mtg / music-approachability-engagement

Classification of music approachability and engagement

Cold

Public
38.5K runs
CPU
GitHub
License

Run with an API

Playground API Examples README Versions

Input

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Stream Type LIVE

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audio

file

Audio file to process

url

string

Shift + Return to add a new line

YouTube URL to process (overrides audio input)

model_type

string

Regards to the downstream type: 2class, 3class, regression

Default: "regression"

Run this model in Node.js with one line of code:

npx create-replicate --model=mtg/music-approachability-engagement

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run mtg/music-approachability-engagement using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "mtg/music-approachability-engagement:a8816edfa80405aa841c0721cc1d5f722ac949744007dd66660218b6e8933c97",
  {
    input: {
      audio: "https://replicate.delivery/mgxm/3a90482a-2a14-435d-8c26-7cb5cb44b5d8/edm.mp3",
      model_type: "2 classes"
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run mtg/music-approachability-engagement using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "mtg/music-approachability-engagement:a8816edfa80405aa841c0721cc1d5f722ac949744007dd66660218b6e8933c97",
    input={
        "audio": "https://replicate.delivery/mgxm/3a90482a-2a14-435d-8c26-7cb5cb44b5d8/edm.mp3",
        "model_type": "2 classes"
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run mtg/music-approachability-engagement using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "mtg/music-approachability-engagement:a8816edfa80405aa841c0721cc1d5f722ac949744007dd66660218b6e8933c97",
    "input": {
      "audio": "https://replicate.delivery/mgxm/3a90482a-2a14-435d-8c26-7cb5cb44b5d8/edm.mp3",
      "model_type": "2 classes"
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

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Generated in

5.5 seconds

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Run time and cost

This model costs approximately $0.00023 to run on Replicate, or 4347 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on CPU hardware. Predictions typically complete within 3 seconds.

Readme

Classification of music approachability and engagement

This demo runs transfer learning models to estimate music approachability and engagement using effnet-discogs embeddings. We include three model types, providing different outcome formats: two classes, three classes, and regression with continuous values:

two classes: low, and high approachability and engagement.
three classes: low, mid, and high approachability and engagement.
regression: continuous values of approachability and engagement from 0 (low) to 1 (high).

These classifiers were trained on in-house MTG datasets.

Source models

effnet-discogs is an EfficientNet architecture trained to predict music styles for 400 of the most popular Discogs music styles.

Transfer learning models

Our models consist of single-hidden-layer MLPs trained on the considered embeddings.

License

These models are part of Essentia Models made by MTG-UPF and are publicly available under CC by-nc-sa and commercial license.