charlesmccarthy/musictunes – Run with an API on Replicate

charlesmccarthy / musictunes

Cold

Public
1 run
L40S

Run with an API

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

A description of the music you want to generate.

input_audio

file

An audio file that will influence the generated music. If `continuation` is `True`, the generated music will be a continuation of the audio file. Otherwise, the generated music will mimic the audio file's melody.

duration

integer

Duration of the generated audio in seconds.

Default: 8

continuation

boolean

If `True`, generated music will continue `melody`. Otherwise, generated music will mimic `audio_input`'s melody.

Default: false

continuation_start

integer

(minimum: 0)

Start time of the audio file to use for continuation.

Default: 0

continuation_end

integer

(minimum: 0)

End time of the audio file to use for continuation. If -1 or None, will default to the end of the audio clip.

multi_band_diffusion

boolean

If `True`, the EnCodec tokens will be decoded with MultiBand Diffusion. Only works with non-stereo models.

Default: false

normalization_strategy

string

Strategy for normalizing audio.

Default: "loudness"

top_k

integer

Reduces sampling to the k most likely tokens.

Default: 250

top_p

number

Reduces sampling to tokens with cumulative probability of p. When set to `0` (default), top_k sampling is used.

Default: 0

temperature

number

Controls the 'conservativeness' of the sampling process. Higher temperature means more diversity.

Default: 1

classifier_free_guidance

integer

Increases the influence of inputs on the output. Higher values produce lower-varience outputs that adhere more closely to inputs.

Default: 3

output_format

string

Output format for generated audio.

Default: "wav"

seed

integer

Seed for random number generator. If None or -1, a random seed will be used.

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

npx create-replicate --model=charlesmccarthy/musictunes

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

const output = await replicate.run(
  "charlesmccarthy/musictunes:1e9110715dc2dc09fb79a58bf34a73e42e49cf8e9157c9feba20b98d2e77f0ef",
  {
    input: {
      top_k: 250,
      top_p: 0,
      duration: 8,
      temperature: 1,
      continuation: false,
      output_format: "wav",
      continuation_start: 0,
      multi_band_diffusion: false,
      normalization_strategy: "loudness",
      classifier_free_guidance: 3
    }
  }
);
console.log(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 charlesmccarthy/musictunes using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "charlesmccarthy/musictunes:1e9110715dc2dc09fb79a58bf34a73e42e49cf8e9157c9feba20b98d2e77f0ef",
    input={
        "top_k": 250,
        "top_p": 0,
        "duration": 8,
        "temperature": 1,
        "continuation": False,
        "output_format": "wav",
        "continuation_start": 0,
        "multi_band_diffusion": False,
        "normalization_strategy": "loudness",
        "classifier_free_guidance": 3
    }
)
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 charlesmccarthy/musictunes 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": "1e9110715dc2dc09fb79a58bf34a73e42e49cf8e9157c9feba20b98d2e77f0ef",
    "input": {
      "top_k": 250,
      "top_p": 0,
      "duration": 8,
      "temperature": 1,
      "continuation": false,
      "output_format": "wav",
      "continuation_start": 0,
      "multi_band_diffusion": false,
      "normalization_strategy": "loudness",
      "classifier_free_guidance": 3
    }
  }' \
  https://api.replicate.com/v1/predictions

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

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/charlesmccarthy/musictunes@sha256:1e9110715dc2dc09fb79a58bf34a73e42e49cf8e9157c9feba20b98d2e77f0ef \
  -i 'top_k=250' \
  -i 'top_p=0' \
  -i 'duration=8' \
  -i 'temperature=1' \
  -i 'continuation=false' \
  -i 'output_format="wav"' \
  -i 'continuation_start=0' \
  -i 'multi_band_diffusion=false' \
  -i 'normalization_strategy="loudness"' \
  -i 'classifier_free_guidance=3'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/charlesmccarthy/musictunes@sha256:1e9110715dc2dc09fb79a58bf34a73e42e49cf8e9157c9feba20b98d2e77f0ef
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "top_k": 250,
      "top_p": 0,
      "duration": 8,
      "temperature": 1,
      "continuation": false,
      "output_format": "wav",
      "continuation_start": 0,
      "multi_band_diffusion": false,
      "normalization_strategy": "loudness",
      "classifier_free_guidance": 3
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

No output yet! Press "Submit" to start a prediction.

Run time and cost

This model runs on Nvidia L40S GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

This model doesn't have a readme.