lucataco/wsrglow | Run with an API on Replicate

Input

Video Player is loading.

Current Time 00:00:000

Duration 00:00:000

Loaded: 0%

Stream Type LIVE

Remaining Time 00:00:000

input

*file

Low-sample rate input file in .wav format

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

npx create-replicate --model=lucataco/wsrglow

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";
import fs from "node:fs";

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

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

const output = await replicate.run(
  "lucataco/wsrglow:56ffe617ab2157bd7a43156860529c83abbe862e2f4fd64263d217d2efac34c1",
  {
    input: {
      input: "https://replicate.delivery/pbxt/JAmpe8J6WAi5YUdXxxatYFU9ZfKKANtSSBfxHjjdbdaMwyXT/demo.wav"
    }
  }
);

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

output = replicate.run(
    "lucataco/wsrglow:56ffe617ab2157bd7a43156860529c83abbe862e2f4fd64263d217d2efac34c1",
    input={
        "input": "https://replicate.delivery/pbxt/JAmpe8J6WAi5YUdXxxatYFU9ZfKKANtSSBfxHjjdbdaMwyXT/demo.wav"
    }
)

# To access the file URL:
print(output.url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output.read())

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 lucataco/wsrglow 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": "lucataco/wsrglow:56ffe617ab2157bd7a43156860529c83abbe862e2f4fd64263d217d2efac34c1",
    "input": {
      "input": "https://replicate.delivery/pbxt/JAmpe8J6WAi5YUdXxxatYFU9ZfKKANtSSBfxHjjdbdaMwyXT/demo.wav"
    }
  }' \
  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/lucataco/wsrglow@sha256:56ffe617ab2157bd7a43156860529c83abbe862e2f4fd64263d217d2efac34c1 \
  -i 'input="https://replicate.delivery/pbxt/JAmpe8J6WAi5YUdXxxatYFU9ZfKKANtSSBfxHjjdbdaMwyXT/demo.wav"'

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/lucataco/wsrglow@sha256:56ffe617ab2157bd7a43156860529c83abbe862e2f4fd64263d217d2efac34c1
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "input": "https://replicate.delivery/pbxt/JAmpe8J6WAi5YUdXxxatYFU9ZfKKANtSSBfxHjjdbdaMwyXT/demo.wav"
    }
  }' \
  http://localhost:5000/predictions

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

Output

Video Player is loading.

Current Time 00:00:000

Duration 00:00:000

Loaded: 0%

Stream Type LIVE

Remaining Time 00:00:000

Generated in

2.3 seconds

Tweak itReport View full prediction

Run time and cost

This model costs approximately $0.0055 to run on Replicate, or 181 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 25 seconds. The predict time for this model varies significantly based on the inputs.