# -*- coding: utf-8 -*- # SPDX-FileCopyrightText: 2016-2024 PyThaiNLP Project # 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 = ["", "", ""] + _GRAPHEMES phonemes = ["", "", "", ""] + _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) + [""] x = [self.g2idx.get(char, self.g2idx[""]) 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: 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, "") 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)