|
|
- # Tacotron 2 (without wavenet)
-
- PyTorch implementation of [Natural TTS Synthesis By Conditioning
- Wavenet On Mel Spectrogram Predictions](https://arxiv.org/pdf/1712.05884.pdf).
-
- This implementation includes **distributed** and **fp16** support
- and uses the [LJSpeech dataset](https://keithito.com/LJ-Speech-Dataset/).
-
- Distributed and FP16 support relies on work by Christian Sarofeen and NVIDIA's
- [Apex Library](https://github.com/nvidia/apex).
-
- 
-
- Visit our [website] for audio samples.
-
- ## Pre-requisites
- 1. NVIDIA GPU + CUDA cuDNN
-
- ## Setup
- 1. Download and extract the [LJ Speech dataset](https://keithito.com/LJ-Speech-Dataset/)
- 2. Clone this repo: `git clone https://github.com/NVIDIA/tacotron2.git`
- 3. CD into this repo: `cd tacotron2`
- 4. Update .wav paths: `sed -i -- 's,DUMMY,ljs_dataset_folder/wavs,g' filelists/*.txt`
- - Alternatively, set `load_mel_from_disk=True` in `hparams.py` and update mel-spectrogram paths
- 5. Install [PyTorch 1.0]
- 6. Install python requirements or build docker image
- - Install python requirements: `pip install -r requirements.txt`
-
- ## Training
- 1. `python train.py --output_directory=outdir --log_directory=logdir`
- 2. (OPTIONAL) `tensorboard --logdir=outdir/logdir`
-
- ## Multi-GPU (distributed) and FP16 Training
- 1. `python -m multiproc train.py --output_directory=outdir --log_directory=logdir --hparams=distributed_run=True,fp16_run=True`
-
- ## Inference demo
- 1. Download our published [Tacotron 2] model
- 2. Download our published [WaveGlow] model
- 3. `jupyter notebook --ip=127.0.0.1 --port=31337`
- 4. Load inference.ipynb
-
- N.b. When performing Mel-Spectrogram to Audio synthesis, make sure Tacotron 2
- and the Mel decoder were trained on the same mel-spectrogram representation.
-
-
- ## Related repos
- [WaveGlow](https://github.com/NVIDIA/WaveGlow) Faster than real time Flow-based
- Generative Network for Speech Synthesis
-
- [nv-wavenet](https://github.com/NVIDIA/nv-wavenet/) Faster than real time
- WaveNet.
-
- ## Acknowledgements
- This implementation uses code from the following repos: [Keith
- Ito](https://github.com/keithito/tacotron/), [Prem
- Seetharaman](https://github.com/pseeth/pytorch-stft) as described in our code.
-
- We are inspired by [Ryuchi Yamamoto's](https://github.com/r9y9/tacotron_pytorch)
- Tacotron PyTorch implementation.
-
- We are thankful to the Tacotron 2 paper authors, specially Jonathan Shen, Yuxuan
- Wang and Zongheng Yang.
-
-
- [WaveGlow]: https://drive.google.com/file/d/1cjKPHbtAMh_4HTHmuIGNkbOkPBD9qwhj/view?usp=sharing
- [Tacotron 2]: https://drive.google.com/file/d/1c5ZTuT7J08wLUoVZ2KkUs_VdZuJ86ZqA/view?usp=sharing
- [pytorch 1.0]: https://github.com/pytorch/pytorch#installation
- [website]: https://nv-adlr.github.io/WaveGlow
|
