ID G2P BERT
ID G2P BERT is a phoneme de-masking model based on the BERT architecture. This model was trained from scratch on a modified Malay/Indonesian lexicon.
This model was trained using the Keras framework. All training was done on Google Colaboratory. We adapted the BERT Masked Language Modeling training script provided by the official Keras Code Example.
Model
Model | #params | Arch. | Training/Validation data |
---|---|---|---|
id-g2p-bert |
200K | BERT | Malay/Indonesian Lexicon |
Training Procedure
Model Config
vocab_size: 32
max_len: 32
embed_dim: 128
num_attention_head: 2
feed_forward_dim: 128
num_layers: 2
Training Setting
batch_size: 32
optimizer: "adam"
learning_rate: 0.001
epochs: 100
How to Use
Tokenizers
id2token = {
0: '',
1: '[UNK]',
2: 'a',
3: 'n',
4: 'ə',
5: 'i',
6: 'r',
7: 'k',
8: 'm',
9: 't',
10: 'u',
11: 'g',
12: 's',
13: 'b',
14: 'p',
15: 'l',
16: 'd',
17: 'o',
18: 'e',
19: 'h',
20: 'c',
21: 'y',
22: 'j',
23: 'w',
24: 'f',
25: 'v',
26: '-',
27: 'z',
28: "'",
29: 'q',
30: '[mask]'
}
token2id = {
'': 0,
"'": 28,
'-': 26,
'[UNK]': 1,
'[mask]': 30,
'a': 2,
'b': 13,
'c': 20,
'd': 16,
'e': 18,
'f': 24,
'g': 11,
'h': 19,
'i': 5,
'j': 22,
'k': 7,
'l': 15,
'm': 8,
'n': 3,
'o': 17,
'p': 14,
'q': 29,
'r': 6,
's': 12,
't': 9,
'u': 10,
'v': 25,
'w': 23,
'y': 21,
'z': 27,
'ə': 4
}
import keras
import tensorflow as tf
import numpy as np
from huggingface_hub import from_pretrained_keras
model = from_pretrained_keras("bookbot/id-g2p-bert")
MAX_LEN = 32
MASK_TOKEN_ID = 30
def inference(sequence):
sequence = " ".join([c if c != "e" else "[mask]" for c in sequence])
tokens = [token2id[c] for c in sequence.split()]
pad = [token2id[""] for _ in range(MAX_LEN - len(tokens))]
tokens = tokens + pad
input_ids = tf.convert_to_tensor(np.array([tokens]))
prediction = model.predict(input_ids)
# find masked idx token
masked_index = np.where(input_ids == MASK_TOKEN_ID)
masked_index = masked_index[1]
# get prediction at those masked index only
mask_prediction = prediction[0][masked_index]
predicted_ids = np.argmax(mask_prediction, axis=1)
# replace mask with predicted token
for i, idx in enumerate(masked_index):
tokens[idx] = predicted_ids[i]
return "".join([id2token[t] for t in tokens if t != 0])
inference("mengembangkannya")
Authors
ID G2P BERT was trained and evaluated by Ananto Joyoadikusumo, Steven Limcorn, Wilson Wongso. All computation and development are done on Google Colaboratory.
Framework versions
- Keras 2.8.0
- TensorFlow 2.8.0
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