Glow-TTS: A Game-Changer in Text-to-Speech Technology
Introduction
Glow-TTS is an innovative project focused on transforming text into speech in a smooth, efficient, and natural manner. Developed by researchers Jaehyeon Kim, Sungwon Kim, Jungil Kong, and Sungroh Yoon, this project introduces a new way of generating speech using advanced machine learning techniques.
Background
In the world of text-to-speech (TTS) systems, recent models such as FastSpeech and ParaNet have made strides by generating mel-spectrograms from text in parallel, which makes these models faster than their predecessors. However, these parallel models have a significant limitation: they rely on autoregressive models for guidance since they need external aligners.
The Glow-TTS Approach
Glow-TTS stands out as a groundbreaking generative flow-based model for TTS. What sets it apart is that it does not require any external aligner to train. Instead, it employs a novel method that combines flow models with dynamic programming to autonomously find the best alignment between text and speech representation. This innovative approach is called monotonic alignment search.
Key Advantages
- Robustness and Generalization: Glow-TTS ensures robust speech synthesis by enforcing strict monotonic alignments. This means it can handle extended utterances more effectively.
- Speed and Quality: The model delivers a significant performance boost, being much faster than the autoregressive Tacotron 2 model, while maintaining similar speech quality.
- Flexibility: The architecture also supports a multi-speaker setting, making it adaptable for various use cases.
Demonstrations and Resources
For those interested in experiencing Glow-TTS firsthand, a demo is available with audio samples. Additionally, a pretrained model is provided for users who want to explore the technology further.
Recent Updates
Recently, the team implemented two notable improvements to enhance Glow-TTS:
- Enhanced Vocoder: By integrating the HiFi-GAN vocoder, the model now produces less noisy outputs. Details and samples of this improvement are available in the HiFi-GAN repository.
- Better Pronunciation: By adding a blank token between input tokens, pronunciation quality has improved. Users can access a config file and a pretrained model for this feature.
Technical Environment
The development environment for Glow-TTS includes an array of tools and packages:
- Python 3.6.9
- Pytorch 1.2.0
- Cython 0.29.12
- Librosa 0.7.1
- Numpy 1.16.4
- Scipy 1.3.0
Additionally, for mixed-precision training, the project utilizes NVIDIA's apex.
Getting Started
To work with Glow-TTS, several prerequisites are necessary:
- Dataset: Download and set up the LJ Speech dataset.
- WaveGlow Model: Initialize the WaveGlow submodule and download the pre-trained model.
- Code Building: Compile the monotonic alignment search code using Cython.
Training and Inference
To train the model, users can run:
sh train_ddi.sh configs/base.json base
For inference, guidance is provided in the inference example.
Acknowledgements
Glow-TTS's development is heavily influenced by contributions from various research repositories, including WaveGlow, Tensor2Tensor, and Mellotron.
Glow-TTS is a significant advancement in the field of text-to-speech generation, offering speed, flexibility, and robustness that set a new benchmark for future developments.