Speech-to-Text APIs
How do I enable speaker diarization and word-level timestamps in Gladia’s async transcription API?
6 min read
Most downstream failures in voice products start here: you don’t know exactly who said what, and you can’t reliably jump to the right moment in the audio. Notes get messy, summaries lose context, CRM syncs mis-assign commitments. Gladia’s async transcription API solves this with speaker diarization and word-level timestamps — but only if you enable them correctly in your requests.
Quick Answer: Turn on speaker diarization and word-level timestamps by enabling the corresponding options in your asynchronous transcription API request. Gladia will then return a transcript segmented by speaker, with precise timestamps for each word.
Frequently Asked Questions
How do I enable speaker diarization and word-level timestamps in the async API?
Short Answer: In your async transcription request, set the diarization option to true and enable word-level timestamps in the request payload. The API response will then include speaker-separated segments and per-word timestamps.
Expanded Explanation:
Gladia’s async engine is designed as a single API surface for transcription + add-ons. Speaker diarization (“who said what?”) and word-level timestamps (“when exactly was it said?”) are exposed as flags in the same call you already use for transcription. Once enabled, the response includes structured segments per speaker and a detailed timing structure per word.
This lets you power downstream workflows — diarized summaries, agent coaching, accurate subtitles, CRM auto-fill — without bolting on separate services. Mono, stereo, and multi-channel files are all supported, so you can use the same pattern for meetings, podcasts, and 8 kHz contact-center calls.
Key Takeaways:
- Enable diarization and timestamps directly in your async transcription request payload.
- The response returns speaker-tagged segments plus word-level timestamps you can use for subtitles, navigation, and analytics.
What’s the process to add diarization and timestamps to an existing async integration?
Short Answer: You keep your existing async transcription flow and just extend the request body to include diarization and word-level timestamp options.
Expanded Explanation:
If you’re already calling Gladia’s asynchronous transcription endpoint, you don’t need a new API or service. You simply add configuration fields telling the engine to perform speaker diarization and to attach timestamps at the word level. The core flow — upload audio, get a job ID, poll for completion, then read the result — stays identical.
Operationally, this keeps your integration surface small: one REST endpoint for transcription, diarization, timestamps, and any other add-ons you choose (custom vocabulary, sentiment analysis, NER, summarization, etc.). You can roll out diarization and timestamps behind a feature flag in your app, without touching your audio pipeline.
Steps:
-
Identify your async endpoint call
Locate the existing code path where you send audio to Gladia’s async transcription API. -
Add diarization and timestamp options
Extend the JSON payload to turn on speaker diarization and word-level timestamps (for example,diarization: trueandword_timestamps: trueor the equivalent fields from the current API docs). -
Update response handling
Adjust your parsing logic to read the diarization segments and per-word timestamps from the response and map them to your UI or downstream workflows (e.g., subtitles, speaker-aware summaries, CRM logging).
Is there a difference between using diarization vs. just word-level timestamps?
Short Answer: Yes — word-level timestamps tell you when each word was spoken, while diarization tells you who spoke it; you typically want both for production workflows.
Expanded Explanation:
Word-level timestamps alone give you precise time alignment with the audio. They’re ideal for subtitles, scrubbing to specific phrases, or aligning transcripts with screen-recordings. But without diarization, everything is a single speaker stream; great for media search, not great for understanding a conversation.
Diarization segments the transcript into “Speaker 1,” “Speaker 2,” and so on. That’s what makes coaching logs, sales call analytics, and meeting notes trustable: you know exactly who committed to what. Combined with word-level timestamps, you unlock both axes — time and speaker — so your automation can trigger off the right person at the right moment.
Comparison Snapshot:
- Option A: Word-level timestamps only
You get precise timing for each word, but no speaker separation. - Option B: Speaker diarization + word-level timestamps
You get “who said what, and when” with segment-level diarization and per-word timing. - Best for:
Any conversational use case (meetings, support calls, sales calls, interviews) where downstream workflows depend on correctly attributing commitments, questions, and identifiers.
What do I need in place to use diarization and word-level timestamps reliably?
Short Answer: You need audio that Gladia can ingest (mono, stereo, or multi-channel) and an async integration that passes the diarization and timestamp flags, plus response handling that consumes the enriched transcript.
Expanded Explanation:
The heavy lifting — diarization and timestamping — is handled by Gladia’s models. From your side, the main requirements are correct request configuration and response parsing. Gladia is built to hold up in real-world audio: noisy meetings, mixed accents, telephony-grade 8 kHz calls, and crosstalk, not just clean demo clips. The diarization engine works across these conditions and across 100+ languages.
Because the async engine is the same one used for production workflows (QA, compliance, coaching, subtitles), you don’t need separate infrastructure. Just ensure your integration is prepared for more detailed JSON: speaker segments and word-level timing arrays.
What You Need:
- Valid async transcription integration
A working call to Gladia’s async transcription API (via REST) that you can extend with diarization and timestamp options. - Response parsing for enriched outputs
Logic to read speaker segments and word-level timestamps from the response and map them into your product’s models, UI, and automations.
How do diarization and word-level timestamps improve results for my product?
Short Answer: They turn a generic transcript into a reliable data backbone for your workflows — from diarized summaries and CRM enrichment to accurate subtitles and analytics.
Expanded Explanation:
Most downstream failures in voice products trace back to missing structure: no speaker separation, no precise timing, and no way to reliably anchor events in the audio. When you combine Gladia’s accurate multilingual async transcription with diarization and word-level timestamps, the transcript becomes safe to automate against.
You can generate error-resistant summaries and action items, clearly tied to who said them. You can build subtitles aligned at the word level. You can drive CRM and ticket enrichment off correctly captured names, emails, and numbers — not random guesses. And because this all comes from a single API, you keep your pipeline simple and auditable.
Why It Matters:
- Fewer broken workflows
Speaker-aware, time-aligned transcripts reduce errors in notes, summaries, QA review, and CRM syncs — especially on noisy calls and multilingual meetings. - Stronger automation and analytics
With “who + when” attached to every word, you can build reliable triggers, precise coaching feedback, and trustworthy reporting on agent behavior and customer sentiment.
Quick Recap
To enable speaker diarization and word-level timestamps in Gladia’s async transcription API, you extend the same transcription request you already use: turn on the diarization and timestamp options, then consume the enriched response. The result is a single API that gives you accurate, multilingual transcripts with “who said what, when” — stable enough to power subtitles, summaries, CRM enrichment, and analytics in real-world audio conditions.