Mms Tts Bgr

🚀 Massively Multilingual Speech (MMS): Chin, Bawm Text-to-Speech

This repository offers a text-to-speech (TTS) model checkpoint for the Chin, Bawm (bgr) language. It's part of Facebook's Massively Multilingual Speech project, aiming to provide speech technology across diverse languages. You can find 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

• Multilingual Support: Part of a project aiming to cover over 1000 languages.
• End - to - End Synthesis: Utilizes the VITS model for direct text - to - waveform synthesis.
• Stochastic Duration Prediction: Allows for different speech rhythms from the same text input.

📦 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-bgr")
tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-bgr")

text = "some example text in the Chin, Bawm 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. It predicts a speech waveform based on an input text sequence. It's a conditional variational autoencoder (VAE) with a posterior encoder, decoder, and conditional prior.

A flow - based module, consisting of a Transformer - based text encoder and multiple coupling layers, predicts a set of spectrogram - based acoustic features. The spectrogram is decoded using a stack of transposed convolutional layers, similar to the HiFi - GAN vocoder. Given the one - to - many nature of the TTS problem (same text can be spoken in multiple ways), the model includes a stochastic duration predictor, enabling it to synthesize speech with different rhythms from the same input text.

The model is trained end - to - end with a combination of losses from variational lower bound and adversarial training. To enhance the model's expressiveness, normalizing flows are applied to the conditional prior distribution. During inference, text encodings are up - sampled according to the duration prediction module and then mapped to 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 requires a fixed seed to generate the same speech waveform.

For the MMS project, a separate VITS checkpoint is trained for each language.

📄 License

The model is licensed under 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}
}