Practical overview
When it fits
Use Tacotron when you want a Python and TensorFlow codebase for local speech-synthesis experiments, a pretrained-model demo, or training on a supported dataset.
Who it is for
- Developers building local text-to-speech prototypes or demonstrations.
- Researchers and practitioners experimenting with speech synthesis models.
- Users preparing supported speech datasets to train a Tacotron model.
Documented uses
- Generate speech audio from text using a pretrained checkpoint.
- Evaluate a trained checkpoint through the demo server or command-line evaluation script.
- Preprocess supported speech data and train a model.
- Inspect training audio and alignments through the documented TensorBoard logs.
Requirements & limits
The repository is public and identified as MIT-licensed. Its documented workflows require a local Python and TensorFlow setup.
- The documented pretrained-model path starts a local server and uses a browser at localhost:9000.
- The README names LJ Speech and Blizzard 2012 as supported out of the box; other datasets need conversion.
- Training requires at least 40 GB of free disk space.
- The README's documented dependency range begins at TensorFlow 1.3; it does not specify support for every newer release.
- The codebase is an independent implementation of the Tacotron paper, not Google's source code.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Run the pretrained speech demo locally
Start Tacotron's documented demo server with a downloaded pretrained checkpoint and enter text for synthesis.
Before you start
- Python 3
- TensorFlow 1.3 or later and the packages in requirements.txt
- A downloaded and unpacked Tacotron pretrained model
Steps
Install Python 3 and TensorFlow, then install the repository requirements with the command below.
pip install -r requirements.txtDownload and unpack the pretrained model using the README's documented command: the command below.
curl https://data.keithito.com/data/speech/tacotron-20180906.tar.gz | tar xzC /tmpStart the demo server with the command below.
python3 demo_server.py --checkpoint /tmp/tacotron-20180906/model.ckptOpen
localhost:9000in a browser and type the text to synthesize.
Expected outcome
A local browser-based demo for entering text for speech synthesis.
Guide limitations
- The README specifies TensorFlow 1.3 or later; it does not establish compatibility with every current TensorFlow release.
- The documented training workflow has an output-length limit controlled by max_iters, outputs_per_step and frame_shift_ms; longer audio may require adjusting max_iters.
Prepare and train on a supported speech dataset
Preprocess an LJ Speech or Blizzard 2012 dataset and start a Tacotron training run.
Before you start
- Python 3, TensorFlow 1.3 or later, and repository requirements
- At least 40 GB of free disk space
- LJ Speech or Blizzard 2012 data unpacked in the documented
~/tacotrondirectory structure
Steps
Unpack the selected dataset into
~/tacotronusing the structure shown in the README.For LJ Speech, run the command below; for Blizzard 2012, use the command below.
python3 preprocess.py --dataset ljspeechpython3 preprocess.py --dataset blizzardStart training with the command below.
python3 train.pyOptionally monitor training with
tensorboard --logdir ~/tacotron/logs-tacotron.
Expected outcome
A training run using the selected supported dataset, with optional TensorBoard monitoring.
Guide limitations
- At least 40 GB of free disk space is required for training.
- Other datasets must be converted to the expected format.
- The README recommends matching relevant hyperparameters between training and evaluation.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 15 October 2026
- Original website title
- keithito/tacotron · Official homepage title · retained in its source language