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Introducing Pika Soundtrack
Sound that understands what happens—and when
August 18, 2026 • By Pika Team
Sound that understands what happens—and when
A silent video can show us what happened, but a complete soundscape makes us feel it. The impact of a foot landing, a voice in the room, and music carrying the emotional arc each do different work. It takes all three—sound effects, voice, and music—to make a scene feel whole.
Today, we’re introducing Pika Soundtrack, a model that turns video into a synchronized, full-scene soundscape. Upload a video and Pika generates motion-aware sound effects, voice, music, and ambience that follow the action, with optional instruction for what to emphasize, include, or leave out. The model runs with exceptional speed in local evaluation; allowing Pika Soundtrack to be up to 2× more cost-efficient than competing video-to-audio models.
See—and hear—the model
Prompt: One continuous, intense drum solo, thirty seconds, no other instruments — a four-count then a groove that builds relentlessly to a frenzied peak: snappy snare, fast hi-hat, deep kick, ripping tom fills, crashing cymbals, a buzz roll, landing on one final hard snare-and-crash hit. Whiplash intensity, room-close and punchy, every hit tight.
Zero prompt to full soundscape
The technical challenge
Pika Soundtrack is not simply sound generation with an image attached. A useful system must solve several problems together:
- Scene understanding: Recognize objects, actions, materials, environments, and implied sound sources.
- Temporal alignment: Place impacts, movement, vocalizations, and transitions at the moments viewers expect them.
- Scene coverage: Balance foreground Foley with background ambience and acoustic space.
- Perspective: Account for distance, framing, camera motion, and the implied listener position.
- Direction: Follow an optional instruction without introducing sounds that contradict the video.
- Long-range consistency: Preserve alignment and ambience beyond the first few seconds and across scene changes.
A soundtrack can be plausible in isolation and still feel wrong against the picture. Pika Soundtrack succeeds only when the sound belongs to the scene and unfolds with it.
To tackle these issues, Pika Soundtrack takes a video together with an optional natural-language instruction and produces a new audio track for the complete scene. The system extracts visual and temporal information from the source, combines it with the user’s direction, and generates audio conditioned on both what appears and when events occur.
Methods
Architecture
The core model is a transformer-based latent diffusion model. It operates over sequences of compressed video, text, and audio tokens, using attention to connect visual events and prompt semantics to the audio tokens being denoised.
The model takes a video and a text prompt as input, then generates a new audio track that matches both the visible motion and the requested sound description. Internally, the video is first compressed into a sequence of visual latent tokens. These tokens preserve timing, object motion, scene layout, and frame-level action cues, but they are much smaller than raw pixels. The text prompt is encoded into semantic tokens that describe what sounds should be present, such as footsteps, wind, water, impact sounds, ambience, or “no dialogue / no music.”
Audio is generated in a compressed latent space rather than directly as waveform samples. The model starts from random, noisy audio latents and gradually denoises them into a clean audio representation. During every denoising step, the audio generator attends to the input video tokens and the text tokens, so it can decide both what sound to create and when it should happen. After denoising, the generated soundtrack is added to the video.
Training
The core objective is a flow-matching / diffusion-style denoising loss over audio latents. During training, we sample a noise level or continuous time step, interpolate between clean target audio latents and noise, and train the transformer to predict the velocity or denoising direction that maps the noisy audio latent back toward the clean target. The video latent is kept fixed as conditioning, so the loss forces the audio trajectory to align with visible motion, scene changes, and prompt semantics rather than learning audio in isolation.
Efficient full-scene inference
Generating a complete soundtrack requires the model to preserve visual context while synthesizing temporally coherent audio across the full scene. Our inference stack is optimized at both the model and serving layers so long-form coverage remains interactive instead of becoming an offline batch process.
We combine distillation, activation and context caching, and sparse attention to reduce the amount of computation repeated across neighboring windows. Distillation compresses the generation trajectory into fewer effective steps, caching reuses visual and temporal features that do not change between adjacent chunks, and sparse attention focuses compute on the local audio/video regions that drive synchronization while still retaining enough global context for scene-level consistency. Together, these optimizations let the system generate full-length soundtrack outputs with low latency while preserving temporal alignment.
Benchmark Results
In this section, we evaluate soundtrack systems on both quality and operational usability. We measure whether each model generates audio that matches the visible scene, stays synchronized over time, and remains plausible across diverse real-world and cinematic videos. Because long videos expose failures that short clips can hide, we evaluate the full benchmark with temporal chunks instead of relying only on the first few seconds.
Across the benchmark, Pika Soundtrack achieves the strongest semantic alignment and the lowest audiovisual desynchronization, while still covering the full input duration while delivering the fastest observed full-duration generation speed.
Our benchmark contains videos of various resolutions, covering physical impacts, animals, weather, water, sports, human motion, music performance, speech-like activity, natural ambience, and designed cinematic scenes.
We compare Pika with LTX-2.3 Foley V2A, HunyuanVideo-Foley, and MMAudio v2.
ImageBind score measures semantic correspondence between video and audio: whether the generated soundtrack belongs with the visible scene. DeSync measures audiovisual temporal misalignment, where lower is better. IS-PANNs and IS-PASST are classifier-based measures of audio quality and diversity.
Pika achieves the strongest semantic alignment and lowest temporal misalignment in this benchmark. MMAudio v2 leads the classifier-based quality and diversity measures. We report both because video sound is not one-dimensional: a system must generate compelling audio, but that audio must also describe the correct scene at the correct time.
Speed and full-duration coverage
Comparing soundtrack latency requires accounting for how much audio each system actually generated. A system that returns only part of a long video should not appear faster because its output was shorter.
These measurements use different execution environments: hosted end-to-end APIs for Hunyuan and MMAudio, summed chunk inference for LTX, and warm self-hosted end-to-end timing for Pika. They are useful operational observations, not a controlled identical-hardware comparison.
However, the achieved speed allows Pika Soundtrack to be up to 2x more cost-efficient than the compared models.
Applications and future work
Pika Soundtrack can add natural Foley to silent footage, build ambience around a scene, generate designed cinematic sound, or create alternative sound directions from the same visual source.
This release is part of our broader work on generative audio. Pika Real-Time Text-to-SFX creates sound directly from language; Pika Soundtrack begins with a visual sequence and determines what should be heard over time.
Future work includes longer-duration generation, stronger event-level control, improved spatial and acoustic perspective, and targeted research on speech and multilingual dubbing.
Availability
Pika Soundtrack is live now in the Pika API Club. Give it a try. We’re excited to see and hear what you make with it.