Clear — on-device speech enhancement

48 kHz on-device speech enhancement. Takes noisy mono or stereo audio (phone mic, untreated room, traffic), returns a podcast-ready file: denoised, dereverbed, voice warm and present.

Try it

iOS / macOS: clear-swift — the Swift SDK with a built-in demo app.

Variants

Variant	Character	When to use
`clear-studio`	Quiet, studio-like — silences near zero	Default. Works across the full range of input quality — phone audio, laptop mic, untreated rooms, USB / XLR podcast captures.
`clear-natural`	Room tone, breath, lip texture preserved	Treated podcast studios, USB / XLR captures, voiceover where the original sound is intentional.

If the source is already clean and you want the model to stay invisible, pick clear-natural. Otherwise clear-studio is the default.

Files

Both variants share the same architecture and realtime cost — only the weights differ.

Variant	File	Format	Size
`clear-studio`	`clear-studio.mlpackage.zip`	Core ML mlpackage (fp16)	~3.8 MB
`clear-studio`	`clear-studio.mlmodelc.zip`	Core ML mlmodelc (fp16, precompiled)	~3.8 MB
`clear-studio`	`clear-studio.onnx`	ONNX (fp32)	~8.5 MB
`clear-natural`	`clear-natural.mlpackage.zip`	Core ML mlpackage (fp16)	~3.8 MB
`clear-natural`	`clear-natural.mlmodelc.zip`	Core ML mlmodelc (fp16, precompiled)	~3.8 MB
`clear-natural`	`clear-natural.onnx`	ONNX (fp32)	~8.5 MB

Use

Swift (iOS / macOS)

import Clear

let clear = try await Clear()
try await clear.enhance(audioURL: inURL, outputURL: outURL)

See clear-swift for the full API and loudness presets (Apple Podcasts, Spotify, YouTube, EBU R128).

ONNX

from huggingface_hub import hf_hub_download
import onnxruntime as ort

path    = hf_hub_download("desert-ant-labs/clear", "clear-studio.onnx")
session = ort.InferenceSession(path, providers=["CPUExecutionProvider"])

Inputs and outputs

Architecture: DeepFilterNet 3 (DFN3-half).
Sample rate: 48 kHz, mono or stereo (per-channel inference).
Inference contract: spec / feat_erb / feat_spec → spec_enhanced. STFT, ERB, and ISTFT are host-side via vDSP (Swift) or pure Kotlin.

Performance

Both variants run at the same speed. Enhancing a 5-minute clip on the Apple Neural Engine:

Device	Chip	Mono	Stereo
iPhone 15 Pro	A17 Pro	4.88 s (61× realtime)	6.53 s (46×)
iPhone 17 Pro	A19 Pro	3.70 s (81× realtime)	5.16 s (58×)

Cold model load is ~0.6 s; later loads ~100 ms via the system ANE cache.

Limitations

Trained on English speech; non-English speech still benefits but has not been measured against per-language ground truth.
Heavy background music or multi-speaker overlap degrades quality.
Mastering is informational only; verify against the platform's actual loudness target before publishing.

Built on

DeepFilterNet 3 by Rikorose — MIT. Fine-tuned on the Desert Ant Labs speech corpus.

License

Released under the Desert Ant Labs Source-Available License v1.0 (see LICENSE.md).

Free for commercial use up to 100,000 Monthly Active Users (MAU).
Above 100,000 MAU a commercial license is required. Contact licensing@desertant.ai.

Citation

@software{clear_2026,
  title  = {Clear: on-device speech enhancement},
  author = {Desert Ant Labs},
  year   = {2026},
  url    = {https://huggingface.co/desert-ant-labs/clear},
}

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