audio.transcribe

Operation: audio.transcribe
Category: media
Tags: audio, whisper, transcription, speech-to-text

Transcribe audio/video to text with word-level timestamps via Whisper

Runtime

Type: Native (built-in)
Timeout: 600s
Retries: 2 (ExponentialWithJitter) Memory: 4096 MB

Inputs

Name	Type	Required	Default	Description
audio_url	Asset	No	—	URL or file:// path to an audio file
audio_path	String	No	—	Local filesystem path to an audio file
video_path	String	No	—	Local path to a video file (audio will be extracted)

Outputs

Name	Type	Description
text	String	Full transcript text
segments	Array<JSON>	Array of {start, end, text} segment objects
words	Array<JSON>	Array of {start, end, word} word-level timestamp objects
language	String	Detected language code
duration_secs	Number	Audio duration in seconds

Default Configuration

{
  "model": "large-v3",
  "prefer_local": true,
  "word_timestamps": true
}

Usage

TypeScript

import { WorkflowBuilder } from "@fabric-platform/sdk";

const workflow = new WorkflowBuilder("my-workflow")
  .node("audio-transcribe", "tool", (n) =>
    n.config({
      operation: "audio.transcribe",
      // ... node-specific config
    })
  )
  .build();

Python

from fabric_platform import FabricClient

fabric = FabricClient(api_key="fab_xxx")

wf_id = fabric.upsert_workflow("my-workflow", nodes=[
    {
        "key": "audio-transcribe",
        "kind": "tool",
    },
])

Rust

use fabric_sdk::FabricClient;

let client = FabricClient::new("http://localhost:3001", api_key)?;

let wf_id = client.upsert_workflow("my-workflow", serde_json::json!({
    "nodes": [{
        "key": "audio-transcribe",
        "kind": "tool"
    }]
})).await?;

curl

curl -X POST http://localhost:3001/v1/workflow-definitions \
  -H "Authorization: Bearer $FABRIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "my-workflow",
    "nodes": [{
      "key": "audio-transcribe",
      "kind": "tool"
    }]
  }'