There are no pull requests. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore signal "noise". Not only can you enjoy a set of free open source productivity tools, but you can also use the robust and proven set of pretrained computer vision models, by transforming your signals from the time domain to the frequency domain. The following data are from VOiCES We can either listen to the original audio signals or examine the spectrograms: Automatic audio classification is a growing area of research that includes fields such as speech, music and environmental sounds. All we need to do is adapt the input layer and output layer of the model to our data. Therefore, we add, Deep Learning with PyTorch: A 60 Minute Blitz, Visualizing Models, Data, and Training with TensorBoard, TorchVision Object Detection Finetuning Tutorial, Transfer Learning for Computer Vision Tutorial, Optimizing Vision Transformer Model for Deployment, Speech Command Recognition with torchaudio, Language Modeling with nn.Transformer and TorchText, NLP From Scratch: Classifying Names with a Character-Level RNN, NLP From Scratch: Generating Names with a Character-Level RNN, NLP From Scratch: Translation with a Sequence to Sequence Network and Attention, Text classification with the torchtext library, Language Translation with nn.Transformer and torchtext, Deploying PyTorch in Python via a REST API with Flask, (optional) Exporting a Model from PyTorch to ONNX and Running it using ONNX Runtime, (beta) Building a Convolution/Batch Norm fuser in FX, (beta) Building a Simple CPU Performance Profiler with FX, (beta) Channels Last Memory Format in PyTorch, Fusing Convolution and Batch Norm using Custom Function, Extending TorchScript with Custom C++ Operators, Extending TorchScript with Custom C++ Classes, Extending dispatcher for a new backend in C++, (beta) Dynamic Quantization on an LSTM Word Language Model, (beta) Quantized Transfer Learning for Computer Vision Tutorial, (beta) Static Quantization with Eager Mode in PyTorch, Single-Machine Model Parallel Best Practices, Getting Started with Distributed Data Parallel, Writing Distributed Applications with PyTorch, Getting Started with Distributed RPC Framework, Implementing a Parameter Server Using Distributed RPC Framework, Distributed Pipeline Parallelism Using RPC, Implementing Batch RPC Processing Using Asynchronous Executions, Combining Distributed DataParallel with Distributed RPC Framework, Training Transformer models using Pipeline Parallelism, Training Transformer models using Distributed Data Parallel and Pipeline Parallelism, Distributed Training with Uneven Inputs Using the Join Context Manager, Preparing data and utility functions (skip this section), Controling resampling quality with parameters. This Setting torch.set_num_threads(1) might help in this AutoEncoder is often referred to as AE for short. Tip If you need to load and resample your audio data on-the-fly, For that purpose we will use a log-scaled mel-spectrogram. waveform (in log scale), the y-axis corresponds to the frequency of the # add `rate` effect with original sample rate after this. Below we write the Encoder class by sublcassing torch.nn.Module, which lets us define the __init__ method storing layers as an attribute, and a forward method describing the forward pass of the network. the extension. Our compressed CODE can be easily used by visualization: It can be seen that, in fact, the CODE compressed by AutoEncoder has been able to initially grasp the existence of different features in each different picture. environment. Implementing a Sparse Autoencoder using KL Divergence with PyTorch Beginning from this section, we will focus on the coding part of this tutorial and implement our through sparse autoencoder using PyTorch. Creating an Autoencoder with PyTorch Autoencoder Architecture Autoencoders are fundamental to creating simpler representations of a more complex piece of data. There are 2 watchers for this library. then you can use torchaudio.sox_effects.apply_effects_file with Examples of dimensionality reduction techniques include principal component analysis (PCA) and t-SNE. As a comparison, here is the equivalent way to get Mel-scale spectrogram Join the PyTorch developer community to contribute, learn, and get your questions answered. If the latent space is 2-dimensional, we can sample latent vectors $z$ from the latent space over a uniform grid and plot the decoded latent vectors on a grid. This is a minimalist, simple and reproducible example. # The first one will fetch all the data and decode them, while. Second, by penalizing the KL divergence in this manner, we can encourage the latent vectors to occupy a more centralized and uniform location. Transform it to a one channel audio signal. Kaldi Pitch feature [1] is pitch detection mechanism tuned for ASR In case of path-like object . There may still be gaps in the latent space because the outputted means may be significantly different and the standard deviations may be small. Because the filter used for interpolation extends infinitely, the It has 13 star(s) with 2 fork(s). By default, torchaudios resample uses the Hann window filter, which is The Convolutional Autoencoder The images are of size 28 x 28 x 1 or a 30976-dimensional vector. For more details, you may refer to DAE (Denoising AutoEncoder). This becomes a problem when we try to use autoencoders as generative models. In addition to this great shortcut we just took, Torchvision enables us to load a model pretrained on Imagenet, so the training will be shorter and more effective. First, we import all the packages we need. frequencies, resampling does not produce perfect results, and a variety In this blog, we will not go over the structure of the training and evaluation loops. uttered in a conference room. some of the higher frequencies. torchaudio implements feature extractions commonly used in audio Though audio signals are temporal in nature, in many cases it is possible to leverage recent advancements in the field of image classification and use popular high performing convolutional neural networks for audio classification. An autoencoder is a neural network that predicts its own input. self.mid will create an output as [batch_size, *, 16] while the following self.syncconv1 layer would try to . However, neural networks have shown considerable power in the unsupervised learning context, where data just consists of points $x$. To add background noise to audio data, you can simply add audio Tensor is a popular augmentation technique applied on spectrogram. Extending it to our diagonal Gaussian distributions is not difficult; we simply sum the KL divergence for each dimension. conversion. We can see that the reconstructed latent vectors look like digits, and the kind of digit corresponds to the location of the latent vector in the latent space. Torchaudio is a library for audio and signal processing with PyTorch. torchaudio.functional.create_fb_matrix can generate the filter bank {display-mode: "form"}. Also published at https://afagarap.github.io/2020/01/26/implementing-autoencoder-in-pytorch.html. extracting the most salient features of the data, and (2) a decoder learns to reconstruct the original data based on the learned representation by the encoder. all the data, instead it only reads the beginning portion of data. Download the jupyter notebook and run this blog post yourself! Below is an implementation of an autoencoder written in PyTorch. This expression applies to two univariate Gaussian distributions (the full expression for two arbitrary univariate Gaussians is derived in this math.stackexchange post). Your model architecture doesn't match the posted image, which seems to use linear layers only. To simplify the implementation, we write the encoder and decoder layers in one class as follows, The. We demonstrate the performance implications As mentioned above, it is divided into two parts: Encoder and Decoder. A pitch extraction algorithm tuned for automatic speech recognition, Ghahremani, B. BabaAli, D. Povey, K. Riedhammer, J. Trmal and S. This is the motivation behind dimensionality reduction techniques, which try to take high-dimensional data and project it onto a lower-dimensional surface. Pytorchs ecosystem includes a variety of open source tools that can jump start our audio classification project and help us manage and support it. In order to use continuous action spaces and have stochastic policies, we have to model the policy $\pi$ directly. Implement Convolutional Autoencoder in PyTorch with CUDA The Autoencoders, a variant of the artificial neural networks, are applied in the image process especially to reconstruct the images. to convert frequency bins to Mel-scale bins. In this way, we may be able to use the compressed CODE for subsequent deep learning processing to recude the computational cost. its value range is normalized within [-1.0, 1.0]. torchaudio implements TimeStrech, TimeMasking and But if you want to briefly describe what AutoEncoder is doing, I think it can be drawn as the following picture. frequency over time. Using a larger lowpass_filter_width Originally published at https://allegro.ai on October 18, 2020. FInally, we write an Autoencoder class that combines these two. the original after the effects. torchaudios I/O functions now support file-like object. To recover a waveform from spectrogram, you can use GriffinLim. other than local file system. Lets plot the latent vector representations of a few batches of data. Implementation of Autoencoder in Pytorch Step 1: Importing Modules We will use the torch.optim and the torch.nn module from the torch package and datasets & transforms from torchvision package. To save audio data in the formats intepretable by common applications, Please checkout the official documentation for the list of [wikipedia]. They are stateless. I also wanted to write some code to generate a GIF of the transition, instead of just a row of images. So we have to control our loss function to make our inputs and outputs look like the better. format argument to tell what format the audio source is. Autoencoder As the dataset is small and we want to mitigate the risk for overfitting, we will use the small but effective ResNet18 model. To analyze traffic and optimize your experience, we serve cookies on this site. A Medium publication sharing concepts, ideas and codes. Total running time of the script: ( 0 minutes 31.806 seconds), Access comprehensive developer documentation for PyTorch, Get in-depth tutorials for beginners and advanced developers, Find development resources and get your questions answered. For the purpose of this blog we will use the UrbanSound8K dataset that contains 8732 labeled sound excerpts (<=4s) of urban sounds from 10 classes, including dog barks, siren and street music. First put the input into the Encoder, which is compressed into a low-dimensional code by the neural network in the encoder architecture, which is the code in the picture, and then the code is input into the Decoder and decoded out the final output. AutoEncoder-with-pytorch has no issues reported. normalize the signal power, then flip the time axis. Note that we could have easily written this entire autoencoder as a single neural network, but splitting them in two makes it conceptually clearer. We can also sample uniformly from the latent space and see how the decoder reconstructs inputs from arbitrary latent vectors. A mel spectrogram is a spectrogram where the frequencies are converted to the mel scale, which takes into account the fact that humans are better at detecting differences in lower frequencies than higher frequencies. FrequencyMasking. Python3 import torch All we have got left to do is to execute our Jupyter notebook, either locally or on a remote machine using Trains Agent and watch the progress of our training on Allegro Trains web app. Both function takes effects in the form of List[List[str]]. Convolutional Autoencoder is a variant of Convolutional Neural Networks that are used as the tools for unsupervised learning of convolution filters. This is generally accomplished by replacing the last layer of a traditional autoencoder with two layers, each of which output $\mu(x)$ and $\sigma(x)$. Generated images from cifar-10 (author's own) It's likely that you've searched for VAE tutorials but have come away empty-handed. We demonstrate these properties through resampling a logarithmic plotted waveform, and the color intensity refers to amplitude. The project is built to facillitate research on using VAEs to model audio. The loading of the datasets metadata is done in the constructor of the class and is configured based on the UrbanSound8K dataset structure. Neural networks are often used in the supervised learning context, where data consists of pairs $(x, y)$ and the network learns a function $f:X \to Y$. which is composed of Spectrogram and MelScale. documentation. The distribution overall of $p(z \mid x)$ appears to be much closer to a Gaussian distribution. like a person talking over a phone in a echoey room with people talking the filter to use to window the interpolation. Building A Marijuana Strain Recommender System, Why You Should Never Trust Data Science Blog Posts, https://afagarap.github.io/2020/01/26/implementing-autoencoder-in-pytorch.html. Note apply_effects_file accepts file-like object or path-like Click to share on Twitter (Opens in new window), Click to share on Facebook (Opens in new window), Click to share on LinkedIn (Opens in new window), Click to share on Reddit (Opens in new window), https://medium.com/pytorch/implementing-an-autoencoder-in-pytorch-19baa22647d1, https://www.kaggle.com/jagadeeshkotra/autoencoders-with-pytorch, https://www.kaggle.com/ljlbarbosa/convolution-autoencoder-pytorch, https://discuss.pytorch.org/t/autoencoders-in-pytorch/844, [PyTorch] Use Embedding Layer To Process Text, [PyTorch] LSTM Principle and Input and Output Format Record, [PyTorch] How To Print Model Architecture And Extract Model Weights, [Announcement] This Website Is Temporarily Suspended From Regular Updates, LeetCode: 1022-Sum of Root To Leaf Binary Numbers Solution, [Linux] Using Ngrok To Set Up A Temporary Server And Forward The Port. you can use torchaudio.save. rcParams ['figure.dpi'] = 200. Your home for data science. Mel-scale spectrogram is a combination of Spectrogram and mel scale Given two inputs $x_1$ and $x_2$, and their corresponding latent vectors $z_1$ and $z_2$, we can interpolate between them by decoding latent vectors between $x_1$ and $x_2$. transforms.Resample or functional.resample. The input data is the classic Mnist. To get the frequency representation of audio signal, you can use The diagram in Figure 3 shows the architecture of the 65-32-8-32-65 autoencoder used in the demo program. Tutorial 8: Deep Autoencoders. beta parameter that allows for the design of the smoothness of the Since we also have access to labels for MNIST, we can colour code the outputs to see what they look like. object. The AutoEncoder architecture is divided into two parts: Encoder and Decoder. The Audio-classification problem is now transformed into an image classification problem. metadata, including the format itself. info function works on file-like object as well. In the next blog post of this series, we will demonstrate how to easily create a machine learning pipeline, using the PyTorch ecosystem. So it will be easier for you to grasp the coding concepts if you are familiar with PyTorch. For example, to save data Introduction to Variational Autoencoders (VAE) in Pytorch. In essence, we force the encoder to find latent vectors that approximately follow a standard Gaussian distribution that the decoder can then effectively decode. In this tutorial, we demonstrated the use of Tochaudio, Torchvision and Allegro Trains for a simple and effective audio classification task. Improve this answer. We will code . implementation does not do that. waveform[:, frame_offset:frame_offset+num_frames]) however, rolloff determines the lowpass filter cutoff and An autoencoder is an artificial neural network that aims to learn how to reconstruct a data. Overview The repo is under construction. Coding a Variational Autoencoder in Pytorch and leveraging the power of GPUs can be daunting. Khudanpur, 2014 IEEE International Conference on Acoustics, Speech and Signal Finally, we formally start training and save the model. 228 1 1 . How to Learn Data Visualization, and What to Do Next. Because all the transforms are subclass of torch.nn.Module, they can SpecAugment Learn about PyTorchs features and capabilities. Learn more, including about available controls: Cookies Policy. This is a beta feature in torchaudio, and only We call $z = e(x)$ a latent vector. The spectrograms below show the frequency representation of the signal, Please see code comments for further explanation: import torch # Use torch.nn.Module to create models class AutoEncoder (torch.nn.Module): def __init__ (self, features: int, hidden: int): # Necessary in order to log C++ API usage and other internals super ().__init__ () self.encoder = torch.nn.Linear . So lets use torchaudio transforms and add the following lines to our snippet: Now the audio file is represented as a two dimensional spectrogram image: Thats exactly what we wanted to achieve. to provide format argument. For humans who visualize most things in 2D (or sometimes 3D), this usually means projecting the data onto a 2D surface. Establish the model and determine the optimizer and loss function. this. VP R&D @ SightX AI | LinkedIn: https://il.linkedin.com/in/dan-malowany-78b2b21, [Notes] (IJCAI2021) UNBERT: User-News Matching BERT for News Recommendation, Comparative Analysis of Machine Learning Algorithms, NLP-Day 20: You Better Pay Attention To Transformers (Part 2), Deploy Machine Learning Web Apps for Free, Flight Data Analysis with Spark ML and Minio, Building Emo Detect Part I(Training a Model With AutoML Vision), [Notes] (SIGIR2022) MM-Rec: Multimodal News Recommendation, https://il.linkedin.com/in/dan-malowany-78b2b21. Saving audio to file To save audio data in the formats intepretable by common applications, you can use torchaudio.save. equivalent transform in torchaudio.transforms. required. # Given the original sample rate used for generating the sweep, # find the x-axis value where the log-scale major frequency values fall in, 'Original Signal Frequency (Hz, log scale)'. To load audio data, you can use torchaudio.load. Below are benchmarks for downsampling and upsampling waveforms between The values encoding can take are one of the following. The implementation involves Conclusion If the latent space is 2-dimensional, then we can transform a batch of inputs $x$ using the encoder and make a scatterplot of the output vectors. Building a deep autoencoder with PyTorch linear layers. that the lowpass_filter_wdith, window type, and sample rates can with librosa. This can be controlled using the torchaudio integrates libsox and provides a rich set of audio I/O. [abstract], In recent years, Convolutional Neural Networks (CNNs) have proven very effective in image classification tasks, which gave rise to the design of various architectures, such as Inception, ResNet, ResNext, Mobilenet and more. The result should look like this: Now comes the best part. This rising field can benefit greatly from the rich experience and various tools developed for computer vision tasks. This penalty term is the KL divergence between $p(z \mid x)$ and $\mathcal{N}(0, 1)$, which is given by detected from either file extension or header, you can provide application. Therefore, we will clip all Mel spectrograms to a preconfigured length and zero pad spectrograms shorter than this length. Instead, the goal is to learn and understand the structure of the data. This object should include data loading as well as data preprocessing. torchaudio provides easy access to common, publicly accessible They are available in torchaudio.functional and Let the input data be X. This is highly effective for cases where we have a repeating sequence of tasks. MelSpectrogram) and augmentation technique called SpecAugment. torchaudio.transforms. You convert the image matrix to an array, rescale it between 0 and 1, reshape it so that it's of size 28 x 28 x 1, and feed this as an input to the network. You can even do: encoder = nn.Sequential (nn.Linear (782,32), nn.Sigmoid ()) decoder = nn.Sequential (nn.Linear (32,732), nn.Sigmoid ()) autoencoder = nn.Sequential (encoder, decoder) @alexis-jacq I want a auto encoder with tied weights, i.e. be serialized using TorchScript. This loss is useful for two reasons. extract features that can be fed to NN models. The encoder learns a non-linear transformation $e:X \to Z$ that projects the data from the original high-dimensional input space $X$ to a lower-dimensional latent space $Z$. # the second one will stop fetching data once it completes decoding. stop as soon as the necessary amount of data is fetched. Note Saving data in encodings with lower bit depth reduces the As such, if we will be able to transfer audio classification tasks into the image domain, we will be able to leverage this rich variety of backbones for our needs. The code below modifies the code above to produce a GIF. A neural layer transforms the 65-values tensor down to 32 values. Spectrogram transform. Since this function does not require input audio/features, there is no An autoencoder is a neural network that predicts its own input. transforms.Resample precomputes and caches the kernel used for For this process, we need RIR data. A Brief Introduction to Autoencoders Deep learning autoencoders are a type of neural network that can reconstruct specific images from the latent code space. It has a neutral sentiment in the developer community. The following examples illustrates this. Intuitively Understanding Variational Autoencoders, Understanding Variational Autoencoders (VAEs), Beyond Vanilla Policy Gradients: Natural Policy Gradients, Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO), Actor-Critic Methods, Advantage Actor-Critic (A2C) and Generalized Advantage Estimation (GAE). To implement this, we do not need to change the Decoder class. In this blog post, we will show how using Torchaudio and Allegro Trains enables simple and efficient audio classification. The training set contains \(60\,000\) images, the test set contains only \(10\,000\). (i.e. We will work with the MNIST Dataset. Cub and StanfordCars, but is easily extensible to any other image dataset the of Mae-Vit model which impelement with PyTorch, no reference any reference code so this is non-official. case. transforms module implements features in object-oriented manner, bits_per_sample argument to change this. Similar to torchaudio.load, when the audio format cannot be Resample it to a preconfigured sample rate Note that this is done here for simplicity. "https://pytorch-tutorial-assets.s3.amazonaws.com/steam-train-whistle-daniel_simon.wav", "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit/source-16k/train/sp0307/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav", "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit/distant-16k/room-response/rm1/impulse/Lab41-SRI-VOiCES-rm1-impulse-mc01-stu-clo.wav", "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit/distant-16k/distractors/rm1/babb/Lab41-SRI-VOiCES-rm1-babb-mc01-stu-clo.wav", "https://pytorch-tutorial-assets.s3.amazonaws.com/steam-train-whistle-daniel_simon.mp3", "https://pytorch-tutorial-assets.s3.amazonaws.com/steam-train-whistle-daniel_simon.gsm", "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit.tar.gz", "VOiCES_devkit/source-16k/train/sp0307/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav", "Waveform with more than 2 channels are not supported. Resampling an audio file is a time consuming function that will significantly slow down the training and will result in decreased GPU utilization. We can see that, compared to the traditional autoencoder, the range of values for latent vectors is much smaller, and more centralized. Sample the latent space to produce output. The Dataset and the Directory Structure Like the last article, we will be using the FashionMNIST dataset in this article. Therefore, it will look as follows: The next thing we will do is define the __ getitem__ method of the Dataset class. This is advantageous Lets also look at the reconstructed digits from the latent space: Variational autoencoders produce a latent space $Z$ that is more compact and smooth than that learned by traditional autoencoders. Then we set the arguments, such as epochs, batch_size, learning_rate, and load the Mnist data set from torchvision. Look at the latent space. The decoder learns to reconstruct the latent features back to the original data. It additionally supports the Kaiser window, torchaudio.save can also handle other formats. ", """Get freqs evenly spaced out in log-scale, between [0, max_sweep_rate // 2]. sox command adds some effects automatically, but torchaudios Note When passing file-like object, info function does not read This blog post is a third of a series on how to leverage PyTorchs ecosystem tools to easily jumpstart your ML/DL project. In this blog we will use three of these tools: For simplification, we will not explain in this blog how to install a Trains-server. We apply it to the MNIST dataset. It is a neural network for unsupervised learning, in other words, it does not require labaled data. providing num_frames and frame_offset arguments is more AutoEncoder actually has a huge family, with quite a few variants, suitable for all kinds of tasks. Imagine that we have a large, high-dimensional dataset. Because a finite number of samples can only represent a finite number of An autoencoder is just the composition of the encoder and the decoder $f(x) = d(e(x))$. What should we look at once weve trained an autoencoder? In this Deep Learning Tutorial we learn how Autoencoders work and how we can implement them in PyTorch. The original formulation of $Q$-learning requires that both the state and action space be small and discrete. In that case your approach seems simpler. The most intuitive understanding is that it can de-noise and dimension reduction. They use a famous. torchaudio.sox_effects module provides ways to apply filiters like This is the PyTorch equivalent of my previous article on implementing an autoencoder in TensorFlow 2.0, which you may read through the following link, An open source machine learning framework that accelerates the path from research prototyping to production deployment, The struggles and the tips: What I learn from doing my own classification project, Free Yourself from Indecision with Weighted Product Models, Integrating Transformers with MedCAT for biomedical NER+L. vae-audio For variational auto-encoders (VAEs) and audio/music lovers, based on PyTorch. resulting file size but loses precision. This example should get you going. In this tutorial, we will take a closer look at autoencoders (AE). sox command on Tensor objects and file-object audio sources PSL PSL. AutoEncoder The AutoEncoder architecture is divided into two parts: Encoder and Decoder. For example, preprocessing and training, as we mentioned at the beginning of this blog post. torchaudio provides powerful audio I/O functions, preprocessing Just turn on microphone By clicking or navigating, you agree to allow our usage of cookies. In variational autoencoders, inputs are mapped to a probability distribution over latent vectors, and a latent vector is then sampled from that distribution. In our last section we have seen what is ResNet and how to implement it. Autoencoder Architecture [Source] The encoding portion of an autoencoder takes an input and compresses this through a. The feature vector is called the "bottleneck" of the network as we aim to . When passing file-like object, you also need to provide format This means that, while the actual data itself might have hundreds of dimensions, the underlying structure of the data can be sufficiently described using only a few dimensions. Both resampling methods use bandlimited sinc Define the model architecture of AutoEncoder. We only need to change the Encoder class to produce $\mu(x)$ and $\sigma(x)$, and then use these to sample a latent vector. Specifically, instead of mapping the input $x$ to a latent vector $z = e(x)$, we map it instead to a mean vector $\mu(x)$ and a vector of standard deviations $\sigma(x)$. As we made sure we report debug data every few iterations, we can check out the debug samples section in the Allegro Trains webapp and make sure that the data being fed into the model makes sense. These parametrize a diagonal Gaussian distribution $\mathcal{N}(\mu, \sigma)$, from which we then sample a latent vector $z \sim \mathcal{N}(\mu, \sigma)$. It provides I/O, signal and data processing functions, datasets, model implementations and application components. masked autoencoders pytorch. The following code produces a row of images showing the interpolation between digits. This function accepts path-like object and file-like object. When writing a python script, you can use the popular argparse package and Allegro Trains will automatically pick it up. An input image x, with 65 values between 0 and 1 is fed to the autoencoder. # Reverbration gives some dramatic feeling, # Because the noise is recorded in the actual environment, we consider that, # the noise contains the acoustic feature of the environment. Not only that, if we can control the code of the intermediate code, even we can use the CODE to generate fake data. subprocesses, such as data loading with multiple worker processes, your \(\mathbb{KL}\left( \mathcal{N}(\mu, \sigma) \parallel \mathcal{N}(0, 1) \right) = \sum_{x \in X} \left( \sigma^2 + \mu^2 - \log \sigma - \frac{1}{2} \right)\). Therefore, depending on the audio format, it cannot get the correct