Tacotron-2 and Tacotron-pytorch
These are competing implementations of the same Tacotron text-to-speech architecture in different deep learning frameworks (TensorFlow vs PyTorch), allowing users to choose based on their preferred framework rather than using them together.
About Tacotron-2
Rayhane-mamah/Tacotron-2
DeepMind's Tacotron-2 Tensorflow implementation
Combines a sequence-to-sequence spectrogram prediction network with WaveNet vocoding for end-to-end text-to-speech synthesis in TensorFlow. Supports modular training of Tacotron and WaveNet components separately or jointly, with configurable hyperparameters for reproduction of paper results or enhanced performance variants. Handles LJSpeech and M-AILABS datasets with preprocessing pipelines and includes Griffin-Lim inversion tools for mel-spectrogram validation.
About Tacotron-pytorch
soobinseo/Tacotron-pytorch
Pytorch implementation of Tacotron
Implements the full Tacotron architecture with encoder-decoder attention, CBHG modules, and mel-spectrogram generation for end-to-end text-to-speech synthesis. Preprocesses text into phoneme indices and audio into spectrograms, supporting the LJSpeech dataset pipeline. Includes separate training and inference scripts for model optimization and TTS sample generation.
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