fast-langdetect 🚀

Overview

fast-langdetect is an ultra-fast and highly accurate language detection library based on FastText, a library developed by Facebook. Its incredible speed and accuracy make it 80x faster than conventional methods and deliver up to 95% accuracy.

• Supported Python 3.9 to 3.14.
• Works offline with the lite model.
• No numpy required (thanks to @dalf).

Background

This project builds upon zafercavdar/fasttext-langdetect with enhancements in packaging. For more information about the underlying model, see the official FastText documentation: Language Identification.

Memory note

The lite model runs offline and is memory-friendly; the full model is larger and offers higher accuracy.

Approximate memory usage (RSS after load):

• Lite: ~45–60 MB
• Full: ~170–210 MB
• Auto: tries full first, falls back to lite only on MemoryError.

Notes:

• Measurements vary by Python version, OS, allocator, and import graph; treat these as practical ranges.
• Validate on your system if constrained; see examples/memory_usage_check.py (credit: script by github@JackyHe398`).
• Run memory checks in a clean terminal session. IDEs/REPLs may preload frameworks and inflate peak RSS (ru_maxrss), leading to very large peaks with near-zero deltas.

Choose the model that best fits your constraints.

Installation 💻

To install fast-langdetect, you can use either pip or pdm:

Using pip

pip install fast-langdetect

Using pdm

pdm add fast-langdetect

FastText Dependency Note

The fasttext module comes from fasttext-predict on Python 3.9–3.13 and from fasttext-predict-v2 on Python 3.14+, which ships 3.14 wheels. Free-threaded builds (3.14t) have no wheels and need a C++ compiler to install.

The deprecated fasttext PyPI package installs into the same fasttext/ directory and can overwrite it. Its predict() fails under NumPy 2, so in that case detect() raises ModelLoadError. To repair it (use fasttext-predict-v2 on Python 3.14+):

pip uninstall -y fasttext
pip install --force-reinstall --no-deps fasttext-predict

Usage 🖥️

For higher accuracy, prefer the full model via detect(text, model='full'). For robust behavior under memory pressure, use detect(text, model='auto') which falls back to the lite model only on MemoryError.

Prerequisites

• If the sample is too long (generally over 80 characters) or too short, accuracy will be reduced.
• The model downloads to the system temporary directory by default. You can customize it by:
  • Setting FTLANG_CACHE environment variable
  • Using LangDetectConfig(cache_dir="your/path")

Quick Start

from fast_langdetect import detect

print(detect("Hello, world!", model="auto", k=1))
print(detect("Hello 世界 こんにちは", model="auto", k=3))

detect always returns a list of candidates ordered by score. Use model="full" for the best accuracy or model="lite" for an offline-only workflow.

Custom Configuration

from fast_langdetect import LangDetectConfig, LangDetector

config = LangDetectConfig(cache_dir="/custom/cache", model="lite")
detector = LangDetector(config)
print(detector.detect("Bonjour", k=1))
print(detector.detect("Hola", model="full", k=1))

Each LangDetector instance maintains its own in-memory model cache. Once loaded, models are reused for subsequent calls within the same instance. The global detect() function uses a shared default detector, so it also benefits from automatic caching.

Create a custom LangDetector instance when you need specific configuration (custom cache directory, input limits, etc.) or isolated model management. Create it once and reuse it: detect(text, config=...) builds a new detector and reloads the model on every call, so it is deprecated and will be removed in 2.0.

🌵 Fallback Policy

Keep It Simple!

• Only MemoryError triggers fallback (in model='auto'): when loading the full model runs out of memory, it falls back to the lite model.
• I/O/network/permission/path/integrity errors raise standard exceptions (e.g., FileNotFoundError, PermissionError) or library-specific errors where applicable — no silent fallback.
• model='lite' and model='full' never fallback by design.

Errors

• Base error: FastLangdetectError (library-specific failures).
• Model loading failures: ModelLoadError.
• Standard Python exceptions (e.g., ValueError, TypeError, FileNotFoundError, MemoryError) propagate when they are not library-specific.

Splitting Text by Language 🌐

For text splitting based on language, please refer to the split-lang repository.

Input Handling

You can control input truncation and normalization via LangDetectConfig:

from fast_langdetect import LangDetectConfig, LangDetector

config = LangDetectConfig(max_input_length=200)
detector = LangDetector(config)
print(detector.detect("Some very long text..." * 5))

• Newlines are always replaced with spaces to avoid FastText errors (silent, no log).
• When truncation happens, an INFO message is logged because it may reduce accuracy.
• The default max_input_length is 80 characters (optimal for accuracy); increase it if you need longer samples, or set None to disable truncation entirely.
• The limit counts characters regardless of language: 80 characters is a full sentence in Chinese or Japanese but only a short one in English. If you need a per-language sampling policy, set max_input_length=None and pass the sample you want to classify.

Cache Directory Behavior

• Default cache: if cache_dir is not set, models are stored under a system temp-based directory specified by FTLANG_CACHE or an internal default. This directory is created automatically when needed.
• User-provided cache_dir: if you set LangDetectConfig(cache_dir=...) to a path that does not exist, the library raises FileNotFoundError instead of silently creating or using another location. Create the directory yourself if that’s intended.

Advanced Options (Optional)

The constructor exposes a few advanced knobs (proxy, normalize_input, max_input_length). These are rarely needed for typical usage and can be ignored. Prefer detect(..., model=...) unless you know you need them.

Language Codes → English Names

fastText reports BCP-47 style tags such as en, zh-cn, pt-br, yue. The detector keeps those codes so you can decide how to display them. Choose the approach that fits your product:

• Small, fixed list? Maintain a hand-written mapping and fall back to the raw code for anything unexpected.

FASTTEXT_DISPLAY_NAMES = {
    "en": "English",
    "zh": "Chinese",
    "zh-cn": "Chinese (China)",
    "zh-tw": "Chinese (Taiwan)",
    "pt": "Portuguese",
    "pt-br": "Portuguese (Brazil)",
    "yue": "Cantonese",
    "wuu": "Wu Chinese",
    "arz": "Egyptian Arabic",
    "ckb": "Central Kurdish",
    "kab": "Kabyle",
}

def code_to_display_name(code: str) -> str:
    return FASTTEXT_DISPLAY_NAMES.get(code.lower(), code)

print(code_to_display_name("pt-br"))
print(code_to_display_name("de"))

• Need coverage for all 176 fastText languages? Use a language database library that understands subtags and scripts. Two popular libraries are langcodes and pycountry.

# pip install langcodes
from langcodes import Language

LANG_OVERRIDES = {
    "pt-br": "Portuguese (Brazil)",
    "zh-cn": "Chinese (China)",
    "zh-tw": "Chinese (Taiwan)",
    "yue": "Cantonese",
}

def fasttext_to_name(code: str) -> str:
    normalized = code.replace("_", "-").lower()
    if normalized in LANG_OVERRIDES:
        return LANG_OVERRIDES[normalized]
    try:
        return Language.get(normalized).display_name("en")
    except Exception:
        base = normalized.split("-")[0]
        try:
            return Language.get(base).display_name("en")
        except Exception:
            return code

from fast_langdetect import detect
result = detect("Olá mundo", model="full", k=1)
print(fasttext_to_name(result[0]["lang"]))

pycountry works similarly (pip install pycountry). Use pycountry.languages.lookup("pt") for fuzzy matching or pycountry.languages.get(alpha_2="pt") for exact lookups, and pair it with a small override dictionary for non-standard tags such as pt-br, zh-cn, or dialect codes like yue.

# pip install pycountry
import pycountry

FASTTEXT_OVERRIDES = {
    "pt-br": "Portuguese (Brazil)",
    "zh-cn": "Chinese (China)",
    "zh-tw": "Chinese (Taiwan)",
    "yue": "Cantonese",
}

def fasttext_to_name_pycountry(code: str) -> str:
    normalized = code.replace("_", "-").lower()
    if normalized in FASTTEXT_OVERRIDES:
        return FASTTEXT_OVERRIDES[normalized]
    try:
        return pycountry.languages.lookup(normalized).name
    except LookupError:
        base = normalized.split("-")[0]
        try:
            return pycountry.languages.lookup(base).name
        except LookupError:
            return code

from fast_langdetect import detect
result = detect("Olá mundo", model="full", k=1)
print(fasttext_to_name_pycountry(result[0]["lang"]))

Load Custom Models

from importlib import resources
from fast_langdetect import LangDetectConfig, LangDetector

with resources.path("fast_langdetect.resources", "lid.176.ftz") as model_path:
    config = LangDetectConfig(custom_model_path=str(model_path))
    detector = LangDetector(config)
    print(detector.detect("Hello world", k=1))

When using a custom model via custom_model_path, the model parameter in detect() calls is ignored since your custom model file is always loaded directly. The model="lite", model="full", and model="auto" parameters only apply when using the built-in models.

Benchmark 📊

For detailed benchmark results, refer to zafercavdar/fasttext-langdetect#benchmark.

References 📚

[1] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, Bag of Tricks for Efficient Text Classification

@article{joulin2016bag,
  title={Bag of Tricks for Efficient Text Classification},
  author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Mikolov, Tomas},
  journal={arXiv preprint arXiv:1607.01759},
  year={2016}
}

[2] A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Jégou, T. Mikolov, FastText.zip: Compressing text classification models

@article{joulin2016fasttext,
  title={FastText.zip: Compressing text classification models},
  author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Douze, Matthijs and J{\'e}gou, H{\'e}rve and Mikolov, Tomas},
  journal={arXiv preprint arXiv:1612.03651},
  year={2016}
}

License 📄

• Code: Released under the MIT License (see LICENSE).
• Models: This package uses the pre-trained fastText language identification models (lid.176.ftz bundled for offline use and lid.176.bin downloaded as needed). These models are licensed under the Creative Commons Attribution-ShareAlike 3.0 (CC BY-SA 3.0) license.
• Attribution: fastText language identification models by Facebook AI Research. See the fastText docs and license for details:
  • https://fasttext.cc/docs/en/language-identification.html
  • https://creativecommons.org/licenses/by-sa/3.0/
• Note: If you redistribute or modify the model files, you must comply with CC BY-SA 3.0. Inference usage via this library does not change the license of the model files themselves.