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---
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,
'-': 1,
'a': 2,
'b': 3,
'c': 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,
'q': 18,
'r': 19,
's': 20,
't': 21,
'u': 22,
'v': 23,
'w': 24,
'y': 25,
'z': 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
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