Grapheme-to-phoneme (G2P) conversion is the process of generating pronunciation for words based on their written form. It has a highly essential role for natural language processing, text-to-speech synthesis and automatic speech recognition systems. This project was adapted from


The following libraries are used:

Install dependencies using pip:

pip3 install -r requirements.txt


The dataset used here was taken from site, as well as some insertions made by me so that the dataset would give more coverage to common words in the daily life of the Brazilian Portuguese. Some ambiguities were also resolved as the intent of this dataset is to contain a specific speaker bias. The dictionary based on São Paulo speakers was chosen.

As in, on which this implementation was based, you could easily provide and use your own language specific pronunciatin doctionary for training G2P. More details about data preparation and contribution could be found in resources.
Feel free to provide resources for other languages.

Attention Model

Both encoder-decoder seq2seq model and attention model could handle G2P problem. Here we train attention based model.

The encoder model get sequence of graphemes and produces states at each timestep. Encoder states used during attention decoding. The decoder attends to appropriate encoder state (according to its state) and produces phonemes.


To start training the model run:


You can also use tensorboard to check the training loss:

tensorboard --logdir log --bind_all

Training parameters could be found at


To get pronunciation of a word:

# PT-BR example
python --sentence 'olá, vamos testar esse projeto.'
o|l|a| |,| |v|a|m|ʊ|s| |t|e|s|t|a| |e|s|i| |p|ɾ|o|ʒ|e|t|ʊ| |.

You could also visualize the attention weights, using --visualize:

# PT-BR example
python --visualize --sentence 'olá, vamos testar esse projeto.'
o|l|a| |,| |v|a|m|ʊ|s| |t|e|s|t|a| |e|s|i| |p|ɾ|o|ʒ|e|t|ʊ| |.