thomasmol/whisper-diarization:c558e6f7 – Run with an API on Replicate

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thomasmol /whisper-diarization:c558e6f7

Playground API

Input

file_string

string

Shift + Return to add a new line

Either provide: Base64 encoded audio file,

file_url

string

Shift + Return to add a new line

Or provide: A direct audio file URL

file

Or an audio file

group_segments

boolean

Group segments of same speaker shorter apart than 2 seconds

Default: true

num_speakers

integer

(minimum: 1, maximum: 50)

Number of speakers, leave empty to autodetect.

language

string

Shift + Return to add a new line

Language of the spoken words as a language code like 'en'. Leave empty to auto detect language.

prompt

string

Shift + Return to add a new line

Prompt, provide names, acronyms and loanwords in a list. Use punctuation for best accuracy.

Default: "AI, Thomas, Audiogest."

offset_seconds

integer

(minimum: 0)

Offset in seconds, used for chunked inputs

Default: 0

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

npx create-replicate --model=thomasmol/whisper-diarization

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 thomasmol/whisper-diarization using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "thomasmol/whisper-diarization:c558e6f7efba0b8e6f4155be9e930d5bd92d788cd3e31396f4c33b5aac984975",
  {
    input: {
      prompt: "AI, Thomas, Audiogest.",
      group_segments: true,
      offset_seconds: 0
    }
  }
);
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 thomasmol/whisper-diarization using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "thomasmol/whisper-diarization:c558e6f7efba0b8e6f4155be9e930d5bd92d788cd3e31396f4c33b5aac984975",
    input={
        "prompt": "AI, Thomas, Audiogest.",
        "group_segments": True,
        "offset_seconds": 0
    }
)
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 thomasmol/whisper-diarization 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": "c558e6f7efba0b8e6f4155be9e930d5bd92d788cd3e31396f4c33b5aac984975",
    "input": {
      "prompt": "AI, Thomas, Audiogest.",
      "group_segments": true,
      "offset_seconds": 0
    }
  }' \
  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/thomasmol/whisper-diarization@sha256:c558e6f7efba0b8e6f4155be9e930d5bd92d788cd3e31396f4c33b5aac984975 \
  -i 'prompt="AI, Thomas, Audiogest."' \
  -i 'group_segments=true' \
  -i 'offset_seconds=0'

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/thomasmol/whisper-diarization@sha256:c558e6f7efba0b8e6f4155be9e930d5bd92d788cd3e31396f4c33b5aac984975
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "prompt": "AI, Thomas, Audiogest.",
      "group_segments": true,
      "offset_seconds": 0
    }
  }' \
  http://localhost:5000/predictions

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

Output

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