Mms Tts Mlg

🚀 Massively Multilingual Speech (MMS): Malagasy Text-to-Speech

This repository offers a text-to-speech (TTS) model checkpoint for the Malagasy (mlg) language. It's part of Facebook's Massively Multilingual Speech project, aiming to provide speech technology across diverse languages. You can find more details about supported languages and their ISO 639 - 3 codes in the MMS Language Coverage Overview, and view all MMS - TTS checkpoints on the Hugging Face Hub: facebook/mms-tts. The MMS - TTS has been available in the 🤗 Transformers library since version 4.33.

✨ Features

The MMS-TTS Malagasy model is based on the VITS architecture, which is an end - to - end speech synthesis model. It can predict speech waveforms based on input text sequences. The model includes a stochastic duration predictor, enabling it to synthesize speech with different rhythms from the same input text.

📦 Installation

MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards. To use this checkpoint, first install the latest version of the library:

pip install --upgrade transformers accelerate

💻 Usage Examples

Basic Usage

from transformers import VitsModel, AutoTokenizer
import torch

model = VitsModel.from_pretrained("facebook/mms-tts-mlg")
tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-mlg")

text = "some example text in the Malagasy language"
inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad():
    output = model(**inputs).waveform

Advanced Usage

# Save the resulting waveform as a .wav file
import scipy

scipy.io.wavfile.write("techno.wav", rate=model.config.sampling_rate, data=output)

# Display the audio in a Jupyter Notebook / Google Colab
from IPython.display import Audio

Audio(output, rate=model.config.sampling_rate)

📚 Documentation

Model Details

VITS (Variational Inference with adversarial learning for end - to - end Text - to - Speech) is an end - to - end speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior.

A set of spectrogram - based acoustic features are predicted by the flow - based module, which is formed of a Transformer - based text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers, much in the same style as the HiFi - GAN vocoder. Motivated by the one - to - many nature of the TTS problem, where the same text input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to synthesise speech with different rhythms from the same input text.

The model is trained end - to - end with a combination of losses derived from variational lower bound and adversarial training. To improve the expressiveness of the model, normalizing flows are applied to the conditional prior distribution. During inference, the text encodings are up - sampled based on the duration prediction module, and then mapped into the waveform using a cascade of the flow module and HiFi - GAN decoder. Due to the stochastic nature of the duration predictor, the model is non - deterministic, and thus requires a fixed seed to generate the same speech waveform.

For the MMS project, a separate VITS checkpoint is trained on each langauge.

📄 License

The model is licensed as CC - BY - NC 4.0.

BibTex citation

This model was developed by Vineel Pratap et al. from Meta AI. If you use the model, consider citing the MMS paper:

@article{pratap2023mms,
    title={Scaling Speech Technology to 1,000+ Languages},
    author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel - Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei - Ning Hsu and Alexis Conneau and Michael Auli},
    journal={arXiv},
    year={2023}
}