DaDaScribe API: YouTube Multi-Language Transcript + SRT in One Request

Most transcription APIs still force you to do this:

Download the YouTube video
Extract the audio with yt-dlp + ffmpeg
Upload the file
Call the transcription endpoint
Call a separate translation API
Map generic speaker labels ("Speaker 0") to real names
Generate SRT files yourself

That’s a lot of moving parts for something that should be simple.

The DaDaScribe API collapses most of that into a single request.

You send a YouTube URL (or a direct audio/video link), specify the source language, optionally add up to 5 target languages + speaker names, and you get back clean .txt transcripts and .srt subtitle files, including translations. Of course, you can also directly upload an audio or video file.

Quick Start

Here’s the minimal flow:

1. Submit a transcription job

bash
curl -X POST https://api.dadascribe.com/v1/transcribe \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "source": "https://www.youtube.com/watch?v=VIDEO_ID",
    "source-language": "en",
    "destination-language": "es,fr,it",
    "diarization": "Lex,Guest"
  }

Response:

{
  "status": "ok",
  "id": "a1B2c3D4e5F6g7H8",
  "count": 1
}

2. Poll for status

curl -X POST https://api.dadascribe.com/v1/status \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"id": "a1B2c3D4e5F6g7H8"}'

3. Download the results

When status is "complete", you get direct URLs to the .txt and .srt files (including translated versions). No auth header required on the download.

Here is the Python version of the same flow:

import requests
import time

API_KEY = "YOUR_API_KEY"
BASE = "https://api.dadascribe.com/v1"

headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

# Submit
resp = requests.post(f"{BASE}/transcribe", headers=headers, json={
    "source": "https://www.youtube.com/watch?v=VIDEO_ID",
    "source-language": "en",
    "destination-language": "es,fr",
    "diarization": "Host,Guest"
})
job_id = resp.json()["id"]

# Poll
while True:
    status = requests.post(f"{BASE}/status", headers=headers, json={"id": job_id}).json()
    if status["status"] == "complete":
        print(status["urls"])
        break
    time.sleep(3)

What Actually Makes This Useful

Here are the features that remove real work from a developer’s pipeline:

Feature	DaDaScribe	Typical Alternatives
YouTube URL as input	First-class	Usually requires extraction
Built-in translation	Over 120 languages	Separate API call
Named speaker diarization	You provide the names	Generic "Speaker 0/1"
Pre-processing pipeline	Noise reduction + cleanup	You handle it
Output formats	.txt + .srt	Often JSON only
Number of core endpoints	3	Usually many more

The pre-processing step is particularly important. Before the speech model ever sees the audio, DaDaScribe runs noise reduction and voice isolation. This is why it handles music (lyrics extraction) and messy real-world recordings better than raw model APIs.

You can see the difference in the Beyoncé "Halo" lyrics demo and the long-form Lex Fridman podcast examples.

How It Compares to the Big Three (Short Version)

OpenAI Whisper API: Excellent model, but no native YouTube support, limited translation (mostly into English), and no built-in diarization.
Deepgram / AssemblyAI: Strong on streaming and accuracy benchmarks. You still need to handle YouTube extraction and translation yourself. Speaker labels are generic.
DaDaScribe: Opinionated toward content pipelines (YouTube, podcasts, multi-language output). Batch-only (no real-time streaming yet). Fewer endpoints, more built-in conveniences.

Full side-by-side comparison (pricing models, retention policy, feature matrix, etc.) is here: DaDaScribe API vs the Big Three