TensorFlowTTS - Real-time Speech Synthesis using TensorFlow's Advanced Architectures

Introduction to TensorFlowTTS

TensorFlowTTS offers cutting-edge speech synthesis capabilities leveraging TensorFlow 2. It's designed for real-time speech generation and encompasses various state-of-the-art architectures like Tacotron-2, MelGAN, Multiband-MelGAN, FastSpeech, and FastSpeech2. The library is optimized for faster than real-time speech synthesis, with the added benefit of deploying on mobile or embedded devices.

Key Features

TensorFlowTTS demonstrates high performance in speech synthesis tasks, offering reliability and scalability.
It allows for fine-tuning on different languages, currently supporting English, French, German, Chinese, and Korean.
The framework is fast and capable of handling both single GPU and multi-GPU setups efficiently.
Models can be converted to TensorFlow Lite (TFlite) for deployment on various platforms.
It supports mixed precision training to accelerate the training process when conditions permit.
TensorFlowTTS also enables C++ inference and includes examples for Android deployment.
The project offers conversion utilities to transfer model weights from PyTorch to TensorFlow, improving computation speed.

Recent Developments

Huggingface Integration: As of August 2021, TensorFlowTTS is integrated with Huggingface Spaces using Gradio for web-based demos.
Language Support Expansion: It added support for French text-to-speech in August 2021 and German in December 2020.
Platform Compatibility: TensorFlowTTS supports iOS for FastSpeech2 and Multiband MelGAN as of March 2021.
TFLite Support: In January 2021, support for TFLite C++ inference was added, facilitating deployment in resource-constrained environments.
New Architectures: A range of vocoders like HiFi-GAN and techniques like Multi-GPU gradient accumulation has been added over time.

Supported Model Architectures

The project supports several models, each tailored to different needs:

MelGAN: Known for fast, efficient waveform synthesis through GANs.
Tacotron-2: Ensures high-quality natural TTS by predicting Mel spectrograms.
FastSpeech & FastSpeech2: These models focus on fast and robust text-to-speech conversion.
Multiband MelGAN: Offers faster waveform generation with high-quality speech output.
Parallel WaveGAN and HiFi-GAN: These optimize for high-quality, efficient speech synthesis using generative adversarial networks.

Installation Guide

Using pip: Simply run pip install TensorFlowTTS to get started quickly.
From Source: For the latest features, clone the repository from GitHub and install the package.

Dataset Preparation

To train models using TensorFlowTTS, users must prepare their datasets in a specific format. Metadata and audio files should be organized, and the data needs to undergo preprocessing steps, including mel spectrogram computations and normalization.

Model Training

TensorFlowTTS provides extensive tutorials and examples for training various models such as Tacotron-2, FastSpeech, FastSpeech2, MelGAN, etc. Each example includes detailed steps for setting up datasets, configuring model parameters, and executing training scripts.

Conclusion

TensorFlowTTS stands out as a robust framework for developers and researchers interested in real-time speech synthesis. Its versatility, combined with strong community support and continuous updates, makes it a valuable asset in the field of text-to-speech technologies.