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…
The DaDaScribe API simplifies the process of obtaining YouTube video transcripts and SRT subtitle files in multiple languages with just a single API request. It eliminates the need for additional steps typically required by other transcription APIs, such as downloading videos, extracting audio, and separate translation or speaker labeling processes.
To use DaDaScribe, you only need to provide a YouTube URL, specify the source language, and optionally list up to five target languages, along with speaker names. The API then returns clean text transcripts and subtitle files in .txt and .srt formats, which includes translations in the specified languages. Alternatively, you can directly upload an audio or video file.
The API workflow consists of three main steps:
1. Submit a transcription job using a POST request to the /transcribe endpoint, including the required API key, video URL, source language, and destination languages.
2. Poll for the status of the job by making a GET request to the /status endpoint with the job ID.
3. Once the status is "complete," the API provides direct URLs to the resulting .txt and .srt files, including any translated versions.
DaDaScribe's pre-processing step—noise reduction and voice isolation—significantly improves the quality of the generated transcripts, particularly for music videos and real-world recordings, compared to other APIs. This API is particularly well-suited for content pipelines like YouTube or podcasts, multi-language subtitle generation, and projects requiring named speakers without post-processing.
However, it may not be the best choice for real-time or streaming transcription, extremely low-cost high-volume transcription, or extremely specialized domain models.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
