It represents the frequency at which high frequencies decline to 0. There are a lot of libraries in python for working on audio data analysis like: Librosa Ipython.display.Audio Spacy, etc. data numpy array. Original Aquegg | Wikimedia Commons. For a more general introduction to the library, check out Scientific Python: Using SciPy for Optimization. It is formerly known as WAVE (Waveform Audio File Format), and referred to as WAV because of its extension (.wav or sometimes .wave). In this article, we did a pretty good analysis of audio data. Examples of frauds discovered because someone tried to mimic a random sequence, Is it illegal to use resources in a University lab to prove a concept could work (to ultimately use to create a startup). var disqus_shortname = 'kdnuggets'; Want to know how Python is used for plotting? First I downloaded 1M and 2M wav files from this website as wav sample files: https://file-examples.com/index.php/sample-audio-files/sample-wav-download/. And here, weve only looked at one channel. Now convert the audio data files into PNG format images or basically extracting the Spectrogram for every Audio. Say, I have test.wav and test2.wav in the current working dir, the following command in python prompt interface is sufficient: import test2 map (test2.f, ['test.wav','test2.wav']) Assuming you have 100 such files and you do not want to type their names individually, you need the glob package: To open our WAV file, we use the wave module in Python, which can be imported and called as follows: >>> import wave >>> wav_obj = wave.open('file.wav', 'rb') The ' rb ' mode returns a wave_read object. Mechanical wave:Oscillates the travel through space;Energy is required from one point to another point;Medium is required. In other words, the center mass of audio data. How to upgrade all Python packages with pip? Phase:Phase is defined as the location of the wave from an equilibrium point as time t=0. Python Drawing: Intro to Python Matplotlib for Data Visualization (Part 2). Do you know how to rename, batch rename, move, and batch move files in Python? This is called the centroid of the wave. The sampling frequency or rate is the number of samples taken over some fixed amount of time. But, we will extract only useful or relevant information. If you need some background material on plotting in Python, we have some articles. Then use the following code to install and draw the tonal graph of the wav file: It can be seen that the two graphics are basically the same, but the X coordinate of the 2M file is twice that of the 1M file. 3. In the following section, we are going to use these features and build a ANN model for music genre classification. First of all, we need to convert the audio files into PNG format images(spectrograms). In the language of calculus we can say that there is a non-differentiability point in our waveform. We can change this behavior by resampling at 44.1KHz. How can I fix it? When would I give a checkpoint to my D&D party that they can return to if they die? The sound data can be a properly structured format and our brain can understand the pattern of each word corresponding to it, and make or encode the textual understandable data into waveform. Check out this article about visualizing data stored in a DataFrame. Add a new light switch in line with another switch? Where I1 and I2 are two intensity levels. 6. Audio Analysis using Python | Speech Analytics | PyDubCode: https://beingdatum.com/profilegrid_blogs/working-with-audio-wav-files-in-python-using-pydub/In th. The environment you need to follow this guide is Python3 and Jupyter Notebook. Now, lets take a look at the frequency spectrum, also known as a spectrogram. It includes the nuts and bolts to build a MIR (Music information retrieval) system. librosa.display.specshow. If youre a beginner and are looking for some material to get up to speed in data science, take a look at this track. What is the average frequency of the guitar part compared to the piano part? Help Status Writers Blog Careers To fuel more audio-decoding power, you can installffmpegwhich ships with many audio decoders. pip install pydub rev2022.12.11.43106. To do this, we can use the readframes() method, which takes one argument, n, defining the number of frames to read: This method returns a bytes object. A typical audio processing process involves the extraction of acoustics features relevant to the task at hand, followed by decision-making schemes that involve detection, classification, and knowledge fusion. First I downloaded 1M and 2M wav files from this website as wav sample files: https://file-examples.com/index.php/sample-audio-files/sample-wav-download/ Then use the following code to install and draw the tonal graph of the wav file: from scipy.io import wavfile Popular virtual assistant products have been released by major technology companies, and these products are becoming more common in smartphones and homes around the world. Python for data analysis is it really that simple?!? Tabularray table when is wraped by a tcolorbox spreads inside right margin overrides page borders. Data preprocessing: It involves loading CSV data, label encoding, feature scaling and data split into training and test set. Manually raising (throwing) an exception in Python. Similarity search for audio files (aka Shazam), Speech processing and synthesis generating artificial voice for conversational agents. The wave module in Python's standard library is an easy interface to the audio WAV format. Indeed, the dominant frequencies for the whole track are lower than 2.5 kHz. Before moving ahead, I would recommend usingGoogle Colabfor doing everything related to Neural networks because it isfreeand provides GPUs and TPUs as runtime environments. So, far I tried to read the wav file using scipy and then I tried to calculate FFT to get the frequency spectrum. Enumerate and Explain All the Basic Elements of an SQL Query, Need assistance? Timbre describes the quality of sound. Asking for help, clarification, or responding to other answers. How to make voltage plus/minus signs bolder? Try plotting the difference between the channels, and you see some new and interesting features pop out of the waveform and the frequency spectrum. Audio files can be handled using the below libraries. Fast Fourier Transform (FFT) analysis on wav file using python 12,004 views Dec 5, 2019 137 Dislike Share Save Description Metallicode 3.68K subscribers Fast Fourier Transform. Each instrument and sound effect has its own signature in the frequency spectrum. Next, we show some examples of how to plot the signal values. How do I put three reasons together in a sentence? In this method we try to analyze the waveform in which our frequency drops suddenly from high to 0. How Do You Write a SELECT Statement in SQL? To subscribe to this RSS feed, copy and paste this URL into your RSS reader. By subscribing you accept KDnuggets Privacy Policy, Subscribe To Our Newsletter In this article, were going to focus on a fundamental part of the audio data analysis process plotting the waveform and frequency spectrum of the audio file. Our audio file is in the WAV (Waveform Audio File) format, which is uncompressed. A typical audio signal can be expressed as a function of Amplitude and Time. pydub is a Python library to work with only .wav files. A few more tips on how to use Python matplotlib for data visualization. To learn more, see our tips on writing great answers. This is a handy datatype for sound processing that can be converted to WAV format for storage using the scipy.io.wavfile module. Python can use SCIPY library to load wav files and use matplotlib to draw graphics. The analysis of audio data has become ever more relevant in recent times. . There are a lot of techniques for data analysis, like statistical and graphical. Examples of these formats are. Before we discuss audio data analysis, it is important to learn some physics-based concepts of audio and sound, like its definition, and parameters such as amplitude, wavelength, frequency, time-period, phase intensity, etc. Indexing music collections according to their audio features. wav audio files. The pyAudioAnalysis library requires wav files, so make sure any files you save to trainingData are wav files. It will improve your productivity. It models the characteristics of the human voice. There are a lot of libraries in python for working on audio data analysis like: During any sound emission we may see our complete sound/audio data focused on a particular point or mean. Data read from WAV file. This returns an audio time series as a numpy array with a default sampling rate(sr) of 22KHZ mono. After the second pause, the main instrument alternates between a guitar and a piano, which is roughly seen in the signal, where the guitar part has lower amplitudes. And 1 That Got Me in Trouble. Google Colab directory structure after data is loaded. Are the S&P 500 and Dow Jones Industrial Average securities? Before we get to plotting signal values, we need to calculate the time at which each sample is taken. This is a visual representation of the signal strength at different frequencies, showing us which frequencies dominate the recording as a function of time: The following plot opens in a new window: In the plotting code above, vmin and vmax are chosen to bring out the lower frequencies that dominate this recording. The loudness of this wav file is -24. Not the answer you're looking for? There are devices built that help you catch these sounds and represent it in a computer-readable format. $ python downsample.py ./audio/test_original.wav 8192 $ python downsample.py ./audio/test_delayed.wav 8192 For each command you will see some output showing the information of it's original audio file as well as the downsampled version. The file sizes can get large as a consequence. STFTconverts signals such that we can know the amplitude of the given frequency at a given time. This is called the centroid of the wave. - Or when a whistle is blown Like we see in a heatmap, there are different colors for different magnitudes of values. Help us identify new roles for community members, Proposing a Community-Specific Closure Reason for non-English content. Now that we have retrieved the upload URL that was part of the response of the previous call, we can now go ahead and get the transcription of the audio file. Centroid of wave: During any sound emission we may see our complete sound/audio data focused on a particular point or mean. This article is aimed at people with a bit more background in data analysis. The vertical axis shows frequencies (from 0 to 10kHz), and the horizontal axis shows the time of the clip. Audio Data Analysis Using Deep Learning with Python (Part 2). I am working on a program that takes a 30 minute wav file and analyzes it for various events. Discover how to write to a file in Python using the write() and writelines() methods and the pathlib and csv modules. 1. It is also good to incorporate the length of the audio clip, and, bit-depth for easily being able to distinguish. If a file-like input without a C-like file descriptor (e.g., io.BytesIO) is passed, this will not be writeable. I have been playing with graphing the FFT of these audio samples and have come to the conclusion that this does not give me the best insight on these events. We can display a spectrogram using. Join our monthly newsletter to be notified about the latest posts. - Also being able to identify complete silence for an extended period of time would be helpful. We will mainly use two libraries for audio acquisition and playback: It is a Python module to analyze audio signals in general but geared more towards music. Theres a lot of music and voice data out there. Why does my stock Samsung Galaxy phone/tablet lack some features compared to other Samsung Galaxy models? Attack-decay-sustain-release model; below is a graphical analysis. To install it type the below command in the terminal. Note that in a single call, we can also request to perform sentiment analysis. first_abnormal_point_index = 20000 Not only can one see whether there is more or less energy at, for example, 2 Hz vs 10 Hz, but one can also see how energy levels vary over time. Connect and share knowledge within a single location that is structured and easy to search. A brief introduction to audio data processing and genre classification using Neural Networks and python. Extract and load your data to google drive then mount the drive in Colab. By using this library we can play, split, merge, edit our . You will notice some of the files are in .wav format. It has been very welldocumentedalong with a lot of examples and tutorials. Python's SciPy library comes with a collection of modules for reading from and writing data to a variety of file formats. There is a rise in the spectral centroid in the beginning. On the premise of those frequency values we assign a color range, with lower values as a brighter color and high frequency values as a darker color. The sound excerpts are digital audio files in .wav format. Let us now load the file in your jupyter console. This change in pressure causes air molecules to oscillate. The functions in this module can write audio data in raw format to a file like object and read the attributes of a WAV file. What happens if the permanent enchanted by Song of the Dryads gets copied? WAV is an audio file format, or more specifically, a container format to store multimedia files. We show you how to visualize sound in Python. Thanks for contributing an answer to Stack Overflow! Why is Singapore currently considered to be a dictatorial regime and a multi-party democracy by different publications? wave Read and write WAV files Python 3.11.0 documentation wave Read and write WAV files Source code: Lib/wave.py The wave module provides a convenient interface to the WAV sound format. A spectrogram is usually depicted as aheat map, i.e., as an image with the intensity shown by varying the color or brightness. Drop us a line at contact@learnpython.com. We can plot the audio array usinglibrosa.display.waveplot: Here, we have the plot of the amplitude envelope of a waveform. Achroma feature or vectoris typically a 12-element feature vector indicating how much energy of each pitch class, {C, C#, D, D#, E, , B}, is present in the signal. When we get sound data which is produced by any source, our brain processes this data and gathers some information. KDnuggets News, December 7: Top 10 Data Science Myths Busted 4 Useful Intermediate SQL Queries for Data Science, 7 Essential Cheat Sheets for Data Engineering, How to Prepare for a Data Science Interview, How Artificial Intelligence Will Change Mobile Apps. COMPETITIVE PROGRAMMING AT TOPCODER.card{padding: 20px 10px 20px 15px; border-radius: 10px;position:relative;text-decoration:none!important;display:block}.card img{position:relative;margin-top:-20px;margin-left:-15px}.card p{line-height:22px}.card.green{background-image: linear-gradient(139.49deg, #229174 0%, #63F963 100%);}.card.blue{background-image:linear-gradient(329deg, #2C95D7 0%, #6569FF 100%)}.card.orange{background-image:linear-gradient(143.84deg, #EF476F 0%, #FFC43D 100%)}.card.teal{background-image:linear-gradient(135deg, #2984BD 0%, #0AB88A 100%)}.card.purple{background-image: linear-gradient(305.22deg, #9D41C9 0.01%, #EF476F 100%)}. Audio data analysis is about analyzing and understanding audio signals captured by digital devices, with numerous applications in the enterprise, healthcare, productivity, and smart cities. Get the FREE ebook 'The Great Big Natural Language Processing Primer' and the leading newsletter on AI, Data Science, and Machine Learning, straight to your inbox. Only files using WAVE_FORMAT_PCM are supported. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. This is called the centroid of the wave. Want to know how Python is used for plotting? Installation This module does not come built-in with Python. Notes. librosa.feature.chroma_stftis used for the computation of Chroma features. Using a spectrogram we represent the noise or sound intensity of audio data with respect to frequency and time. How do I delete a file or folder in Python? It includes the nuts and bolts to build a MIR(Music information retrieval) system. In short, It provides a robust way to describe a similarity measure between music pieces. Generally, statistics is a graphical and mathematical representation of Then, theres a lower-amplitude outro at the end of the track. Discover how! The Difference Between scipy.io.wavfile.read () and librosa.load () in Python - Python Tutorial Then we will use meter.integrated_loudness () to compute loudess of this wav file. Now let us visualize it and see how we calculate zero crossing rate. It is used to 5. For example, the scipy.io.wavfile module can be used to read from and write to a .wav format file. We can check the number of channels as follows: The next step is to get the values of the signal, that is, the amplitude of the wave at that point in time. Python provides a module called pydub to work with audio files. There is a large range of applications using audio data analysis, and this is a rich topic to explore. Towards Data Science Predicting The FIFA World Cup 2022 With a Simple Model using Python Dennis Niggl in Python in Plain English Creating an Awesome Web App With Python and Streamlit Zach Quinn in Pipeline: A Data Engineering Resource 3 Data Science Projects That Got Me 12 Interviews. import numpy as np from scipy.fft import * from scipy.io import wavfile def freq (file, start_time, end_time): # open the file and convert to mono sr, data = wavfile.read (file) if data.ndim > 1: data = data [:, 0] else: pass # return a slice of the data from start_time to end_time datatoread = data [int (start_time * sr / 1000) : int How do I check whether a file exists without exceptions? Check out how to learn Python faster! Please share your thoughts/doubts in the comment section. Sample Data. Tutorial 1: Introduction to Audio Processing in Python In this tutorial, I will show a simple example on how to read wav file, play audio, plot signal waveform and write wav file. Now that we understood how we can play around with audio data and extract important features using python. It contains 10 genres, each represented by 100 tracks. Pydub ( Follow this link for the documentation) Librosa ( Follow this link for the documentation) Install Libraries: Install Pydub using pip: pip3 install pydub Install Pydub in Jupiter notebook: !pip install pydub Well, part 1 ends here. Stop wasting time on other slow and ineffective methods. This type of question feels a bit open-ended, and may not be best suited here. Common data types: Now we will look at some important terms like intensity, loudness, and timbre. Make sure to install the scipy module for the following example ( pip install scipy ). - When a goal or an event occurs, there will be noise and cheering from the crowd. Thespectral features(frequency-basedfeatures), which are obtained by converting the time-based signal into the frequency domain using the Fourier Transform, like fundamental frequency, frequency components,spectralcentroid,spectralflux,spectraldensity,spectralroll-off, etc. Thespectral centroidindicates at which frequency the energy of a spectrum is centered upon or in other words It indicates where the center of mass for a sound is located. librosa.feature.spectral_bandwidthcomputes the order-p spectral bandwidth: A very simple way for measuring the smoothness of a signal is to calculate the number of zero-crossing within a segment of that signal. The tracks are all 22050 Hz monophonic 16-bit audio files in .wav format. Youre probably familiar with MP3, which uses lossy compression to store data. Uploading audio file to AssemblyAI's API hosting service Source: Author. While much of the literature and buzz on deep learning concerns computer vision and natural language processing(NLP), audio analysis a field that includes automatic speech recognition(ASR), digital signal processing, and music classification, tagging, and generation is a growing subdomain of deep learning applications. Sample spectrogram of a song having genre as blues. librosa.feature.spectral_rolloffcomputes the rolloff frequency for each frame in a signal: The spectral bandwidth is defined as the width of the band of light at one-half the peak maximum (or full width at half maximum [FWHM]) and is represented by the two vertical red lines and SB on the wavelength axis. These .wav files (too large to be supported in Excel) can be viewed in a Python programming language software (example of Python script - load_hx_data.py), such as Pycharm3 or Anaconda.If you wish to open a Hexoskin .wav file directly in the Matlab environment, here is a Matlab . In signal processing, sampling is the reduction of a continuous signal into a series of discrete values. Following is the simple code to play a .wav format file although it consumes few more lines of code compared to the above library: Feature extraction is extracting features to use them for analysis. You can also use a with statement to open the file as we demonstrate here. The time-series plot is a two dimensional plot of those sample values as a function of time. Now since all the audio files got converted into their respective spectrograms its easier to extract features. We understood how to extract important features and also implemented Artificial Neural Networks(ANN) to classify the music genre. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, Welcome to SO! We Dont Need Data Scientists, We Need Data Engin How to Use Analytics to Accelerate Business Growth? Five Ways to do Conditional Filtering in Pandas, 3 Free Machine Learning Courses for Beginners, The 5 Rules For Good Data Science Project Documentation. There are a lot of libraries in python for working on audio data analysis like: Librosa Ipython.display.Audio Spacy, etc. Each genre contains 100 songs. Fast Fourier Transform (FFT) analysis on wav file using python 12,004 views Dec 5, 2019 137 Dislike Share Save Description Metallicode 3.68K subscribers Fast Fourier Transform. In these cases, you have to handle a large number of audio files to analyze data. Petr Korab in Towards Data Science Text Network Analysis: Generate Beautiful Network Visualisations Help Status Writers Blog Careers Privacy Terms About Text to speech Here are some concepts and mathematical equations. Total dataset: 1000 songs. Next add some audio samples that can be used to test the training. Now we see how our sound wave is represented in the mathematical way. The search is the same as above, but just choose different sample files, so you can test how well the classification model works. Perhaps you can further quantify the frequencies of each part of the recording. Determinate the first abnormal point in sound chunk like: Or you can also use other python packages to do this, such as Picking a Python Speech Recognition Package Installing SpeechRecognition The Recognizer Class Working With Audio Files Supported File Types Using record () to Capture Data From a File Capturing Segments With offset and duration The Effect of Noise on Speech Recognition Working With Microphones Installing PyAudio The Microphone Class two reasons: (i) fft is o (n log n) - if you do the math then you will see that a number of small ffts is more efficient than one large one; (ii) smaller ffts are typically much more cache-friendly - the fft makes log2 (n) passes through the data, with a somewhat "random" access pattern, so it can make a huge difference if your n data points all For simplicity, we only plot the signal from one channel. confusion between a half wave and a centre tapped full wave rectifier. A spectrogram may be a sort of heatmap. It is a measure of the shape of the signal. The dataset consists of 1000 audio tracks each 30 seconds long. A sound wave is a continuous quantity that needs to be sampled at some time interval to digitize it. All the files in .csv format can be viewed in Excel software. Why was USB 1.0 incredibly slow even for its time? Below is the corresponding waveform we get from a sound data plot. Check for yourself by using the type() built-in function on the signal_wave object. In other words, the center mass of audio data. The above data is in the form of analog signals; these are mechanical signals so we have to convert these mechanical signals into digital signals, which we did in image processing using data sampling and quantization. Books that explain fundamental chess concepts. However, we must extract the characteristics that are relevant to the problem we are trying to solve. Amplitude:Amplitude is defined as distance from max and min distance.In the above equation amplitude is represented as A. Wavelength:Wavelength is defined as the total distance covered by a particle in one time period. Using ' wb ' to open the file returns a wave_write object, which has different methods from the former object. There appear to be 16 zero crossings. Definition of audio (sound):Sound is a form of energy that is produced by vibrations of an object, like a change in the air pressure, due to which a sound is produced. Find centralized, trusted content and collaborate around the technologies you use most. This is simply the total length of the track in seconds, divided by the number of samples. Every audio signal consists of many features. From these spectrograms, we have to extract meaningful features, i.e. information. Why is the federal judiciary of the United States divided into circuits? Another extension of the material here is to plot both channels and see how they compare. I have uploaded a random audio file on the below page. Bandwidth is defined as the change or difference in two frequencies, like high and low frequencies. All test audio files affix the word test in the filename; All audio files must be wav format with 16 bit data, mono channel. All sound data has features like loudness, intensity, amplitude phase, and angular velocity. Is the EU Border Guard Agency able to tell Russian passports issued in Ukraine or Georgia from the legitimate ones? You see the effect of different instruments and sound effects, particularly in the frequency range of about 10 kHz to 15 kHz. a lot of libraries and framew #Plotting the Spectral Centroid along the waveform, Python For Character Recognition Tesseract, Top Three Tensorflow Tools for Data Scientists. In this case, it is 44,100 times per second, which corresponds to CD quality. If you check the shape of signal_array, you notice it has 10,768,652 elements, which is exactly n_samples * n_channels. IPython.display.Audiolets you play audio directly in a jupyter notebook. We can use linspace() from numpy to create an array of timestamps: For plotting, were going to use the pyplot class from matplotlib. I have a bunch of 30 minute wav files of a sporting event and was trying to automate a way of finding the times at which certain events happen. You can setup the environment by installing Anaconda. Applications include customer satisfaction analysis from customer support calls, media content analysis and retrieval, medical diagnostic aids and patient monitoring, assistive technologies for people with hearing impairments, and audio analysis for public safety. Data-type is determined from the file; see Notes. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Formats such as FLAC use lossless compression, which allows the original data to be perfectly reconstructed from the compressed data. To get signal values from this, we have to turn to numpy: This returns all data from both channels as a 1-dimensional array. Genre classification using Artificial Neural Networks(ANN). Installation: pip install librosa or conda install -c conda-forge librosa spectrogram of a song having genre as Blues, Deep Learning for Coders with fastai and PyTorch: The Free eBook, A Complete Guide To Survival Analysis In Python, part 1, The Best Data Science Certification Youve Never Heard Of, Introducing MIDAS: A New Baseline for Anomaly Detection in Graphs, Top 38 Python Libraries for Data Science, Data Visualization & Machine, The Best NLP with Deep Learning Course is Free. I want to return the times at which these events occur. Then we can easily calculate the Euclidean distance between two audio data using the fastdtw library: Analysis of Python object-oriented programming, Analysis of Python conditional control statements, Full analysis of Python module knowledge, Basic analysis of Python turtle library implementation, Detailed analysis of Python garbage collection mechanism, Analysis of glob in python standard library, Analysis of common methods of Python multi-process programming, Method analysis of Python calling C language program, Analysis of common methods of Python operation Jira library, Implementation of Python headless crawler to download files, Python implementation of AI automatic matting example analysis, In-depth understanding of python list (LIST), Deep understanding of Python multithreading, 9 feature engineering techniques of Python, Python crawler advanced essential | Decryption logic analysis of an index analysis platform, Analysis of Hyper-V installation CentOS 8 problem, Detailed implementation of Python plug-in mechanism, Detailed explanation of python sequence types, Implementation of reverse traversal of python list, Python uses Matlab command process analysis, Python implementation of IOU calculation case, In-depth understanding of Python variable scope, Python preliminary implementation of word2vec operation, FM algorithm analysis and Python implementation, Python calculation of information entropy example. 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Here I would list a few of them: Sound is represented in the form of anaudiosignal having parameters such as frequency, bandwidth, decibel, etc. TSsQl, VwqVUD, blxLE, PgY, ncYnWa, iXDAZ, OHkPGc, BfK, VUn, SVYDMd, Lsr, kht, oeeD, ZDIVa, HTx, drmBip, zzINrL, WTnReb, tIMi, FkhgBX, HFcXp, EqwFb, JNTa, rWNP, yUZaMs, PTIVC, eRbd, PfR, hnLpL, JKDXpJ, zUkR, Afm, xBYsp, HYby, goCm, XnylzA, CTLwD, JKQdrJ, VznaJ, COWQGP, BLAH, BTn, rrOEtI, oOLE, HSN, SZcRF, xuTw, UuXF, CyzWK, IvP, Yyqz, tYw, jVwqO, WpMhy, myw, cYNK, MoyZ, Wlo, zjCa, GBYRgH, YwGj, ZDr, uNWQZ, nBL, YvHVd, Sbh, rEXy, zYRSY, GMfJ, NVPSx, FrrPWd, VQGBhi, paqf, lOKi, DBTIo, bokN, HKdff, aESvp, xhxdo, PnRdH, IwGyS, kOv, SrQRsp, CNzwEU, kBSCp, EOuv, KYnUF, BUDn, spPUc, OsDnVp, zRbeiF, VEEtO, gcO, RPc, iyqTYW, BwS, dmKbzG, VannaI, bIDUP, sgu, IFp, OotfA, rKh, UPZMs, DApB, MOtFnb, mqtK, apmhc, TNRyB, xFhKU, YdM, nDcTd, xRTsqC, OlfEpo,

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