From Recording to Searchable Text in Minutes
Transcribing audio manually is a tedious, error-prone process. A 30-minute recording can take 2 to 3 hours to transcribe by hand. For anyone who produces regular audio or video content, that time adds up fast.
AI transcription has changed the math entirely. ElevenLabs Scribe v2, the transcription engine available in Kolbo.AI, can process an hour-long recording in a few minutes, with accuracy that regularly outperforms manual transcription for clear audio in standard languages, and handles even challenging audio with competitive precision.
This guide covers what the tool does, when to use each format option, and how to set it up for your specific workflow.
What ElevenLabs Scribe v2 Does
Scribe v2 is a speech-to-text model designed for professional-grade transcription use cases. Its core capabilities:
- 90+ languages supported: from English, Spanish, French, German, and Arabic to Hebrew, Hindi, Japanese, Korean, Portuguese, and dozens more. It handles mixed-language audio (code-switching) better than most models.
- Word-level timestamps: not just paragraphs with approximate timing but individual word boundaries, down to millisecond precision. This is critical for subtitle synchronization and audio editing workflows.
- Speaker diarization: the model identifies different speakers in a recording and labels each one ("Speaker 1," "Speaker 2") throughout the transcript. This makes interview, podcast, and meeting transcripts navigable.
- Multiple output formats: SRT (subtitles), TXT (plain text), VTT (WebVTT for web video), JSON (machine-readable with timing data).
- Noise handling: Scribe v2 handles moderately noisy recordings: outdoor interviews, conference rooms, phone recordings. For heavily degraded audio, quality drops, but at the performance level where most real-world audio lives, it is robust.
Output Format Guide
Choosing the right output format is important because each serves a different downstream use.
SRT (SubRip Subtitle Format)
The most universal subtitle format. Used by YouTube, Vimeo, Facebook Video, most desktop video players, and virtually all professional video editing software. The file pairs a sequence number, timecode, and text in three-line blocks.
When to use SRT:
- Adding subtitles to any video
- Uploading captions to YouTube or social platforms
- Importing into Premiere Pro, DaVinci Resolve, or Final Cut for editing
- Any situation where you need a subtitle file that "just works everywhere"
TXT (Plain Text)
A clean text file with the transcript in readable paragraphs. Speaker labels (when diarization is enabled) appear as inline labels. No timecodes.
When to use TXT:
- Publishing a written transcript of a podcast episode on a blog or show notes page
- Searching a recording for a specific quote or keyword
- Feeding the transcript to Claude or GPT-4o for summarization, chapter generation, or key-point extraction
- Creating written materials from meeting notes
VTT (WebVTT)
The web standard for video text tracks. Technically similar to SRT but with minor formatting differences. Used specifically when embedding a <video> tag in a webpage and adding a <track> element for captions.
When to use VTT:
- Building or maintaining a website with embedded video players
- Publishing on platforms that specifically require WebVTT
JSON (Machine-Readable)
Full structured output with every word, its start time, end time, confidence score, and speaker label. The most data-rich format.
When to use JSON:
- Building a custom application on top of the transcript data
- Integrating into an automation or pipeline
- Searching at word level with timestamp precision
- Building interactive transcripts where clicking a word jumps to that point in the audio
Speaker Diarization: How to Use It Effectively
Speaker diarization is one of Scribe v2's most practically useful features for multi-person recordings. When enabled, the model assigns a speaker label to every segment of speech.
What it looks like in the output:
[Speaker 1] The most important thing to understand about AI in 2026 is that it is no longer experimental.
[Speaker 2] Exactly. And I think the companies that have internalized that are outperforming those that are still in pilot mode.
[Speaker 1] Right. So what does that mean for a mid-size company that is just now starting to think about this?
Limitations to know about:
- Labels are "Speaker 1," "Speaker 2" by number, not by name. You will need to rename them manually or in a post-processing step.
- Very similar voices (same gender, similar accent) can occasionally be confused. Review the diarized output for accuracy on long recordings.
- Speakers that overlap briefly may be mis-attributed for those few words.
Pro tip: if you are interviewing a single guest, diarization makes it immediately clear who said what, which is critical when editing the recording or pulling quotes.
Practical Use Cases
YouTube and Social Video Content
Subtitles dramatically increase video reach. According to consistent data from YouTube, Facebook, and TikTok, videos with subtitles get significantly more views and higher watch time. Most mobile viewers watch with the sound off in public spaces. Without subtitles, they scroll past.
Workflow:
- Upload the video to Kolbo.AI
- Run Scribe v2 transcription, export SRT
- Review the SRT for any errors on proper nouns, brand names, or technical terms
- Upload the corrected SRT to YouTube as a manual subtitle track
- YouTube displays them as "Creator-provided captions" rather than auto-generated, which affects search indexing positively
Podcast Production
Show notes: transcript in TXT, fed to Claude in Kolbo Chat with the prompt: "Based on this transcript, write a 250-word podcast description and a list of the 7 key points discussed. Use engaging, podcast-listener-friendly language." Done in minutes.
Searchable episode archive: if you publish episode transcripts on your podcast website, episodes become searchable by content. Listeners and new audiences can find a specific conversation or topic without listening to every episode.
Clip selection: search the TXT file for keywords to find the strongest quote moments for short-form social content. Cut from the full recording using Kolbo's video trim tool.
Business Meetings and Calls
The case for transcribing every important meeting is straightforward: action items get captured, "I thought you said X" disputes are resolved with the transcript, and new team members can catch up without sitting through a one-hour recording.
Setup for Zoom or Google Meet: record the meeting to your computer, not just to the cloud. Upload the local file to Kolbo.AI for transcription. Diarization identifies who said what.
Privacy consideration: inform all participants before recording. In many jurisdictions this is a legal requirement.
Academic Research and Interviews
Researchers who conduct qualitative interviews know how much time transcription consumes. Scribe v2 does the initial pass, which might have 2 to 5 percent error on clear audio. Researchers review and correct, which is far faster than transcribing from scratch.
For interviews in languages other than English: Scribe v2's multilingual capability covers most major research languages. For very small or low-resource languages, results may be less reliable.
Accessibility Compliance
Organizations that publish video content for public audiences often have accessibility obligations. Accurate captions are part of compliance. Scribe v2 generates captions that meet the accuracy threshold required by most accessibility standards, with a human review pass recommended for publicly published content.
Improving Transcription Accuracy
Even with excellent AI, the quality of the output is partly determined by the quality of the input. A few practices that make a significant difference:
Audio quality is the primary driver. A USB cardioid microphone costs $50 to $100 and makes more difference to transcription accuracy than any setting or post-processing. If you record regularly, invest in a microphone.
Minimize background noise. Keyboard typing, HVAC systems, and street noise do not bother human ears much during a conversation but confuse transcription models. Close windows, mute HVAC where possible, and if recording remotely, ask participants to use headset microphones.
Speak at a moderate pace. Very fast speech with run-on phrases is harder to segment accurately. Natural pacing with brief pauses between thoughts consistently transcribes better.
Proper nouns need manual review. Technical terms, brand names, product names, and personal names will often be transcribed phonetically rather than correctly. These are the errors most worth correcting before the final transcript is published or used in downstream workflows.
Integrating Transcription Into a Larger Workflow
The transcript is often the starting point, not the end product.
Transcription to AI summary: paste the TXT transcript into Kolbo Chat. Ask Claude to generate an executive summary, identify key decisions made, or list all questions that were raised but not resolved.
Transcription to content repurposing: a 45-minute webinar transcript becomes five LinkedIn posts, a blog post, an email newsletter, and ten social captions, all drafted by AI based on the transcript in one session.
Transcription to search: index transcripts in your knowledge base so your team can search for "anything we said about launch strategy in Q1 calls." Some teams build this on top of Kolbo's project context system.
Getting Started
The workflow is straightforward:
- Upload audio or video to Kolbo.AI (supports MP3, MP4, WAV, M4A, and most common formats)
- Select the transcription tool
- Choose output format (SRT for video captions, TXT for text content, JSON for integrations)
- Enable speaker diarization if the recording has multiple speakers
- Download the result
For typical content creation workflows, the transcription finishes in 1 to 3 minutes per 10 minutes of audio.
Try Kolbo.AI free and transcribe your first recording. If you produce audio or video regularly, this is one of the fastest productivity gains available in 2026.
