DiffWave is a fast, high-quality neural vocoder and waveform synthesizer.

Overview

DiffWave

PyPI Release License

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.

What's new (2021-11-09)

  • unconditional waveform synthesis (thanks to 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)

  • fast inference procedure
  • stable training
  • high-quality synthesis
  • mixed-precision training
  • multi-GPU training
  • command-line inference
  • programmatic inference API
  • PyPI package
  • audio samples
  • pretrained models
  • unconditional waveform synthesis

Big thanks to Zhifeng Kong (lead author of DiffWave) for pointers and bug fixes.

Audio samples

22.05 kHz audio samples

Pretrained models

22.05 kHz pretrained model (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, VCTK). By default, this implementation assumes a sample rate of 22.05 kHz. If you need to change this value, edit 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(). You can specify which GPUs to use by setting the CUDA_DEVICES_AVAILABLE environment variable before running the training module.

Inference API

Basic usage:

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

Owner
LMNT
LMNT
The source code for the Cutoff data augmentation approach proposed in this paper: "A Simple but Tough-to-Beat Data Augmentation Approach for Natural Language Understanding and Generation".

Cutoff: A Simple Data Augmentation Approach for Natural Language This repository contains source code necessary to reproduce the results presented in

Dinghan Shen 49 Dec 22, 2022
Replication Package for "An Empirical Study of the Effectiveness of an Ensemble of Stand-alone Sentiment Detection Tools for Software Engineering Datasets"

Replication Package for "An Empirical Study of the Effectiveness of an Ensemble of Stand-alone Sentiment Detection Tools for Software Engineering Data

2 Oct 06, 2022
Implementation and replication of ProGen, Language Modeling for Protein Generation, in Jax

ProGen - (wip) Implementation and replication of ProGen, Language Modeling for Protein Generation, in Pytorch and Jax (the weights will be made easily

Phil Wang 71 Dec 01, 2022
Exploring the Dual-task Correlation for Pose Guided Person Image Generation

Dual-task Pose Transformer Network The source code for our paper "Exploring Dual-task Correlation for Pose Guided Person Image Generation“ (CVPR2022)

63 Dec 15, 2022
Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Time Using Noisy Proxies

Deconfounding Temporal Autoencoder (DTA) This is a repository for the paper "Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Tim

Milan Kuzmanovic 3 Feb 04, 2022
PyTorch implementation of our ICCV2021 paper: StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation

StructDepth PyTorch implementation of our ICCV2021 paper: StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimat

SJTU-ViSYS 112 Nov 28, 2022
A forwarding MPI implementation that can use any other MPI implementation via an MPI ABI

MPItrampoline MPI wrapper library: MPI trampoline library: MPI integration tests: MPI is the de-facto standard for inter-node communication on HPC sys

Erik Schnetter 31 Dec 22, 2022
Deep Federated Learning for Autonomous Driving

FADNet: Deep Federated Learning for Autonomous Driving Abstract Autonomous driving is an active research topic in both academia and industry. However,

AIOZ AI 12 Dec 01, 2022
QuALITY: Question Answering with Long Input Texts, Yes!

QuALITY: Question Answering with Long Input Texts, Yes! Authors: Richard Yuanzhe Pang,* Alicia Parrish,* Nitish Joshi,* Nikita Nangia, Jason Phang, An

ML² AT CILVR 61 Jan 02, 2023
This is an official pytorch implementation of Lite-HRNet: A Lightweight High-Resolution Network.

Lite-HRNet: A Lightweight High-Resolution Network Introduction This is an official pytorch implementation of Lite-HRNet: A Lightweight High-Resolution

HRNet 675 Dec 25, 2022
Official implementation of deep-multi-trajectory-based single object tracking (IEEE T-CSVT 2021).

DeepMTA_PyTorch Officical PyTorch Implementation of "Dynamic Attention-guided Multi-TrajectoryAnalysis for Single Object Tracking", Xiao Wang, Zhe Che

Xiao Wang(王逍) 7 Dec 03, 2022
Game Agent Framework. Helping you create AIs / Bots that learn to play any game you own!

Serpent.AI - Game Agent Framework (Python) Update: Revival (May 2020) Development work has resumed on the framework with the aim of bringing it into 2

Serpent.AI 6.4k Jan 05, 2023
Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

Modeling High-Frequency Limit Order Book Dynamics Using Machine Learning Framework to capture the dynamics of high-frequency limit order books. Overvi

Chang-Shu Chung 1.3k Jan 07, 2023
Model-based Reinforcement Learning Improves Autonomous Racing Performance

Racing Dreamer: Model-based versus Model-free Deep Reinforcement Learning for Autonomous Racing Cars In this work, we propose to learn a racing contro

Cyber Physical Systems - TU Wien 38 Dec 06, 2022
Sample code and notebooks for Vertex AI, the end-to-end machine learning platform on Google Cloud

Google Cloud Vertex AI Samples Welcome to the Google Cloud Vertex AI sample repository. Overview The repository contains notebooks and community conte

Google Cloud Platform 560 Dec 31, 2022
LF-YOLO (Lighter and Faster YOLO) is used to detect defect of X-ray weld image.

This project is based on ultralytics/yolov3. LF-YOLO (Lighter and Faster YOLO) is used to detect defect of X-ray weld image. The related paper is avai

26 Dec 13, 2022
NovelD: A Simple yet Effective Exploration Criterion

NovelD: A Simple yet Effective Exploration Criterion Intro This is an implementation of the method proposed in NovelD: A Simple yet Effective Explorat

29 Dec 05, 2022
Code for ICCV2021 paper SPEC: Seeing People in the Wild with an Estimated Camera

SPEC: Seeing People in the Wild with an Estimated Camera [ICCV 2021] SPEC: Seeing People in the Wild with an Estimated Camera, Muhammed Kocabas, Chun-

Muhammed Kocabas 187 Dec 26, 2022
CVPR 2021: "The Spatially-Correlative Loss for Various Image Translation Tasks"

Spatially-Correlative Loss arXiv | website We provide the Pytorch implementation of "The Spatially-Correlative Loss for Various Image Translation Task

Chuanxia Zheng 89 Jan 04, 2023
Powerful unsupervised domain adaptation method for dense retrieval.

Powerful unsupervised domain adaptation method for dense retrieval

Ubiquitous Knowledge Processing Lab 191 Dec 28, 2022