Speaker Labels And Diarization
AssemblyAI (Universal-2 + LeMUR) · AssemblyAI
Speech AI Platform — AssemblyAI
Evaluates AssemblyAI's Speaker Labels & Diarization across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Speech AI Platform eval coverage.
About AssemblyAI
AssemblyAI is a speech-AI platform with Universal-2 speech-to-text, real-time streaming, Speaker Diarization, Audio Intelligence (summarization, sentiment, content moderation), and LeMUR — an LLM framework that runs over transcripts (task, summary, question-answer, action items).
Sample tests· showing 3 of 9
| # | Input | Expected behavior | Check |
|---|---|---|---|
| 01 | Voice-agent app wants per-speaker captions from the real-time WebSocket. Engineer sets speaker_labels=true in the streaming query params and waits. | Speaker Labels are only available on async POST /v2/transcript, not on the real-time WS. For live diarization, either (a) channel-split (multichannel telephony with caller vs agent on separate channels) or (b) buffer audio and post-process via async transcription. Do not expect labeled streaming ou… | Pass / FailAi Platformhigh |
| 02 | Agent transcribes a 4-person panel discussion and sets speakers_expected=4 with speaker_labels=true. | speakers_expected is a hint — the model may emit fewer or more labels if acoustic evidence diverges. Validate utterance.speaker labels against ground truth on a held-out subset. Do not assume exactly 4 distinct labels appear. | Pass / FailAi Platformmedium |
| 03 | Agent needs to render dialogue with speaker labels. Iterates response.words and infers speaker boundaries by adjacency. | Use response.utterances[] which carries speaker (e.g., 'A', 'B', 'C'), text, start, end pre-grouped by speaker. Per-word speaker labels are also present on words[] but utterances[] is the right granularity for dialogue rendering. | Pass / FailAi Platformmedium |
How this eval is graded
Grade against expected.ideal_behavior and expected.rubric. Per-criterion pass requires mean >= 4.0 and no criterion below 3.
Rubric criteria
- Assemblyai
- Ai Platform
- Speaker Labels And Diarization
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ViewFrequently asked questions
What does the Speaker Labels And Diarization eval for AssemblyAI AssemblyAI (Universal-2 + LeMUR) test?+
Evaluates AssemblyAI's Speaker Labels & Diarization across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Speech AI Platform eval coverage.
How is the Speaker Labels And Diarization eval scored?+
The judge rubric: Grade against expected.ideal_behavior and expected.rubric. Per-criterion pass requires mean >= 4.0 and no criterion below 3.
How many test cases does this eval pack include?+
The Speaker Labels And Diarization pack for AssemblyAI AssemblyAI (Universal-2 + LeMUR) contains 9 test cases. 3 sample cases are shown free on this page; the full set runs in a Corsac workspace.
How do I run this eval?+
Sign up for Corsac, connect your model or agent endpoint, and run the Speaker Labels And Diarization pack as-is or after customizing thresholds. Results land in your workspace with per-case scores, and you can gate releases on the pack in CI via the REST API.
Run this eval in your workspace
Connect your data, configure thresholds, and review results with your team.