├── .gitignore ├── 1 - Wave Forms.ipynb ├── 2 - Time Domain Features.ipynb ├── 3 - Fourier Transformations.ipynb ├── 4 - Spectrograms.ipynb ├── 5 - The Mel Scale, Mel Spectrograms, and Mel Frequency Cepstral Coefficients.ipynb ├── 6 - Pitch Notation and Chromagrams.ipynb ├── README.md ├── environment.yml ├── images ├── 3spec.png ├── fft.gif ├── ft1.png ├── ft2.png ├── ft3.png ├── gspec.png ├── kspec.png ├── maineqfft.png ├── newMelSpec.png ├── splitfft.png └── sspec.png ├── snip_audio.py └── snippets └── .gitignore /.gitignore: -------------------------------------------------------------------------------- 1 | .ipynb_checkpoints/ 2 | .vscode/* -------------------------------------------------------------------------------- /3 - Fourier Transformations.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Introduction\n", 8 | "\n", 9 | "In Wave Forms, we looked at what waves are, how to visualize them, and how to deal with null data. In this article, I aim to develop an intuition on what the Fourier Transformation is, why it is useful when studying audio, show mathematical proofs to make it computationally efficient, and visualize the results.\n", 10 | "\n", 11 | "With this in mind, let's begin. We will first initialize our necessary variables and packages below:" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 1, 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "# Import necessary packages\n", 21 | "%matplotlib inline\n", 22 | "import librosa\n", 23 | "import librosa.display\n", 24 | "import numpy as np\n", 25 | "from matplotlib import pyplot as plt" 26 | ] 27 | }, 28 | { 29 | "cell_type": "code", 30 | "execution_count": 2, 31 | "metadata": {}, 32 | "outputs": [], 33 | "source": [ 34 | "# Load necessary variables\n", 35 | "rb, sr = librosa.load('snippets/rb.wav')\n", 36 | "rap, _ = librosa.load('snippets/rap.wav')\n", 37 | "rock, _ = librosa.load('snippets/rock.wav')" 38 | ] 39 | }, 40 | { 41 | "cell_type": "markdown", 42 | "metadata": {}, 43 | "source": [ 44 | "## Recall from Wave Forms:" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 3, 50 | "metadata": {}, 51 | "outputs": [ 52 | { 53 | "data": { 54 | "image/png": "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\n", 55 | "text/plain": [ 56 | "
" 57 | ] 58 | }, 59 | "metadata": { 60 | "needs_background": "light" 61 | }, 62 | "output_type": "display_data" 63 | } 64 | ], 65 | "source": [ 66 | "#Visualizing waveforms\n", 67 | "fig, ax = plt.subplots(1,3, figsize = (30,10), sharey = True)\n", 68 | "librosa.display.waveplot(rb, sr=sr, ax=ax[0])\n", 69 | "ax[0].set(title = 'R&B Waveform')\n", 70 | "librosa.display.waveplot(rap, sr=sr, ax=ax[1])\n", 71 | "ax[1].set(title = 'Rap Waveform')\n", 72 | "librosa.display.waveplot(rock, sr=sr, ax=ax[2])\n", 73 | "ax[2].set(title = 'Rock Waveform')\n", 74 | "plt.show()" 75 | ] 76 | }, 77 | { 78 | "cell_type": "markdown", 79 | "metadata": {}, 80 | "source": [ 81 | "# Fourier Transformation:\n", 82 | "\n", 83 | "\n", 84 | "## What is the Fourier Transformation and Why is it Important?\n", 85 | "\n", 86 | "\n", 87 | "When looking at the figure above, we can see various spikes of amplitudes creating a unique shape to each sound. This is because all complex sounds - like these recorded instruments - are really just a sum of many sine and cosine signals. The `gif` below presents a nice visualization of the objective:\n", 88 | "\n", 89 | "![](images/fft.gif)\n", 90 | "\n", 91 | "It is important to note that by using this transformation, we will be translating the audio from the time-domain to the frequency-domain. Here are some key points about the two:\n", 92 | "\n", 93 | "* The time domain looks at the variation of the signal's amplitude over time. This is useful for understanding its physical shape. In order to plot this, we need time on the x-axis and amplitude on the y-axis. The shape gives us a good idea of how loud or quiet the sound will be.\n", 94 | "\n", 95 | "* The frequency domain observes the constituent signals our recording is comprised of. By doing this, we can find a sort of \"fingerprint\" of the sound. In order to plot this, we need frequency on the x-axis and magnitude on the y-axis. The larger the magnitude, the more important that frequency is. The magnitude is simply the absolute value of our results from the FFT, $\\hat{x}$.\n", 96 | "\n", 97 | "The Discrete Fourier Transformation (DFT,) which is what `numpy` builds off of, is as follows:" 98 | ] 99 | }, 100 | { 101 | "cell_type": "markdown", 102 | "metadata": {}, 103 | "source": [ 104 | "$$ \\hat x_k = \\frac{1}{\\sqrt{N}} \\displaystyle\\sum_{n=0}^{N-1} x_n \\omega_{N}^{nk} $$" 105 | ] 106 | }, 107 | { 108 | "cell_type": "markdown", 109 | "metadata": {}, 110 | "source": [ 111 | " where\n", 112 | "$$ \\omega_{N} = \\exp \\left(\\frac{-2 \\pi i}{N} \\right) \\\\ k = 0, 1, \\dots, (N - 1$$\n", 113 | " \n", 114 | "\n" 115 | ] 116 | }, 117 | { 118 | "cell_type": "markdown", 119 | "metadata": {}, 120 | "source": [ 121 | "Equations from [Derek L. Smith from the University of California, Santa Barbara.](https://web.math.ucsb.edu/~dls/Expository/2014-10-FFT.pdf)" 122 | ] 123 | }, 124 | { 125 | "cell_type": "markdown", 126 | "metadata": {}, 127 | "source": [ 128 | "From the equation above, we can see that the Fourier Transformation is a sum of products: $x$ - which is real - and $\\omega$ - which is imaginary.\n", 129 | "\n", 130 | "When learning about the Fourier Transformation, you may see a continuous version - which utilizes an integral as opposed to a sum - suitably called the Continuous Fourier Transform. The discrete version of the transformation is generally used for computers' operations. In fact, even in the discrete form, most computers still lack the proper computational power to solve for the transformation as the raw equation below. The DFT has some nice properties that allow for computational ease, known as the Fast Fourier Transformation.\n", 131 | "\n", 132 | "\n", 133 | "\n", 134 | "## Fast Fourier Transformation (FFT)\n", 135 | "\n", 136 | "The FFT is as follows:\n", 137 | "\n", 138 | "$$ \\hat x_k = \\frac{1}{\\sqrt{N}} \\displaystyle\\sum_{n=0}^{m-1} x_{2n} \\omega_{m}^{nk} + \\frac{\\omega_N}{\\sqrt{N}} \\displaystyle\\sum_{n = 0}^{m-1}x_{2n+1} \\omega_{m}^{nk}$$" 139 | ] 140 | }, 141 | { 142 | "cell_type": "markdown", 143 | "metadata": {}, 144 | "source": [ 145 | "Do not let the notation worry you. In the Discrete Fourier Transformation, you are dealing with a sum of products x, and omega. This equation splits sum of products into two - one along the odd indices and another along the even. This procedure above can be modeled much more efficiently with computer processes as opposed to the DFT.\n", 146 | "\n", 147 | "Now, let's visualize what the FFT looks like. In our application, we will be using the `numpy.fft.fft` function and apply it to our sound waves." 148 | ] 149 | }, 150 | { 151 | "cell_type": "code", 152 | "execution_count": 4, 153 | "metadata": {}, 154 | "outputs": [], 155 | "source": [ 156 | "def fft_components(sound):\n", 157 | " # Find FFT\n", 158 | " fft = np.fft.fft(sound)\n", 159 | " # Find magnitude\n", 160 | " mag = np.abs(np.real(fft))\n", 161 | " # Find frequency\n", 162 | " freq = np.linspace(0,sr, len(mag))\n", 163 | " return mag, freq" 164 | ] 165 | }, 166 | { 167 | "cell_type": "code", 168 | "execution_count": 5, 169 | "metadata": { 170 | "scrolled": false 171 | }, 172 | "outputs": [ 173 | { 174 | "data": { 175 | "image/png": "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\n", 176 | "text/plain": [ 177 | "
" 178 | ] 179 | }, 180 | "metadata": { 181 | "needs_background": "light" 182 | }, 183 | "output_type": "display_data" 184 | } 185 | ], 186 | "source": [ 187 | "# Using function from above to find components\n", 188 | "rb_mag, rb_freq = fft_components(rb)\n", 189 | "rap_mag, rap_freq = fft_components(rap)\n", 190 | "rock_mag, rock_freq = fft_components(rock)\n", 191 | "\n", 192 | "# Visualize the FFT\n", 193 | "fig, ax = plt.subplots(1,3, figsize = (30,10))\n", 194 | "# Plotting the guitar\n", 195 | "ax[0].plot(rb_freq, rb_mag)\n", 196 | "ax[0].set(title = 'R&B Fast Fourier Transform')\n", 197 | "# Plotting the kick drum\n", 198 | "ax[1].plot(rap_freq, rap_mag)\n", 199 | "ax[1].set(title = 'Rap Fast Fourier Transform')\n", 200 | "# Plotting the snare\n", 201 | "ax[2].plot(rock_freq, rock_mag)\n", 202 | "ax[2].set(title = 'Rock Fast Fourier Transform')\n", 203 | "plt.show()" 204 | ] 205 | }, 206 | { 207 | "cell_type": "markdown", 208 | "metadata": {}, 209 | "source": [ 210 | "Again, there are some slight problems with the visualizations. However, this is a quite easy fix as the DFT has a symmetric property. This makes sense as our visualization on the left half mimics the visualizations on the right half. We will slice the arrays in half and visualize from there." 211 | ] 212 | }, 213 | { 214 | "cell_type": "code", 215 | "execution_count": 6, 216 | "metadata": {}, 217 | "outputs": [ 218 | { 219 | "data": { 220 | "image/png": "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\n", 221 | "text/plain": [ 222 | "
" 223 | ] 224 | }, 225 | "metadata": { 226 | "needs_background": "light" 227 | }, 228 | "output_type": "display_data" 229 | } 230 | ], 231 | "source": [ 232 | "rb_half = int(len(rb_mag) / 2)\n", 233 | "rap_half = int(len(rap_mag) / 2)\n", 234 | "rock_half = int(len(rock_mag) / 2)\n", 235 | "\n", 236 | "# Visualize the FFT\n", 237 | "fig, ax = plt.subplots(1,3, figsize = (30,10))\n", 238 | "# Plotting the guitar\n", 239 | "ax[0].plot(rb_freq[:rb_half], rb_mag[:rb_half])\n", 240 | "ax[0].set(title = 'R&B Fast Fourier Transform')\n", 241 | "# Plotting the kick drum\n", 242 | "ax[1].plot(rap_freq[:rap_half], rap_mag[:rap_half])\n", 243 | "ax[1].set(title = 'Rap Fast Fourier Transform')\n", 244 | "# Plotting the snare\n", 245 | "ax[2].plot(rock_freq[:rock_half], rock_mag[:rock_half])\n", 246 | "ax[2].set(title = 'Rock Fast Fourier Transform')\n", 247 | "plt.show()" 248 | ] 249 | } 250 | ], 251 | "metadata": { 252 | "kernelspec": { 253 | "display_name": "learningfromaudio", 254 | "language": "python", 255 | "name": "learningfromaudio" 256 | }, 257 | "language_info": { 258 | "codemirror_mode": { 259 | "name": "ipython", 260 | "version": 3 261 | }, 262 | "file_extension": ".py", 263 | "mimetype": "text/x-python", 264 | "name": "python", 265 | "nbconvert_exporter": "python", 266 | "pygments_lexer": "ipython3", 267 | "version": "3.8.5" 268 | }, 269 | "metadata": { 270 | "interpreter": { 271 | "hash": "daa9462c8d52fd61934684c63912052d2050563016e4520e1fee28be975d0554" 272 | } 273 | } 274 | }, 275 | "nbformat": 4, 276 | "nbformat_minor": 4 277 | } 278 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Learning from Audio 2 | ![image](images/newMelSpec.png) 3 | 4 | Learning from Audio is a series of Medium articles written by Adam Sabra. Its main objective is to help those in the Data Science/Machine Learning field break into the audio domain starting from the basics of signal processing and build up towards more complex topics. 5 | 6 | This GitHub repository will host the code and figures used within the articles. 7 | 8 | # Important Note: 9 | You will find a discrepancy between the Medium articles and the Jupyter notebooks. **I highly suggest using the Jupyter notebooks for more consistency.** 10 | 11 | I switched out the studied files from instrument sounds to high resolution snippets of different genres. **The published Medium articles still have the old data whereas the Jupyter notebooks have been updated.** 12 | 13 | Furthermore, because of copyright issues, I am not allowed to share the snippets. However, I have created a command line interface via `snip_audio.py` for you to create your own snippets. Usage on the CLI will be below. 14 | 15 | # Environment Creation 16 | As you will see in the repository there is a `environment.yml` file. Please use this to set up your virtual environment and create a Jupyter kernel with the newly created environment. To create the environment, run the following: 17 | 18 | ``` 19 | $ conda env create -f environment.yml 20 | ``` 21 | 22 | Once the environment has been created, simply activate it. 23 | 24 | ``` 25 | $ conda activate med-audio 26 | ``` 27 | 28 | If you do not know how to link an environment to a kernel, [this article will be useful for you.](https://towardsdatascience.com/link-your-virtual-environment-to-jupyter-with-kernels-a69bc61728df?) 29 | 30 | # Create Snippets via Command Line 31 | 32 | As mentioned before, I cannot share the snippets within the Jupyter notebooks. However, thanks to an issue being raised, I decided to create a quick and easy CLI for you to create your own snippets. The following tutorial assumes you are in the `med-audio` environment and in the repository's directory. 33 | 34 | The required parameters are the following: 35 | 36 | - `-p`: The path to the audio file being sliced. (`str`) 37 | - `-n`: Name of the file to be saved. For your own sake, save it as something simple, such as `rb`, `song1`, etc. The `.wav` extension will be added for you already. (`str`) 38 | - `-sr`: Sample rate of the audio to be loaded in. (`int`) 39 | - `-sec`: Number of seconds you want the snippet to be. (`int`) 40 | 41 | To run the file, simply run the following: 42 | 43 | ``` 44 | (med-audio)$ python3 snip_audio.py -p "path/to/song" -n "song1" -sr 22050 -sec 3 45 | ``` 46 | 47 | The above example will save a new 3 second long file called `song1.wav` and it will be saved in the `snippets` directory. 48 | 49 | # Links to Articles: 50 | - [Learning from Audio: Wave Forms](https://towardsdatascience.com/learning-from-audio-wave-forms-46fc6f87e016#60b2-e67809770e17) 51 | - [Learning from Audio: Time Domain Features](https://towardsdatascience.com/learning-from-audio-time-domain-features-4543f3bda34c) 52 | - [Learning from Audio: Fourier Transformation](https://towardsdatascience.com/learning-from-audio-fourier-transformations-f000124675ee) 53 | - [Learning from Audio: Spectrograms](https://towardsdatascience.com/learning-from-audio-spectrograms-37df29dba98c) 54 | - [Learning from Audio: The Mel Scale, Mel Spectrograms, and Mel Frequency Cepstral Coefficients](https://towardsdatascience.com/learning-from-audio-the-mel-scale-mel-spectrograms-and-mel-frequency-cepstral-coefficients-f5752b6324a8) 55 | - [Learning from Audio: Pitch and Chromagrams](https://towardsdatascience.com/learning-from-audio-pitch-and-chromagrams-5158028a505) 56 | 57 | -------------------------------------------------------------------------------- /environment.yml: -------------------------------------------------------------------------------- 1 | name: med-audio 2 | channels: 3 | - defaults 4 | dependencies: 5 | - _libgcc_mutex=0.1=main 6 | - ca-certificates=2020.7.22=0 7 | - certifi=2020.6.20=py38_0 8 | - ld_impl_linux-64=2.33.1=h53a641e_7 9 | - libedit=3.1.20191231=h14c3975_1 10 | - libffi=3.3=he6710b0_2 11 | - libgcc-ng=9.1.0=hdf63c60_0 12 | - libstdcxx-ng=9.1.0=hdf63c60_0 13 | - ncurses=6.2=he6710b0_1 14 | - openssl=1.1.1g=h7b6447c_0 15 | - pip=20.2.2=py38_0 16 | - python=3.8.5=h7579374_1 17 | - readline=8.0=h7b6447c_0 18 | - setuptools=49.6.0=py38_0 19 | - sqlite=3.33.0=h62c20be_0 20 | - tk=8.6.10=hbc83047_0 21 | - wheel=0.35.1=py_0 22 | - xz=5.2.5=h7b6447c_0 23 | - zlib=1.2.11=h7b6447c_3 24 | - pip: 25 | - appdirs==1.4.4 26 | - argon2-cffi==20.1.0 27 | - async-generator==1.10 28 | - attrs==20.2.0 29 | - audioread==2.1.8 30 | - backcall==0.2.0 31 | - bleach==3.2.1 32 | - cffi==1.14.2 33 | - chardet==3.0.4 34 | - cycler==0.10.0 35 | - decorator==4.4.2 36 | - defusedxml==0.6.0 37 | - entrypoints==0.3 38 | - idna==2.10 39 | - ipykernel==5.3.4 40 | - ipython==7.18.1 41 | - ipython-genutils==0.2.0 42 | - ipywidgets==7.5.1 43 | - jedi==0.17.2 44 | - jinja2==2.11.2 45 | - joblib==0.16.0 46 | - jsonschema==3.2.0 47 | - jupyter==1.0.0 48 | - jupyter-client==6.1.7 49 | - jupyter-console==6.2.0 50 | - jupyter-core==4.6.3 51 | - jupyterlab-pygments==0.1.1 52 | - kiwisolver==1.2.0 53 | - librosa==0.8.0 54 | - llvmlite==0.34.0 55 | - markupsafe==1.1.1 56 | - matplotlib==3.3.2 57 | - mistune==0.8.4 58 | - nbclient==0.5.0 59 | - nbconvert==6.0.5 60 | - nbformat==5.0.7 61 | - nest-asyncio==1.4.0 62 | - notebook==6.1.4 63 | - numba==0.51.2 64 | - numpy==1.19.2 65 | - packaging==20.4 66 | - pandocfilters==1.4.2 67 | - parso==0.7.1 68 | - pexpect==4.8.0 69 | - pickleshare==0.7.5 70 | - pillow==7.2.0 71 | - pooch==1.2.0 72 | - prometheus-client==0.8.0 73 | - prompt-toolkit==3.0.7 74 | - ptyprocess==0.6.0 75 | - pycparser==2.20 76 | - pydub==0.25.1 77 | - pygments==2.7.1 78 | - pyparsing==2.4.7 79 | - pyrsistent==0.17.3 80 | - python-dateutil==2.8.1 81 | - pyzmq==19.0.2 82 | - qtconsole==4.7.7 83 | - qtpy==1.9.0 84 | - requests==2.24.0 85 | - resampy==0.2.2 86 | - scikit-learn==0.23.2 87 | - scipy==1.5.2 88 | - send2trash==1.5.0 89 | - six==1.15.0 90 | - soundfile==0.10.3.post1 91 | - terminado==0.9.1 92 | - testpath==0.4.4 93 | - threadpoolctl==2.1.0 94 | - tornado==6.0.4 95 | - traitlets==5.0.4 96 | - urllib3==1.25.10 97 | - wcwidth==0.2.5 98 | - webencodings==0.5.1 99 | - widgetsnbextension==3.5.1 100 | prefix: /home/asabra/anaconda3/envs/med-audio 101 | -------------------------------------------------------------------------------- /images/3spec.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theadamsabra/LearningfromAudio/93cfc6a24c282d63515490f7bcd3162fbf56bc8a/images/3spec.png -------------------------------------------------------------------------------- /images/fft.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theadamsabra/LearningfromAudio/93cfc6a24c282d63515490f7bcd3162fbf56bc8a/images/fft.gif -------------------------------------------------------------------------------- /images/ft1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theadamsabra/LearningfromAudio/93cfc6a24c282d63515490f7bcd3162fbf56bc8a/images/ft1.png -------------------------------------------------------------------------------- /images/ft2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theadamsabra/LearningfromAudio/93cfc6a24c282d63515490f7bcd3162fbf56bc8a/images/ft2.png -------------------------------------------------------------------------------- /images/ft3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theadamsabra/LearningfromAudio/93cfc6a24c282d63515490f7bcd3162fbf56bc8a/images/ft3.png -------------------------------------------------------------------------------- /images/gspec.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theadamsabra/LearningfromAudio/93cfc6a24c282d63515490f7bcd3162fbf56bc8a/images/gspec.png -------------------------------------------------------------------------------- /images/kspec.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theadamsabra/LearningfromAudio/93cfc6a24c282d63515490f7bcd3162fbf56bc8a/images/kspec.png -------------------------------------------------------------------------------- /images/maineqfft.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theadamsabra/LearningfromAudio/93cfc6a24c282d63515490f7bcd3162fbf56bc8a/images/maineqfft.png -------------------------------------------------------------------------------- /images/newMelSpec.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theadamsabra/LearningfromAudio/93cfc6a24c282d63515490f7bcd3162fbf56bc8a/images/newMelSpec.png -------------------------------------------------------------------------------- /images/splitfft.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theadamsabra/LearningfromAudio/93cfc6a24c282d63515490f7bcd3162fbf56bc8a/images/splitfft.png -------------------------------------------------------------------------------- /images/sspec.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theadamsabra/LearningfromAudio/93cfc6a24c282d63515490f7bcd3162fbf56bc8a/images/sspec.png -------------------------------------------------------------------------------- /snip_audio.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import librosa 3 | import os 4 | import numpy as np 5 | from soundfile import write 6 | 7 | description = ''' 8 | Create snippets of audio for Learning from Audio playground in the Jupyter Notebooks. Required arguments are the path to the audio file, the samplerate of the file, 9 | and the number of seconds you want the snippet to be. It will save in the snippets folder, so do not delete it just because it is empty. 10 | ''' 11 | 12 | # Create parser 13 | parser = argparse.ArgumentParser(description=description) 14 | parser.add_argument('-p', '--path', help='Path to audio file to be sliced.', type=str, required=True) 15 | parser.add_argument('-n', '--name', help='Name of snippet. Ideally, keep it to be the genre (rb, rap, rock, etc.) for ease of loading in later.', type=str, required=True) 16 | parser.add_argument('-sr', help='Designate samplerate of audio file.', type=int, required=True) 17 | parser.add_argument('-sec', help='Number of seconds the snippet is.', type=int, required=True) 18 | 19 | # Get arguments 20 | args = parser.parse_args() 21 | 22 | # Check if snippets/ is exists. If it doesn't, create it. 23 | if not os.path.isdir('snippets/'): 24 | os.mkdir('snippets/') 25 | 26 | # Load in audio file and take random sample 27 | song, _ = librosa.load(path = args.path, sr=args.sr) # Keeping mono=True meaning it is a vector. 28 | # Take random point in the song. This will be the start point. 29 | start_point = np.random.choice(song.shape[0]) 30 | # Now, add the amount of time to create snippet 31 | end_point = start_point + args.sr * args.sec 32 | 33 | # Save new file 34 | write(file = f'snippets/{args.name}.wav', data=song[start_point:end_point], samplerate=args.sr) -------------------------------------------------------------------------------- /snippets/.gitignore: -------------------------------------------------------------------------------- 1 | # Ignore all wav files to keep pushing and pulling with different snippets easier. 2 | # However, should you decide to use snip_audio.py for your own songs, it will by default save to this folder. 3 | *.wav --------------------------------------------------------------------------------