Text to Sound Effects: How AI Generates Audio from Description

If you have used a tool like Midjourney or DALL-E, you already understand the core idea: you type a description, and a neural network generates matching content. Text-to-image is now a familiar concept. Text-to-audio is the same paradigm applied to sound — and for people who work with video, games, or podcasts, it can be a useful production tool.

Sound effects are notoriously time-consuming to source. A one-minute video might need twenty distinct SFX clips: footsteps on gravel, a door creaking, wind through leaves, a distant car horn. Traditionally, each of these requires either recording in the field, buying from a library, or hunting through royalty-free archives for something imperfect. Text-to-audio generation can shorten that pipeline. You type “footsteps on dry gravel, slow pace, morning atmosphere” and may get a usable 10-second WAV file, or at least a rough draft to refine.

This article covers how text-to-audio generation works, the models that power it, how to prompt effectively, and where the technology stands in 2026 — including the practical workflows that video editors and game developers can use today.

What Text-to-Audio (Non-Speech) Actually Is

It helps to distinguish text-to-audio from text-to-speech, because the two are often confused. TTS takes text and generates a human voice speaking those words. Text-to-audio (sometimes called “generative audio” or “text-to-SFX”) takes a description and generates any sound that is not speech — rain, engines, footsteps, explosions, ambient room tone, musical instruments, animal calls.

The technical difference matters. TTS models learn the mapping from phonemes to vocal tract acoustics — a constrained and well-studied problem. Text-to-audio models must learn the mapping from arbitrary text descriptions to arbitrary sound events, with no fixed vocabulary of sounds and no standard way to describe them. A “door creaking” in a horror game sounds different from a “door creaking” in a cartoon. A “gunshot” varies wildly by weapon type, distance, and environment.

This open-endedness makes text-to-audio harder than TTS. It also makes it more valuable for production work, because the variety of sounds creators need is essentially unlimited.

How Text-to-Audio Models Work

The internal architecture of text-to-audio models borrows heavily from text-to-image. Most systems use some form of latent diffusion: a process that starts with random noise and gradually refines it into a coherent audio signal, guided by a text embedding from a language model.

The Latent Diffusion Pipeline for Audio

The diffusion process for audio works in three stages:

Stage 1: Text encoding. The input prompt is converted into a vector representation using a pretrained language model. CLAP (Contrastive Language-Audio Pretraining) is the most common choice — it is a dual-encoder model trained on text-audio pairs, so its text embeddings are aligned with audio features. Some newer models use T5 or FLAN-T5 for richer semantic understanding.

Stage 2: Latent diffusion. A UNet or transformer processes the text embedding alongside a latent noise tensor. At each denoising step, the model predicts and removes a portion of the noise. The number of steps controls quality — 50 steps produces recognizable audio, 200+ steps produces cleaner results at the cost of generation time. The diffusion happens in a compressed latent space rather than directly on waveforms, which keeps computation manageable.

Stage 3: Decoding. The denoised latent is passed through a decoder that reconstructs the audio signal. This decoder could output mel-spectrograms (which are then converted to waveforms via a vocoder like HiFi-GAN), or it could output discrete audio codec tokens that are decoded by a neural codec like EnCodec.

The result is a waveform intended to match the text description — not by selecting from a library, but by generating new audio from the model.

Spectrogram Generation vs. Audio Codec Tokens

There are two main approaches to representing audio during generation:

Spectrogram-based models generate mel-spectrograms (visual representations of frequency over time) and then convert them to audio with a vocoder. This is the approach used by AudioLDM and AudioLDM 2. The advantage is interpretability — you can visualize what the model is generating. The disadvantage is that spectrograms lose phase information, and the vocoder can introduce artifacts.

Codec-based models generate discrete tokens using a neural audio codec (like EnCodec or DAC). The model predicts token sequences, and the codec decodes them to waveforms. This is the approach used by AudioCraft (AudioGen + MusicGen). The advantage is higher fidelity and better handling of complex sounds. The disadvantage is that token sequences are harder to inspect and debug.

In practice, both approaches produce good results. Codec-based models tend to sound cleaner, especially for complex sounds with sharp transients (impacts, clicks, bursts). Spectrogram-based models can be more predictable for sustained, tonal sounds.

Key Models in 2026

AudioLDM 2

AudioLDM 2 (by researchers at Hong Kong Baptist University and Microsoft) is built on a latent diffusion architecture with a FLAN-T5 text encoder. It was one of the first models to demonstrate reliable text-to-SFX generation, and it remains the most accessible option for experimentation.

AudioLDM 2 generates audio at 16kHz with controllable duration (up to about 30 seconds). It handles a wide range of sound classes: environmental sounds (rain, wind, fire), human sounds (footsteps, applause, coughing), and mechanical sounds (engines, alarms, tools). The quality can be useful but is not always studio-grade; audio may have a compressed or “muffled” quality compared to real recordings.

The model runs on approximately 4GB of VRAM for the base variant, making it viable on consumer GPUs. CPU-only generation may be too slow for practical interactive use.

Stable Audio (Stability AI)

Stable Audio by Stability AI (the company behind Stable Diffusion) applies the same diffusion philosophy to audio. It uses a VAE to compress audio into a compact latent representation, then diffuses in that space conditioned on text and duration embeddings.

Stable Audio generates 44.1kHz stereo audio — significantly higher resolution than AudioLDM’s 16kHz mono. The duration is controllable from 1 to 90 seconds. The quality is strong among available models; generated sounds can be convincing, especially for tonal sounds like instruments and ambient textures.

The tradeoff is compute. Stable Audio requires approximately 8GB of VRAM and generation takes 10-30 seconds on an NVIDIA RTX 4090. The model weights are available under a non-commercial license, with commercial use requiring a Stability AI membership.

AudioCraft (Meta: AudioGen + MusicGen)

AudioCraft is Meta’s family of generative audio models. It includes AudioGen (for sound effects) and MusicGen (for music), plus EnCodec as the underlying neural audio codec.

AudioGen uses a language modeling approach rather than diffusion: it quantizes audio into discrete tokens with EnCodec, then trains a transformer to predict token sequences conditioned on text. This gives it strong coherence over longer durations — a 10-second AudioGen clip of “rain on a car roof” maintains consistent acoustic properties throughout.

AudioCraft code and models have permissive project materials, but teams should verify current license terms for the exact model and weights they use. Hardware needs and audio quality vary by model size, backend, and prompt.

M2UGen (Microsoft)

M2UGen by Microsoft Research is a multi-modal model that generates audio from text, images, or video. Rather than treating text-to-audio as an isolated task, M2UGen fuses embeddings from a vision encoder, an audio encoder, and a text encoder into a shared latent space, then generates audio with a diffusion decoder.

M2UGen is notable because it introduces the possibility of audio generation directly from video content. A video editor could feed in a clip and have M2UGen generate matching Foley audio — footsteps that sync with on-screen movement, ambient sound that matches the setting.

The multi-modal conditioning is the differentiator, not necessarily raw audio quality. Verify current license terms before using it in commercial work.

Quality Comparison Table

“Good” quality means the generated sound is recognizable and usable but has audible compression artifacts. “Excellent” means the sound is difficult to distinguish from a studio recording in blind listening.

Prompt Engineering for Sound Effects

Prompting a text-to-audio model is not the same as prompting a text-to-image model. Sound has different perceptual dimensions, and the prompt must capture them effectively.

Be Specific About the Sound Source

Vague prompts produce vague audio. “A car” yields a generic motor hum that does not match any real car sound. “A diesel truck engine idling at a traffic light, low rumble, echo from surrounding buildings” produces a recognizable sound with spatial character. The model needs to know not just what makes the sound but how it is being heard.

For video editors matching audio to footage, specificity about the recording perspective helps: “close microphone on a hand clapping, dry room, no reverb” versus “a single hand clap in a large concert hall with 2-second reverb tail.”

Use Adjectives of Sound Quality

Text-to-audio models understand timbre descriptors. “Harsh,” “muffled,” “bright,” “boomy,” “crisp,” “warm,” and “metallic” all produce measurable differences in output. A prompt like “sharp metallic clang, like a blacksmith striking an anvil” directs the latent diffusion toward a specific region of the sound space that a flat prompt would miss.

Specify Duration Where Supported

Stable Audio and AudioLDM 2 accept explicit duration parameters. Using them is important because the model will otherwise produce a default-length clip. A 5-second “rain ambience” sounds fundamentally different from a 30-second one — the longer clip has time for variation in rain intensity, distant thunder, and spatial movement.

When the model supports duration control, use it in the prompt: “gentle rain on a window, steady intensity, 15 seconds, stereo field with rain center and subtle thunder on the left channel.”

Avoid Overloading the Prompt

Text-to-audio models have limited “capacity” — give them too many distinct sounds and they blend everything into mush. A prompt like “dog barking, car horn, people talking, footsteps on concrete, door slamming” produces an unrecognizable wall of noise. The model cannot separate these into distinct events. Instead, generate each sound separately and composite them in a DAW.

A practical rule: one primary sound per generation, plus up to two contextual descriptors (environment and recording quality).

Practical Workflows

Video Editing

Text-to-audio is transformative for video editors who work with library footage, stock video, or B-roll. Instead of browsing Soundly or Artlist for minutes to find the right clip, they can generate matching audio in seconds.

A typical workflow on a Mac with DaVinci Resolve or Final Cut Pro:

1. Export the video timeline’s timecode ranges for each segment needing audio
2. For each segment, describe the needed sound in text
3. Generate audio with a local or cloud model
4. Import the WAV file and place it on the timeline
5. Adjust gain and apply EQ to blend with existing audio

The time savings can be substantial. A short video that previously required a long SFX search may move faster with text-to-audio generation, provided the editor has learned to prompt effectively.

For editors who need consistency across cuts — for example, the same footsteps sound across six shots of the same scene — the workflow requires generating one clip and repeating it, since current models cannot produce the exact same sound twice from the same prompt.

Game Development

Game developers have different requirements. They need:

• Loopable audio. Ambient sounds must loop seamlessly. Text-to-audio models do not produce loopable output natively, so generated clips need crossfade looping in Ableton Live or Reaper.
• Multiple variations. A game needs 3-5 variations of each sound to avoid repetition fatigue. Text-to-audio models naturally produce different variations from the same prompt (because of the random noise seed), so generating a batch and selecting the best ones works well.
• Real-time considerations. Mobile games and web games need small file sizes. Generated audio at 16kHz mono in MP3 or Ogg Vorbis is typically under 50KB per sound, which is acceptable for most games.

For Unity developers, generated audio can be imported as .wav files and assigned to AudioSource components. For Unreal Engine, the same process applies with Sound Cues and MetaSounds.

Podcast Production

Podcast editors use text-to-audio for:

• Transition sounds. “A whoosh transition sound, short, 0.5 seconds, clean” between segments.
• Atmosphere. “Coffee shop ambience, low chatter, distant espresso machine, warm acoustic” for interview segments shot in cafes.
• Sound design moments. “A record scratch sound, vintage vinyl, followed by silence” for dramatic edits.

The key advantage for podcast editors is speed. A whoosh that would take 30 seconds to find in a library takes 3 seconds to generate. For daily podcast production, this compounds into hours saved per week.

Rapid Prototyping for Sound Designers

Professional sound designers use text-to-audio as a rapid prototyping tool. Rather than spending an hour creating a complex sound from synthesized layers and field recordings, they generate a rough version from text, evaluate whether the direction works, and then recreate it with higher fidelity using traditional tools.

This “generative-first” workflow mirrors how visual designers use AI: generate options quickly, select the best direction, refine manually. The model does not replace the sound designer, but it shortens the exploration phase from hours to minutes.

Local vs. Cloud: Running AudioCraft on Mac

The local-vs-cloud tradeoff for text-to-audio mirrors the same debate that plays out in TTS. Cloud APIs offer convenience and quality, but local inference provides privacy, offline availability, and no per-generation costs.

All four major text-to-audio models can run locally on a Mac with an M-series chip, though the experience varies significantly by model.

AudioCraft is one practical option for local Mac experimentation. Runtime depends heavily on model size, hardware, backend, and prompt. Installation requires Python 3.10+ and PyTorch:

git clone https://github.com/facebookresearch/audiocraft
cd audiocraft
pip install -e .
python -m audiocraft.sample --model facebook/audiogen-medium \
  --prompt "footsteps on wooden floor, indoors, close mic" \
  --duration 5

AudioLDM 2 also runs on Mac via PyTorch with MPS acceleration, but the MPS backend has compatibility issues with some operations. Expect frequent fallback to CPU.

Stable Audio’s model requires more VRAM than Apple Silicon’s unified memory efficiently provides for the full-quality variant. A quantized version running through MLX may become viable as the community ports audio diffusion models to Apple’s framework, but as of mid-2026, Stable Audio remains a cloud-first option on Mac.

The practical roadmap for Mac users:

1. AudioCraft (now): Runs locally with acceptable quality and speed on M2+ chips. Best for daily SFX generation.
2. AudioLDM 2 (now): Runs locally but slower. Best for batch generation when quality requirements are moderate.
3. Stable Audio (late 2026): Expect MLX ports and/or web API as the primary Mac access path.

Spokio focuses on speech, not sound-effect generation. It is an offline Mac TTS app powered by Chatterbox Turbo for English voice generation, with local voice cloning, batch export, and no cloud uploads for text, audio, or voice samples.

Key Limitations

Text-to-audio generation is impressive but not yet a replacement for all recorded or synthesized sound effects. The main limitations in 2026:

Consistency

The same prompt with the same model produces different audio every time (the random noise seed ensures variation). This is useful for generating options but problematic when you need reproducible results. There is no standard “seed control” mechanism across models yet, though some implementations expose the random seed parameter for re-generation.

Duration Control

Outside of Stable Audio, duration control is coarse. AudioCraft generates fixed-length clips (approximately 12 seconds). AudioLDM 2 supports variable duration but the quality degrades significantly at the limits — short clips under 3 seconds and long clips over 25 seconds both show increased artifacts.

Transient Sharpness

The hardest sounds for generative models are those with sharp, fast-changing transients: gunshots, glass breaking, impacts, clicks. These require precise timing at the sample level, and diffusion models tend to “smear” them into softer, less defined sounds. A generated gunshot might sound more like a heavy door slam. A glass break might sound like gravel being poured.

This is the most noticeable quality gap between generated and recorded SFX, and it is the area where traditional Foley recording remains strictly superior.

Polyphonic Confusion

As noted in the prompting section, models struggle when asked to generate multiple simultaneous sounds. Two related sounds (e.g., “rain and distant thunder”) work reasonably because they occupy complementary frequency ranges. Two unrelated sounds (“dog barking and piano playing”) produce a blended mess. Compositing individual generations in a DAW remains the only reliable approach for multi-source audio.

Licensing Uncertainty

The training data and output rights for text-to-audio models vary by provider. Anyone producing content for broadcast or distribution should verify the model license, training-data claims, and output usage terms for the exact tool they use.

The Future: Integration with Video Editing Workflows

The medium-term trajectory for text-to-audio is not as a standalone tool but as a feature embedded inside video editing and game development software.

Imagine a DaVinci Resolve timeline where you right-click an empty audio track, type “footsteps on gravel, matching this walking shot,” and the audio is generated, synced to the video, and placed on the timeline with proper gain staging. This is not hypothetical — the components exist, they just need integration.

Adobe is the most likely first mover, given its investment in Firefly generative AI across Photoshop, Premiere Pro, and After Effects. A Firefly Audio model that generates SFX from clips would directly replace the need to browse Adobe Stock Audio.

For game engines, the integration path is different. Rather than generating audio at edit time, game audio systems could generate sound effects at runtime based on game state. A Unity Audio Source that accepts a text description (“explosion near player, muffled by distance and wall”) and generates a unique sound each time the event triggers — individualized Foley for every bullet, footstep, and door. This would eliminate the repetition problem in games where the same explosion sound plays hundreds of times.

The compute requirements for runtime generation are the blocker. Mobile devices and consoles do not have the GPU headroom. But as models distill (compressing AudioGen from 1.5B parameters to 300M without significant quality loss) and as hardware improves, runtime generation becomes plausible within the current console generation’s lifecycle.

Getting Started

If you want to try text-to-audio generation today:

1. For quick experimentation: Use AudioLDM 2 on Hugging Face Spaces — no installation, just a browser. Generate 2-3 clips, see what the output quality looks like for your use case.
2. For local generation on Mac: Install AudioCraft via pip. The medium model (AudioGen) balances quality and speed. Expect 30-60 seconds per generation on M2+ hardware.
3. For production-quality audio: Use Stable Audio’s web interface or API. The quality gap over open models is significant enough that paid cloud access may be worth it for client-facing work.
4. For game development: Generate batches of 5-10 variations per sound, select the best, and loop/crossfade in your audio editor before importing to Unity or Unreal.

Text-to-SFX is useful, rapidly improving, and still imperfect at the edges. For video editors and game developers who work with sound daily, current tools can already save time on some projects. The next few years will determine how quickly consistency, transient handling, duration control, and licensing clarity improve.