username116
commented
2 weeks ago I've written these wiki pages to show what I've done with Essentia, and the script I use.
It's written with an automatic translator. The code is written by a beginner. And there are foreign language variable names and messages in the code. Sorry about that.
You can reuse and modify it if you like.
Hi,
This is not a request, it's just to let you know.
I've been able to use Essentia for a few days now, and I can find the same kind of results as with AcousticBrainz. I can calculate several genre and mood tags. It seems to me that the Tensorflow models are replacing the old Gaia models (I haven't made a comparison though).
I can use Essentia in 3 different ways:
on Windows with Python and Tensorflow I followed this step by step (with a few changes). It takes time. (I don't know if this procedure will always remain valid.) I can give my
.whlfile for windows x64 (the Python wheel). I don't know if it works, it would allow to go directly to step E. It would be much easier.Screenshot
I've copied the messages [here](https://gist.github.com/username116/7d99224ee10206186d054ac16e618036). Here the results are percentages, but it's just a formatting, initially the results are a number between 0 and 1 with many decimals, like [this](https://gist.github.com/username116/1d41da3fd7d9fba0451357092c92b671). 
Example of a python script to calculate a mood
In the working directory of your choice: In a `tracks` subfolder, I've placed my `.flac` audio file (adapt the path according to the name of your audio file). In a `models` subfolder, I've placed the `.pb` and `.json` files of the tensorflow models. (otherwise, you need to adapt the paths) Link to the 'happy' model for this example: [.pb file](https://essentia.upf.edu/models/legacy/classifiers/mood_happy/mood_happy-musicnn-msd-2.pb), [.json file](https://essentia.upf.edu/models/legacy/classifiers/mood_happy/mood_happy-musicnn-msd-2.json). Copy this into a `.py` file, e.g. `my_script.py`: ```python import json from essentia.standard import MonoLoader, TensorflowPredictMusiCNN audio_file = 'tracks/07. Dancing barefoot.flac' audio = MonoLoader(sampleRate=16000, filename=audio_file)() with open('models/mood_happy-musicnn-msd-2.json', 'r') as json_file: metadata = json.load(json_file) activations = TensorflowPredictMusiCNN(graphFilename='models/mood_happy-musicnn-msd-2.pb')(audio) for label, probability in zip(metadata['classes'], activations.mean(axis=0)): print(f'{label}: {100 * probability:.1f}%') ``` (I don't claim it's a good script. Chances are it's a bad one.) In a command prompt, navigate to this working directory with the `cd` command, and launch the script with `my_script.py` in Windows, or `python3 my_script.py` in Ubuntu. A result: ``` happy: 78.5% non_happy: 44.8% ``` The other model files can be found [here](https://essentia.upf.edu/models.html), or [here](https://mtg.github.io/essentia-labs/news/tensorflow/2020/01/16/tensorflow-models-released/) with descriptions, or [here](https://essentia.upf.edu/models/). In the essentia repository, you'll find examples of python scripts and tutorials with Jupyter notebooks (so you can run code directly in the tutorial). Or simply start here: [documentation](https://essentia.upf.edu/documentation.html). Or there are examples at the end of [the article](https://mtg.github.io/essentia-labs/news/tensorflow/2020/01/16/tensorflow-models-released/).
installation in WSL (Windows Subsystem for Linux) with Python and Tensorflow (I'm on Windows 10)
Details
(maybe there's a Windows feature to enable first elsewhere) [Install WSL](https://learn.microsoft.com/windows/wsl/install) in PowerShell as administrator: ``` wsl --install ``` Possibly in PowerShell, if you get an error message: ``` wsl --update ``` If Ubuntu is not installed automatically (you know it's installed when it asks you to choose a username and password): ``` wsl --install -d Ubuntu ``` I had some difficulties with Debian to install Essentia, so I recommend Ubuntu (it's the default distribution). In an Ubuntu terminal, install Python: ``` sudo apt install python3-pip ``` Install essentia with [this command](https://pypi.org/project/essentia-tensorflow/): ``` pip install essentia-tensorflow ```
Screenshot
The same script in an Ubuntu terminal: 
with essentia.js and converted Tensorflow models
Details
I've modified this [demo page](https://mtg.github.io/essentia.js/examples/demos/mood-classifiers/) a little to use other genre and mood models. My version [is here](https://username116.github.io/essentia.js/examples/demos/mood-classifiers/index.html) ([repository](https://github.com/username116/essentia.js/tree/edit)). You need to open the console and look at the `*`. What I've done is a rough draft, then I found the other methods, so I didn't go much further. I'm not sure it works well in Firefox, apparently there are [differences](https://mtg.github.io/essentia.js/docs/api/tutorial-3.%20Machine%20learning%20inference%20with%20Essentia.js.html#tensorflowjs-backends) in how Firefox and Chrome work, so it's best to try it in a Chrome-based browser (Vivaldi in my case). I manage to run this page locally with some kind of server (the Live Preview extension in VS Codium), otherwise there are errors because of the scripts that are downloaded. I haven't looked any further. You need converted models for essentia.js (`.bin` + `.json`). Most of them are already on the [models repository](https://essentia.upf.edu/models/). Otherwise I've managed to do some conversions following [this tutorial](https://github.com/MTG/essentia.js/wiki/Converting-Essentia-TensorFlow-Models), but it doesn't work with all models.