File size: 6,002 Bytes
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 | # -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: 2016-2025 PyThaiNLP Project
# SPDX-FileType: SOURCE
# SPDX-License-Identifier: Apache-2.0
"""
Romanization of Thai words based on machine-learnt engine in ONNX runtime ("thai2rom")
"""
import json
import numpy as np
from onnxruntime import InferenceSession
from pythainlp.corpus import get_corpus_path
_MODEL_ENCODER_NAME = "thai2rom_encoder_onnx"
_MODEL_DECODER_NAME = "thai2rom_decoder_onnx"
_MODEL_CONFIG_NAME = "thai2rom_config_onnx"
class ThaiTransliterator_ONNX:
def __init__(self):
"""
Transliteration of Thai words.
Now supports Thai to Latin (romanization)
"""
# get the model, download it if it's not available locally
self.__encoder_filename = get_corpus_path(_MODEL_ENCODER_NAME)
self.__decoder_filename = get_corpus_path(_MODEL_DECODER_NAME)
self.__config_filename = get_corpus_path(_MODEL_CONFIG_NAME)
# loader = torch.load(self.__model_filename, map_location=device)
with open(str(self.__config_filename)) as f:
loader = json.load(f)
OUTPUT_DIM = loader["output_dim"]
self._maxlength = 100
self._char_to_ix = loader["char_to_ix"]
self._ix_to_char = loader["ix_to_char"]
self._target_char_to_ix = loader["target_char_to_ix"]
self._ix_to_target_char = loader["ix_to_target_char"]
# encoder/ decoder
# Load encoder decoder onnx models.
self._encoder = InferenceSession(self.__encoder_filename)
self._decoder = InferenceSession(self.__decoder_filename)
self._network = Seq2Seq_ONNX(
self._encoder,
self._decoder,
self._target_char_to_ix["<start>"],
self._target_char_to_ix["<end>"],
self._maxlength,
target_vocab_size=OUTPUT_DIM,
)
def _prepare_sequence_in(self, text: str):
"""
Prepare input sequence for ONNX
"""
idxs = []
for ch in text:
if ch in self._char_to_ix:
idxs.append(self._char_to_ix[ch])
else:
idxs.append(self._char_to_ix["<UNK>"])
idxs.append(self._char_to_ix["<end>"])
return np.array(idxs)
def romanize(self, text: str) -> str:
"""
:param str text: Thai text to be romanized
:return: English (more or less) text that spells out how the Thai text
should be pronounced.
"""
input_tensor = self._prepare_sequence_in(text).reshape(1, -1)
input_length = [len(text) + 1]
target_tensor_logits = self._network.run(input_tensor, input_length)
# Seq2seq model returns <END> as the first token,
# As a result, target_tensor_logits.size() is torch.Size([0])
if target_tensor_logits.shape[0] == 0:
target = ["<PAD>"]
else:
target_tensor = np.argmax(target_tensor_logits.squeeze(1), 1)
target = [self._ix_to_target_char[str(t)] for t in target_tensor]
return "".join(target)
class Seq2Seq_ONNX:
def __init__(
self,
encoder,
decoder,
target_start_token,
target_end_token,
max_length,
target_vocab_size,
):
super().__init__()
self.encoder = encoder
self.decoder = decoder
self.pad_idx = 0
self.target_start_token = target_start_token
self.target_end_token = target_end_token
self.max_length = max_length
self.target_vocab_size = target_vocab_size
def create_mask(self, source_seq):
mask = source_seq != self.pad_idx
return mask
def run(self, source_seq, source_seq_len):
# source_seq: (batch_size, MAX_LENGTH)
# source_seq_len: (batch_size, 1)
# target_seq: (batch_size, MAX_LENGTH)
batch_size = source_seq.shape[0]
start_token = self.target_start_token
end_token = self.target_end_token
max_len = self.max_length
# target_vocab_size = self.decoder.vocabulary_size
outputs = np.zeros((max_len, batch_size, self.target_vocab_size))
expected_encoder_outputs = list(
map(lambda output: output.name, self.encoder.get_outputs())
)
encoder_outputs, encoder_hidden, _ = self.encoder.run(
input_feed={
"input_tensor": source_seq,
"input_lengths": source_seq_len,
},
output_names=expected_encoder_outputs,
)
decoder_input = np.array([[start_token] * batch_size]).reshape(
batch_size, 1
)
encoder_hidden_h_t = np.expand_dims(
np.concatenate(
# [encoder_hidden_1, encoder_hidden_2], dim=1
(encoder_hidden[0], encoder_hidden[1]),
axis=1,
),
axis=0,
)
decoder_hidden = encoder_hidden_h_t
max_source_len = encoder_outputs.shape[1]
mask = self.create_mask(source_seq[:, 0:max_source_len])
for di in range(max_len):
decoder_output, decoder_hidden = self.decoder.run(
input_feed={
"decoder_input": decoder_input.astype("int32"),
"decoder_hidden_1": decoder_hidden,
"encoder_outputs": encoder_outputs,
"mask": mask.tolist(),
},
output_names=[
self.decoder.get_outputs()[0].name,
self.decoder.get_outputs()[1].name,
],
)
topi = np.argmax(decoder_output, axis=1)
outputs[di] = decoder_output
decoder_input = np.array([topi])
if decoder_input == end_token:
return outputs[:di]
return outputs
_THAI_TO_ROM_ONNX = ThaiTransliterator_ONNX()
def romanize(text: str) -> str:
return _THAI_TO_ROM_ONNX.romanize(text)
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