Mms Tts Urd Script Arabic

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

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

✨ Features

• Multilingual Support: Part of a project aiming to provide speech technology for over 1000 languages.
• End - to - End Synthesis: Based on the VITS model for direct text - to - speech conversion.
• Stochastic Rhythm: Capable of synthesizing 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-urd-script_arabic")
tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-urd-script_arabic")

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

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

Advanced Usage

Saving the Output as a .wav File

import scipy

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

Displaying 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) consisting of a posterior encoder, decoder, and conditional prior.

A set of spectrogram - based acoustic features are predicted by the flow - based module, which includes a Transformer - based text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers, similar to the HiFi - GAN vocoder. Due to the one - to - many nature of the TTS problem (the same text can be spoken in multiple ways), the model has a stochastic duration predictor, allowing 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, the text encodings are up - sampled based on the duration prediction module and then mapped to the waveform using a cascade of the flow module and HiFi - GAN decoder. Because of 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 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}
}