Get started with CTranslate2 with end-to-end examples using machine translation models.

See also

The Transformers guide which contains a bunch of examples for various models.


Start using CTranslate2 from Python by converting a pretrained model and running your first translation.

1. Install the Python packages

pip install ctranslate2 OpenNMT-py==2.* sentencepiece

2. Download the English-German Transformer model trained with OpenNMT-py

wget https://s3.amazonaws.com/opennmt-models/transformer-ende-wmt-pyOnmt.tar.gz
tar xf transformer-ende-wmt-pyOnmt.tar.gz

3. Convert the model to the CTranslate2 format

ct2-opennmt-py-converter --model_path averaged-10-epoch.pt --output_dir ende_ctranslate2

4. Translate texts with the Python API

import ctranslate2
import sentencepiece as spm

translator = ctranslate2.Translator("ende_ctranslate2/", device="cpu")
sp = spm.SentencePieceProcessor("sentencepiece.model")

input_text = "Hello world!"
input_tokens = sp.encode(input_text, out_type=str)

results = translator.translate_batch([input_tokens])

output_tokens = results[0].hypotheses[0]
output_text = sp.decode(output_tokens)


This code should print the sentence:

Hallo Welt!

If that’s the case, you successfully converted and executed a translation model with CTranslate2! Consider browsing the other sections for more information and examples.


Start using the CTranslate2 library in your own C++ project.

1. Compile and Install CTranslate2

mkdir build && cd build
cmake ..
make -j4 install

It is important that the library is getting installed into a directory that is on the CMAKE_PREFIX_PATH, otherwise you can install to a custom directory, e.g.:

export CTRANSLATE_INSTALL_PATH=$(pwd)/install
make -j4 install

See the installation guide for more information.

2. Add CTranslate2 to your CMakeLists.txt

cmake_minimum_required (VERSION 2.8.11)


add_executable (main main.cpp)
target_link_libraries(main CTranslate2::ctranslate2)

3. Have a model ready in the CTranslate2 format

ct2-transformers-converter --model Helsinki-NLP/opus-mt-en-de --output_dir opus-mt-en-de

4. Write the translation C++ code using the API

You will need to have your input string tokenised, which depends on what type of model you are using. Have a look at the guides to get tokens from your input string.

#include <iostream>
#include <vector>

#include "ctranslate2/translator.h"

int main(int argc, char* argv[]) {
  const std::string model_path("opus-mt-en-de");
  const ctranslate2::models::ModelLoader model_loader(model_path);

  ctranslate2::Translator translator(model_loader);

  const std::vector<std::vector<std::string>> batch = {{"▁Hello", "▁World", "!", "</s>"}};
  const auto translation = translator.translate_batch(batch);

  for (const auto& token : translation[0].output())
    std::cout << token << ' ';
  std::cout << std::endl;

5. Compile and run the example

cmake .

If you have installed the CTranslate library to a custom path use:


This code should print the output tokens:

▁Hall o ▁Welt !

If that’s the case, you successfully converted and executed a translation model with CTranslate2!


(De)tokenisation is handled outside of CTranslate2, so make sure to properly tokenise the input and detokenise the output.