SttEfficacyScorer
Scores transcript fidelity vs spoken audio (1-10). Parses JSON score and reasoning; metadata may include wer_estimate, omissions, hallucinations, substitutions, severity when returned by the model.
Overview
Scores transcript fidelity vs spoken audio (1-10). Parses JSON score and reasoning; metadata may include wer_estimate, omissions, hallucinations, substitutions, severity when returned by the model.
Use Cases
- General AI evaluation and quality assessment
How It Works
This scorer uses LLM-as-Judge technology to evaluate responses. It prompts a large language model with specific evaluation criteria and the content to assess, then analyzes the LLM's judgment to produce a score and detailed reasoning.
Input Schema
| Parameter | Type | Required | Description |
|---|---|---|---|
| model | any | Yes | - |
| stt_data | any | Yes | - |
| stt_data.metadata | any | Yes | - |
Output Schema
| Field | Type | Description |
|---|---|---|
| score | any | - |
| passed | any | - |
| reasoning | any | - |
| metadata | any | - |
Score Interpretation
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Frequently Asked Questions
When should I use this scorer?
Use SttEfficacyScorer when you need to evaluate stt_efficacy and audio aspects of your AI outputs. It's particularly useful for general ai evaluation and quality assessment.
Why doesn't this scorer need expected output?
This scorer evaluates quality aspects that don't require comparison against a reference answer. It uses the system prompt and context as the implicit ground truth.
Can I customize the threshold?
Yes, the default threshold of 7 can be customized when configuring the scorer.
Quick Info
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