Tacotron 2 github

Yet another PyTorch implementation of Tacotron 2 with reduction factor and faster training speed. The project is highly based on these, tacotron 2 github. I made some modification to improve speed and performance of both training and inference.

This implementation supports both single-, multi-speaker TTS and several techniques to enforce the robustness and efficiency of the model. Unlike many previous implementations, this is kind of a Comprehensive Tacotron2 where the model supports both single-, multi-speaker TTS and several techniques such as reduction factor to enforce the robustness of the decoder alignment. The model can learn alignment only in 5k. Note that only 1 batch size is supported currently due to the autoregressive model architecture. Skip to content.

Tacotron 2 github

A TensorFlow implementation of Google's Tacotron speech synthesis with pre-trained model unofficial. This can greatly reduce the amount of data required to train a model. In April , Google published a paper, Tacotron: Towards End-to-End Speech Synthesis , where they present a neural text-to-speech model that learns to synthesize speech directly from text, audio pairs. However, they didn't release their source code or training data. This is an independent attempt to provide an open-source implementation of the model described in their paper. The quality isn't as good as Google's demo yet, but hopefully it will get there someday Pull requests are welcome! Install the latest version of TensorFlow for your platform. For better performance, install with GPU support if it's available. This code works with TensorFlow 1. You can use other datasets if you convert them to the right format. Tunable hyperparameters are found in hparams. Hyperparameters should generally be set to the same values at both training and eval time.

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This implementation includes distributed and fp16 support and uses the LJSpeech dataset. Results from Tensorboard while Training:. This does train much faster and better than the normal training, however this may start by overflowing for a few steps, with messages similar to the following, before it starts training correctly:. Below are the inference results after , and steps respectively, for the input text: "You stay in Wonderland and I show you how deep the rabbit hole goes. Around step , is when the network started to construct a proper alignment graph and make understandable sounds. This implementation uses code from the following repos: Keith Ito , Prem Seetharaman as described in our code.

Yet another PyTorch implementation of Tacotron 2 with reduction factor and faster training speed. The project is highly based on these. I made some modification to improve speed and performance of both training and inference. Currently only support LJ Speech. You can modify hparams. You can find alinment images and synthesized audio clips during training. The text to synthesize can be set in hparams. You can download pretrained models from Realeases. The hyperparameter for training is also in the directory.

Tacotron 2 github

Tacotron 2 - PyTorch implementation with faster-than-realtime inference. This implementation includes distributed and automatic mixed precision support and uses the LJSpeech dataset. Visit our website for audio samples using our published Tacotron 2 and WaveGlow models. Training using a pre-trained model can lead to faster convergence By default, the dataset dependent text embedding layers are ignored. When performing Mel-Spectrogram to Audio synthesis, make sure Tacotron 2 and the Mel decoder were trained on the same mel-spectrogram representation. This implementation uses code from the following repos: Keith Ito , Prem Seetharaman as described in our code. Skip to content. You signed in with another tab or window. Reload to refresh your session.

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Language: All Filter by language. Feature prediction model can separately be trained using:. Select adress with Tunable hyperparameters are found in hparams. You can find alinment images and synthesized audio clips during training. This implementation includes distributed and fp16 support and uses the LJSpeech dataset. You signed in with another tab or window. Folders and files Name Name Last commit message. Checkpoints and code originate from following sources:. Updated Aug 20, Jupyter Notebook. Before proceeding, you must pick the hyperparameters that suit best your needs.

While browsing the Internet, I have noticed a large number of people claiming that Tacotron-2 is not reproducible, or that it is not robust enough to work on other datasets than the Google internal speech corpus. Although some open-source works 1 , 2 has proven to give good results with the original Tacotron or even with Wavenet , it still seemed a little harder to reproduce the Tacotron 2 results with high fidelity to the descriptions of Tacotron-2 T2 paper.

Star 5. Folders and files Name Name Last commit message. Skip to content. The hyperparameter for training is also in the directory. Latest commit. A vocoder is not implemented. Here is the expected loss curve when training on LJ Speech with the default hyperparameters:. You switched accounts on another tab or window. All other options are well explained in the hparams. The following section lists the requirements in order to start training the Tacotron 2 and WaveGlow models. Notifications Fork 25 Star This script takes text as input and runs Tacotron 2 and then WaveGlow inference to produce an audio file.