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Nateraw’s autotune model on Replicate applies pitch correction to a voice recording. To try it, send the model an audio-file URL and a scale value through Replicate’s API; the result is an audio URL you can retrieve. Replicate also documents local execution with Docker, but its model page and API schema do not establish that this is a DAW plugin or provide the controls of a full vocal-processing suite.

What the model does—and what it does not document

Replicate describes Nateraw’s model as “pitch correction on your voice.” It accepts an audio file and returns an audio URI. The documented interface is a model API, not a graphical plugin workflow.

The available model page and schema do not explain the processing method, correction quality, supported audio formats, maximum recording length, privacy or retention terms, or the license for local use. They also do not document professional controls such as note-level editing or correction-strength settings. Do not assume those details based on other autotune tools.

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What you need before making a prediction

  • A Replicate account and API token. Keep the token private; the API examples use it to authenticate requests.
  • An audio recording accessible to the request as a URL. The model’s schema identifies the input as an audio-file URI; it does not establish file-format or duration limits.
  • A value for the string input named scale. Replicate’s example uses Gb:min, but the schema describes this field as “Strategy for normalizing audio.” The documentation does not clarify how that value affects processing, so treat it as an example rather than assuming a particular musical-key behavior.
  • A way to call the API: Replicate’s client library for Python or Node.js, or an HTTP request.

If you still need to record a voice, an existing recording or a built-in microphone may be enough to create an input file. Replicate does not require a USB microphone or name a compatible model.

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Run the model with Python

  1. Create or access an API token. Use your Replicate account’s API token and configure it as the Replicate client expects. Do not put a secret token in public code or share it with others.
  2. Install the Python client. Follow the current installation instructions for Replicate’s Python package before running the example.
  3. Pass an audio URL and scale value. The example below follows the documented input shape. Replace the example URL with a URL that points to your own audio file; example.com is only a placeholder, not a working test file.
  4. Call the pinned model version and read the returned URL. The version identifier is the one used in Replicate’s API example. The model’s output is a URI; the code prints its URL.
import replicate

output = replicate.run(
    "nateraw/autotune:53d58aea27ccd949e5f9d77e4b2a74ffe90e1fa534295b257cea50f011e233dd",
    input={
        "scale": "Gb:min",
        "audio_file": "https://example.com/your-audio.wav"
    }
)
print(output.url)

Use the Replicate API guide for the current Python, Node.js, and HTTP request examples. Those examples require a Replicate API token and demonstrate retrieving the output audio URL.

Understand the inputs and output

The API schema lists audio_file and scale as inputs. It also lists output_format, with wav as its default, and an output type of URI. The API example demonstrates getting a URL from the returned output. The published material does not establish that every input format or URL source will work, nor does it explain whether changing the scale value changes pitch in a specific way.

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Once the prediction completes, open or download the returned audio URL to listen to the result. If the response is an error rather than an output URI, check that your token is configured, the input URL is accessible to the service, and the request follows the current schema.

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Hosted API or local Docker execution?

For a first attempt, the hosted Replicate API is the most directly documented route: provide the inputs, run a prediction, and retrieve the result. Replicate says the model runs on CPU and can also be run locally with Docker. Its page notes that the first hosted call may take longer while the model boots.

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The model page does not provide a README, and the reviewed documentation does not specify local-use licensing or a detailed Docker setup. Check the model’s current page and version instructions before relying on local execution.

Cost and runtime are estimates

On the Replicate model page snapshot accessed October 7, 2026, the displayed estimate was approximately $0.0061 per run and typical completion time was approximately 61 seconds. Replicate says cost and prediction time can vary with inputs, and the figures shown on the page can change; another page snapshot displayed different estimates. Treat these as time-specific estimates, not a guaranteed price or service-level commitment.

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Common beginner questions

Is this an Autotune plugin for a DAW?

The documented offering is a model called through Replicate’s API, with an option for local Docker execution. The reviewed materials do not establish a DAW plugin.

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Can I set a song key or tune strength?

The API has a string input called scale, and the example value is Gb:min. However, the schema describes the field as “Strategy for normalizing audio” and does not explain its behavior. The reviewed interface does not document a separate tune-strength control.

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Does it support my file type or recording length?

The schema identifies an audio-file URI but the reviewed materials do not state accepted formats or duration limits. Check the current model page and API schema before preparing a large or unusual file.

How good is the pitch correction?

The model is described as pitch correction for a voice, but the reviewed official materials do not provide accuracy measurements or quality benchmarks. Listen to the returned audio to decide whether it suits your recording.

Does Replicate keep my audio private?

The reviewed model documentation does not specify privacy, storage, or retention terms for submitted audio. Consult Replicate’s current service terms and privacy documentation before sending sensitive recordings.

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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.