Key topics: speaker diarization, speaker labeling, speaker segmentation, speech-to-text, TXT transcript import, SRT subtitle export

For multi-person meetings, interviews, podcasts, or online courses, recording is rarely the hard part. Cleaning up the transcript is: you still need to proofread the text and rewind again and again to confirm who said each line. Once several speakers are involved, a long block of text without names or timestamps is hard to read—and even harder to search, quote, or turn into captions.

Taption speaker diarization can segment multi-speaker conversations by voice, then replace "Speaker A" and "Speaker B" with real names so the transcript clearly shows who spoke when. This guide walks through importing media and an existing TXT transcript, setting the speaker count, completing speaker labeling, and exporting a transcript or subtitle file.

What's the difference between speaker diarization and speaker labeling?

Speaker diarization answers "who spoke when": the system uses voice characteristics to split audio into segments for different speakers. Speaker labeling is the follow-up cleanup step—replacing temporary system tags with host, guest, or real names.

• Speaker diarization: Automatically separate multiple voices and segment text by speaker
• Speaker labeling: Change Speaker A and Speaker B into names like Amy or Steven
• Timeline alignment: Keep each text segment matched to the correct audio or video position
• Structured output: Preserve speaker context for meeting notes, interview quotes, and captions

If you only have continuous text, start with the transcript speaker labeling workflow so Taption can realign the text to the original media and separate speakers. Before you begin, prepare the original audio or video file, a matching TXT transcript, and an approximate count of the main speakers.

Step 1: Import your audio or video file

After signing in to Taption, upload the recording or video from the files page. Prefer a clear original file with less background noise. If people talk over each other or volume levels differ a lot, you may still need to confirm speaker attribution manually.

If you need to generate text from scratch, you can also use audio/video to transcript. This guide focuses on the case where you already have a TXT transcript and want speaker labels and timestamps added automatically.

Step 2: Choose the language and import your TXT transcript

After the file uploads, select the main language spoken in the recording. Then, under text generation method, choose "Import TXT text file" and upload a plain-text transcript that matches the media. The TXT does not need speaker names yet, but the text order should follow the recording.

Turn on AI speaker segmentation and set the speaker count

Under text segmentation method, choose "AI label speakers and segment text by different speakers," then set the total number of speakers in the video. The closer that count is to reality, the more consistent the speaker labels tend to stay.

• Count the main speakers first; adjust brief interruptions manually afterward
• If the audio includes narration, a host, and guests, include each of them in the speaker count
• Confirm the language and TXT content are correct before processing to reduce rework

Step 3: Proofread the transcript and replace speaker names

When transcription finishes, play a few different sections and check that the text, timeline, and speaker changes line up. Click a speaker tag to rename system labels to real identities such as Amy, Steven, host, or interviewee—so every segment from the same person stays consistent.

Prioritize these three types of segments while proofreading

• Passages where two speakers switch quickly or overlap
• Moments with background noise, remote call audio, or sudden volume changes
• Opening/closing narration and speakers who appear only briefly

Speaker diarization works best as a usable first draft that someone familiar with the content finalizes. Proofread names, titles, and proper nouns before export so mistakes do not carry into meeting notes or final captions.

Step 4: Export a transcript or subtitle file

After the text and speaker labels look right, open the export menu and choose a transcript format such as TXT, or export an SRT subtitle file. If the next step is putting the content back on video, continue with the auto captioning workflow to proofread, translate, or export the video.

• TXT: Best for meeting notes, interview write-ups, and quoting content
• SRT: Keeps subtitle text and timecodes for video platforms or editing workflows
• Before export: Spot-check speaker names, wording, and the timeline again

FAQ: How can I get better speaker diarization results?

Does the speaker count have to be exact?

Enter the actual number of main speakers when you can. If you are unsure, count people who speak throughout the recording. Speakers who only appear briefly at the start can be included or left out based on importance, then corrected manually afterward.

Do I still need to proofread after importing TXT?

Yes. TXT solves the text source problem, but AI still has to align that text to the audio and detect speaker changes. Overlapping speech, background noise, and remote mics can all affect results, so spot-check before final delivery.

When is speaker labeling most useful?

It works especially well for multi-person meetings, research or customer interviews, podcast conversations, online courses, and panels. Clear speaker labels help readers follow the dialogue, make it easier for teams to search decisions and quote remarks, and keep the transcript ready for caption production.

Conclusion: Separate who is speaking before you clean up the transcript

Transcript speaker diarization turns continuous text into content with speakers, segments, and a timeline. Follow the sequence—import media, upload TXT, run AI speaker segmentation, name the speakers, and export the format you need—to build a transcript that is easier to proofread and reuse.

If you regularly handle meetings, interviews, or podcasts, try Taption's speaker diarization workflow. Let AI create a structured first draft, then have someone who knows the content finish the proofreading—usually a better fit for ongoing media workflows than labeling everything by hand from scratch.