tmappdev/cosy_voice_cloner – Run with an API on Replicate

tmappdev / cosy_voice_cloner

Cold

Public
52 runs
A100 (80GB)

Run with an API

Playground API Examples README Versions

Input

ref_audio

*file

Reference audio file (3-10 seconds)

prompt_text

string

Shift + Return to add a new line

Text of the reference audio (optional)

Default: ""

prompt_language

string

Language of reference audio

Default: "粤语"

text

*string

Shift + Return to add a new line

Text to synthesize

text_language

string

Language of the text to synthesize

Default: "粤语"

how_to_cut

string

How to split text

Default: "按标点符号切"

top_k

integer

(minimum: 1, maximum: 100)

GPT top_k parameter

Default: 15

top_p

number

(minimum: 0, maximum: 1)

GPT top_p parameter

Default: 1

temperature

number

(minimum: 0, maximum: 1)

GPT temperature parameter

Default: 1

ref_free

boolean

Enable reference-free mode

Default: false

speed

number

(minimum: 0.6, maximum: 1.65)

Speech speed adjustment

Default: 1

reference_files

file[]

Add multiple files

Optional additional reference files to blend

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

npx create-replicate --model=tmappdev/cosy_voice_cloner

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

const output = await replicate.run(
  "tmappdev/cosy_voice_cloner:b66eda68a3970fa7c3862578e07cf1bbb4c8889b4080e7e2a0bac2f047582514",
  {
    input: {
      speed: 1,
      top_k: 15,
      top_p: 1,
      ref_free: false,
      how_to_cut: "按标点符号切",
      prompt_text: "",
      temperature: 1,
      text_language: "粤语",
      prompt_language: "粤语"
    }
  }
);

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

output = replicate.run(
    "tmappdev/cosy_voice_cloner:b66eda68a3970fa7c3862578e07cf1bbb4c8889b4080e7e2a0bac2f047582514",
    input={
        "speed": 1,
        "top_k": 15,
        "top_p": 1,
        "ref_free": False,
        "how_to_cut": "按标点符号切",
        "prompt_text": "",
        "temperature": 1,
        "text_language": "粤语",
        "prompt_language": "粤语"
    }
)
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 tmappdev/cosy_voice_cloner 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": "b66eda68a3970fa7c3862578e07cf1bbb4c8889b4080e7e2a0bac2f047582514",
    "input": {
      "speed": 1,
      "top_k": 15,
      "top_p": 1,
      "ref_free": false,
      "how_to_cut": "按标点符号切",
      "prompt_text": "",
      "temperature": 1,
      "text_language": "粤语",
      "prompt_language": "粤语"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

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

Run time and cost

This model runs on Nvidia A100 (80GB) GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

This model doesn't have a readme.