AI Transcription: Turn Video and Audio Into TXT and SRT

AI transcription turns spoken audio from a video, interview, or podcast into editable text. With Pipe2.ai Transcription, upload a video or audio file and receive both a plain TXT transcript and a timestamped SRT file; you can also detect the language, label speakers, and correct repeatedly misheard words.

Choose a workflow for interviews, podcasts, or subtitles

For an interview or podcast, start with TXT when your goal is a written record or a quotation. Enable speaker detection for a conversation, then check each label against the recording before attributing a statement. For a solo voiceover, leave speaker detection off unless you need it.

For automatic subtitle generation, start with SRT. Review the words and timing, then attach the SRT and the original video to Captions to produce visible subtitles. Transcription generates the subtitle file; Captions renders it into the video.

Transcribe from video or an exported voice recording

To transcribe from video, upload the video itself; you do not need to extract an MP3 first. The pipeline uses its audio track. For example, a recorded interview can become a TXT for checking quotations and an SRT for preparing subtitles in the same run.

For a voice-recorder recording, export the audio file first and upload it to Transcription. This workflow starts with an existing file; it does not record a live conversation. Choose speaker detection for an interview, or leave it off for a single-speaker memo.

Before you run, decide whether the next deliverable is written text or on-screen subtitles. Review TXT against the recording for a written record. For subtitles, check the SRT wording and timing, then use the reviewed file with the original video in Captions.

Generate a video transcript in five steps

1. Choose a video with a clear audio track. Transcription needs spoken audio. A silent video cannot produce a transcript.
2. Upload the file. Open Transcription and attach the video or audio asset you want to convert.
3. Set the language and speakers. Leave Language on Auto when you are unsure. For interviews or panels, enable Speaker Detection and optionally enter the expected number of speakers.
4. Add recurring word corrections. If a name, acronym, or product term is usually misheard the same way, map the recognised spelling to the correct one. Corrections are case-sensitive and match whole words.
5. Run and review both files. Use TXT for reading and editing. Use SRT when timing matters or when the transcript will become subtitles.

The current pipeline uses ElevenLabs Scribe v2. Its public form accepts one uploaded video or audio asset, Auto plus ten named language choices, optional speaker detection, an optional speaker-count hint from 0 to 20, and optional word corrections. The outputs are separate SRT and TXT assets.

Choose TXT or SRT for the next job

The two outputs contain the same spoken content in different forms.

Output	What it contains	Best used for
TXT	Continuous readable text without subtitle timing blocks	Notes, quotations, search, summaries, drafts, and editorial review
SRT	Numbered text blocks with start and end timestamps	Subtitles, sentence-aware trimming, and any workflow that must stay aligned with the video

Keep both. TXT is faster to read, while SRT preserves the timing needed for production. If the next step is to make visible captions, follow the guide to adding subtitles to a video after reviewing the SRT.

Get cleaner speech-to-text results

Recognition quality starts with the recording. Move the microphone closer to the speaker, reduce background music and room echo, and avoid having several people talk over one another. Exporting a louder file cannot restore words that were never captured clearly.

Set a language when you know it; otherwise Auto is the practical starting point. Speaker Detection is useful when attribution matters, but speaker labels still need human review. A model can separate voices imperfectly when they sound similar, interrupt each other, or enter from different recording sources.

Use Corrections for consistent, predictable errors rather than general rewriting. For example, if one brand name is always rendered as a similar common word, a whole-word correction can fix that spelling in both output files. It will not repair unclear sentences or decide what a speaker intended to say.

Review the transcript before you publish it

An AI transcript is a working document, not proof that every word is correct. Read it while listening to the source and pay special attention to:

• Names, organisations, technical terms, and acronyms
• Numbers, prices, dates, and measurements
• Speaker changes in interviews and group discussions
• Proper nouns that ordinary spell-checking may “correct” incorrectly
• Sections with noise, music, accents, or overlapping speech
• SRT blocks that begin too early, end too late, or contain too much text

If you plan to quote someone, verify the quotation against the recording. If you plan to create subtitles, correct the wording and timing before the text becomes part of the finished video.

Turn the transcript into captions or clips

Transcription does not burn text into the picture. It creates reusable files so you can inspect them before making another asset.

For visible subtitles, attach the reviewed SRT and source video to the Captions pipeline. For short-form editing, one transcript can support the entire workflow: find useful moments, cut at sentence boundaries, and caption the resulting clips. The guide to turning a long video into shorts explains that sequence, while sentence-aware video trimming covers clean cut points in detail.

This separation is useful. You can correct one SRT, reuse it across several caption styles, and keep the TXT version for descriptions, notes, or a written edit without transcribing the source again.

What the generator does not do

A transcript generator converts speech to text; it does not automatically summarise the recording, choose highlights, translate the transcript, or create a captioned video. Those are separate editorial or production steps.

It also needs an audio track. Visual text shown silently on screen is not speech and will not become part of the transcript. When the recording contains important slides, labels, or screen text, review those separately and add them to your notes if they matter.

Start with one representative recording rather than your largest archive. Check how it handles your speakers, vocabulary, and audio conditions, add any recurring corrections, and then use the same review checklist for the rest of the batch.