How to Transcribe Audio to Text: Step-by-Step

To transcribe audio to text with TransPocket, upload an audio or video file (or paste a YouTube URL), select the language and number of speakers, start the transcription, then review and export the result as DOCX, TXT, CSV, SRT, or VTT. The service is AI-powered, supports 10+ languages, and reports an average Word Error Rate (WER) of 5.8%. This walkthrough covers the full process, from file preparation to export, plus the settings that affect your result.

Before You Start

Confirm two things before uploading:

• File format. TransPocket accepts common audio and video formats, including MP3, MP4, WAV, and M4A. If your recording is in another format, convert it first.
• Speaker count. If more than one person talks in the recording, decide how many speakers there are. You set this in the upload or import dialog, and it tells the tool how many voices to separate.

If your source is a YouTube video, you don't need to download it first — pasting the URL is enough.

Step-by-Step Transcription

1. Upload your audio or paste a YouTube link

Open TransPocket and add your source. For a local file, upload it in a supported format. For a YouTube video, paste the URL directly — no software or download is required.

Expected result: the file or link is accepted and ready for settings.

2. Set language and speaker count

Choose the language of the recording (the service supports 10+ languages) and enter the number of speakers if you want them labeled. Speaker recognition is optional but useful for interviews, meetings, and multi-person recordings.

What to expect: speaker labeling adds roughly 20–30 seconds of processing time on top of the normal transcription.

3. Start transcription and monitor progress

Begin the transcription. A real-time progress indicator shows live status updates while the audio is processed. The engine is Whisper Large-v3, and the site describes processing as fast, with results in seconds for typical files.

Expected result: a draft transcript appears when processing finishes.

4. Review the transcript

Read through the output and correct names, technical terms, and any misheard words. The stated average WER of 5.8% means roughly 6 words in 100 may need a fix — clean audio and a single speaker usually do better, while background noise, accents, and overlapping speech tend to raise the error rate.

If speaker labels are wrong, check that the speaker count you entered matches the actual number of people.

5. Export in the format you need

Choose an export format based on what you'll do with the text:

YouTube-sourced audio can also be exported as an audio file, according to the site's FAQ.

Common Snags and Fixes

• Unsupported file type: convert to MP3, WAV, M4A, or MP4 before uploading.
• Speakers merged into one block: re-run with the correct speaker count; remember it adds 20–30 seconds.
• More errors than expected: check for background noise or overlapping voices, and review the language setting — a mismatch is a frequent cause of poor output.
• Subtitle timing looks off: export as SRT or VTT rather than DOCX, since those formats carry timestamps.

What You Get

The workflow is: prepare a supported file → upload or paste a URL → set language and speakers → transcribe with live progress → review → export. For quick single-file jobs, the default settings are enough; for interviews and meetings, set the speaker count up front so the transcript arrives already separated by voice.