LD-LAudio-V1: Long-Form Video-to-Audio Extension

Updated 4 July 2026

LD-LAudio-V1 introduces dual lightweight adapters to inject long-range context, enabling seamless long-form video-to-audio synthesis.
It leverages a clean, human-annotated long-form dataset (LPSE-1) to produce pure sound effects with improved temporal alignment and semantic continuity.
Empirical results show significant gains in audio quality and boundary consistency, though precise frame-level synchronization remains a challenge.

LD-LAudio-V1, introduced in "LD-LAudio-V1: Video-to-Long-Form-Audio Generation Extension with Dual Lightweight Adapters" (Zhang et al., 14 Aug 2025), is a long-form extension of a state-of-the-art short-form video-to-audio (V2A) generator. It is designed specifically to synthesize temporally coherent audio for videos lasting tens of seconds or longer, while avoiding the "cut-and-stitch" artifacts that arise when one simply applies short-clip generation repeatedly. The method combines a clean and human-annotated long-form dataset with a lightweight architectural extension built on top of a base short-form V2A model, targeting semantically aligned audio for silent videos and pure sound-effect generation.

1. Problem setting and motivation

The task is to generate an audio sequence $a$ with equivalent duration to a long video $v$ . The key difficulty is that long-form audio requires both local, frame-level synchronization and global, clip-to-clip coherence. Short-form methods typically work on clips under about 10 seconds, where the model only needs to learn local correspondence. They do well on short segments but are not designed for minute-scale synthesis. Long-form methods that process the video in segments often lose global context, and repeated independent generation causes discontinuities at boundaries (Zhang et al., 14 Aug 2025).

LD-LAudio-V1 is motivated by the claim that long-form V2A is not just a scaling problem. If one naïvely segments a long video into independent short clips, or fine-tunes only on short training videos, the model tends to lose global context, produce inconsistent sound events across boundaries, and create audible splicing artifacts. Existing long-form efforts are also limited by data quality: many long-form datasets are noisy, containing speech, music, voice-over, or irrelevant sounds, which weakens learning for pure Foley and sound-effect generation. In this formulation, boundary continuity and semantic continuity are treated as first-order requirements rather than secondary consequences of better short-form synthesis.

2. Architectural formulation

LD-LAudio-V1 extends a state-of-the-art short-form V2A model, specifically the MMAudio-L-44.1kHz family in the experiments, with a dual lightweight adapter mechanism. The base generative backbone remains a unified multimodal synthesis transformer; the long-form capability comes from adding two small adapters that inject long-range context into the short-form conditions without retraining the full model (Zhang et al., 14 Aug 2025).

The method uses multiple types of input features: CLIP visual features at 8 fps, 1024-dim; CLIP text tokens, 77 tokens, 1024-dim; VAE latent space at 31.25 fps, 20-dim; and Synchformer features at 24 fps, 768-dim, then projected and up-sampled to frame-aligned features at 31.25 fps. All features are treated as 1D temporal tokens, and no absolute position encoding is used, allowing the model to generalize to durations not seen in training.

For local generation, the model first extracts frame-level visual features, frame-aligned synchronization features, and clip-level semantic features. These are projected into a common hidden dimension and fused into two kinds of conditioning signals: $c_f$ , which provides fine-grained local conditioning, and $c_g$ , which provides global semantic conditioning. The clip-level contextualization module aggregates clip visual and text encodings, then averages and concatenates them into a global representation $F_g^{con}$ , which is fused with timestamp embeddings to produce $c_g$ .

The long-form extension introduces two adapters: $h_{syn}$ , a synchronization adapter, and $h_{global}$ , a global-context adapter. For a long video split into $L$ clips $\{V_{clip}^{(i)}\}_{i=1}^L$ , the model computes $v$ 0, $v$ 1, and $v$ 2 over the multi-clip sequence. The adapters then inject long-range context into the base conditions:

$v$ 3

The generation backbone is unchanged and uses these final conditions in each transformer block:

$v$ 4

The stated intuition is that $v$ 5 injects multi-clip semantic context so events remain consistent across the whole long video, while $v$ 6 injects long-range synchronization priors so the model preserves event timing and transitions. This is a parameter-efficient design, with only about a 4% parameter increase over the base model.

3. Dataset and training regime

A central component of LD-LAudio-V1 is LPSE-1, described as the first long-form clean sound effects dataset for long-form V2A research (Zhang et al., 14 Aug 2025). Its key properties are exact and restrictive: it contains more than 6K videos, over 20K audio-visual events, and 120 event categories; each clip is longer than 60 seconds; the videos depict real-life audio-visual scenes; and the audio is manually verified to contain pure sound effects only, with no voice-over, no music, no speech, and no irrelevant audio artifacts.

The training strategy is data-driven. Rather than redesigning the generator from scratch, the method extends a strong short-form model using long-form supervision and the new dataset. The reported comparisons are against MMAudio-L-44.1kHz Zeroshot, MMAudio-L-44.1kHz Finetuned on short training videos, and MMAudio-L-44.1kHz Long-form with dual adapters. The key contribution is that long-form behavior is achieved not by full retraining, but by attaching the adapters and training them with long-form data, preserving the short-form competence of the base generator.

Algorithmically, long videos are handled as multi-clip sequences. Instead of generating each short chunk independently, the model extracts local clip conditions for the current segment and global conditions over the entire multi-clip input. These are fused through the adapters before audio generation. The method does not present a separate explicit boundary-stitching algorithm in the provided formulation; the core improvement comes from architectural conditioning and long-form data rather than a novel objective.

4. Evaluation protocol, metrics, and empirical results

The evaluation covers three axes: audio quality and distribution matching, temporal alignment, and long-form consistency (Zhang et al., 14 Aug 2025). The reported metrics are $v$ 7, $v$ 8, $v$ 9, $c_f$ 0, $c_f$ 1, $c_f$ 2, $c_f$ 3, $c_f$ 4, $c_f$ 5, $c_f$ 6, and $c_f$ 7. The long-form consistency metrics are defined operationally in the text: Energy $c_f$ 8 is the average energy change within 10 ms before and after each segmentation point between adjacent short clips; Energy $c_f$ 9 compares the generated audio’s average boundary energy change against the ground truth; and Semantic Relevance (Sem.Rel.) measures consistency of semantics across clips. The ground-truth row reports $c_g$ 0 and $c_g$ 1.

The key comparison is against the short-video fine-tuned baseline.

Metric	Fine-tuned	LD-LAudio-V1
$c_g$ 2	450.00	327.29
$c_g$ 3	34.88	22.68
$c_g$ 4	3.75	1.28
$c_g$ 5	2.49	2.07
$c_g$ 6	1.78	1.53
$c_g$ 7	4.17	4.30
$c_g$ 8	0.25	0.28
$c_g$ 9	0.3013	0.1349
$F_g^{con}$ 0	0.0531	0.0288
$F_g^{con}$ 1	2.73	3.28

These correspond to the reported improvements $F_g^{con}$ 2: 450.00 $F_g^{con}$ 3 327.29 (+27.27%), $F_g^{con}$ 4: 34.88 $F_g^{con}$ 5 22.68 (+34.98%), $F_g^{con}$ 6: 3.75 $F_g^{con}$ 7 1.28 (+65.87%), $F_g^{con}$ 8: 2.49 $F_g^{con}$ 9 2.07 (+16.87%), $c_g$ 0: 1.78 $c_g$ 1 1.53 (+14.04%), $c_g$ 2: 4.17 $c_g$ 3 4.30 (+3.12%), $c_g$ 4: 0.25 $c_g$ 5 0.28 (+12.00%), $c_g$ 6: 0.3013 $c_g$ 7 0.1349 (+55.23%), $c_g$ 8: 0.0531 $c_g$ 9 0.0288 (+45.76%), and $h_{syn}$ 0: 2.73 $h_{syn}$ 1 3.28 (+20.15%). The only metric that slightly worsens relative to the short-video fine-tuned model is $h_{syn}$ 2: 1.38 $h_{syn}$ 3 1.51.

Against the zero-shot MMAudio baseline, the long-form model improves quality and semantic relevance in nearly every measure, while preserving overall efficiency. Zero-shot has better $h_{syn}$ 4 and slightly better $h_{syn}$ 5, but lower quality and consistency overall. The reported cost is modest: Base MMAudio-L-44.1kHz: 1.03B params, Fine-tuned version: 1.03B params, Long-form with dual adapters: 1.07B params; and inference time for a 60s clip is 61.27 s for the base and fine-tuned models versus 62.75 s for the long-form model. The reported findings therefore tie the strongest gains to long-range acoustic coherence, perceptual quality, and boundary continuity rather than to reduced synchronization error on $h_{syn}$ 6.

5. Relation to adjacent systems and terminology

LD-LAudio-V1 belongs to a line of research on video-conditioned audio generation, but it should not be conflated with Tri-Ergon. Tri-Ergon, presented in "Tri-Ergon: Fine-grained Video-to-Audio Generation with Multi-modal Conditions and LUFS Control" (Li et al., 2024), is a new, related controllable video-to-audio system rather than an alias for LD-LAudio-V1. Tri-Ergon is a diffusion-based V2A model operating in a learned latent space; it incorporates textual, auditory, and pixel-level visual prompts through LanguageBind, introduces LUFS embedding for precise manual control of the loudness changes over time for individual audio channels, and generates 44.1 kHz high-fidelity stereo audio clips of varying lengths up to 60 seconds. LD-LAudio-V1 instead addresses long-form coherence by extending a short-form V2A generator with dual lightweight adapters and a clean long-form dataset, with the primary goal of reducing splicing artifacts and temporal inconsistencies.

A separate research direction is represented by "UniAudio 1.5: LLM-driven Audio Codec is A Few-shot Audio Task Learner" (Yang et al., 2024). UniAudio 1.5 is a frozen-Llama-based few-shot audio learner built around LLM-Codec, which transfers audio into the textual space by representing audio tokens with words or sub-words in the vocabulary of LLMs. Its central motivation is cross-modal in-context learning without parameter update, and it studies tasks such as speech emotion classification, audio classification, text-to-speech generation, and speech enhancement. This suggests a different problem formulation: UniAudio 1.5 focuses on few-shot audio task learning through shared token space, whereas LD-LAudio-V1 focuses on long-form video-to-audio generation through parameter-efficient conditioning extensions.

Within this landscape, LD-LAudio-V1 is most precisely understood as a parameter-efficient long-form conditioning extension to a short-form V2A generator, not as a general controllable-audio framework and not as an LLM-native audio tokenization system.

6. Limitations and prospective directions

The reported limitations are explicit (Zhang et al., 14 Aug 2025). First, $h_{syn}$ 7 does not improve over the fine-tuned short-form baseline, so precise synchronization remains an open issue. Second, the method is evaluated primarily on pure sound effects, so extension to speech, music, or more complex mixed-audio scenes is not demonstrated. Third, the architecture is a lightweight extension over chunked processing; it does not propose a fully unified end-to-end long-form generator that eliminates chunking altogether.

The main empirical conclusions are correspondingly narrow and technical. Long-form V2A needs explicit long-range conditioning; a clean long-form dataset matters; dual lightweight adapters are effective and efficient; global + synchronization context is complementary; and artifact reduction is measurable, especially on boundary energy continuity and semantic relevance. Likely future directions include stronger synchronization modules, better boundary-aware training objectives, joint modeling of sound effects plus speech/music, larger and more diverse long-form datasets, and more explicit splicing/transition modeling across clips.

In this sense, LD-LAudio-V1 is best understood as a targeted response to a specific failure mode of short-form V2A systems: they can produce locally plausible sound, but they do not reliably preserve global context across long durations. The contribution of LD-LAudio-V1 is to operationalize long-range conditioning with minimal architectural overhead and to pair that design with a long-form dataset whose audio content is constrained to pure sound effects, making long-form coherence a measurable and trainable objective rather than a side effect.