Category: Wavegan github

Wavegan github

Time-frequency TF representations provide powerful and intuitive features for the analysis of time series such as audio.

wavegan github

But still, generative modeling of audio in the TF domain is a subtle matter. Consequently, neural audio synthesis widely relies on directly modeling the waveform and previous attempts at unconditionally synthesizing audio from neurally generated TF features still struggle to produce audio at satisfying quality.

In this contributionfocusing on the short-time Fourier transform, we discuss the challenges that arise in audio synthesis based on generated TF features and how to overcome them. We demonstrate the potential of deliberate generative TF modeling with TiFGAN, which generates audio successfully using an invertible TF representation and improves on the current state-of-the-art for audio synthesis with GANs.

The TiFGAN architecture additionally relies on the guidelines and principles for generating short-time Fourier data that we presented in the accompanying paper. We first normalize the STFT magnitude to have maximum value 1, such that the log-magnitude is confined in -inf, 0]. Generation of, and synthesis from, the log-magnitude STFT is the main focus of this contribution. Nonetheless, we also trained a variant architecture TiFGAN-MTF for which we additionally provided the time- and frequency-direction derivatives of the unwrapped, demodulated phase.

It may seem straightforward to restore the phase from its time-direction derivative by summation along frequency channels as proposed by Engel et Al. Even on real, unmodified STFTs, the resulting phase misalignment introduces cancellation between frequency bands resulting in energy loss, see Figure above 2 for a simple example.

In practice, such cancellations often leads to clearly perceptible changes of timbre see below. Moreover, in areas of small STFT magnitude, the phase is known to be unreliable see Balazs et Al and highly sensitive to distortions see Alaifari et Alsuch that it cannot be reliably modelled and synthesis from generated phase derivatives is likely to introduce more distortion.

Phase-gradient heap integration PGHI see Prusa et Al relies on the phase-magnitude relations and bypasses phase instabilities by avoiding integration through areas of small magnitude, leading to significantly better and more robust phase estimates see Figure above 4.

PGHI often outperforms more expensive, iterative schemes relying on alternate projection, e. Sound examples Phase examples. For the purpose of this contribution, we restrict to generating 1 second of audio data, sampled at 16kHz. For the short-time Fourier transform, we chose for the analysis window a sampled Gaussian and fix the minimal redundancy that we consider reliable, i.

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Since the Nyquist frequency is not expected to hold significant information for the considered signals, we drop it to arrive at a representation size of x, which is well suited to processing using strided convolutions. For the reconstruction of the phase we use phase-gradient heap integration PGHI see Prusa et Al which requires no iteration, such that reconstruction time is comparable to simply integrating the phase derivatives.

Sound examples Results obtained training on a speech dataset obtained as a subset of spoken digits "zero" through "nine" sc09 from the "Speech Commands Dataset". The dataset is not curated, some samples are noisy or poorly labeled, the considered subset consists of approximately 23, samples.

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Different phase recovery strategies Overview of spectral changes resulting from different phase reconstruction methods. Phase from the time-direction derivative Your browser does not support the audio element. Original 2 Your browser does not support the audio element.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Official implementation of WaveGANa machine learning algorithm which learns to generate raw audio waveforms. WaveGAN is a machine learning algorithm which learns to synthesize raw waveform audio by observing many examples of real audio.

In this repository, we include an implementation of WaveGAN capable of learning to generate up to 4 seconds of audio at 16kHz. For comparison, we also include an implementation of SpecGAN, an approach to audio generation which applies image-generating GANs to image-like audio spectrograms.

WaveGAN is capable of learning to synthesize audio in many different sound domains. In the above figure, we visualize real and WaveGAN-generated audio of speech, bird vocalizations, drum sound effects, and piano excerpts. These sound examples and more can be heard here. WaveGAN can now be trained on datasets of arbitrary audio files previously required preprocessing. You can use any folder containing audio, but here are a few example datasets to help you get started:.

Here is how you would begin or resume training a WaveGAN on random clips from a directory containing longer audio, i.

wavegan github

If you are instead training on datasets of short sound effects e. Because our codebase buffers audio clips directly from files, it is important to change the data-related command line arguments to be appropriate for your dataset see [ data-considerations] data considerations below.

We currently do not support training on multiple GPUs. The WaveGAN training script is configured out-of-the-box to be appropriate for training on random slices from a directory containing longer audio files e. If your clips are extremely short i. This may slightly increase training speed. If you choose a larger generation length, you will likely want to reduce the number of model parameters to train more quickly e.

If you are modeling more than 2 channels, each audio file must have the exact number of channels specified. To back up checkpoints every hour GAN training may occasionally collapse so it's good to have backups. If you are training on the SC09 dataset, this command will slowly calculate inception score at each checkpoint.

The primary focus of this repository is on WaveGAN, our raw audio generation method. For comparison, we also include an implementation of SpecGAN, an approach to generating audio by applying image-generating GANs on image-like audio spectrograms. This implementation only generates spectrograms of one second in length at 16khz. Before training a SpecGAN, we must first compute mean and variance of each spectrogram bin to use for normalization. This may take a while you can also measure these statistics on a subset of the data.

An example usage is below; see this Colab notebook for additional features. Our paper uses Inception score to roughly measure model performance. If you plan to directly compare to our reported numbers, you should run this script on a directory of 50, bit PCM WAV files with samples each.

To reproduce our paper results 9. Using this implementation, we created a procedural drum machine powered by a WaveGAN trained on drum sound effects. Skip to content.

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Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. WaveGAN: Learn to synthesize raw audio with generative adversarial networks. Python GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Befor running, make sure you have the sc09 dataset, and put that dataset under your current filepath. For sc09 task, make sure sc09 dataset under your current project filepath befor run your code. This repo is based on chrisdonahue's and jtcramer's implementation.

wavegan github

Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Latest commit 3c99f1d Mar 12, Quick Start: Installation sudo apt-get install libav-tools Download dataset sc09 : sc09 raw WAV filesutterances of spoken english words '0'-'9' piano : Piano raw WAV files Run For sc09 task, make sure sc09 dataset under your current project filepath befor run your code.

For piano piano dataset, 2 X Tesla P40 takes hours to get reasonable result. Contributions This repo is based on chrisdonahue's and jtcramer's implementation. You signed in with another tab or window.You can combine these state-of-the-art non-autoregressive models to build your own great vocoder!

parallel-wavegan 0.3.4

Please check our samples in our demo HP. Different cuda version should be working but not explicitly tested. All of the codes are tested on Pytorch 1. Note that your cuda version must be exactly matched with the version used for pytorch binary to install apex. Note that we specify cuda version used to compile pytorch wheel. This repository provides Kaldi -style recipes, as the same as ESPnet. Currently, the following recipes are supported. The integration with job schedulers such as slurm can be done via cmd.

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If you want to use it, please check this page. All of the hyperparameters is written in a single yaml format configuration file. Please check this example in ljspeech recipe. The speed of the training is 0.

You can monitor the training progress via tensorboard. If you want to accelerate the training, you can try distributed multi-gpu training based on apex. You need to install apex for distributed training. Please make sure you already installed it. Then you can run distributed multi-gpu training via following command:. In the case of distributed training, batch size will be automatically multiplied by the number of gpus. Please be careful. If you want to accelerate the inference more, it is worthwhile to try the conversion from pytorch to tensorflow.

The example of the conversion is available in the notebook Provided by dathudeptrai. Here the results are summarized in the table. You can listen to the samples and download pretrained models from the link to our google drive. If you want to check more results, please access at our google drive.

wavegan github

The author would like to thank Ryuichi Yamamoto r9y9 for his great repository, paper and valuable discussions. Tomoki Hayashi kan-bayashi E-mail: hayashi.GANs are a state-of-the-art method for generating high-quality images. However, researchers have struggled to apply them to more sequential data such as audio and music, where autoregressive AR models such as WaveNets and Transformers dominate by predicting a single sample at a time.

While this aspect of AR models contributes to their success, it also means that sampling is painfully serial and slow, and techniques such as distillation or specialized kernels are required for real-time generation.

Unlike the WaveNet autoencoders from the original paper that used a time-distributed latent code, GANSynth generates the entire audio clip from a single latent vector, allowing for easier disentanglement of global features such as pitch and timbre. Using the NSynth dataset of musical instrument notes, we can independently control pitch and timbre. You can hear this in the samples below, where we first hold the timbre constant, and then interpolate the timbre over the course of the piece:.

Similar to previous work we found it difficult to directly generate coherent waveforms because upsampling convolution struggles with phase alignment for highly periodic signals. Consider the figure below:. The red-yellow curve is a periodic signal with a black dot at the beginning of the wave each cycle.

If we try to model this signal by chopping it into periodic frames black dotted lineas is done for both upsampling convolutions in GANs and short-time fourier transforms STFTthe distance between the beginning of the frame dotted line and the beginning of the wave dot changes over time black solid line. For a strided convolution, this means the convolution needs to learn all the phase permutations for a given filter, which is very inefficient.

This difference black line is called the phase and it precesses over time because the wave and frames have different periodicities.

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We call this the instantaneous frequency IF because the definition of frequency is the change in phase in time.

An STFT compares a frame of signal to many different frequencies, which leads to the speckled phase patterns as in the image below. In contrast, when we extract the instantaneous frequencies, we see bold consistent lines reflecting the coherent periodicity of the underlying sound.

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We also find that progressive training P and increasing the frequency resolution of the STFT H boosts performance by helping to separate closely-spaced harmonics. The graph below shows the results of user listening tests, where users were played audio examples from two different methods and asked which of the two they preferred:.

Beyond the many quantitative measures in the paper, we also can see qualitatively that the GANs that generate instantaneous frequencies IF-GANs also produce much more coherent waveforms. The top row of the figure below shows the generated waveform modulo the fundamental periodicity of a note. Notice that the real data completely overlaps itself as the waveform is extremely periodic. In the Rainbowgrams CQTs with color representing instantaneous frequency below, the real data and IF models have coherent waveforms that result in strong consistent colors for each harmonic, while the PhaseGAN has many speckles due to phase discontinuities, and the WaveGAN model is very irregular.

This work represents an initial foray into using GANs to generate high-fidelity audio, but many interesting questions remain. While the methods above worked well for musical signals, they still produced some noticeable artifacts for speech synthesis. Some recent related work builds on this by exploring new methods for recovering the phase from generated spectrograms with fewer artifacts.

Other promising directions include using multi-scale GAN conditioninghandling variable length outputs, and perhaps replacing upsampling convolution generators with flexible differentiable synthesizers. If you extend or use this work, please cite the paper where it was introduced:.

You can hear this in the samples below, where we first hold the timbre constant, and then interpolate the timbre over the course of the piece: Consistent Timbre Interpolation Bach's Prelude Suite No. Try it yourself with the Colab Notebook. How does it work?Generative models are successfully used for image synthesis [ 11 ] in the recent years.

But when it comes to other modalities like audio, text etc little progress has been made. Recent works focus on generating audio from a generative model in an unsupervised setting. We explore the possibility of using generative models conditioned on class labels. Concatenation based conditioning and conditional scaling were explored in this work with various hyper-parameter tuning methods [ 22 ] [ 15 ].

Work in progress. Generative adversarial networks [ 8 ] are being widely used for synthesizing realistic images [ 13201 ]. But very little has been explored in the area of audio generation. A few research works has been made in the area of unsupervised generative models in audio. One of them is WaveGAN [ 5 ]which trains a generative model in an unsupervised setting. In this work we use WaveGAN model as our baseline model.

Audio samples generated from WaveGAN are human-recognizable and have a relatively good inception scores [ 2 ]. But the samples generated are completely random. The need for generating synthetic data has many applications in various fields.

For example, in digital image enhancement, synthesizing new images can be used for smoothing denoisingfilling missing pieces inpaintingimproving resolution super-resolution imaging etc. Other areas include reinforcement learning, where agents can be trained with more training examples, and automatic speech recognition, where the synthesized data from generative models can be used as training data for training acoustic models.

More direct usage is in the field of semi-supervised learning techniques [ 231822 ]. In the recent ACM webinar [ 12 ] Ian Goodfellow talks about the semi-supervised learning techniques and their applications in various fields.

In this work we explore a way to generate audio samples conditioned on class labels. That is, given a class label whether the generator of GAN can generate a particular audio waveform.

In the history of GAN such type of conditioning has been explored in the synthesis of images. Most of the notable advancements were made in the field of image synthesis. In his paper, He Huang et al. Usage of generative models in natural language processing is also in progress with notabld fields including text modelling [ 2821 ]dialogue generation [ 16 ]and neural machine translation [ 27 ].

When it comes to audio generation, several approaches have been explored. Nonetheless, models that can generate audio waveforms directly as opposed to some other representation that can be converted into audio afterwards, such as spectrograms or piano rolls are only starting to be explored. Autoregressive models were initially used for the generation of raw audio. WaveNet by Van Den Oord et al. While a single WaveNet can capture the characteristics of many different speakers with equal fidelity and can switch between them by conditioning on the speaker identity, WaveRNN [ 14 ] describes a single-layer recurrent neural network with a dual softmax layer that matches the quality of the state-of-the-art WaveNet model.

WaveNets have been applied to music generation as well. The paper by Sander Dieleman et al. The application of GAN in speech were used mainly for audio enhancements techniques and little on raw audio generation.

SEGAN by Santiago Pascual et al [ 19 ] uses deep networks which operates on waveform level, training the model end-to-end for denoising waveform chunks.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

GANSynth: Making music with GANs

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. You can combine these state-of-the-art non-autoregressive models to build your own great vocoder! Please check our samples in our demo HP. Different cuda version should be working but not explicitly tested.

All of the codes are tested on Pytorch 1. Note that your cuda version must be exactly matched with the version used for pytorch binary to install apex. Note that we specify cuda version used to compile pytorch wheel. This repository provides Kaldi -style recipes, as the same as ESPnet. Currently, the following recipes are supported.

The integration with job schedulers such as slurm can be done via cmd. If you want to use it, please check this page. All of the hyperparameters is written in a single yaml format configuration file. Please check this example in ljspeech recipe. The speed of the training is 0. You can monitor the training progress via tensorboard.

If you want to accelerate the training, you can try distributed multi-gpu training based on apex. You need to install apex for distributed training. Please make sure you already installed it. Then you can run distributed multi-gpu training via following command:.

In the case of distributed training, batch size will be automatically multiplied by the number of gpus. Please be careful. If you want to accelerate the inference more, it is worthwhile to try the conversion from pytorch to tensorflow. The example of the conversion is available in the notebook Provided by dathudeptrai. Here the results are summarized in the table. You can listen to the samples and download pretrained models from the link to our google drive.

If you want to check more results, please access at our google drive. The author would like to thank Ryuichi Yamamoto r9y9 for his great repository, paper and valuable discussions. Tomoki Hayashi kan-bayashi E-mail: hayashi. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Python Branch: master.

Find file. Sign in Sign up. Go back.


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