JETS TTS Model for LJSpeech
| Property | Value |
|---|---|
| Author | imdanboy |
| Framework | ESPnet2 |
| Dataset | LJSpeech |
| Model Type | Text-to-Speech (TTS) |
| Repository | Hugging Face |
What is ljspeech_tts_train_jets_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave?
This is an advanced text-to-speech model built using the JETS (Joint End-to-End Training for Speech synthesis) architecture within the ESPnet2 framework. The model utilizes phone-level encoding and Tacotron-style grapheme-to-phoneme (G2P) processing specifically optimized for English speech synthesis.
Implementation Details
The model implements a sophisticated architecture with multiple key components including a transformer-based encoder-decoder structure, along with specialized pitch and energy predictors. It features 4 encoder and decoder layers, with 1024 units each, and employs multi-head attention with 2 heads.
- Generator architecture with 256-dimensional attention dimension
- Advanced duration predictor with 2 layers and 256 channels
- Sophisticated pitch predictor with 5 layers and 256 channels
- Energy predictor featuring 2 layers with 3x3 kernel size
- HiFiGAN-style multi-scale multi-period discriminator
Core Capabilities
- High-quality speech synthesis at 22050Hz sampling rate
- Efficient processing with 256-hop length and 1024-point FFT
- Advanced pitch modeling with F0 range of 80-400Hz
- Token-level processing with 78 unique phoneme tokens
- Integrated global mean-variance normalization for consistent output
Frequently Asked Questions
Q: What makes this model unique?
This model combines JETS architecture with phone-level processing and specialized G2P conversion, making it particularly effective for English TTS tasks. Its integrated pitch and energy prediction capabilities enable more natural-sounding speech synthesis.
Q: What are the recommended use cases?
The model is ideal for applications requiring high-quality English speech synthesis, particularly when phone-level control is needed. It's well-suited for audiobook generation, virtual assistants, and other applications requiring natural-sounding speech output.
