complete MOS scores together with their 95 % confidence intervals (CI95). Table 7 presents MOS for Overall quality (OVL), Vocal Melodic Attractiveness (MEL), and Vocal-Instrument Harmony (HAM). Table 8 lists MOS for Song Structure Clarity (SSC), Audio Sound Quality (AQ), and Lyrics Alignment Accuracy (LYC). For objective evaluations, the error bars very small due the large number of test samples. Specifically, 95% confidence level, the error margin for MuQ-T and MuQ-A scores across all models is less than 0.01, and for content enjoyment (CE), content usefulness (CU), and production quality (PQ), the error margin is less than 0.015. For production complexity (PC), the error margin less than 0.05. The narrow intervals confirm that inter-rater variability is low and that the trends reported in Section are statistically reliable:
Superiority of LeVo. LeVo's MOS scores remain significantly higher than every open- source system across all six dimensions. The non-overlapping CIs corroborate LeVo's superiority.
Competitiveness with industry systems. While Suno-V4.5 leads most dimensions, its CIs overlap with (or are exceeded by) LeVo's on many dimensions, underscoring our claim of state-of-thh-an art alignment performance even against proprietary systems.
Effectiveness of each module. Ablation rows show consistent, statistically meaningful drops. These non-overlapping intervals validate the contribution of every design choice.
F Contribution of Each Module to Audio Quality
To investigate how different components Lo contribute to the final audio quality, we conducted Mean Opinion Score (MOS) listening test 40 unseen songs. The evaluated audio included:
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Table Subjective results on audio quality generated by different modules within Le
Models
AQ(Audio Quality)
GT
3.81
VAE Recon
3.79
Codec Recon
3.75
LeI.M
0.21
(1) ground-truth recordings, (2) VAE-reconstruutted audio, (3) Codec- redee-rrconstructedd audio, and (4) LeVo-generated audio using the same lyrics. As shown in Table the overall degradation in audio quality mainly arises from three sources: VAE reconstruction loss (0.02). Codec reconstruction loss (0.04), and LeLM modeling loss (0.09). Among these, the loss introduced by LeLM modeling is the most pronounced. While Le achieves higher perceived audio quality than models such as SongGen and LeVo w/o AR Decoder-bout both of which generate dual-track tokens- still lags behind the Codec reconstruction, revealing room for improvement the generative modeling stage. In addition, both the VAE and Codec modules contribute to minor degradation during the token-to-audio process.
G Smooth Transitions in Interpolation-bassed Multi-Preference Alignment Method
To further demonstrate the flexibility of our proposed interpolation-basee multi-preference alignment method, we measured its behavior under four weight settings that progressively move from no preference control DPO, it serves as the baseline for all subsequent comparisons. The results are presented in Table TO Steering the model with single preference- -tht assigning an interpolation coefficient of (1, 0) for Lyric Aligmment Preferences (Strategy 1), (0. 1. 0) for Prompt Consistency Preference (Strategy 2). or (0, 0, for Musicality Preference (Strategy 3) enhances exactly the targeted dimension. Concretely, Strategy 1 reduces the phoneme eπror rate (PER) from 10.6% to 7.0%, Strategy 2 raises MuQ-T and MuQ-A 0.34 and 0.83, respectively, and Strategy delivers the highest Audiobox-Aestherie scores. When two preferences are mixed with equal weights, the model exhibits smooth trade-offs: combining Strategy and Strategy slightly relaxes PER (from 6.5 % to 7.0 %) compared to using Strategy alone, but yields noticeable gains in MuQ-T and MuQ-A; blending Strategy with Strategy 3 preserves most the PER benefit while further improving to using Strategy alone; pairing Strategy 2 with Strategy 3 simultaneously maintains high MuQ scores and Audtebox-Aesthetcc scores. Finally, the uniform three-way interpolation (0.33, 0.33, 0.33) produces consistent improvements across all metrics. Taken together, these results verify that by simply adjusting the inference-ting interpolation coefficients (without retraining or additional fine- tuning), this approach can achieve smooth transition in performance to satisfy diverse application needs more flexibly than conventional data-mixing approaches.
uniform blend of the three preferences. When the model trained without
Aesthetic scores compared
Table 10: Comparison of various preference combinations across multiple objective metrics. The overall first and second results are marked with bold and underline, respectively.
Content Scores
Models
FAD 4
MuQ-T
MuQ-A +
PER +
CE
CU
PC
PQs
w/o DPO
2.60
0.31
0.82
10.6
7.70
7.86
5.89
8.39
Strategy 1
2.85
0.30
0.81
6.5
7.72
7.86
5.97
8.42
Strategy 2
2.89
0.34
0.83
10.3
7.75
7.87
5.96
8.43
Strategy 3
2.63
0.32
0.82
11.2
7.78
7.93
6.16
8.45
Strategy 1&2
2.91
0.33
0.83
7.0
7.67
7.83
5.93
8.40
Strategy 1&3
2.75
0.31
0.82
8.3
7.76
7.90
6.10
8.44
Strategy 2&3
2.73
0.34
0.83
10.9
7.75
7.92
6.01
8.43
Strategy 1&2&3
2.68
0.34
0.83
7.2
7.78
7.90
6.03
8.46
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Table 11: Pearson Correlation between subjective evaluation results and Meta Aestttoxi
metrics.
OVL
MEL.
HAM
SSC
AQ
LYC
CE
0.32
0.44
0.28
0.07
0.31
-0.01
CU
0.26
0.36
0.16
0.02
0.27
-0.14
PC
-0.08
-0.14
0.32
0.44
-0.14
0.23
PQ
0.21
0.07
-0.06
0.01
0.15
-0.12
H Risks of Memorization and Content Leakage
We further assess the potential risks of memorization and content leakage in Le To examine whether the model inadvertently reproduces training data, we randomly selected 40 songs from the training set and generated corresponding outputs using the same input conditions. We then evaluated similarities between the generated and original songs by computing the 5-gram overlap and I. Levenshtein distance similarity of the extracted dual-track token sequences, supplemented by manual inspection melodic patterns. The quantitative results show extremely low similarity scores scores- 0.0001 for 5-gram and 0.0012 for Levenshtein distance and perceptually similar melodies were identified through human evaluation. These results suggest that LeVo does not memorize or directly replicate its training data, even when prompted with identical inputs.
We attribute this low memorization tendency to two main factors. First, the relatively modest model scale (2B parameters) compared to the large training corpus of 2 million songs reduces the likelihood of overfitting to specific samples. Second, the multi-preference alignment stage following pre- training serves as additional regularization process, further mitigating memorization by optimizing the model under diverse alignment objectives rather than maximizing likelihood on training data. Together, these results indicate that Vo maintains low risk of content leakage, supporting its reliability for safe and responsible music generation.
I Correlation between Human Evaluation Results and Objective Metrics
To better analyze the limitations of current objective metrics in music generation, compared subjective and objective evaluation results. Overall, we observed a noticeable discrepancy between human judgments and automatic metries. To quantify this relationship. calculated the Pearson Correlation Coefficients between human evaluation results and objective scores.
A weak correlation was found between FAD and AQ (r -0.244), suggesting that distribution-level metrics such as FAD may fail to capture subtle acoustic differences that influence human perception. In contrast, PER and LYC exhibit strong correlation (r -0.77), indicating that ASR -asbdsed metrics effectively reflect crric-following accuracy. Nevertheless, these metrics are still affected by factors such as background accompaniment and vocal pitch, which can introduce recognition errors.
For the Meta shown in Table II The results show that content enjoyment (CE) exhibits the strongest correlations with OVL (0.32), MEL (0.44), and AQ (0.31), suggesting that these objective metrics are most aligned with human enjoyment of music. Content usefulness (CU) shows similar but slightly weaker patterns. Production complexity (PC) correlates most strongly with HAM (0.32) and SSC (0.44), indicating sensitivity to variations in vocal and accompaniment elements. In contrast, production quality (PQ) shows only weak correlations with all subjective dimensions, reflecting its limited ability to capture human perceeved quality.
-esthetti metrics and subjective evaluation results, the correlations were
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Overall Quality
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Focus: The overall musicality and naturalness of the piece
*1
Overall quality
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Exceptionally engaging and well-structure music; flows naturally with outstancing listenaility and virtually no flws.
4: High-quality music with clear structure and natural flow; minor imperfections may be present but do not detract from the overall experience.
User 68973
3: Avarage quality, the structure is mostly intact, though some af3 unnatural or awkoward moments are notiocable but not ovenwhelming.
2: Noticeable flavs in the music; structure may be confusing or the performance foels stiff, affecting the listoning experience.
I: Very poor quality; the music is sorgantted and unnatural, making
it difficult to onjoy.
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Figure 4: The screenshot of MOS test in overall quality.
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Vocal Melodic Attractiveness
usst User 68973
Focus: The beauty, smoothness, and appeal of the vocal melody
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1+ Vocal Melodic Attractiveness
melocy is captiveting, besutifully crefted, effortlessly;
highly expressive and appeeling. ouer 60513
melocy is pleasam: smooth, good appeal strong Guest User 68973 expressivenesss
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melocy average, reesonably smooth, moderalely
appealing. though somewhet limited expressiveness. melocy beauty smoothness, little appeal expressiveness, (891
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melocy chaotic uneppealing, lacking both smoothness and expressiveneess
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Figure 5: The screenshot of MOS test in vocal melodic attractiveness.
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xjshwewhdjva.x
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Vocal-Instrument Harmony
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The coherence and integration between vocals and
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accompaniment
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11
Vocal Instrument Harmony
accompaniment are perfectly blended, highly
coordineted, sound returally harmonious. accompaniment work well logether, only minor imbalance.
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Vocals and accompaniment are gencrally in sync, though somo lack notionable.
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Vocals and accompaniment are highly discordant, severely impacting the listening experience.
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Figure 6: The screenshot of the MOS test in vocal-instrument harmony.
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Song Structure Clarity
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Song Structure Clarity
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structure well-dffined,
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structure mostly clear, with distinct sections and generally ly smooth transition.
structure somewhat with some distinction sedions, though transitions may feel awvoward.
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structure vague, with soctions hand distingutsh and transitiors: feeling unnetural.
structure cheotic difficult to fallow, locking logical progression.
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Figure 7: The screenshot of the MOS test in song structure clarity.
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xjshwawhdjva3x
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Audio Sound Quality
Guest 6837s
Focus: The clarity, absence of noise, dynamic range, and technical quality of the audio
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*1 Audio Sound Quality
S: Excallant sound qualit; crystal clear with no notse, wide dynamic
4: Very gocd sound quality: minimaal nobe,, good dynamic range, and Guest User 68973
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range, and rich dotall. cvedd user 600
plenty detail.
3: Average sound cuality; occasional noise, moderate dynamic range,
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Figure 8: The screenshot of the MOS test in audio sound quality.
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*1+ Lyrics Following Accuracy
5: Lyrics perfectly metched to the target, with clear prorundation and precise rhythm; noticoable crrors
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4: Lyrics are mostly accurate, with only minor mistakkes undear pronunciations that do not hinder understanding.
3: tyrics show some discrepancles, with several minor errors that partially affect comprehensiion.
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2: Lyrics differ significaritly from the target, with frequert errors that make understanding dificcult.
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I: tyrics are largely incorrect unintelligbble making It hard recognize understand the intendod content.
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Figure The screenshot of the MOS test lyrics following accuracy.
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Reviews:
[{"review_id": "A6uOahP2sE", "reviewer": "Reviewer_YZXD", "summary": "The paper proposed a song generation method based on lyrics, prompt audio, and text descriptions. The proposed method consists of 3 stages. First, dual-track (vocal and accompaniments) and mixed track based language model is trained to have a long-form characteristics of the song. Then, the dual-track tokens are injected into DiT like diffusion model for modeling high quality sound quality. Finally, to enhance the quality of the trained model's performance, the authors conducted DPO fine-tuning. At this stage, the authors collected thousands of human annotations over the generated music and lyrics, then with lyric alignment measurement algorithm and language music similarity measurement algorithm, the authors trained some reward model that predicts diverse thresholds of the quality. Then, the authors created large win-lose pairs using this reward model and fine-tuned the whole model to improve the performance of the model. In the experiments, the authors collected 2 million songs and generated according metadata using several algorithms. For example, they extracted vocals using separation model, and extracted lyric and timestamps through Whisper and wav2vec 2.0. And, musical section information was extracted using All-In-One model, and finally tags are extracted using Qwen2-Audio. Finally, they evaluated the generated music for both objective and subjective evaluations. In objective evaluation, they used FAD, PER, and text, audio alignment metrics with Meta Audiobox-Aesthetic scores. In subjective evaluation, the authors measured 6 MOS metrics. Overall, the proposed method showed superior performance than the public algorithms and competitive performance over industry models. Also, they reported the results of ablation studies, and it verified that the proposed dual-track model, DiT, DPO fine-tuning, and individual DPO methods are all effective, respectively. The paper is well-written and the experiments and evaluation seems correct.", "questions": "The concern lies on how the authors collected the training music data. They acquired 2 million songs, but it's not explained in the paper.", "limitations": "-", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "The use of dual-track and DPO fine-tuning steps seems novel and effective. Especially, how the authors constructed the training dataset and how the authors created reward model and used it for DPO fine-tuning are well described and will be beneficial for the future researchers.", "was_revised": true, "final_justification": "If the paper is accepted, I strongly urge the authors to keep this in mind and ensure that the reported results, training datasets, the released data collection codebase, and the released model checkpoints are all properly aligned.", "rebuttal": "Thank you very much for your time and effort in reviewing our paper and for your recognition of our work. We hope our response below resolves your concerns and questions.\n\n**Regarding Question 1 and Ethical Concerns:** **Data Source**\n\nRegarding the training music data, we clarify this in the Supplementary Material, specifically in Appendix A (lines 6–7). We used a combination of licensed open-source datasets such as **DISCO-10M** [1*] and the **Million Song Dataset** [2*], along with a small portion of copyrighted proprietary data. This setup is similar to previous works in the field, including SongCreator [16], MusiCot [17], and YuE [18].\n\n**Regarding Formatting Concerns: Letter Size**\n\nWe appreciate your valuable suggestion regarding the letter size. We will address this issue in the final version of the paper to ensure clarity.\n\n**References:**\n\n[1*] Lanzendörfer, L., Grötschla, F., Funke, E., et al. \"Disco-10M: A large-scale music dataset.\" *Advances in Neural Information Processing Systems*, 2023, 36: 54451-54471.\n\n[2*] Bertin-Mahieux, T., Ellis, D. P. W., Whitman, B., & Lamere, P. \"The Million Song Dataset.\" *In ISMIR*, volume 2, page 10, 2011.\n\n[16] Lei, S., Zhou, Y., Tang, B., et al. \"Songcreator: Lyrics-based universal song generation.\" *Advances in Neural Information Processing Systems*, 37:80107–80140, 2024.\n\n[17] Lam, M. W. Y., Xing, Y., You, W., et al. \"Analyzable chain-of-musical-thought prompting for high-fidelity music generation.\" *arXiv* *preprint* *arXiv:2503.19611*, 2025.\n\n[18] Yuan, R., Lin, H., Guo, S., et al. \"YuE: Scaling open foundation models for long-form music generation.\" *arXiv* *preprint* *arXiv:2503.08638*, 2025.\n\nNote: [N] denotes the reference in the paper and [N*] represents the reference in the Appendix."}, {"review_id": "5jid5IdL9D", "reviewer": "Reviewer_arNL", "summary": "This paper explores direct preference optimization for lyric-conditioned music generation. The authors proposed a novel framework to construct training pairs for lyric alignment, prompt consistency and musicality preference modeling. The experimental results demonstrate the effectiveness of the proposed framework in improving the quality of the generated music with DPO, especially in terms of  lyric alignment. The ablation study also highlights the necessities of each proposed component.", "questions": "- (Line 71-73) \"Compared to single token prediction (only using mixed or dual-track tokens) or straightforward joint training, our method simultaneously optimizes musicality, vocal–instrument harmony, and sound quality.\" -> Is this true? I thin the same applies to the baseline models.\n- (Figure 2) The illustration for the prefix tokens is confusing. If I understand it correctly, they are fed as a sequence of tokens. Yet, the current illustration makes it look like they are being \"summed\" in some embedding space and fed to the model as one single token. It's also unclear to me what \"Prompt Embedding\" means here. Please clarify this.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n\n- This paper clearly demonstrates the benefits of DPO for lyric-conditioned  music generation. The idea of adapting DPO for music generation is well executed, and the experimental results do show its effectiveness, most significantly observed in the improvement of lyric alignment.\n- The authors compared the proposed method with many baseline models, including both industrial and academic models. It's impressive that the model can achieve competitive results to some of the leading commercial models in the subjective listening test.\n\n### Weaknesses\n\n- Error bars are missing, making it hard to see if any of these differences are significant.\n- Table 1 is somewhat misleading without a row for \"LeVo w/o DPO\", i.e., the first row of Table 3. The effectiveness of the proposed DPO approach in improving PER is clear, yet its effectiveness in improving MuQ-T, MuQ-A and Meta's AudioBox-Aesthetic metrics are not quite clear as the numbers are close.\n- Significance is claimed in multiple places without any significance test, including line 80 (\"the proposed LeVo model significantly improves over the open-source music generation models ...\") line 315 (\"causes a significant decline in FAD, PER and PC ...\"), and line 321 (\"leads to significant decreases in OVL, MEL and LYC ...\"). Please weaken these claim.\n- Reproducibility can be a big issue. The dataset is massive with 2 million songs totaling 110k hours. Even if the code is released, there will be no easy way to reproduce the results from scratch.", "was_revised": true, "final_justification": "The authors have addressed the issues of missing error bars and unsupported significance claims in their rebuttal. I think this paper introduces many new and reusable ideas to lyric-conditioned music generation, and am thus recommending accepting this paper.", "rebuttal": "We thank the reviewer for recognizing our work. We appreciate the constructive comments provided, which will help us further improve our paper.  We hope our response below resolves your concerns and questions.\n\n**Regarding Weakness 1 and 3: Missing Significance Tests**\n\nWe sincerely appreciate your valuable suggestions. To support the statistical significance of the differences in our subjective evaluations, we have added the complete MOS scores along with their 95% confidence intervals (CI95), as shown in Tables 4 and 5 in the Appendix. For objective evaluations, the error bars are very small due to the large number of test samples. Following prior works, such as MusiCot [17] and YUE [18], we did not include the error bars in the paper. Specifically, at a 95% confidence level, the error margin for MuQ-T and MuQ-A scores across all models is less than 0.01, and for content enjoyment (CE), content usefulness (CU), and production quality (PQ), the error margin is less than 0.015. For production complexity (PC), the error margin is less than 0.05. We thank you again for your suggestion, and we will include these results in the final version of the paper to further support our claims.\n\n**Regarding Weakness 2: Table 1 Misleading without \"LeVo w/o DPO\" Row**\n\nThank you for your comment. In this paper, we placed the ablation results regarding DPO in Table 3. The first row of Table 3 corresponds to \"LeVo w/o DPO\", and the last row, \"Interpolation\", represents the LeVo model. We apologize for any confusion caused. In the final version, we will clearly label \"LeVo (Interpolation)\" and add a \"LeVo w/o DPO\" row to Table 1.\n\nRegarding the effectiveness of DPO in musicality, while the differences are not highly pronounced in the AudioBox-Aesthetic metrics, they remain significant at a 95% confidence level, as calculated by the confidence intervals. Additionally, subjective evaluations better demonstrate the differences in musicality. As shown in Tabel 2, removing DPO led to a significant decrease in subjective metrics: OVL dropped by 0.24 points, MEL by 0.22 points, showing a substantial reduction in musicality. \n\nRegarding the effectiveness of DPO in prompt consistency, the effect is less noticeable due to the limited capability of the MuQ model. We further conducted a MOS listening test to evaluate the alignment between  the prompt and the generated music. In this experiment, \"LeVo\" scored 3.175 ± 0.043, while \"LeVo w/o DPO\" scored 3.055 ± 0.057, indicating that DPO improves prompt consistency. (The value before the ± represents the mean score, while the value after the ± represents the error margin calculated at a 95% confidence level.)\n\n**Regarding Weakness 4: Reproducibility**\n\nTo help readers reproduce our work, we have provided detailed information about dataset construction, model architecture, and training strategies in the paper and supplementatory material. Additionally, we commit to releasing the code and full checkpoints as soon as the paper is published.\n\n**Regarding Question 1: Claims of Lines 71-73**\n\nAs mentioned in line 70 of the paper, we aimed to highlight the advantages brought by our parallel prediction strategy for mixed and dual-track tokens and the LeLM structure. As shown in Table 2, it can be observed that both YuE and SongGen, which predict dual-track tokens only, perform significantly worse than \"LeVo w/o DPO\" in overall quality (OVL), vocal melodic attractiveness (MEL), vocal-instrument harmony (HAM), and audio sound quality (AQ), confirming the advantage of our method in musicality, vocal-instrument harmony, and sound quality. When DPO is applied, compared to \"LeVo w/o Dual-track\" (which predicts only mixed tokens) and \"LeVo w/o AR decoder\" (which directly jointly trains to predict mixed and dual-track tokens), \"LeVo\" achieves better pefemance in musicality, vocal-instrument harmony, and sound quality.\n\n**Regarding Question 2: Clarity of Figure 2**\n\nWe apologize for any confusion caused by Figure 2. The token sequences representing lyrics, text descriptions, and audio prompts are concatenated as a sequence and fed into the model, not summed. \"Prompt Embedding\" refers to the hidden states output by the last layer of the LeLM's language model after processing the concatenated token sequence (comprising lyrics, text descriptions, and audio prompts tokens). These states are passed to the AR Decoder to provide contextual information about the conditions. We will update the figure and method section to clarify this process and eliminate any potential confusion in the final version of the paper.\n\n**References:**\n\n[17] Lam, M. W. Y., Xing, Y., You, W., et al. \"Analyzable chain-of-musical-thought prompting for high-fidelity music generation.\" *arXiv* *preprint* *arXiv:2503.19611*, 2025.\n\n[18] Yuan, R., Lin, H., Guo, S., et al. \"YuE: Scaling open foundation models for long-form music generation.\" *arXiv* *preprint* *arXiv:2503.08638*, 2025."}, {"review_id": "6P4ZazKxVk", "reviewer": "Reviewer_XdTb", "summary": "This paper proposes a language model LeLM and a music codec for lyrics-to-song generation. The authors identify challenges in AI music generation such as synchronization loss, limited high quality data, inefficient prediction for long songs, and noisy or unreliable annotations. LeVo addresses these challenges by parallel prediction of mixed and dual-track tokens to guide overall structure while improving details. The authors also introduce a DPO-based multi-preference alignment method to fine tune for human preferences. Experiments show that LeVo has higher instruction-following ability, and achieves the highest Audiobox-Aesthetic scores among open-source approaches, and shows overall competitive results with academic baselines and industry models.", "questions": "What music data is the LeLM component trained on? Is the training data limited to Chinese and English songs?\n\nI was not able to access the appendix. When I click on the supplementary materials link, it shows \"no healthy upstream.\"", "limitations": "As the authors discussed, generated audio quality is constrained by data quality and leaves a performance gap compared to SOTA closed-source models. Further fine-tuning  is needed.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n(1) The idea of parallel mixed and dual-track token prediction with modular training is novel and addresses limitations of prior works like Jukebox and SongGen. \n\n(2) The proposed multi-dimensional DPO fine-tuning is a novel and interesting extension of RLHF methods for AI music generation.\n\n(3) Experimental results are strong, covering both objective and subjective evaluations, along with ablation studies.\n\nWeaknesses:\n\n(1) Performance of the proposed framework falls short of SOTA closed-source models like Suno.\n\n(2) It is not clear whether the training data for LeLM covers languages beyond Chinese and English. If the dataset is bilingual, the model may have limited ability to generalize to other languages and cultural song styles.", "was_revised": true, "final_justification": "Thank you for the detailed rebuttal. Your explanation of how LeVo compares with other open-source approaches and industry systems addresses my concerns about its competitiveness despite the data limitations. I was able to access the appendix successfully this time, and I appreciate the additional detail provided there.", "rebuttal": "We are grateful for the reviewer’s overall positive response and the constructive comments provided. Below, we provide detailed responses to all the concerns you raised.\n\n**Regarding Weaknesses 1 and Limitations: Performance of the Proposed Framework Falls Short of** **SOTA** **Closed-Source Models like Suno**\n\nDue to copyright issues with music data, our model, as well as other studies such as MusiCot [17], YUE [18], and DiffRhythm [33], have relied primarily on open-source data and a small portion of in-house copyrighted data for training. These datasets are not only limited in quantity but also have lower quality (mainly sourced from non-professional singers), which significantly affects the musicality, background noise, and vocal pronunciation of the generated songs. In contrast, industry systems like Suno are likely able to access more extensive and higher-quality datasets.\n\nAlthough our performance is constrained by the quality of the data, our proposed model structure, training strategy, and the multi-preference alignment method allow LeVo to outperform existing open-source models in various dimensions. Moreover, LeVo is competitive with current state-of-the-art industry systems such as Mureka and Haimian, with close or superior performance to Suno in terms of vocal-instrument harmony, audio sound quality, and lyrics following accuracy. This demonstrates the potential of our approach. We believe that with access to licensed higher-quality data, LeVo could further improve. We will also explore increasing the model size, dataset size, and data quality in future work.\n\n**Regarding Weakness 2 and Question 1: The Component of** **Training Data**\n\nWe sincerely appreciate your valuable suggestions regarding the training data. As mentioned in Appendix A (lines 6-7), our training data consists of licensed open-source datasets, such as DISCO-10M [1*] and the Million Song Dataset [2*], along with a small portion of copyrighted in-house data. Due to copyright limitations, the available data currently covers primarily Chinese and English songs, so LeVo is currently limited to these two languages.\n\nHowever, we believe that the structure and training methodology of LeLM are not language-dependent. Once enough data for other languages is available, we expect that the success seen in Chinese and English can be replicated in other languages. This limitation is solely due to the current availability of training data, and we plan to continuously collect data in more languages and demonstrate its capabilities across multiple languages in future work.\n\n**Regarding Question 2: Not Able to Access the Appendix**\n\nThank you for your feedback. We have re-tested the link to the supplementary materials, and it successfully downloads and unzips the appendix. We suspect that the issue may have been caused by a network problem on your end. Since new links cannot be provided during the rebuttal phase, we kindly suggest that you attempts to click on the “Supplementary Material” link again to access it.\n\n**References:**\n\n[17] Lam, M. W. Y., Xing, Y., You, W., et al. \"Analyzable chain-of-musical-thought prompting for high-fidelity music generation.\" *arXiv* *preprint* *arXiv:2503.19611*, 2025.\n\n[18] Yuan, R., Lin, H., Guo, S., et al. \"YuE: Scaling open foundation models for long-form music generation.\" *arXiv* *preprint* *arXiv:2503.08638*, 2025.\n\n[33] Ning Z, Chen H, Jiang Y, et al. DiffRhythm: Blazingly fast and embarrassingly simple end-to-end full-length song generation with latent diffusion[J]. arXiv preprint arXiv:2503.01183, 2025.\n\n[1*] Lanzendörfer, L., Grötschla, F., Funke, E., et al. \"Disco-10M: A large-scale music dataset.\" *Advances in Neural Information Processing Systems*, 2023, 36: 54451-54471.\n\n[2*] Bertin-Mahieux, T., Ellis, D. P. W., Whitman, B., & Lamere, P. \"The Million Song Dataset.\" *In ISMIR*, volume 2, page 10, 2011.\n\nNote: [N] denotes the reference in the paper and [N*] represents the reference in the Appendix."}, {"review_id": "MDbuYF7hi8", "reviewer": "Reviewer_7iGT", "summary": "This paper presents LeVo, a conditional music generation model. The authors propose a three-stage training pipeline: (1) autoregressive pretraining on mixed audio tokens, (2) fine-tuning on separate vocal and accompaniment tokens to improve fidelity and musicality, and (3) preference alignment using direct preference optimization (DPO) to enhance lyrics alignment, prompt consistency, and musicality. LeVo addresses key limitations of prior works, including low audio fidelity, vocal-instrument mismatch, and noisy training data. Extensive experiments and ablation studies validate the effectiveness of the proposed methods.", "questions": "- How are the residual vector quantization (RVQ) tokens handled during training and inference? Are they flattened into a sequential token stream or processed in parallel (e.g., stacked across channels)?\n- What types of prompts were used for evaluation? Were they sufficiently diverse in terms of genre, emotion, and complexity to rigorously assess the model’s capabilities across different generation scenarios?\n- Is the overall audio quality constrained by the capacity of the Stable Audio VAE decoder used in the codec? Or is there evidence that further improvements can be achieved independent of the codec's ceiling? A more detailed analysis of each module’s contribution to the final audio quality would be valuable for understanding where the main bottlenecks lie.\n- Line 312: AR decoder?", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n- The paper is clearly written and well-structured. The motivation and proposed methods are easy to follow and logically presented.\n- The literature review is thorough and well-contextualized.\n- The authors conduct comprehensive objective and subjective evaluations, including careful ablation studies, to support the effectiveness of their approach.\n- The paper makes effective use of a variety of pretrained models (e.g., DEMUCS, MuQ-MuLan, MuCodec, All-in-One, Qwen2-Audio, Whisper) for both data preparation and evaluation. These choices are well-justified and help address the limitations of noisy or incomplete training data.\n\nWeaknesses\n- The reliance on multiple pretrained models introduces a dependency on their quality. For instance, LeVo’s relatively lower performance in song structure clarity may be due to limitations in the All-in-One model. A deeper analysis of how such dependencies impact final performance would strengthen the work.\n- There are notable discrepancies between objective and subjective evaluation results (e.g., PER vs. LYC), but the paper lacks sufficient discussion to interpret these gaps. A more nuanced analysis would both guide future research and clarify the limitations of current evaluation metrics.\n- The MOS evaluation lacks clear calibration or reference targets. Including real human or professionally produced examples in the listening test could help contextualize the reported scores.\n- The training data includes licensed materials (e.g., DISCO-10M, Million Song Dataset), but the paper does not discuss the ethical implications or risk of memorization. An analysis of potential overfitting or content leakage would improve the ethical transparency of the work.", "was_revised": true, "final_justification": "The paper is clearly written and well-structured. The motivation and proposed methods are logically presented and easy to follow. The literature review is thorough and well-contextualized within the scope of related work. The proposed approach, which leverages dual-track tokens and Direct Preference Optimization (DPO), effectively addresses the challenges of text-to-music generation.\nThe authors have addressed all of the questions I previously raised. Additionally, they conducted a correlation analysis between subjective and objective metrics and evaluated potential memorization of training data to further clarify and support their results.", "rebuttal": "We sincerely appreciate your time and efforts in reviewing our paper. Your constructive feedback has significantly improved our work. Below, we provide detailed responses to your concerns and questions.\n\n**Regarding Weakness 1: Dependency on** **Pre-trained Model** **Quality**\n\nWe sincerely appreciate your valuable suggestion. Due to the lack of labeled music data and the high cost of annotation, we have relied on pre-trained models for data labeling. As you mentioned, the quality of these models can affect the overall performance. Our experiments on a small dataset revealed that the current pre-trained models achieve around 76% accuracy in song structure labeling and approximately 50% accuracy in song description labeling. In contrast, LeVo achieves 80% accuracy in song structure and 52% in song description. This initial experiment shows that, with our proposed model structure, training methods, and DPO-based multi-preference alignment method, LeVo's song structure clarity and prompt adherence surpass the accuracy of the labeling models. However, due to the limitations of pre-trained models, there is still a gap between these aspects and current state-of-the-art industry systems. We will further investigate this dependency and add more accurate and extensive experimental results in the final version of the paper. Additionally, we are currently working to improve the labeling accuracy of pre-trained models and introducing human-annotated data for fine-tuning to explore how to enhance model performance further.\n\n**Regarding Weakness 2: Discrepancy between Objective and Subjective Evaluation Results**\n\nAs the reviewer mentioned, objective metrics in music evaluation have limitations, which result to the observed differences between objective and subjective results. Given that music is an auditory art, we set up multiple dimensions for subjective experiments to provide a more accurate and persuasive human auditory perception. To explore the limitations of objective metrics, we calculated the Pearson Correlation between human evaluation results and objective metrics. We found a weak correlation between FAD and AQ (-0.24), suggesting that distribution-level metrics may not capture subtle acoustic differences. However, there is a strong correlation between PER and LYC (-0.77), indicating that ASR models can capture the lyrics following accuracy. Nevertheless, ASR models are affected by background music, vocal pitch, and other factors, leading to some errors.\n\nFor the Meta Audiobox-Aesthetic scores and the subjective evaluation results, the following correlations were observed:\n\n|      | OVL   | MEL   | HAM   | SSC  | AQ    | LYC   |\n| ---- | ----- | ----- | ----- | ---- | ----- | ----- |\n| CE   | 0.32  | 0.44  | 0.28  | 0.07 | 0.31  | -0.01 |\n| CU   | 0.26  | 0.36  | 0.16  | 0.02 | 0.27  | -0.14 |\n| PC   | -0.08 | -0.14 | 0.32  | 0.44 | -0.14 | 0.23  |\n| PQ   | 0.21  | 0.07  | -0.06 | 0.01 | 0.15  | -0.12 |\n\nThe results indicate that CE (content enjoyment) has the strongest correlations with OVL (0.32), MEL (0.44), and AQ (0.31), indicating that CE is likely linked to human preferences for music. CU (content usefulness) also shows some correlations in these aspects but weaker than CE. PC (production complexity) has the strongest correlations with HAM (0.32) and SSC (0.44), showing sensitivity to variationgs in vocal and accompaniment elements. PQ (production quality) shows weak correlations with most subjective results, indicating limitations in capturing these six subjective properties. We will include this analysis in the final version of the paper to clarify the limitations of current metrics.\n\n**Regarding Weakness 3: MOS Evaluation Lacking Clear Calibration or Reference Targets**\n\nThanks for your constructive suggestion. As commercial products like Suno and Haimian often prohibit the input of real song lyrics to prevent content leakage, we followed the experimental setups from MusiCot [17] and YUE [18], using lyrics generated by LLMs for the evaluation. During the experiments, we asked participants to listen to the entire music generated by different models with the same input before scoring, thereby obtaining scores that reflect the relative differences between models. Your suggestion is valuable, and in future work, we will attempt to manually produce a set of songs based on the generated lyrics for more accurate MOS evaluation.\n\n**Regarding Weakness 4 and Ethical Concerns: Ethical Implications and Risk of Memorization**\n\nAs the reviewer rightly pointed out, the risks of memorization and content leakage are important issues in song generation, and they have often been overlooked in prior work. We conducted a new experiment where we selected 40 songs from the training set and generated corresponding songs using LeVo. We checked the similarities between the generated songs and the original ones by calculating 5-gram and Levenshtein distance similarity for the extracted dual-track token sequences and manually checking the similarity of the melodies.  The results showed that the 5-gram similarity was 0.0001, and the Levenshtein distance similarity was 0.0012, with no similar melodies found through manual inspection. These results indicate that using the same input is unlikely to cause training data leakage. This might be due to the relatively small model size (2B parameters) compared to the training dataset of 2 million songs, which prevents severe overfitting. Additionally, LeVo's multi-preference alignment stage after pre-training helps reduce the memorization risk. We will include these experimental results in the final version of the paper, and we hope to explore content leakage and better training strategies in future work.\n\n**Regarding Question 1: Handling of RVQ Tokens**\n\nFor the mixed token, its primary role is to guide the overall arrangement of melody, rhythm, and tempo, so we only predict the first layer of RVQ tokens. For dual-track tokens, as shown in Table 2 of Appendix B, we found that using a 2-layer dual-track token (one for vocals and one for accompaniment) closely matches the performance of a 4-layer mixed token. Therefore, in our experiments, we used a 2-layer dual-track token, and these layers were processed in parallel through cross-channel stacking.\n\n**Regarding Question 2: Evaluation of Prompts**\n\nFor audio prompts, we randomly selected unseen music segments from different genres for evaluation. For text descriptions, we used a LLM to generate diverse content, covering a wide range of genres, emotions, and complexities to rigorously assess the model's capabilities across different scenarios.\n\n**Regarding Question 3: Contribution of Each Module to Audio Quality**\n\nThanks for your insightful suggestion. We conducted a new experiment where we selected 40 unseen songs and performed a MOS listening test on the audio sound quality of ground truth audio, VAE-reconstructed audio, Codec-reconstructed audio, and LeVo-generated audio (using the same lyrics). The results are as follows:\n\n| Model                | AQ   |\n| -------------------- | ---- |\n| Ground Truth         | 3.81 |\n| VAE Reconstruction   | 3.79 |\n| Codec Reconstruction | 3.75 |\n| LeVo                 | 3.66 |\n\nThe results show that the overall loss in audio quality is primarily caused by three factors: VAE reconstruction loss (a decrease of 0.02), Codec reconstruction loss (a decrease of 0.04), and LeLM modeling loss (a decrease of 0.09). The loss caused by LeLM modeling is the most significant. While LeVo performs better than models like SongGen and LeVo w/o AR Decoder (both of which predict dual-track tokens), there is still a gap compared to Codec reconstruction. Additionally, the VAE module and Codec module also cause some loss when rendering tokens into audio. We will include this analysis in the final version of the paper and explore further improvements in audio quality from these two aspects in future work.\n\n**Regarding Question 4: Line 312 -** **AR** **Decoder**\n\nThank you for pointing out the typo. We will correct this in the final version of the paper.\n\n**References:**\n\n[17] Lam, M. W. Y., Xing, Y., You, W., et al. \"Analyzable chain-of-musical-thought prompting for high-fidelity music generation.\" *arXiv* *preprint* *arXiv:2503.19611*, 2025.\n\n[18] Yuan, R., Lin, H., Guo, S., et al. \"YuE: Scaling open foundation models for long-form music generation.\" *arXiv* *preprint* *arXiv:2503.08638*, 2025."}]

Author rebuttal:

Meta-review: This paper introduces LeVo, a conditional music generation model trained through a three-stage pipeline: (1) autoregressive pretraining on mixed audio tokens, (2) fine-tuning on separated vocal and accompaniment tokens to improve fidelity, and (3) preference alignment via direct preference optimization (DPO) to enhance lyric alignment and consistency. The proposed method addresses key limitations of prior work, such as low fidelity, vocal–instrument mismatch, and noisy training data.

All reviewers find the submission valuable, novel, and well-executed, with strong empirical results. In the rebuttal, the authors satisfactorily addressed concerns regarding missing results (e.g., error bars, baselines without DPO), reproducibility, and datasets details. I strongly encourage the authors to incorporate these clarifications and maintain their commitment to open-sourcing in the final version.

In light of these strengths, I recommend acceptance.
Decision: Accept (poster)