Ear

Detect spoken language from 30 seconds audio.

On-device spoken language identification across 102 languages.

• SDKs, install and examples: https://desertant.com/docs/ear/

Detect the spoken language of a recording with Ear before you transcribe it, so you run the right transcription model. Ear works on 5-30s of speech and takes 7.6ms on a MacBook Pro, entirely on the device.

Ear is a spoken language identification model for 102 languages. Ear gives each language a confidence score. When the top two are too close to call, Ear flags the answer as unreliable. You can then ask the user or fall back to a general model instead of transcribing in the wrong language. The model is 5.0MB as Core ML for Apple platforms and 10.7MB as LiteRT for Android, Linux, Windows and Node.

Platforms	iOS, macOS, tvOS, visionOS, Android, Linux, Windows, Browser, Node
Languages	102
Weights	v0.2.0

Install

Swift (requirements)

.package(url: "https://github.com/Desert-Ant-Labs/desert-ant-core.git", from: "4.2.1")

Then add the Ear product to your target.

Kotlin (requirements)

implementation("ai.desertant:ear:4.2.1")

JavaScript (requirements)

npm i @desert-ant-labs/ear @litertjs/core   # browser
npm i @desert-ant-labs/ear                  # Node, prebuilt native core

Files

File	Format	Size	Contents
ear.mlmodelc	Core ML, 6-bit weights, compiled	5.0MB	The detector for iOS, macOS, tvOS and visionOS
ear.tflite	LiteRT, int8	10.7MB	The detector for Android, Linux, Windows, Node and the web
ear_meta.json	JSON	2.2kB	The front end's geometry and the tensor names per runtime
languages.json	JSON	0.6kB	The language codes, in output order
mel_filters.f32	Binary table	64kB	The mel filterbank, 80 by 201, little-endian

Load the detector for your platform together with ear_meta.json, languages.json and mel_filters.f32. The SDK uses them to prepare the audio, so there's no separate model for that step.

Inputs and outputs

• In: 16kHz mono audio, up to 30s.
• Out: a probability for each language, in the order of languages.json. The language with the highest probability is Ear's answer.
• Reliable or not: an answer is reliable when the top two probabilities are at least 0.25 apart. The SDK always marks Norwegian, Swedish and Danish as unreliable.

Languages

Ear covers 102 languages, from Afrikaans to Zulu, listed in output order in languages.json. For any other language, Ear returns the closest supported language, so check that each answer is reliable before picking a transcription model.

Results

The right transcription model, 99% of the time. Measured on the files in this repo, with speakers who weren't in the training data.

Accuracy by clip length. How often Ear's top answer is the right language.

Corpus	Clips per length	5s	10s	15s	20s	30s
Common Voice, 15 languages	1,500	0.946	0.980	0.989	0.991	0.995
VoxPopuli, 16 languages	825 to 458	0.978	0.996	0.997	0.998	1.000
NST, Norwegian and Swedish	200	0.985	0.995	1.000	1.000	1.000
Multilingual LibriSpeech, 7 languages	532	0.979	0.992	0.996	0.998	0.994
English, LibriSpeech audiobooks	100	0.990	1.000	1.000	1.000	1.000
English, AMI meetings	100	0.950	1.000	1.000	1.000	1.000
English, CHiME-6 dinner parties	32	0.938	1.000	1.000	1.000	1.000
English, NOTSOFAR-1 meetings	100	0.900	0.890	0.960	0.960	0.980

Routing, every clip. Right model: the clip went to a transcription model that handles its language. Wrong model: it went to one that doesn't. Reliable: the answer passed the reliability rule. Right when reliable: how many reliable answers were correct.

Corpus	Clips	Right model	Wrong model	Reliable	Right when reliable
Common Voice	7,500	0.990	0.014	0.986	0.994
VoxPopuli	3,221	0.998	0.000	0.995	0.999
NST	1,000	1.000	0.000	always unreliable
Multilingual LibriSpeech	2,660	0.997	every language handled	0.994	0.998
English, four conditions	1,660	0.980	every language handled	0.972	0.991

Whole recordings. Ear's answers on three 30s clips from each recording, averaged.

Corpus	Recordings	Right model	Reliable	Right when reliable	Reliable and wrong
Common Voice	457	0.993	0.996	0.996	2
VoxPopuli	256	1.000	1.000	1.000	0
NST	80	1.000	always unreliable		0
Multilingual LibriSpeech	173	1.000	1.000	1.000	0
English, four conditions	128	0.984	0.984	0.992	1

More test sets, Core ML build.

Set	Clips	Accuracy	Right model	Wrong model
FLEURS test, the 32 product languages	640	0.977	0.997	0.000
FLEURS test, all 102 languages	2,040	0.954	0.993	0.010
VoxLingua107 dev, YouTube audio, languages Ear offers	1,094	0.942	0.973
VoxLingua107 dev, YouTube audio, languages Ear does not offer	515			0.012
120 VoxPopuli recordings, whole files	120		1.000

Performance

7.6ms per clip on the Apple Neural Engine. One 30s clip, after warm-up, median.

Build	Device	Compute	Size	Latency
Core ML, 6-bit weights	MacBook Pro, M5 Pro, macOS 26	Apple Neural Engine, 100% resident	5.0MB	7.6ms
LiteRT, int8	MacBook Pro, M5 Pro, macOS 26	CPU, 4 threads	10.7MB	26.8ms

Limitations

• Far-field recordings. Ear is less accurate when the speakers are far from the microphone. On NOTSOFAR-1 meetings, Ear gets the language right 90% of the time on 5s clips, versus 98% on 30s clips. For room recordings, pass Ear the full 30s, and when an answer is unreliable, ask a user to confirm the language.
• Galician. Ear labels Galician as Spanish about 4% of the time.
• Norwegian, Swedish and Danish. Ear tells Norwegian and Swedish apart 98.5-100% of the time on NST. The SDK still marks all three as unreliable, so you can route them to a transcription model that handles them well.
• Mixed-language recordings. Ear returns one language per clip, even if the speakers switch languages.
• Languages outside the 102. Ear returns the closest supported language. The reliability rule catches most of these answers, but not all of them.
• Ear detects the language and doesn't transcribe. For a transcript, pair Ear with Voz or another transcription model.

Credits

Open models and tools this work builds on, with their licenses:

• openai/whisper-tiny, Apache-2.0

None of them is redistributed here. Dataset attributions are in THIRD_PARTY_NOTICES.md.

License

Desert Ant Labs Source-Available License. Free for most apps, and a commercial license is required at scale. Full terms are at the link. Licensing: licensing@desertant.com.

See THIRD_PARTY_NOTICES.md.

Citation

@software{ear_2026,
  title  = {Ear: On-device spoken language identification across 102 languages},
  author = {Desert Ant Labs},
  year   = {2026},
  url    = {https://huggingface.co/desert-ant-labs/ear},
}

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© 2026 Desert Ant Labs · https://desertant.com