Comprehensive-Transformer-TTS - PyTorch Implementation
Overview
Comprehensive-Transformer-TTS is a PyTorch-based project designed to offer a state-of-the-art Text-to-Speech (TTS) solution using Non-Autoregressive Transformer models. The project's goal is to continuously improve with insights from the research community to deliver the ultimate TTS experience. Comprehensive-Transformer-TTS supports various advanced Transformer models and both supervised and unsupervised duration modeling techniques.
Key Components
Transformers
The project integrates several advanced Transformer models to enhance the TTS process:
- Fastformer: An efficient model that uses additive attention mechanisms.
- Long-Short Transformer: Designed for both language and vision tasks, balancing efficiency and performance.
- Conformer: Combines convolutional neural networks with Transformer models specifically for speech recognition.
- Reformer: Focused on reducing computational load while maintaining effectiveness.
- Transformer: The traditional Transformer model underpinning many modern language models.
Prosody Modeling
Prosody refers to the rhythm, stress, and intonation of speech. The project is working-in-progress to include:
- DelightfulTTS: A speech synthesis system by Microsoft designed for high-quality prosody modeling.
- Rich Prosody Diversity Modeling: Uses a phone-level mixture density network to capture prosody variation.
Duration Modeling
Understanding and predicting the duration of phonemes is crucial for natural-sounding speech. The project supports:
- Supervised Duration Modeling with FastSpeech 2, aimed at providing high-quality, fast end-to-end TTS.
- Unsupervised Duration Modeling using a technique that frees the system from external alignment tools.
Performance and Configuration
Performance Comparison
Comprehensive-Transformer-TTS provides a comparison of different models' performance, demonstrating variation in memory usage and training times. This data assists users in selecting the best model suited to their needs.
Configuration Options
Users can adjust:
- Building Blocks: Choosing different Transformer models (e.g.
transformer_fs2,fastformer). - Prosody Modeling: Different options based on ongoing research.
- Duration Modeling: Choose between supervised and unsupervised methods.
Quickstart and Practical Usage
Dependencies and Setup
Users can easily install the required Python dependencies, and a Docker setup is available for those who prefer a containerized environment.
Inference and Synthesis
The project allows single-speaker and multi-speaker TTS synthesis using pretrained models. Users can control the pitch, volume, and speaking rate to tailor the output to their specifications.
Training
Detailed instructions are provided for training using datasets like LJSpeech and VCTK. The project supports mixed precision training to optimize speed and memory usage.
Visualization with TensorBoard
Users can visualize training results such as loss curves, mel-spectrograms, and audio syntheses using TensorBoard.
Advanced Studies and Notes
Ablation studies offer insights into the impact of different Transformer block types and pitch conditioning methods on the quality and expressiveness of the generated speech. Additionally, the project integrates advanced speaker embedding options for multi-speaker settings.
Updates and Community
The project is actively maintained with regular updates reflecting the latest research. Contributions and suggestions from the community are highly valued to enhance the project further.
Conclusion
Comprehensive-Transformer-TTS stands as a flexible and advanced tool for TTS researchers and developers, combining state-of-the-art models and techniques with practical usability. The focus remains on delivering a robust, high-quality speech synthesis experience.