基于Mellotron歌唱合成系统`内含预训练模型以及环境搭建教程可以直接推理使用.zip

 ## WaveGlow: a Flow-based Generative Network for Speech Synthesis ### Ryan Prenger, Rafael Valle, and Bryan Catanzaro In our recent [paper], we propose WaveGlow: a flow-based network capable of generating high quality speech from mel-spectrograms. WaveGlow combines insights from [Glow] and [WaveNet] in order to provide fast, efficient and high-quality audio synthesis, without the need for auto-regression. WaveGlow is implemented using only a single network, trained using only a single cost function: maximizing the likelihood of the training data, which makes the training procedure simple and stable. Our [PyTorch] implementation produces audio samples at a rate of 4850 kHz on an NVIDIA V100 GPU. Mean Opinion Scores show that it delivers audio quality as good as the best publicly available WaveNet implementation. Visit our [website] for audio samples. ## Setup 1. Clone our repo and initialize submodule ```command git clone https://github.com/NVIDIA/waveglow.git cd waveglow git submodule init git submodule update ``` 2. Install requirements `pip3 install -r requirements.txt` 3. Install [Apex] ## Generate audio with our pre-existing model 1. Download our [published model] 2. Download [mel-spectrograms] 3. Generate audio `python3 inference.py -f <(ls mel_spectrograms/*.pt) -w waveglow_256channels.pt -o . --is_fp16 -s 0.6` N.b. use `convert_model.py` to convert your older models to the current model with fused residual and skip connections. ## Train your own model 1. Download [LJ Speech Data]. In this example it's in `data/` 2. Make a list of the file names to use for training/testing ```command ls data/*.wav | tail -n+10 > train_files.txt ls data/*.wav | head -n10 > test_files.txt ``` 3. Train your WaveGlow networks ```command mkdir checkpoints python train.py -c config.json ``` For multi-GPU training replace `train.py` with `distributed.py`. Only tested with single node and NCCL. For mixed precision training set `"fp16_run": true` on `config.json`. 4. Make test set mel-spectrograms `python mel2samp.py -f test_files.txt -o . -c config.json` 5. Do inference with your network ```command ls *.pt > mel_files.txt python3 inference.py -f mel_files.txt -w checkpoints/waveglow_10000 -o . --is_fp16 -s 0.6 ``` [//]: # (TODO) [//]: # (PROVIDE INSTRUCTIONS FOR DOWNLOADING LJS) [pytorch 1.0]: https://github.com/pytorch/pytorch#installation [website]: https://nv-adlr.github.io/WaveGlow [paper]: https://arxiv.org/abs/1811.00002 [WaveNet implementation]: https://github.com/r9y9/wavenet_vocoder [Glow]: https://blog.openai.com/glow/ [WaveNet]: https://deepmind.com/blog/wavenet-generative-model-raw-audio/ [PyTorch]: http://pytorch.org [published model]: https://ngc.nvidia.com/catalog/models/nvidia:waveglow_ljs_256channels [mel-spectrograms]: https://drive.google.com/file/d/1g_VXK2lpP9J25dQFhQwx7doWl_p20fXA/view?usp=sharing [LJ Speech Data]: https://keithito.com/LJ-Speech-Dataset [Apex]: https://github.com/nvidia/apex