Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
DiffWave is a fast, high-quality neural vocoder and waveform synthesizer.
| Date | Stars |
|---|---|
| 2026-07-24 | 885 |
| 2026-07-25 | 885 |
| 2026-07-28 | 885 |
| 2026-07-30 | 885 |
| 2026-08-18 | 885 |
| 2026-08-20 | 884 |
| 2026-08-25 | 885 |
| 2026-09-08 | 884 |
| 2026-09-11 | 884 |
| 2026-09-18 | 885 |
| 2026-09-20 | 885 |
Today
— stars today
This week
+1 stars this week
This month
+1 stars this month
Momentum
0.0
growth rate 0.11%/day
# DiffWave  [](https://github.com/lmnt-com/diffwave/blob/master/LICENSE) **We're hiring!** If you like what we're building here, [come join us at LMNT](https://explore.lmnt.com). 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). ## What's new (2021-11-09) - unconditional waveform synthesis (thanks to [Andrechang](https://github.com/Andrechang)!) ## What's new (2021-04-01) - fast sampling algorithm based on v3 of the DiffWave paper ## What's new (2020-10-14) - new pretrained model trained for 1M steps - updated audio samples with output from new model ## Status (2021-11-09) - [x] fast inference procedure - [x] stable training - [x] high-quality synthesis - [x] mixed-precision training - [x] multi-GPU training - [x] command-line inference - [x] programmatic inference API - [x] PyPI package - [x] audio samples - [x] pretrained models - [x] unconditional waveform synthesis Big thanks to [Zhifeng Kong](https://github.com/FengNiMa) (lead author of DiffWave) for pointers and bug fixes. ## Audio samples [22.05 kHz audio samples](https://lmnt.com/assets/diffwave) ## 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) ## Install Install using pip: ``` pip install diffwave ``` or from GitHub: ``` git clone https://github.com/lmnt-com/diffwave.git cd diffwave pip install . ``` ### 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). #### 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 - [DiffW
Excerpt of 4,272 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c1d0ee85ec8b9a51, topic:deep-learning, topic:neural-network, topic:pytorch
matched fp:c1d0ee85ec8b9a51, topic:text-to-speech, topic:tts, topic:speech-synthesis
