JETS: Joint End-to-end Text-to-Speech Model
| Property | Value |
|---|---|
| Framework | ESPnet2 |
| Dataset | LJSpeech |
| Author | imdanboy |
| Repository | HuggingFace |
What is JETS?
JETS is a sophisticated text-to-speech model implemented in the ESPnet2 framework. It combines a transformer-based architecture with advanced features for high-quality speech synthesis, including pitch prediction, energy prediction, and HiFiGAN vocoder integration.
Implementation Details
The model employs a complex architecture with both generator and discriminator components. The generator features 4 encoder and decoder layers with 256 attention dimensions and 1024 units. It implements advanced features like conformer-based processing and multi-scale discriminators.
- Transformer-based encoder-decoder architecture with 4 layers each
- Attention mechanism with 2 heads and 256 dimensional embeddings
- Duration, pitch, and energy predictors for enhanced prosody control
- HiFiGAN-based vocoder with multi-scale and multi-period discrimination
Core Capabilities
- High-quality speech synthesis with natural prosody
- Phoneme-based text processing with 78 distinct tokens
- 22.05kHz sampling rate output
- Advanced feature prediction for pitch and energy modeling
Frequently Asked Questions
Q: What makes this model unique?
JETS stands out for its comprehensive approach to TTS, combining transformer architecture with advanced prosody modeling and high-quality vocoder integration. It uses both pitch and energy prediction modules while maintaining efficient training through gradient stopping mechanisms.
Q: What are the recommended use cases?
This model is well-suited for applications requiring high-quality English speech synthesis, particularly where natural prosody and clear articulation are important. It's ideal for audiobook generation, virtual assistants, and other applications requiring human-like speech output.
