pip3 install ctranslate2

• OpenNMT-py:

ct2-opennmt-py-converter --model_path model.pt --output_dir enja_ctranslate2 --quantization int8

• OpenNMT-tf:

ct2-opennmt-tf-converter --model_path model --output_dir enja_ctranslate2 --src_vocab source.vocab --tgt_vocab target.vocab --model_type TransformerBase --quantization int8

import ctranslate2 import sentencepiece as spm

Set file paths

source_file_path = "test.en" target_file_path = "test.ja"

sp_source_model_path = "spm_model.en" sp_target_model_path = "spm_model.ja"

ct_model_path = "enja_ctranslate2/"

Load the source SentecePiece model

sp = spm.SentencePieceProcessor() sp.load(sp_source_model_path)

Open the source file

with open(source_file_path, "r") as source: lines = source.readlines()

source_sents = [line.strip() for line in lines]

Subword the source sentences

source_sents_subworded = sp.encode_as_pieces(source_sents)

Translate the source sentences

translator = ctranslate2.Translator(ct_model_path, device="cpu") # or "cuda" for GPU translations = translator.translate_batch(source_sents_subworded, batch_type="tokens", max_batch_size=4096) translations = [translation.hypotheses[0] for translation in translations]

Load the target SentecePiece model

sp.load(sp_target_model_path)

Desubword the target sentences

translations_desubword = sp.decode(translations)

Save the translations to the a file

with open(target_file_path, "w+", encoding="utf-8") as target: for line in translations_desubword: target.write(line.strip() + "\n")

print("Done")