phonemize 0.2.3

Multilingual grapheme-to-phoneme (G2P) conversion using Transformer models.

Phonimize

Phonimize is a multilingual grapheme-to-phoneme (G2P) conversion library built with Transformer models. It’s designed for high accuracy, fast inference, and simple integration into text-to-speech (TTS) or other speech-related systems.

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Key Features

• Easy-to-use API: A simple interface for both training and inference.
• Multilingual Support: Train a single model on multiple languages.
• High Performance: Fast and accurate predictions powered by Transformer models.
• Custom Training: Effortlessly train your own models in just a few lines of code.
• Optimized for TTS: Ideal for both real-time and offline text-to-speech pipelines.

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Installation

To install Phonimize, use the following command:

pip install phonimize

Quickstart

Load a pre-trained model and perform phoneme prediction with this simple example:

from phonimize import Phonemizer

# Load the pre-trained model from a checkpoint
phonemizer = Phonemizer.from_checkpoint("phonemize_m1.pt")

# Phonemize an English text
result = phonemizer("Phonemizing an English text is imposimpable!", lang="en_us")

# Print the result
print(result)

Output:

foʊnɪmaɪzɪŋ æn ɪŋglɪʃ tɛkst ɪz ɪmpəzɪmpəbəl!

Training Your Own Model

You can easily train your own forward or autoregressive Transformer model. All configuration parameters are defined in a simple YAML file (e.g., configs/forward.yaml).

from phonimize.preprocess import preprocess
from phonimize.train import train

# Define your training data
train_data = [
    ("en_us", "young", "jʌŋ"),
    ("de", "benützten", "bənʏt͡stn̩")
] * 1000

# Define your validation data
val_data = [
    ("en_us", "young", "jʌŋ"),
    ("de", "benützten", "bənʏt͡stn̩")
] * 100

# Specify the configuration file
config_file = "configs/forward.yaml"

# Preprocess the data
preprocess(
    config_file=config_file,
    train_data=train_data,
    val_data=val_data,
    deduplicate_train_data=False
)

# Train the model
train(rank=0, num_gpus=1, config_file=config_file)

Checkpoints will be saved in the directory specified in your configuration file.

Inference Example

To perform inference with your trained model:

from phonimize import Phonemizer

# Load your custom model from a checkpoint
phonemizer = Phonemizer.from_checkpoint("checkpoints/best_model.pt")

# Get the phonemes for a given text
phonemes = phonemizer("Phonemizing text is simple!", lang="en_us")
print(phonemes)

To inspect detailed predictions, including confidence scores:

result = phonemizer.phonemise_list(["Phonemizing text is simple!"], lang="en_us")

for word, pred in result.predictions.items():
    print(f"Word: {word}, Phonemes: {pred.phonemes}, Confidence: {pred.confidence}")

TorchScript Export

For optimized performance, you can easily export your trained Transformer model to TorchScript:

import torch
from phonimize import Phonemizer

# Load the model from a checkpoint
phonemizer = Phonemizer.from_checkpoint("checkpoints/best_model.pt")

# Convert the model to a TorchScript module
scripted_model = torch.jit.script(phonemizer.predictor.model)
phonemizer.predictor.model = scripted_model

# Run inference with the TorchScript model
phonemizer("Running the TorchScript model!")

Pre-trained Models

This model has been modified for the phonemize library.

Model	Language	Dataset	Repo Version
phonemize_m1	en_us	cmudict	0.1.0

Acknowledgment

Phonimize is inspired by DeepPhonemizer, and has been refactored and optimized for simplicity, speed, and modern Python environments.

Phonimize is compatible with Python 3.8+ and distributed under the MIT license. Learn more at: https://github.com/arcosoph/phonemize

License

This project is released under the MIT License.