This repository is Kyubyong Park’s heavily documented TensorFlow implementation of Tacotron, Google’s fully end-to-end text-to-speech synthesis model that turned raw text directly into speech spectrograms. It remains a widely cited teaching implementation of the paper that launched modern neural TTS. It is an open-source project on GitHub with around 1,800 stars; the code dates from the TensorFlow 1.x era and was last updated in 2022.
Tacotron replaced the traditional multi-stage speech pipeline (text analysis, acoustic model, vocoder) with a single sequence-to-sequence network with attention. This implementation follows the paper closely, with extensive comments and training scripts for the LJSpeech and web-collected Korean/English corpora, spectrogram visualizations and sample outputs. Although production TTS has moved on to newer architectures, the repo is still valuable for learning how attention-based mel-spectrogram generation works and as a baseline in speech research courses.
Free. The implementation is open source under the Apache-2.0 license.
On the output side, the implementation pairs the predicted mel spectrograms with a Griffin-Lim reconstruction step to produce listenable waveforms, and the training code logs attention alignments so you can watch the model learn to read. The repository also links the author’s companion implementations of Deep Voice 3 and other landmark speech papers, which together form a small curriculum of classic neural speech synthesis in TensorFlow.
Speech researchers, students and engineers studying neural text-to-speech who want a readable reference implementation of the original Tacotron architecture.