Introduce code from Rust monorepo. This is a mess

2026-10-21 08:06:47 +00:00 · 2022-02-28 05:43:54 -05:00
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+![WaveGlow](waveglow_logo.png "WaveGLow")
+
+## 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