Ear.
设备端口语语种识别，在转写开始前，凭一小段音频判定所说语言，覆盖 99 种语言。
凭 30 秒音频判定语言。
一条语音留言进来，转写器被设成了英语。30 秒后，Ear 说这是葡萄牙语，于是接着运行的是葡萄牙语转写器，而不是一个自信地返回一堆胡话的英语转写器。
Ear 不会听完整个文件。Ear 找出听起来最像语音的三个三十秒片段，跳过片头曲和静音，在 250 毫秒内说出语言。答案附带一个把握度，以及一个标记，告诉你是否该信任它。
当前两个候选太接近、无法区分时，Ear 会如实说明。你可以去问用户，或退回到一个通用模型，而不是用错误的语言去转写。一个 SDK，覆盖每个平台：Apple 上用 Swift，Android 上用 Kotlin，浏览器和 Node 里用 JavaScript。什么都不上传。
98.5% 的情况选对转写器。
在 162 段录音上，Ear 标为可靠的答案里有 98.5% 分流到了正确的转写器；在 12 个普通上传和 10 个重制成播客的样本上，没有一个自信的答案是错的。
端到端穿过 SDK 测得，已公布在模型卡上。混在更响音乐下的语音，约有 60% 的情况读对。
先选对转写器
指向错误语言的转写器，会自信地返回一堆胡话。在会议记录器或语音笔记应用里，对前 30 秒运行 Ear，再用真正所说的语言启动转写、字幕和翻译。
整理一批录音存档
在 Node 脚本或 Mac 应用里，通宵把一库语音笔记、通话或节目过一遍 Ear，到早上每个文件都标好了语言。什么都不上传，也不按文件计费。
分流一通电话或一条语音信箱
在客服应用或即时通讯产品里，在一通来电或一条语音信箱到达真人之前，先说出它的语言。这通电话就会落到会说这门语言的人那里，或落到对的语音模型上。
在浏览器里做字幕
一款网页视频工具在客户端识别所说的语言，浏览器和 Node 里用同一个 SDK，并据此选好字幕语言，上传者从此不用自己选。
灵感
用 Ear 构建的点子。把提示词复制到你的编码 agent 里就能开始。
Detect a caller's language and route the voicemail.
In a voicemail or call app, run Desert Ant's Ear on the first ~30 seconds of audio to identify the spoken language on-device, then route the message. For a very short voicemail, accumulate audio first, and treat a low-reliability answer as unknown and ask. Build it with the Desert Ant SDK. Ear (Swift, Kotlin, JavaScript / TypeScript). Install and API: https://desertant.com/docs/ear/. Install: SwiftPM desert-ant-core; Maven ai.desertant:ear:3.1.0; npm i @desert-ant-labs/ear @litertjs/core # browser. SDK source: https://github.com/Desert-Ant-Labs/desert-ant-core. Machine-readable catalog of every model and SDK: https://desertant.com/llms.txt.
Sort an archive of recordings by spoken language.
Build a batch tool that runs Desert Ant's Ear over a folder of recordings to tag each by spoken language, so a mixed archive sorts itself. On-device. Build it with the Desert Ant SDK. Ear (Swift, Kotlin, JavaScript / TypeScript). Install and API: https://desertant.com/docs/ear/. Install: SwiftPM desert-ant-core; Maven ai.desertant:ear:3.1.0; npm i @desert-ant-labs/ear @litertjs/core # browser. SDK source: https://github.com/Desert-Ant-Labs/desert-ant-core. Machine-readable catalog of every model and SDK: https://desertant.com/llms.txt.
Detect the language of a voice note, then transcribe with the right model.
Before transcribing, run Desert Ant's Ear on the first ~30 seconds to identify the spoken language on-device, then run Voz. Handle low confidence by asking the user instead of guessing. Build it with the Desert Ant SDK. Ear (Swift, Kotlin, JavaScript / TypeScript). Install and API: https://desertant.com/docs/ear/. Voz (Swift). Install and API: https://desertant.com/docs/voz/. SDK source: https://github.com/Desert-Ant-Labs/desert-ant-core. Machine-readable catalog of every model and SDK: https://desertant.com/llms.txt.
Pick the dominant language of a recording to route it.
Use Desert Ant's Ear on-device to identify the dominant spoken language of a recording (from its most speech-like 30-second windows) and route it to the right transcriber or team. Build it with the Desert Ant SDK. Ear (Swift, Kotlin, JavaScript / TypeScript). Install and API: https://desertant.com/docs/ear/. Install: SwiftPM desert-ant-core; Maven ai.desertant:ear:3.1.0; npm i @desert-ant-labs/ear @litertjs/core # browser. SDK source: https://github.com/Desert-Ant-Labs/desert-ant-core. Machine-readable catalog of every model and SDK: https://desertant.com/llms.txt.
Detect the language of a voicemail or dictation before transcribing.
Run Desert Ant's Ear on the first ~30 seconds of a voicemail or dictation on-device to identify the language, then pick the right transcriber. Ear needs about 30 seconds, so accumulate audio before deciding on very short clips. Build it with the Desert Ant SDK. Ear (Swift, Kotlin, JavaScript / TypeScript). Install and API: https://desertant.com/docs/ear/. Install: SwiftPM desert-ant-core; Maven ai.desertant:ear:3.1.0; npm i @desert-ant-labs/ear @litertjs/core # browser. SDK source: https://github.com/Desert-Ant-Labs/desert-ant-core. Machine-readable catalog of every model and SDK: https://desertant.com/llms.txt.
Gate transcription so it only runs on languages you support.
Use Desert Ant's Ear to detect the spoken language on-device and skip or reroute recordings in languages your transcriber does not support. Build it with the Desert Ant SDK. Ear (Swift, Kotlin, JavaScript / TypeScript). Install and API: https://desertant.com/docs/ear/. Install: SwiftPM desert-ant-core; Maven ai.desertant:ear:3.1.0; npm i @desert-ant-labs/ear @litertjs/core # browser. SDK source: https://github.com/Desert-Ant-Labs/desert-ant-core. Machine-readable catalog of every model and SDK: https://desertant.com/llms.txt.
模型能做什么
- 99 种语言，从南非荷兰语到约鲁巴语，涵盖主要的欧洲、亚洲和非洲语言。
- 挑选听起来最像语音的三个三十秒窗口，而不是按位置去读，因此一段音乐片头或一段静音骗不了它。按位置挑选时，一段五分钟、只有十分之一是语音的录音，只有 4% 的概率能找对语言。
- 返回语言、一个把握度、排好序的候选，以及一个
isReliable标记。当前两个太接近、难以定夺时该标记为 false，对彼此混淆的挪威语、瑞典语和丹麦语也为 false。 - 任意采样率、已解码的音频都可用；在 Apple 上文件 URL 也可用。
- 权重在首次使用时下载并缓存；构造时不做任何工作，也不发起任何下载。
快速上手
只需几行代码，即可为你的 iOS or macOS, Android or web 应用加上 口语语种检测。 Ear 文档.
// Swift Package Manager
.package(url: "https://github.com/Desert-Ant-Labs/desert-ant-core.git", from: "3.1.0")
// target dependency
.product(name: "Ear", package: "desert-ant-core")import Ear
let ear = Ear() // downloads on first use
let detection = try await ear.identify(contentsOf: url)
detection.language // "pt"
detection.isReliable // trueAdd Ear from Desert Ant Labs to this Swift project (iOS, macOS). What it does: Ear：设备端口语语种识别. SDK: Swift (iOS, macOS) Repo: https://github.com/Desert-Ant-Labs/desert-ant-core#readme // Swift Package Manager .package(url: "https://github.com/Desert-Ant-Labs/desert-ant-core.git", from: "3.1.0") // target dependency .product(name: "Ear", package: "desert-ant-core") Reference: - Model page: https://desertant.com/models/ear/ - Full catalog and other models: https://desertant.com/llms.txt Add the SDK, then follow its README for the exact API and current version. Do not invent API names or method signatures; confirm them against the README.
// build.gradle.kts (Maven Central)
implementation("ai.desertant:ear:3.1.0")import ai.desertant.ear.Ear
val ear = Ear(context) // downloads on first use
val detection = ear.identify(samples, 16_000.0)
detection.language // "pt"
detection.isReliable // true
ear.close()Add Ear from Desert Ant Labs to this Kotlin project (Android).
What it does: Ear：设备端口语语种识别.
SDK:
Kotlin (Android)
Repo: https://github.com/Desert-Ant-Labs/desert-ant-core#readme
// build.gradle.kts (Maven Central)
implementation("ai.desertant:ear:3.1.0")
Reference:
- Model page: https://desertant.com/models/ear/
- Full catalog and other models: https://desertant.com/llms.txt
Add the SDK, then follow its README for the exact API and current version. Do not invent API names or method signatures; confirm them against the README.
npm i @desert-ant-labs/ear @litertjs/core # browser
npm i @desert-ant-labs/ear # Nodeimport { Ear } from "@desert-ant-labs/ear";
const ear = await Ear.load();
const detection = await ear.identify(samples, 16000);
detection.language // "pt"
detection.isReliable // trueAdd Ear from Desert Ant Labs to this JavaScript / TypeScript project (Web, Node.js). What it does: Ear：设备端口语语种识别. SDK: JavaScript / TypeScript (Web, Node.js) Repo: https://github.com/Desert-Ant-Labs/desert-ant-core#readme npm i @desert-ant-labs/ear @litertjs/core # browser npm i @desert-ant-labs/ear # Node Reference: - Model page: https://desertant.com/models/ear/ - Full catalog and other models: https://desertant.com/llms.txt Add the SDK, then follow its README for the exact API and current version. Do not invent API names or method signatures; confirm them against the README.
规格
- 语言
- 99
- 输入
- 录音中的三个三十秒窗口；任意采样率
- 速度
- 每次识别约 250 毫秒，三个窗口
- 平台
- iOS、macOS、tvOS、visionOS（Core ML）；Android、Linux、Windows（LiteRT）；浏览器（WebAssembly 和 LiteRT.js）；Node
Ear 说出语言，但不做转写。在更响的音乐下的语音，大约十次里读对六次；挪威语、瑞典语和丹麦语彼此容易混淆，因此 Ear 会按设计把这几种标为不可靠。短于 30 秒的录音只得到一个窗口和一个把握较低的答案。请把不可靠的答案当作一个要问用户的问题，而不是一个转写器设置。
常见问题
Ear 是什么？
设备端口语语种识别，在转写开始前，凭一小段音频判定所说语言，覆盖 99 种语言。
Ear 在设备上运行吗？
是的。Ear 在设备上运行，不调用任何服务器，因此数据始终留在用户手中。
Ear 支持哪些平台？
Ear 以面向 Swift, Kotlin, JavaScript / TypeScript 的原生设备端 SDK 形式提供。
Ear 的价格是多少？
每个模型的每个 SDK 均可免费支持最多 10 万台月活跃设备。每位用户调用模型的次数不设上限。 如需定制授权，请联系我们。
Ear 的准确度和速度如何？
在 162 段录音上，Ear 标为可靠的答案里有 98.5% 分流到了正确的转写器；在 12 个普通上传和 10 个重制成播客的样本上，没有一个自信的答案是错的。