Model Selection Language Detection
Deepgram · Deepgram
Speech AI Platform — Deepgram
Evaluates Deepgram's Model Selection & Language Detection across 8 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Speech AI Platform eval coverage.
About Deepgram
Deepgram is a speech-AI platform offering streaming and batch speech-to-text (Nova), Aura text-to-speech, speaker diarization, redaction, and smart formatting across 30+ languages — used by voice-agent platforms, contact centers, and media teams.
Sample tests· showing 3 of 8
| # | Input | Expected behavior | Check |
|---|---|---|---|
| 01 | New wss://api.deepgram.com/v1/listen integration; latency and accuracy tradeoffs; Nova-3 is current flagship. | Default to model=nova-3 for English streaming agent; document fallback path to nova-2 if SKU constraints; measure WER/latency empirically per deployment. | Pass / FailAi Platformhigh |
| 02 | Existing production URL wss://api.deepgram.com/v1/listen?model=nova-2; ops wants upgrade checklist. | Document param change to model=nova-3; run shadow traffic comparison; rollback switch retained; note nova-2 for constrained runtimes if needed. | Pass / FailAi Platformmedium |
| 03 | Multilingual archive with rare dialect; engineer proposes whisper for batch-only offline pipeline. | Use whisper on batch POST /v1/listen when docs indicate language coverage advantage; benchmark against nova-3; note whisper streaming limitations if agent needs live. | Pass / FailAi Platformmedium |
How this eval is graded
Grade the agent's response against expected.ideal_behavior and expected.rubric. Score 1-5 per criterion (4+ passes; no criterion below 3). Penalize failure_modes. For safety outcomes, correct behavior is refuse/abstain or redact per policy.
Rubric criteria
- Deepgram
- Ai Platform
- Model Selection Language Detection
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ViewFrequently asked questions
What does the Model Selection Language Detection eval for Deepgram Deepgram test?+
Evaluates Deepgram's Model Selection & Language Detection across 8 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 Model Selection Language Detection eval scored?+
The judge rubric: Grade the agent's response against expected.ideal_behavior and expected.rubric. Score 1-5 per criterion (4+ passes; no criterion below 3). Penalize failure_modes. For safety outcomes, correct behavior is refuse/abstain or redact per policy.
How many test cases does this eval pack include?+
The Model Selection Language Detection pack for Deepgram Deepgram contains 8 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 Model Selection Language Detection 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.