Python-PyTorch实现了Tacotron语音合成模型

# tacotron_pytorch [](https://travis-ci.org/r9y9/tacotron_pytorch) PyTorch implementation of [Tacotron](https://arxiv.org/abs/1703.10135) speech synthesis model. Inspired from [keithito/tacotron](https://github.com/keithito/tacotron). Currently not as much good speech quality as [keithito/tacotron](https://github.com/keithito/tacotron) can generate, but it seems to be basically working. You can find some generated speech examples trained on [LJ Speech Dataset](https://keithito.com/LJ-Speech-Dataset/) at [here](http://nbviewer.jupyter.org/github/r9y9/tacotron_pytorch/blob/master/notebooks/Test%20Tacotron.ipynb). If you are comfortable working with TensorFlow, I'd recommend you to try https://github.com/keithito/tacotron instead. The reason to rewrite it in PyTorch is that it's easier to debug and extend (multi-speaker architecture, etc) at least to me. ## Requirements - PyTorch - TensorFlow (if you want to run the training script. This definitely can be optional, but for now required.) ## Installation ``` git clone --recursive https://github.com/r9y9/tacotron_pytorch pip install -e . # or python setup.py develop ``` If you want to run the training script, then you need to install additional dependencies. ``` pip install -e ".[train]" ``` ## Training The package relis on [keithito/tacotron](https://github.com/keithito/tacotron) for text processing, audio preprocessing and audio reconstruction (added as a submodule). Please follows the quick start section at https://github.com/keithito/tacotron and prepare your dataset accordingly. If you have your data prepared, assuming your data is in `"~/tacotron/training"` (which is the default), then you can train your model by: ``` python train.py ``` Alignment, predicted spectrogram, target spectrogram, predicted waveform and checkpoint (model and optimizer states) are saved per 1000 global step in `checkpoints` directory. Training progress can be monitored by: ``` tensorboard --logdir=log ``` ## Testing model Open the notebook in `notebooks` directory and change `checkpoint_path` to your model.