annahung31/emopia – Run with an API on Replicate

annahung31 / emopia

Emotional conditioned music generation using transformer-based model.

Cold

Public
3.2K runs
T4
GitHub
Paper
License

Run with an API

Playground API Examples README Versions

Input

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

npx create-replicate --model=annahung31/emopia

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 annahung31/emopia using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "annahung31/emopia:ad93577dbfe239c7604a49495ac579176157bb2a6f5fa1e0906433fd7acff792",
  {
    input: {
      seed: -1,
      emotion: "High valence, high arousal"
    }
  }
);

// 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 annahung31/emopia using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "annahung31/emopia:ad93577dbfe239c7604a49495ac579176157bb2a6f5fa1e0906433fd7acff792",
    input={
        "seed": -1,
        "emotion": "High valence, high arousal"
    }
)
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 annahung31/emopia 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": "annahung31/emopia:ad93577dbfe239c7604a49495ac579176157bb2a6f5fa1e0906433fd7acff792",
    "input": {
      "seed": -1,
      "emotion": "High valence, high arousal"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Video Player is loading.

Current Time 00:00:000

Duration 00:00:000

Loaded: 0%

Stream Type LIVE

Remaining Time 00:00:000

Tweak itReport

This output was created using a different version of the model, annahung31/emopia:56a03a92.

Run time and cost

This model costs approximately $0.0032 to run on Replicate, or 312 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 Nvidia T4 GPU hardware. Predictions typically complete within 15 seconds. The predict time for this model varies significantly based on the inputs.

Readme

This is a demo accompanying the repository EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation. The paper has been accepted by International Society for Music Information Retrieval Conference 2021.

The piano music was generated by a Transformer-based model and it’s in MIDI format. During the training phase, the model takes a sequence of music token as input and also output a sequence of music token. We use a published piano dataset called AILabs1k7 to pre-train the model, and then use the self-collected dataset EMOPIA to finetune and condition it.

Motivated by CTRL, we prepend an emotion token to each music sequence to make the model aware of the emotion.

The objective and subjective evaluations show that the generation quality becomes more stable when the model is pre-trained with a larger dataset, and our Transformer-based model is capable of generating music with a given target emotion to a certain degree.

There is still room for improvement in the conditioning ability. So take a look at EMOPIA and create some fantastic works using it!