File size: 6,633 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 | # -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: 2016-2025 PyThaiNLP Project
# SPDX-FileType: SOURCE
# SPDX-License-Identifier: Apache-2.0
"""
Thai Word-to-Phoneme (Thai W2P)
GitHub : https://github.com/wannaphong/Thai_W2P
"""
from typing import Union
import numpy as np
from pythainlp.corpus import download, get_corpus_path
_GRAPHEMES = list(
"พจใงต้ืฮแาฐฒฤๅูศฅถฺฎหคสุขเึดฟำฝยลอ็ม"
+ " ณิฑชฉซทรฏฬํัฃวก่ป์ผฆบี๊ธญฌษะไ๋นโภ?"
)
_PHONEMES = list(
"-พจใงต้ืฮแาฐฒฤูศฅถฺฎหคสุขเึดฟำฝยลอ็ม"
+ " ณิฑชฉซทรํฬฏ–ัฃวก่ปผ์ฆบี๊ธฌญะไษ๋นโภ?"
)
_MODEL_NAME = "thai_w2p"
class _Hparams:
batch_size = 256
enc_maxlen = 30 * 2
dec_maxlen = 40 * 2
num_epochs = 50 * 2
hidden_units = 64 * 8
emb_units = 64 * 4
graphemes = ["<pad>", "<unk>", "</s>"] + _GRAPHEMES
phonemes = ["<pad>", "<unk>", "<s>", "</s>"] + _PHONEMES
lr = 0.001
hp = _Hparams()
def _load_vocab():
g2idx = {g: idx for idx, g in enumerate(hp.graphemes)}
idx2g = dict(enumerate(hp.graphemes))
p2idx = {p: idx for idx, p in enumerate(hp.phonemes)}
idx2p = dict(enumerate(hp.phonemes))
# note that g and p mean grapheme and phoneme respectively.
return g2idx, idx2g, p2idx, idx2p
class Thai_W2P():
def __init__(self):
super().__init__()
self.graphemes = hp.graphemes
self.phonemes = hp.phonemes
self.g2idx, self.idx2g, self.p2idx, self.idx2p = _load_vocab()
self.checkpoint = get_corpus_path(_MODEL_NAME, version="0.2")
if self.checkpoint is None:
download(_MODEL_NAME, version="0.2")
self.checkpoint = get_corpus_path(_MODEL_NAME)
self._load_variables()
def _load_variables(self):
self.variables = np.load(self.checkpoint, allow_pickle=True)
# (29, 64). (len(graphemes), emb)
self.enc_emb = self.variables.item().get("encoder.emb.weight")
# (3*128, 64)
self.enc_w_ih = self.variables.item().get("encoder.rnn.weight_ih_l0")
# (3*128, 128)
self.enc_w_hh = self.variables.item().get("encoder.rnn.weight_hh_l0")
# (3*128,)
self.enc_b_ih = self.variables.item().get("encoder.rnn.bias_ih_l0")
# (3*128,)
self.enc_b_hh = self.variables.item().get("encoder.rnn.bias_hh_l0")
# (74, 64). (len(phonemes), emb)
self.dec_emb = self.variables.item().get("decoder.emb.weight")
# (3*128, 64)
self.dec_w_ih = self.variables.item().get("decoder.rnn.weight_ih_l0")
# (3*128, 128)
self.dec_w_hh = self.variables.item().get("decoder.rnn.weight_hh_l0")
# (3*128,)
self.dec_b_ih = self.variables.item().get("decoder.rnn.bias_ih_l0")
# (3*128,)
self.dec_b_hh = self.variables.item().get("decoder.rnn.bias_hh_l0")
# (74, 128)
self.fc_w = self.variables.item().get("decoder.fc.weight")
# (74,)
self.fc_b = self.variables.item().get("decoder.fc.bias")
def _sigmoid(self, x):
return 1 / (1 + np.exp(-x))
def _grucell(self, x, h, w_ih, w_hh, b_ih, b_hh):
rzn_ih = np.matmul(x, w_ih.T) + b_ih
rzn_hh = np.matmul(h, w_hh.T) + b_hh
rz_ih, n_ih = (
rzn_ih[:, : rzn_ih.shape[-1] * 2 // 3],
rzn_ih[:, rzn_ih.shape[-1] * 2 // 3 :],
)
rz_hh, n_hh = (
rzn_hh[:, : rzn_hh.shape[-1] * 2 // 3],
rzn_hh[:, rzn_hh.shape[-1] * 2 // 3 :],
)
rz = self._sigmoid(rz_ih + rz_hh)
r, z = np.split(rz, 2, -1)
n = np.tanh(n_ih + r * n_hh)
h = (1 - z) * n + z * h
return h
def _gru(self, x, steps, w_ih, w_hh, b_ih, b_hh, h0=None) -> np.ndarray:
if h0 is None:
h0 = np.zeros((x.shape[0], w_hh.shape[1]), np.float32)
h = h0 # initial hidden state
outputs = np.zeros((x.shape[0], steps, w_hh.shape[1]), np.float32)
for t in range(steps):
h = self._grucell(x[:, t, :], h, w_ih, w_hh, b_ih, b_hh) # (b, h)
outputs[:, t, ::] = h
return outputs
def _encode(self, word: str) -> np.ndarray:
chars = list(word) + ["</s>"]
x = [self.g2idx.get(char, self.g2idx["<unk>"]) for char in chars]
x = np.take(self.enc_emb, np.expand_dims(x, 0), axis=0)
return x
def _short_word(self, word: str) -> Union[str, None]:
self.word = word
if self.word.endswith("."):
self.word = self.word.replace(".", "")
self.word = "-".join([i + "อ" for i in list(self.word)])
return self.word
return None
def _predict(self, word: str) -> str:
short_word = self._short_word(word)
if short_word is not None:
return short_word
# encoder
enc = self._encode(word)
enc = self._gru(
enc,
len(word) + 1,
self.enc_w_ih,
self.enc_w_hh,
self.enc_b_ih,
self.enc_b_hh,
h0=np.zeros((1, self.enc_w_hh.shape[-1]), np.float32),
)
last_hidden = enc[:, -1, :]
# decoder
dec = np.take(self.dec_emb, [2], axis=0) # 2: <s>
h = last_hidden
preds = []
for _ in range(20):
h = self._grucell(
dec,
h,
self.dec_w_ih,
self.dec_w_hh,
self.dec_b_ih,
self.dec_b_hh,
) # (b, h)
logits = np.matmul(h, self.fc_w.T) + self.fc_b
pred = logits.argmax()
if pred == 3:
break
preds.append(pred)
dec = np.take(self.dec_emb, [pred], axis=0)
preds = [self.idx2p.get(idx, "<unk>") for idx in preds]
return preds
def __call__(self, word: str) -> str:
if not any(letter in word for letter in self.graphemes):
pron = [word]
else: # predict for oov
pron = self._predict(word)
return "".join(pron)
_THAI_W2P = Thai_W2P()
def pronunciate(text: str) -> str:
"""
Convert a Thai word to its pronunciation in Thai letters.
Input should be one single word.
:param str text: Thai text to be pronunciated
:return: A string of Thai letters indicating
how the input text should be pronounced.
"""
return _THAI_W2P(text)
|