Language Detector

The MediaPipe Language Detector task lets you identify the language of a piece of text. This task operates on text data with a machine learning (ML) model and outputs a list of predictions, where each prediction consists of an ISO 639-1 language code and a probability.

Open in Web Demos Edit in StackBlitz

Quickstart

Install the SDK for your platform, initialize the task with a model, and run inference.

Web

npmnpm install @mediapipe/tasks-textWeb demo·Edit in StackBlitz·Setup guide

import { FilesetResolver, LanguageDetector } from "@mediapipe/tasks-text";

const MODEL_URL = "https://storage.googleapis.com/mediapipe-models/language_detector/language_detector/float32/1/language_detector.tflite";

const textFiles = await FilesetResolver.forTextTasks(
  "https://cdn.jsdelivr.net/npm/@mediapipe/tasks-text@latest/wasm"
);
const languageDetector = await LanguageDetector.createFromOptions(textFiles, {
  baseOptions: { modelAssetPath: MODEL_URL },
});

const inputText = "Bonjour tout le monde, comment allez-vous ?";
const detectionResult = languageDetector.detect(inputText);

Android (Kotlin)

Gradleimplementation("com.google.mediapipe:tasks-text:latest.release")Sample code·Setup guide

import com.google.mediapipe.tasks.core.BaseOptions
import com.google.mediapipe.tasks.text.languagedetector.LanguageDetector
import com.google.mediapipe.tasks.text.languagedetector.LanguageDetector.LanguageDetectorOptions

val baseOptions = BaseOptions.builder()
    .setModelAssetPath("language_detector.tflite")
    .build()
val options = LanguageDetectorOptions.builder()
    .setBaseOptions(baseOptions)
    .setScoreThreshold(0.5f)
    .build()

val languageDetector = LanguageDetector.createFromOptions(context, options)

val inputText = "Bonjour tout le monde, comment allez-vous ?"
val result = languageDetector.detect(inputText)

Android (Java)

Gradleimplementation 'com.google.mediapipe:tasks-text:latest.release'Sample code·Setup guide

import com.google.mediapipe.tasks.core.BaseOptions;
import com.google.mediapipe.tasks.text.languagedetector.LanguageDetector;
import com.google.mediapipe.tasks.text.languagedetector.LanguageDetector.LanguageDetectorOptions;
import com.google.mediapipe.tasks.text.languagedetector.LanguageDetectorResult;

BaseOptions baseOptions = BaseOptions.builder()
    .setModelAssetPath("language_detector.tflite")
    .build();
LanguageDetectorOptions options = LanguageDetectorOptions.builder()
    .setBaseOptions(baseOptions)
    .setScoreThreshold(0.5f)
    .build();

LanguageDetector languageDetector = LanguageDetector.createFromOptions(context, options);

String inputText = "Bonjour tout le monde, comment allez-vous ?";
LanguageDetectorResult result = languageDetector.detect(inputText);

iOS

SwiftPMhttps://github.com/google-ai-edge/mediapipe → MediaPipeTasksTextPackage.swift·Sample code·Setup guide

import Foundation
import MediaPipeTasksText

guard let modelPath = Bundle.main.path(forResource: "language_detector", ofType: "tflite") else { return }

let options = LanguageDetectorOptions()
options.baseOptions.modelAssetPath = modelPath
options.scoreThreshold = 0.5

let languageDetector = try LanguageDetector(options: options)

let inputText = "Bonjour tout le monde, comment allez-vous ?"
let result = try languageDetector.detect(text: inputText)

Python

pippip install mediapipeSample code·Setup guide

import mediapipe as mp

BaseOptions = mp.tasks.BaseOptions
LanguageDetector = mp.tasks.text.LanguageDetector
LanguageDetectorOptions = mp.tasks.text.LanguageDetectorOptions

model_path = "language_detector.tflite"
options = LanguageDetectorOptions(base_options=BaseOptions(model_asset_path=model_path), score_threshold=0.5)

input_text = "Bonjour tout le monde, comment allez-vous ?"
with LanguageDetector.create_from_options(options) as detector:
    detection_result = detector.detect(input_text)

Task details

This section describes the capabilities, inputs, outputs, and configuration options of this task.

Features

Score threshold - Filter results based on prediction scores
Label allowlist and denylist - Specify the categories detected

Inputs

Language Detector accepts the following input data type:

String

Outputs

The Language Detector outputs a list of predictions containing:

Language code: An ISO 639-1 language / locale code (e.g. "en" for English, "uz" for Uzbek, "ja-Latn" for Japanese (romaji)) as a string.
Probability: the confidence score for this prediction, expressed as a probability between zero and one as floating point value.

Configuration options

This task has the following configuration options:

Web

Option Name	Description	Value Range	Default Value
`displayNamesLocale`	Locale code for display names provided in the model metadata, if available.	Locale code	`"en"`
`maxResults`	Maximum number of top-scored language predictions to return (`-1` returns all results).	Integer	`-1`
`scoreThreshold`	Prediction score threshold overriding the model metadata default. Predictions lower than this score are rejected.	Float `[0.0, 1.0]`	Not set
`categoryAllowlist`	Optional list of allowed BCP 47 language codes. Mutually exclusive with `categoryDenylist`.	List of strings	Not set
`categoryDenylist`	Optional list of excluded BCP 47 language codes. Mutually exclusive with `categoryAllowlist`.	List of strings	Not set

Android

Option Name	Description	Value Range	Default Value
`displayNamesLocale`	Locale code for display names provided in the model metadata, if available.	Locale code	`"en"`
`maxResults`	Maximum number of top-scored language predictions to return (`-1` returns all results).	Integer	`-1`
`scoreThreshold`	Prediction score threshold overriding the model metadata default. Predictions lower than this score are rejected.	Float `[0.0, 1.0]`	Not set
`categoryAllowlist`	Optional list of allowed BCP 47 language codes. Mutually exclusive with `categoryDenylist`.	List of strings	Not set
`categoryDenylist`	Optional list of excluded BCP 47 language codes. Mutually exclusive with `categoryAllowlist`.	List of strings	Not set

iOS

Option Name	Description	Value Range	Default Value
`displayNamesLocale`	Locale code for display names provided in the model metadata, if available.	Locale code	`"en"`
`maxResults`	Maximum number of top-scored language predictions to return (`-1` returns all results).	Integer	`-1`
`scoreThreshold`	Prediction score threshold overriding the model metadata default. Predictions lower than this score are rejected.	Float `[0.0, 1.0]`	Not set
`categoryAllowlist`	Optional list of allowed BCP 47 language codes. Mutually exclusive with `categoryDenylist`.	List of strings	Not set
`categoryDenylist`	Optional list of excluded BCP 47 language codes. Mutually exclusive with `categoryAllowlist`.	List of strings	Not set

Python

Option Name	Description	Value Range	Default Value
`display_names_locale`	Locale code for display names provided in the model metadata, if available.	Locale code	`"en"`
`max_results`	Maximum number of top-scored language predictions to return (`-1` returns all results).	Integer	`-1`
`score_threshold`	Prediction score threshold overriding the model metadata default. Predictions lower than this score are rejected.	Float `[0.0, 1.0]`	Not set
`category_allowlist`	Optional list of allowed BCP 47 language codes. Mutually exclusive with `category_denylist`.	List of strings	Not set
`category_denylist`	Optional list of excluded BCP 47 language codes. Mutually exclusive with `category_allowlist`.	List of strings	Not set

Task output

Each LanguageDetectorResult returns a list of languages (LanguagePrediction) ordered by descending probability:

Field	Type / Range	Description
`languageCode`	`string` (BCP-47 / ISO 639-1)	Predicted language code, such as `"fr"`, `"en"`, `"ja"`, or `"und"` when below threshold (`language_code` in Python).
`probability`	`float` (`[0.0, 1.0]`)	Confidence score for the predicted language (`score` on Android).

Example `LanguageDetectorResult` payload

The following example shows the language prediction for "Il y a beaucoup de bouches qui parlent et fort peu de têtes qui pensent.":

{
  languages: [
    { languageCode: "fr", probability: 0.9998 }
  ]
}

Models

We offer a default, recommended model when you start developing with this task.