Practical overview
When it fits
Use it for hands-on speech-synthesis experimentation when you can work with its Python and PyTorch requirements and manage model data or checkpoints.
Who it is for
- Developers studying or adapting Transformer-based speech synthesis in PyTorch
- Researchers experimenting with training on paired text and audio
- Users who want to generate speech samples from the documented pretrained checkpoints
Documented uses
- Prepare paired text-and-audio data for model training
- Train the autoregressive Transformer and its post-network in separate stages
- Generate a TTS WAV file using the synthesis script and checkpoint files
Requirements & limits
- The README specifies PyTorch 0.4.0 and Python 3.
- Training requires paired text and WAV data; LJSpeech is the documented example.
- Synthesis requires checkpoint files and setting the restore step.
- The author reports that the example model was not yet converged at 160k steps and performed less well on long sentences.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Train the model with LJSpeech
Prepare LJSpeech data and train the Transformer and post-network stages.
Before you start
- Python 3
- PyTorch 0.4.0
- Packages listed in the repository requirements file
- LJSpeech text-and-WAV dataset
Steps
Download and extract LJSpeech.
Set the extracted data directory as data_path in hyperparams.py and adjust other hyperparameters as needed.
Run prepare_data.py.
Run train_transformer.py, followed by train_postnet.py.
Expected outcome
The documented two-stage training process produces a trained autoregressive attention network and post-network.
Guide limitations
- The README notes that learning rate and gradient clipping are important training parameters.
- The author describes the published 160k-step samples as not yet converged and reports lower performance on long sentences.
Generate a speech WAV sample
Run the documented synthesis script using a pretrained checkpoint.
Before you start
- Python 3
- PyTorch 0.4.0
- Packages listed in the repository requirements file
- Pretrained autoregressive model and post-network files placed in checkpoint/
Steps
Obtain the pretrained model files referenced by the repository README.
Place the files in the checkpoint/ directory.
Run synthesis.py and make sure the restore step is set.
Expected outcome
The synthesis script is documented as generating a TTS WAV file.
Guide limitations
- The README does not provide further synthesis configuration details in the documented steps.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 7 November 2026
- Original website title
- soobinseo/Transformer-TTS · Official homepage title · retained in its source language