OTranscribe: A free and open tool for transcribing audio interviews

Original Article ↗ Hacker News Discussion ↗

Manual transcription tools like OTranscribe still attract fans for their simplicity, offline support, and precise control, even as automatic speech-to-text systems become more powerful and accessible. Commenters compare OTranscribe’s no-frills, browser-based workflow with a growing ecosystem of AI-powered services built on Whisper and similar models, covering use cases from diarized interviews to movie subtitles and live captions. The overall sentiment is that fully automated transcription is rapidly commoditizing, but high-quality results often still rely on good editing interfaces and human oversight.

What OTranscribe Is (and Isn’t)

  • Tool is a browser-based UI that helps with manual transcription: playback speed, easy pause/play, keyboard control.
  • Does not perform automatic speech-to-text; users type everything themselves (confirmed in FAQ excerpt).
  • Works offline if the site is preloaded; can be self‑hosted (MIT licensed) or saved as an offline web app.
  • Praised for being simple, distraction‑free, and well-suited for interviews and travel/offline use.
  • Some find it “too simple” for many modern use cases.

Expectations Around AI and Automation

  • Several commenters initially assume it’s an automatic ASR tool and are corrected.
  • Others are surprised it has no AI integration, but context is that it was written years ago before current AI wave and is not actively developed.
  • Some argue manual transcription still matters, even with AI, for proofing, attribution, and handling edge cases.

Alternative Tools: Automatic Transcription & Diarization

  • Many suggestions based on Whisper and derivatives:
    • CLI and libraries: Whisper, whisper.cpp (much faster on CPU), WhisperX, whisper‑diarization.
    • Hosted services/APIs: Spectropic (with diarization, some LLM post‑processing for speaker names), Audiogest, TurboScribe, VideotoTextAI, Talio.
    • Desktop/mobile: Aiko (iOS, offline Whisper), various macOS and Electron apps, oTranscribe+ (browser/Electron with Vosk via WASM), Pixel Recorder and Live Transcribe, FUTO’s Android tools, Transcribro.
  • Several tools generate subtitles (SRT/VTT), handle YouTube downloads, or provide chat-with-transcript features.

Real-Time, Local-First, and Accessibility Use Cases

  • Strong interest in:
    • Real-time, word-by-word transcription.
    • Fully local processing for privacy and for people who are hard of hearing.
  • Some report good results with local-first apps and Android’s built‑in captioning/transcription; others still searching for open-source, punctuated, real‑time solutions.

Quality, Hallucination, and Post-Processing

  • Whisper and similar models are viewed as high quality but can be slow on CPU and may hallucinate in dead air.
  • LLMs can be chained after transcription to:
    • Remove filler words.
    • Correct errors, punctuate, and infer speaker names.
  • One experiment with a multimodal LLM shows near‑perfect transcription with nuanced punctuation, but post‑processing pipelines can also “over-correct” phrasing.

Language and Dialect Issues

  • OTranscribe itself is language-agnostic (you type whatever you hear).
  • Multiple tools claim broad language support; specific mentions of Japanese and Brazilian Portuguese queries, with mixed clarity on accent handling.