README.md

# DiffWave
**Note**  
This code is an adaptation of the original work by the LMNT team. [Original repo link](https://img.shields.io/github/license/lmnt-com/diffwave); version 0.17.   

**Part of the original README follows here:**    
  

DiffWave is a fast, high-quality neural vocoder and waveform synthesizer. It starts with Gaussian noise and converts it into speech via iterative refinement. The speech can be controlled by providing a conditioning signal (e.g. log-scaled Mel spectrogram). The model and architecture details are described in [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://arxiv.org/pdf/2009.09761.pdf).

## Pretrained models
[22.05 kHz pretrained model](https://lmnt.com/assets/diffwave/diffwave-ljspeech-22kHz-1000578.pt) (31 MB, SHA256: `d415d2117bb0bba3999afabdd67ed11d9e43400af26193a451d112e2560821a8`)

This pre-trained model is able to synthesize speech with a real-time factor of 0.87 (smaller is faster).

### Pre-trained model details
- trained on 4x 1080Ti
- default parameters
- single precision floating point (FP32)
- trained on LJSpeech dataset excluding LJ001* and LJ002*
- trained for 1000578 steps (1273 epochs)

## Getting started
Using GitHub:
```
git clone https://gitlab.lisn.upsaclay.fr/guaranda-cabezas/diffwave
cd diffwave
```

### Training
Before you start training, you'll need to prepare a training dataset. The dataset can have any directory structure as long as the contained .wav files are 16-bit mono (e.g. [LJSpeech](https://keithito.com/LJ-Speech-Dataset/), [VCTK](https://pytorch.org/audio/_modules/torchaudio/datasets/vctk.html)). By default, this implementation assumes a sample rate of 22.05 kHz. If you need to change this value, edit [params.py](https://github.com/lmnt-com/diffwave/blob/master/src/diffwave/params.py).

```
python -m diffwave.preprocess /path/to/dir/containing/wavs
python -m diffwave /path/to/model/dir /path/to/dir/containing/wavs

# in another shell to monitor training progress:
tensorboard --logdir /path/to/model/dir --bind_all
```

You should expect to hear intelligible (but noisy) speech by ~8k steps (~1.5h on a 2080 Ti).

#### Training with trajectories
You don't need to run any preprocessing.
```
python src/diffwave/__main__.py /path/to/model/dir /path/to/file/containing/trajectories --data_type trajectories_x #or trajectories if you want to use 3D data
```

#### Multi-GPU training
By default, this implementation uses as many GPUs in parallel as returned by [`torch.cuda.device_count()`](https://pytorch.org/docs/stable/cuda.html#torch.cuda.device_count). You can specify which GPUs to use by setting the [`CUDA_DEVICES_AVAILABLE`](https://developer.nvidia.com/blog/cuda-pro-tip-control-gpu-visibility-cuda_visible_devices/) environment variable before running the training module.

### Inference API
Basic usage:

```python
from diffwave.inference import predict as diffwave_predict

model_dir = '/path/to/model/dir'
spectrogram = # get your hands on a spectrogram in [N,C,W] format
audio, sample_rate = diffwave_predict(spectrogram, model_dir, fast_sampling=True)

# audio is a GPU tensor in [N,T] format.
```

### Inference CLI
```
python -m diffwave.inference --fast /path/to/model /path/to/spectrogram -o output.wav
```

## References
- [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://arxiv.org/pdf/2009.09761.pdf)
- [Denoising Diffusion Probabilistic Models](https://arxiv.org/pdf/2006.11239.pdf)
- [Code for Denoising Diffusion Probabilistic Models](https://github.com/hojonathanho/diffusion)