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e4b9a7b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | # -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: 2016-2025 PyThaiNLP Project
# SPDX-FileType: SOURCE
# SPDX-License-Identifier: Apache-2.0
"""
English-Thai Machine Translation
from VISTEC-depa Thailand Artificial Intelligence Research Institute
Website: https://airesearch.in.th/releases/machine-translation-models/
"""
import os
from fairseq.models.transformer import TransformerModel
from sacremoses import MosesTokenizer
from pythainlp.corpus import download, get_corpus_path
_EN_TH_MODEL_NAME = "scb_1m_en-th_moses"
# SCB_1M-MT_OPUS+TBASE_en-th_moses-spm_130000-16000_v1.0.tar.gz
_EN_TH_FILE_NAME = "SCB_1M-MT_OPUS+TBASE_en-th_moses-spm_130000-16000_v1.0"
_TH_EN_MODEL_NAME = "scb_1m_th-en_spm"
# SCB_1M-MT_OPUS+TBASE_th-en_spm-spm_32000-joined_v1.0.tar.gz
_TH_EN_FILE_NAME = "SCB_1M-MT_OPUS+TBASE_th-en_spm-spm_32000-joined_v1.0"
def _get_translate_path(model: str, *path: str) -> str:
return os.path.join(get_corpus_path(model, version="1.0"), *path)
def _download_install(name: str) -> None:
if get_corpus_path(name) is None:
download(name, force=True, version="1.0")
def download_model_all() -> None:
"""
Download all translation models in advance
"""
_download_install(_EN_TH_MODEL_NAME)
_download_install(_TH_EN_MODEL_NAME)
class EnThTranslator:
"""
English-Thai Machine Translation
from VISTEC-depa Thailand Artificial Intelligence Research Institute
Website: https://airesearch.in.th/releases/machine-translation-models/
:param bool use_gpu : load model using GPU (Default is False)
"""
def __init__(self, use_gpu: bool = False):
self._tokenizer = MosesTokenizer("en")
self._model_name = _EN_TH_MODEL_NAME
_download_install(self._model_name)
self._model = TransformerModel.from_pretrained(
model_name_or_path=_get_translate_path(
self._model_name,
_EN_TH_FILE_NAME,
"models",
),
checkpoint_file="checkpoint.pt",
data_name_or_path=_get_translate_path(
self._model_name,
_EN_TH_FILE_NAME,
"vocab",
),
)
if use_gpu:
self._model = self._model.cuda()
def translate(self, text: str) -> str:
"""
Translate text from English to Thai
:param str text: input text in source language
:return: translated text in target language
:rtype: str
:Example:
Translate text from English to Thai::
from pythainlp.translate import EnThTranslator
enth = EnThTranslator()
enth.translate("I love cat.")
# output: ฉันรักแมว
"""
tokens = " ".join(self._tokenizer.tokenize(text))
translated = self._model.translate(tokens)
return translated.replace(" ", "").replace("▁", " ").strip()
class ThEnTranslator:
"""
Thai-English Machine Translation
from VISTEC-depa Thailand Artificial Intelligence Research Institute
Website: https://airesearch.in.th/releases/machine-translation-models/
:param bool use_gpu : load model using GPU (Default is False)
"""
def __init__(self, use_gpu: bool = False):
self._model_name = _TH_EN_MODEL_NAME
_download_install(self._model_name)
self._model = TransformerModel.from_pretrained(
model_name_or_path=_get_translate_path(
self._model_name,
_TH_EN_FILE_NAME,
"models",
),
checkpoint_file="checkpoint.pt",
data_name_or_path=_get_translate_path(
self._model_name,
_TH_EN_FILE_NAME,
"vocab",
),
bpe="sentencepiece",
sentencepiece_model=_get_translate_path(
self._model_name,
_TH_EN_FILE_NAME,
"bpe",
"spm.th.model",
),
)
if use_gpu:
self._model.cuda()
def translate(self, text: str) -> str:
"""
Translate text from Thai to English
:param str text: input text in source language
:return: translated text in target language
:rtype: str
:Example:
Translate text from Thai to English::
from pythainlp.translate import ThEnTranslator
then = ThEnTranslator()
then.translate("ฉันรักแมว")
# output: I love cat.
"""
return self._model.translate(text)
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