Speech to Text

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calibrate-agent stt

The interactive UI guides you through the full evaluation process:

Language selection — pick from 10+ supported Indic languages
Provider selection — choose providers (only those supporting your language are shown)
Input directory — path to the directory containing your audio files and reference transcripts

The input directory should have this structure:

/path/to/data/
├── stt.csv
└── audios/
    ├── audio_1.wav
    └── audio_2.wav

The stt.csv file contains the reference transcriptions:

id	text
audio_1	Hi
audio_2	Madam, my name is Geeta Shankar

All audio files should be in WAV format. The evaluation script expects files at audios/<id>.wav where <id> matches the id column in your CSV.

Refer to the sample dataset for a template.

Output directory — where results will be saved (defaults to ./out)
API keys — enter the API keys for the selected providers

The evaluation runs providers in parallel (max 2 at a time), showing the transcriptions as they are generated.

Evaluator configuration

By default, a text LLM judge — routed through OpenRouter (set OPENROUTER_API_KEY in your environment) — evaluates whether each transcription matches the reference text semantically using the built-in semantic_match evaluator; expand Default evaluator: semantic_match below for the exact system_prompt from the codebase. You can customize the judge model and add multiple evaluators by passing an optional config file with --config:

calibrate-agent stt -p deepgram google -i ./data -o ./out --config config.json

Each evaluator’s system_prompt is sent as the system message to its own dedicated LLM judge call (one call per evaluator, run in parallel). The user message contains the source/transcription pair. The config file supports:

{
  "evaluators": [
    {
      "id": "semantic-match-id",
      "name": "semantic_match",
      "system_prompt": "You are a highly accurate evaluator. You will be given a source text and a transcription. Mark True if the values represented by both strings match semantically.",
      "judge_model": "openai/gpt-5.4-mini"
    },
    {
      "id": "completeness-id",
      "name": "completeness",
      "system_prompt": "You are a highly accurate evaluator. You will be given a source text and a transcription. Mark True if all information from the source text is present in the transcription.",
      "judge_model": "openai/gpt-5.4-mini"
    }
  ]
}

Key	Type	Description
`evaluators`	array	List of evaluators. Each one becomes its own LLM call per row.
`evaluators[].id`	string	Optional unique id. Output `config.json` includes the raw `evaluators` list and an `evaluators_map` from id to name.
`evaluators[].name`	string	Unique evaluator name. Becomes the column name in the leaderboard.
`evaluators[].system_prompt`	string	Full system prompt used for this evaluator’s LLM judge call.
`evaluators[].judge_model`	string	OpenRouter model id for this evaluator (default: `openai/gpt-5.4-mini`). Use any model in the OpenRouter catalog.

Each evaluator also accepts:

Key	Type	Description
`type`	string	`"binary"` (default) or `"rating"`
`scale_min`	integer	Required when `type` is `"rating"`. Lowest allowed score.
`scale_max`	integer	Required when `type` is `"rating"`. Highest allowed score.

Binary evaluators produce per-row pass/fail and a mean pass-rate. Rating evaluators produce an integer score on your scale and a mean score in the leaderboard. When multiple evaluators are defined, each is scored independently — one LLM call per evaluator per row, all run in parallel — and appears as a separate column in the results and leaderboard. Refer to the sample config for a template.

The --config flag is optional. When omitted, a single built-in semantic_match evaluator scores semantic match between source and transcription.

semantic_match (default evaluator system prompt)

Matches DEFAULT_STT_EVALUATOR in calibrate/judges.py when no --config is passed.

You are a highly accurate evaluator evaluating the transcription output of an STT model.

You will be given two strings - one is the source string used to produce an audio and the other is the transcription of that audio.

You need to evaluate if the two strings are the same.

# Important Instructions:
- Check whether the values represented by both the strings match. E.g. if one string says 1,2,3 but the other string says "one, two, three" or "one, 2, three", they should be considered the same as their underlying value is the same. However, if the actual values itself are different, e.g. for the name of a person or address or the value of any other key detail - that difference should be noted.
- Ignore differences like a word being split up into more than 1 word by spaces. Look at whether the values mean the same in both the strings.
- Minor differences in values of entities (e.g. proper nouns, numbers) matter and should be considered an error.
- If all the "values" for the strings match, mark it as True. Else, False.

Output

Once all the providers have completed, it displays a leaderboard measuring key metrics along with bar charts for better visualization. Each provider output directory includes:

results.csv with one row per input audio. In addition to id, gt, pred, WER, evaluator scores, and evaluator reasoning, STT runs include audio_duration_seconds.
metrics.json with aggregate wer, evaluator summaries, and a cost object. The cost object includes provider, pricing_model, currency, billing_unit, total_seconds, audio_minutes, the native per-minute rate (cost_per_minute_currency, denominated in currency), and cost_usd. Providers billed in a non-USD currency (e.g. Sarvam in INR) report the native-currency total cost_in_currency and, when the live exchange rate is reachable, the conversion_rate used (native-currency units per 1 USD) and cost_usd. If the exchange rate can’t be fetched, the native-currency cost is still reported and cost_usd is omitted, so that provider is left out of the USD comparison rather than failing the run. Cost is a proportional estimate from bundled rates; provider invoices may differ because of per-file rounding, minimums, tiers, or regional pricing.
leaderboard/stt_leaderboard.xlsx with a cost_usd column in the summary sheet (the one cost figure comparable across providers; per-unit rates and native-currency figures stay in each provider’s metrics.json).

You can also view the generated transcript and metrics for each row of your dataset including the LLM judge score and reasoning.

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Evaluator configuration

Output

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​Evaluator configuration

​Output

Learn more about metrics

​Resources

Integrations

Get started

Evaluator configuration

Output

Resources