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Now Hear This: Pika SFX
Production-ready sound from language, generated faster than real-time
August 18, 2026 • By Pika Team
Production-ready sound from language, generated faster than real-time
Today, we’re introducing Pika SFX, a new model that turns a written description into a focused, ready-to-use sound effect in real-time.
Describe a single event—glass shattering or a cartoon whoosh—or direct a complete sound sequence with timing, environment, texture, and mood. Pika SFX generates an audio clip that follows the request while avoiding unrelated speech, music, and background noise unless they are explicitly part of the prompt.
Write the sound you want. Pika SFX generates up to 20 seconds of audio for video, games, creative tools, and editing workflows.
Listen to the model
A single event
glass shattering
Material, space, and perspective
A heavy metal door slamming shut in a large empty warehouse, followed by a long metallic echo.
A sequence of events
A cork popping out of a champagne bottle, followed by fizzy liquid being poured into a glass, the bubbles softly crackling as they settle.
Layered sound design
An old malfunctioning machine: gears grinding irregularly, steam hissing from a valve, electrical sparks crackling, a loose bolt rattling, ending with a heavy clunk and a power-down whir.
Designed fantasy sound
A magical spell being cast: shimmering sparkles building up, an ethereal choir-like swell, then a powerful whoosh release with glittering chimes trailing off.
The technical challenge
A useful sound prompt can be as short as “a metal door slamming in a warehouse.”
Or it can describe a sequence of events: “A cork pops out of a champagne bottle, followed by fizzy liquid pouring into a glass, with bubbles softly crackling as they settle.”
Prompt fidelity, however, is only the first of three constraints. The second is audio quality: sounds should have crisp, accurate transients, plausible acoustics and reverberation, and no unwanted content beyond what the prompt specifies. The third is efficiency: how can we generate high-quality audio quickly and cost-effectively, without sacrificing fidelity or controllability?
Pika SFX is designed to tackle all three challenges at once, combining prompt fidelity, audio quality, and efficient generation in a single system.
Methods
Architecture overview
Pika SFX translates a natural-language description into a complete 44.1 kHz stereo audio waveform. A transformer-based text encoder first maps the prompt into conditioning embeddings that capture the requested sound sources, physical actions, acoustic environment, timing, texture, and creative direction.
The generative core is a text-conditioned diffusion transformer (DiT) operating in a compressed acoustic latent space. Rather than generating waveform samples directly, the transformer progressively constructs a compact representation of the sound. This reduces the sequence length and computational cost while preserving both fine acoustic details—such as transients, texture, and reverberation—and broader temporal structure. The model can therefore represent anything from a single impact to evolving ambience and layered multi-event sequences.
A semantic-acoustic autoencoder then decodes the generated latents into the final stereo waveform.
We designed the system around three goals:
- Prompt fidelity: preserve the objects, actions, environment, and sequence described by the user.
- Production quality: generate clean, balanced audio with minimal artifacts or unrelated content.
- Efficient generation: preserve useful sound quality within a distilled, few-step inference path suitable for interactive applications.
This architecture provides the foundation; the inference stack determines how quickly it can be served.
Training for efficient generation
We train Pika SFX in three stages. First, the model learns to transform random noise into structured audio using a flow-matching objective. During training, it observes intermediate points between noise and real audio and learns the direction—or velocity—that moves each point toward a clean audio representation. This teaches the diffusion transformer to model diverse sounds, temporal structure, and text–audio relationships.
Next, we apply teacher–student distillation. A high-quality teacher follows a longer, multi-step generation path, while the student learns to predict the teacher’s final result from an intermediate noisy state. This effectively shortens and straightens the generation trajectory, allowing the model to produce useful audio with far fewer inference steps.
Finally, we refine the distilled student model through post-training on high-quality curated data with human feedback. Together, these stages allow us to generate high-fidelity content efficiently.
Real-time interactive inference
We treat inference as an end-to-end systems problem, optimizing the model, hardware, and serving stack for continuous generation.
At a high level, our approach combines two ideas:
- Few-step generation. We distill the model to generate high-quality audio in fewer inference steps, substantially reducing sequential computation.
- Deployment-aware optimization. We optimize for the specific combination of model architecture, hardware, output duration, and serving configuration instead of treating each component in isolation.
Together, these choices turn inference from a cold batch job into a warm, continuous generation loop.
Evaluation
Evaluation setup
The benchmark uses 50 prompts and 454 seconds of requested audio per model, with requested durations from 2 to 20 seconds. It covers short and detailed prompts across impacts, Foley, animals, nature, vehicles, human sounds, ambience, spatial motion, and layered multi-event sequences.
Generation performance
Across the same 50 prompts of requested audio per model, Pika SFX completed its end-to-end path in 0.847 seconds on average. For context, the fastest hosted APIs in this benchmark averaged 2.47 seconds for ElevenLabs v2 and 2.64 seconds for Stable Audio 3 SFX.
Figure: Generation latency versus requested audio duration across text-to-SFX models. Pika SFX achieves sub-second end-to-end generation, including CPU processing and file saving. Lines show means; shaded bands show ±1 standard deviation. Lower is faster.
Audio quality
Evaluating generated sound is difficult. A clip can match a prompt in embedding space and still be noisy, poorly balanced, or awkward to place in an edit. We therefore evaluated both prompt-related metrics and the practical qualities of the resulting audio.
What we measure
The evaluation separates prompt alignment from practical audio quality:
- CLAPScore: similarity between the generated audio and prompt in a shared embedding space.
- AQAScore: whether the prompted events are present, as assessed by a multimodal model.
- Content usefulness: whether the generated content is practically usable, using the corresponding Meta Audiobox Aesthetics axis.
- Production quality: whether the audio is clean and ready for creative work, using the corresponding Meta Audiobox Aesthetics axis.
Results
Across this benchmark, Pika SFX achieved the highest content usefulness and production quality scores among the systems evaluated.
Pika SFX leads the measurements closest to using the result in a creative workflow—whether the content is useful and whether the audio sounds production-ready.
Applications and future work
Pika SFX is designed as a building block. It can generate an isolated effect for an edit, create a sequence from a detailed direction, supply ambience for a scene, or bring sound generation directly into an application through the API.
This release is one part of our broader work on generative audio. Pika SFX begins with language. Pika Video-to-SFX begins with the picture itself, generating effects and ambience aligned with visible events and motion across a video.
Availability
Pika SFX is available now through the Pika API Club. Developers can generate downloadable MP3 sound effects from text prompts, with configurable durations from 1 to 20 seconds and optional controls for negative prompts, seed, inference steps, and guidance scale.
We’re excited to hear what you create.