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https://github.com/ksw0306/FloWaveNet
A Pytorch implementation of "FloWaveNet: A Generative Flow for Raw Audio"
https://github.com/ksw0306/FloWaveNet
clarinet generative-flow glow pytorch wavenet
Last synced: 3 months ago
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A Pytorch implementation of "FloWaveNet: A Generative Flow for Raw Audio"
- Host: GitHub
- URL: https://github.com/ksw0306/FloWaveNet
- Owner: ksw0306
- License: mit
- Created: 2018-11-03T09:21:42.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2019-04-23T13:57:02.000Z (over 5 years ago)
- Last Synced: 2024-07-12T22:11:39.257Z (4 months ago)
- Topics: clarinet, generative-flow, glow, pytorch, wavenet
- Language: Python
- Homepage:
- Size: 57.6 KB
- Stars: 492
- Watchers: 42
- Forks: 112
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# FloWaveNet : A Generative Flow for Raw Audio
This is a PyTorch implementation of our work ["FloWaveNet : A Generative Flow for Raw Audio".](https://arxiv.org/abs/1811.02155) (We'll update soon.)
For a purpose of parallel sampling, we propose FloWaveNet, a flow-based generative model for raw audio synthesis.
FloWaveNet can generate audio samples as fast as ClariNet and Parallel WaveNet, while the training procedure is really easy and stable with a single-stage pipeline. Our generated audio samples are available at [https://ksw0306.github.io/flowavenet-demo/](https://ksw0306.github.io/flowavenet-demo/). Also, our implementation of ClariNet (Gaussian WaveNet and Gaussian IAF) is available at [https://github.com/ksw0306/ClariNet](https://github.com/ksw0306/ClariNet)
# Requirements
- PyTorch 0.4.1
- Python 3.6
- Librosa
# Examples
#### Step 1. Download Dataset
- LJSpeech : [https://keithito.com/LJ-Speech-Dataset/](https://keithito.com/LJ-Speech-Dataset/)
#### Step 2. Preprocessing (Preparing Mel Spectrogram)
`python preprocessing.py --in_dir ljspeech --out_dir DATASETS/ljspeech`
#### Step 3. Train
##### Single-GPU training
`python train.py --model_name flowavenet --batch_size 2 --n_block 8 --n_flow 6 --n_layer 2 --block_per_split 4`
##### Multi-GPU training
`python train.py --model_name flowavenet --batch_size 8 --n_block 8 --n_flow 6 --n_layer 2 --block_per_split 4 --num_gpu 4`
NVIDIA TITAN V (12GB VRAM) : batch size 2 per GPU
NVIDIA Tesla V100 (32GB VRAM) : batch size 8 per GPU
#### Step 4. Synthesize
`--load_step CHECKPOINT` : the # of the pre-trained model's global training step (also depicted in the trained weight file)
`--temp`: Temperature (standard deviation) value implemented as z ~ N(0, 1 * TEMPERATURE^2)
ex) `python synthesize.py --model_name flowavenet --n_block 8 --n_flow 6 --n_layer 2 --load_step 100000 --temp 0.8 --num_samples 10 --block_per_split 4`
# Sample Link
Sample Link : [https://ksw0306.github.io/flowavenet-demo/](https://ksw0306.github.io/flowavenet-demo/)
Our implementation of ClariNet (Gaussian WaveNet, Gaussian IAF) : [https://github.com/ksw0306/ClariNet](https://github.com/ksw0306/ClariNet)
- Results 1 : Model Comparisons (WaveNet (MoL, Gaussian), ClariNet and FloWaveNet)
- Results 2 : Temperature effect on Audio Quality Trade-off (Temperature T : 0.0 ~ 1.0, Model : FloWaveNet)
- Results 3 : Analysis of ClariNet Loss Terms (Loss functions : 1. Only KL 2. KL + Frame 3. Only Frame)
- Results 4 : Causality of WaveNet Dilated Convolutions (FloWaveNet : Non-causal WaveNet Affine Coupling Layers, FloWaveNet_causal : Causal WaveNet Affine Coupling Layers)
# Reference
- WaveNet vocoder : [https://github.com/r9y9/wavenet_vocoder](https://github.com/r9y9/wavenet_vocoder)
- glow-pytorch : [https://github.com/rosinality/glow-pytorch](https://github.com/rosinality/glow-pytorch)