File size: 4,971 Bytes
ddb547c 84b3e03 ddb547c 84b3e03 b496440 84b3e03 ddb547c 84b3e03 ba49116 84b3e03 0b1e973 84b3e03 |
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 |
---
language:
- id
- ms
license: apache-2.0
tags:
- g2p
- text2text-generation
inference: false
---
# ID G2P LSTM
ID G2P LSTM is a grapheme-to-phoneme model based on the [LSTM](https://doi.org/10.1162/neco.1997.9.8.1735) architecture. This model was trained from scratch on a modified [Malay/Indonesian lexicon](https://huggingface.co/datasets/bookbot/id_word2phoneme).
This model was trained using the [Keras](https://keras.io/) framework. All training was done on Google Colaboratory. We adapted the [LSTM training script](https://keras.io/examples/nlp/lstm_seq2seq/) provided by the official Keras Code Example.
## Model
| Model | #params | Arch. | Training/Validation data |
| ------------- | ------- | ----- | ------------------------ |
| `id-g2p-lstm` | 596K | LSTM | Malay/Indonesian Lexicon |
## Training Procedure
<details>
<summary>Model Config</summary>
latent_dim: 256
num_encoder_tokens: 28
num_decoder_tokens: 32
max_encoder_seq_length: 24
max_decoder_seq_length: 25
</details>
<details>
<summary>Training Setting</summary>
batch_size: 64
optimizer: "rmsprop"
loss: "categorical_crossentropy"
learning_rate: 0.001
epochs: 100
</details>
## How to Use
<details>
<summary>Tokenizers</summary>
g2id = {
' ': 27,
'-': 0,
'a': 1,
'b': 2,
'c': 3,
'd': 4,
'e': 5,
'f': 6,
'g': 7,
'h': 8,
'i': 9,
'j': 10,
'k': 11,
'l': 12,
'm': 13,
'n': 14,
'o': 15,
'p': 16,
'q': 17,
'r': 18,
's': 19,
't': 20,
'u': 21,
'v': 22,
'w': 23,
'y': 24,
'z': 25,
'’': 26
}
p2id = {
'\t': 0,
'\n': 1,
' ': 31,
'-': 2,
'a': 3,
'b': 4,
'd': 5,
'e': 6,
'f': 7,
'g': 8,
'h': 9,
'i': 10,
'j': 11,
'k': 12,
'l': 13,
'm': 14,
'n': 15,
'o': 16,
'p': 17,
'r': 18,
's': 19,
't': 20,
'u': 21,
'v': 22,
'w': 23,
'z': 24,
'ŋ': 25,
'ə': 26,
'ɲ': 27,
'ʃ': 28,
'ʒ': 29,
'ʔ': 30
}
</details>
```py
import keras
import numpy as np
from huggingface_hub import from_pretrained_keras
latent_dim = 256
bos_token, eos_token, pad_token = "\t", "\n", " "
num_encoder_tokens, num_decoder_tokens = 28, 32
max_encoder_seq_length, max_decoder_seq_length = 24, 25
model = from_pretrained_keras("bookbot/id-g2p-lstm")
encoder_inputs = model.input[0]
encoder_outputs, state_h_enc, state_c_enc = model.layers[2].output
encoder_states = [state_h_enc, state_c_enc]
encoder_model = keras.Model(encoder_inputs, encoder_states)
decoder_inputs = model.input[1]
decoder_state_input_h = keras.Input(shape=(latent_dim,), name="input_3")
decoder_state_input_c = keras.Input(shape=(latent_dim,), name="input_4")
decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]
decoder_lstm = model.layers[3]
decoder_outputs, state_h_dec, state_c_dec = decoder_lstm(
decoder_inputs, initial_state=decoder_states_inputs
)
decoder_states = [state_h_dec, state_c_dec]
decoder_dense = model.layers[4]
decoder_outputs = decoder_dense(decoder_outputs)
decoder_model = keras.Model(
[decoder_inputs] + decoder_states_inputs, [decoder_outputs] + decoder_states
)
def inference(sequence):
id2p = {v: k for k, v in p2id.items()}
input_seq = np.zeros(
(1, max_encoder_seq_length, num_encoder_tokens), dtype="float32"
)
for t, char in enumerate(sequence):
input_seq[0, t, g2id[char]] = 1.0
input_seq[0, t + 1 :, g2id[pad_token]] = 1.0
states_value = encoder_model.predict(input_seq)
target_seq = np.zeros((1, 1, num_decoder_tokens))
target_seq[0, 0, p2id[bos_token]] = 1.0
stop_condition = False
decoded_sentence = ""
while not stop_condition:
output_tokens, h, c = decoder_model.predict([target_seq] + states_value)
sampled_token_index = np.argmax(output_tokens[0, -1, :])
sampled_char = id2p[sampled_token_index]
decoded_sentence += sampled_char
if sampled_char == eos_token or len(decoded_sentence) > max_decoder_seq_length:
stop_condition = True
target_seq = np.zeros((1, 1, num_decoder_tokens))
target_seq[0, 0, sampled_token_index] = 1.0
states_value = [h, c]
return decoded_sentence.replace(eos_token, "")
inference("mengembangkannya")
```
## Authors
ID G2P LSTM was trained and evaluated by [Ananto Joyoadikusumo](https://anantoj.github.io/), [Steven Limcorn](https://stevenlimcorn.github.io/), [Wilson Wongso](https://w11wo.github.io/). All computation and development are done on Google Colaboratory.
## Framework versions
- Keras 2.8.0
- TensorFlow 2.8.0
|