deterministic transcript normalization
token-batched module orchestration
concrete glossary, homophones, spoken_word, and grammar modules built on reusable proposal / validator contracts
structured run reporting and work-dir diagnostics

The previous working implementation has been preserved as audita_prototype inside this repository. Its full regression suite lives under tests/audita_prototype.

Development

This project is set up for uv.

uv sync --extra dev
uv run pytest

Usage

Process a transcript with the current framework implementation:

uv run audita process transcript.json --glossary glossary.yaml --output corrected.json

The framework currently runs this default module sequence:

glossary
homophones
glossary
spoken_word
grammar

Resolved run instance names are auto-numbered for repeats, so the default report pipeline is:

glossary_1
homophones
glossary_2
spoken_word
grammar

The default module sequence is fully implemented today:

glossary proposes glossary-supported acoustic corrections
homophones proposes conservative homophone and mistranscription corrections
spoken_word proposes conservative dysfluency cleanup
grammar proposes punctuation, capitalization, and spacing cleanup only

To run a custom module sequence, pass --modules:

uv run audita process transcript.json --glossary glossary.yaml --modules grammar --output corrected.json

To also write a structured JSON report:

uv run audita process transcript.json --glossary glossary.yaml --output corrected.json --report-json report.json

From a checked-out repository, you can also use the root launcher:

./audita process transcript.json --glossary glossary.yaml --output corrected.json

For a system-wide command, install the source tree under /usr/local/src/audita, sync dependencies there, and symlink the root launcher into your PATH:

cd /usr/local/src/audita
uv sync --extra dev
ln -s /usr/local/src/audita/audita /usr/local/bin/audita
audita process transcript.json --glossary glossary.yaml --output corrected.json

Without --output, Audita writes the corrected transcript JSON to stdout and progress logs to stderr. --report-json writes a separate machine-readable run report and never mixes report data into stdout.

Useful configuration can be supplied by CLI flag or environment variable. CLI flags take precedence over environment variables. Normal runs now require LLM API credentials, because the glossary, homophones, spoken_word, and grammar modules make real LLM calls. AUDITA_LLM_API_KEY and --llm-api-key are the preferred provider-neutral credential surfaces, while OPENROUTER_API_KEY remains supported as a backward-compatible fallback.

Environment variable	CLI flag	Default	Purpose
`AUDITA_MODULES`	`--modules`	`glossary,homophones,glossary,spoken_word,grammar`	Comma-separated logical module keys to run; CLI overrides the environment value
`AUDITA_LLM_API_KEY`	`--llm-api-key`	unset	Preferred provider-neutral LLM API credential; CLI overrides both environment-key variants
`AUDITA_MODEL`	`--model`	`openrouter/google/gemma-4-31b-it`	LLM model name sent to the configured OpenAI-compatible endpoint
`AUDITA_BASE_URL`	`--base-url`	`https://openrouter.ai/api/v1`	OpenAI-compatible API base URL
`AUDITA_MAX_RETRIES`	`--max-retries`	`3`	Maximum Instructor retries for structured responses
`AUDITA_MAX_SECTION_TOKENS`	`--max-section-tokens`	`6144`	Maximum estimated tokens per transcript batch
`AUDITA_GLOSSARY_CONFIDENCE_THRESHOLD`	`--glossary-confidence-threshold`	`0.8`	Minimum confidence required for glossary proposals to survive validation
`AUDITA_GRAMMAR_CONFIDENCE_THRESHOLD`	`--grammar-confidence-threshold`	`0.8`	Minimum confidence required for grammar proposals to survive validation
`AUDITA_HOMOPHONES_CONFIDENCE_THRESHOLD`	`--homophones-confidence-threshold`	`0.8`	Minimum confidence required for homophone proposals to survive validation
`AUDITA_SPOKEN_WORD_CONFIDENCE_THRESHOLD`	`--spoken-word-confidence-threshold`	`0.8`	Minimum confidence required for spoken-word proposals to survive validation
`AUDITA_NORMALIZE_MAX_SEGMENT_GAP`	`--normalize-max-segment-gap`	`4.0`	Same-speaker gaps eligible for deterministic merging
`AUDITA_NORMALIZE_ELLIPSIS_GAP`	`--normalize-ellipsis-gap`	`3.5`	Same-speaker gaps above this value are joined with `...`
`AUDITA_NORMALIZE_MAX_SEGMENT_DURATION`	`--normalize-max-segment-duration`	`60.0`	Maximum merged segment duration
`AUDITA_NORMALIZE_MAX_SEGMENT_TOKENS`	`--normalize-max-segment-tokens`	`2048`	Maximum merged segment prompt payload size
`AUDITA_WORK_DIR`	`--work-dir`	`/tmp/audita`	Per-run scratch diagnostics directory
`AUDITA_WORK_DIR_RETENTION`	`--work-dir-retention`	`auto`	Whether to retain the per-run work directory: `auto`, `always`, or `never`

Set AUDITA_MODULES=grammar to run only the grammar module by default, or override it per command with --modules.

OpenRouter remains the default out of the box:

export AUDITA_LLM_API_KEY=your-openrouter-key
audita process transcript.json --glossary glossary.yaml --output corrected.json

You can point Audita at any OpenAI-compatible endpoint by changing AUDITA_BASE_URL and, if needed, AUDITA_MODEL. For example, a local vLLM server:

export AUDITA_LLM_API_KEY=local-dev-key
export AUDITA_BASE_URL=http://localhost:8000/v1
export AUDITA_MODEL=meta-llama/Llama-3.1-8B-Instruct
audita process transcript.json --glossary glossary.yaml --output corrected.json

Or the actual OpenAI API:

export AUDITA_LLM_API_KEY=your-openai-key
export AUDITA_BASE_URL=https://api.openai.com/v1
export AUDITA_MODEL=gpt-4.1-mini
audita process transcript.json --glossary glossary.yaml --output corrected.json

AUDITA_WORK_DIR stores per-run diagnostics while processing. Under the default AUDITA_WORK_DIR_RETENTION=auto, clean successful runs are removed, while failed runs and successful runs with final skipped corrections are preserved. Use always to keep every run directory and never to remove successful run directories even when skips remain. Failed runs always preserve the run directory and include an authoritative report.json alongside normalization and prompt/response diagnostics.

Prototype Archive

The archived prototype remains importable as audita_prototype and is still covered by its original regression suite. This is intentional: the new audita package is a framework-oriented rewrite, not a thin wrapper around the old code.