> Speaker Pagkilala & Diarization

> Awtomatikong makilala at label ang iba't ibang mga speaker sa iyong mga transcription ng audio at video. Alam eksakto sino ang sinabi kung ano.

> Gumagana sa publikong magagamit na audio at video. DRM-protected na nilalaman ay hindi suportado.

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Pag-record: 0:00
Real-time Ang Ōmi (おみ, lit.
Pinahusay > Wika (tumpak)
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Ano ang mga uri ng diyabetis?

Ang speaker diarization ay ang proseso ng paghati ng isang audio stream sa mga segment ayon sa pagkakakilanlan ng tagapagsalita. Sa mas simpleng mga salita, ito ay tumutugon sa tanong na "sino ang nagsalita kailan?" This is essential for multi-speaker recordings like meetings, interviews, podcasts, conference calls, and legal proceedings where knowing who said what is just as important as what was said.

STT.ai uses advanced neural speaker diarization models that can detect and label speakers in real time. The system creates speaker embeddings -- numerical representations of each voice's unique characteristics -- and clusters them to distinguish between different people. This works even when speakers have similar voices or frequently interrupt each other.

> Paano gumagana ang Speaker Detection

1. Pagtukoy ng Aktibidad ng Tinig

Ang mga ito ay maaaring tumukoy sa: Mga instrumentong pangmusika, mga instrumentong pangmusikang pang-awitin, mga instrumentong pangmusikang pang-kompyuter.

> 2. Speaker pag-embed

Ang bawat segment ng pananalita ay nagiging isang speaker embedding -- isang compact vector na nahuhuli ang mga natatanging katangian ng boses ng tagapagsalita.

3. Pag-cluster at Pag-label

Ang mga embeddings ay pinagsama-sama upang bumuo ng mga segment mula sa parehong tagapagsalita, at pagkatapos ay ang bawat cluster ay itinalaga ng isang label (Tagapagsalaysay 1, Tagapagsalita 2, atbp.).

> Gamitin ang mga kaso para sa Speaker Detection

> Transcript ng pulong
> Awtomatikong label ang bawat kalahok sa mga recordings pulong. Bumuo ng mga minuto na may malinaw na pagbibigay ng sino ang sinabi kung ano.
> Podcast transcription
> Pagkilala sa pagitan ng host at mga bisita sa podcast episodes. Lumikha ng mga tala ng palabas na may tamang speaker pag-aari.
> Transcript ng Interbyu
> I-separate ang tagapanayam at mga sagot ng interviewee para sa pananaliksik, journalism, at hiring dokumentasyon.
Legal & Pagtupad
> Gumawa ng opisyal na talaan ng mga depositions, hearing, at mga tawag sa pagsunod na may malinaw na pagkakakilanlan ng tagapagsalita.

> Speaker Pagkilala sa STT.ai

Speaker detection is available on all paid plans. When you transcribe audio or video with speaker detection enabled, the transcript will include speaker labels inline with the text. You can also export speaker-labeled transcripts in all supported formats including SRT, VTT, DOCX, JSON, and PDF.

Speaker 1 [00:00:01]: Welcome to the meeting, everyone. Let's start with the quarterly review. Speaker 2 [00:00:05]: Thanks. I have the numbers ready. Revenue is up 23% quarter over quarter. Speaker 1 [00:00:12]: That's great news. Can you walk us through the breakdown?

The system can detect up to 20 distinct speakers in a single recording. For best results, ensure each speaker has at least a few seconds of solo speech. Overlapping speech is handled but may reduce accuracy in heavily cross-talked segments.

> Subukan speaker detection ngayon

> I-upload ang isang multi-speaker recording at makita speakers awtomatikong naka-label.

tl> Magsisimulang Mag-translate ng Libre

Mga Madalas Itanong

speaker detection runs in your browser: paste a URL, upload a file, or record from your mic. STT.ai picks the AI model and returns the transcript in under 5 minutes. Export as TXT, SRT, VTT, DOCX, JSON, or PDF.

Yes — every visitor gets 600 free minutes/month on STT.ai, usable for speaker detection the same as any other workflow. Paid plans starting at $5/month unlock longer files, private transcripts, and priority queueing.

speaker detection runs on the same AI models as the rest of STT.ai — our best models reach 95-97% accuracy on clean speech (3-5% Word Error Rate on benchmarks). Switch models on the fly if the first pass is below your target.

speaker detection can run on any of STT.ai's 10+ models — STT.ai Enhanced (most accurate), Whisper Large V3 (99 languages), NVIDIA Canary (#1 WER on supported langs), Whisper Turbo (fast), Moonshine (lightweight), and more.

Yes. Every transcript exports as SRT or VTT — works with YouTube, Vimeo, TikTok, VLC, and every major video player. The burn-subtitles tool overlays them onto video as hardsubs.

Yes. Speaker diarization automatically labels each voice (Speaker 1, Speaker 2, ...) and you can rename them in the built-in editor. Works across all models and languages.

Most speaker detection jobs finish in under 5 minutes. A 1-hour audio file typically completes in 2-3 minutes with our fastest models. Speed depends on chosen model and current GPU load.

speaker detection accepts 20+ formats — MP3, WAV, M4A, FLAC, OGG, MP4, MKV, MOV, WebM, AVI, and more. Output to TXT, SRT, VTT, DOCX, JSON, or PDF.

Yes. Audio files submitted to speaker detection are processed and deleted by default. Pro plans add client-side encryption — even if STT.ai's database is breached, your transcripts are unreadable without your key. Data is never used for model training without explicit opt-in.

Yes. STT.ai offers a REST API with Python and Node.js SDKs, plus an MCP server for Claude and Cursor — all usable for speaker detection workflows. Free API tier includes 100 minutes/month.

Yes. Every transcript opens in the built-in editor where you can correct words, rename speakers, adjust timestamps, and add notes. All changes save automatically.

Every transcript gets a unique shareable URL. Export to DOCX or PDF for email. Pro plans add password-protected and permanent links — useful for client work.

STT.ai handles 1,300+ platforms including YouTube, Vimeo, TikTok, SoundCloud, Zoom, Google Meet, podcast hosts, and more. URL transcription works with publicly-available content only — DRM-protected sources can't be transcribed.