🐤 Nix-TTS

Lightweight and End-to-end Text-to-Speech via Module-wise Distillation

Rendi Chevi, Radityo Eko Prasojo, Alham Fikri Aji, Andros Tjandra, Sakriani Sakti

This is a repository for our paper, 🐤 Nix-TTS (Accepted to IEEE SLT 2022). We released the pretrained models, an interactive demo, and audio samples below.

[[📄 Paper Link](Coming Soon!)] [🤗 Interactive Demo] [📢 Audio Samples]

Abstract    Several solutions for lightweight TTS have shown promising results. Still, they either rely on a hand-crafted design that reaches non-optimum size or use a neural architecture search but often suffer training costs. We present Nix-TTS, a lightweight TTS achieved via knowledge distillation to a high-quality yet large-sized, non-autoregressive, and end-to-end (vocoder-free) TTS teacher model. Specifically, we offer module-wise distillation, enabling flexible and independent distillation to the encoder and decoder module. The resulting Nix-TTS inherited the advantageous properties of being non-autoregressive and end-to-end from the teacher, yet significantly smaller in size, with only 5.23M parameters or up to 89.34\% reduction of the teacher model; it also achieves over 3.04$\times$ and 8.36$\times$ inference speedup on Intel-i7 CPU and Raspberry Pi 3B respectively and still retains a fair voice naturalness and intelligibility compared to the teacher model.

Getting Started with Nix-TTS

Clone the nix-tts repository and move to its directory

git clone https://github.com/rendchevi/nix-tts.git
cd nix-tts

Install the dependencies

• Install Python dependencies. We recommend python >= 3.8

  pip install -r requirements.txt 

• Install espeak in your device (for text tokenization).

  sudo apt-get install espeak

  Or follow the official instruction in case it didn't work.

Download your chosen pre-trained model here.

*** Here we compute how much the model run faster than real-time as the inverse of Real Time Factor (RTF). The complete table of all models speedup is detailed on the paper.

And running Nix-TTS is as easy as:

from nix.models.TTS import NixTTSInference
from IPython.display import Audio

# Initiate Nix-TTS
nix = NixTTSInference(model_dir = "<path_to_the_downloaded_model>")
# Tokenize input text
c, c_length, phoneme = nix.tokenize("Born to multiply, born to gaze into night skies.")
# Convert text to raw speech
xw = nix.vocalize(c, c_length)

# Listen to the generated speech
Audio(xw[0,0], rate = 22050)

Acknowledgement

• This research is fully and exclusively funded by Kata.ai, where the authors work as part of the Kata.ai Research Team.
• Some of the complex parts of our model, as mentioned in the paper, are adapted from the original implementation of VITS and Comprehensive-Transformer-TTS.