Module Catalog¶
This catalog is generated from the ModuleRegistry.
Regenerate: python3 scripts/release/regen_module_docs.py (or make docs-gen).
Available Modules¶
Module |
Description |
Category |
Dependencies |
Determinism |
|---|---|---|---|---|
acts |
Dialogue Act Classification |
medium |
None |
T0 |
conversation_loops |
Conversation Loop Detection |
light |
None |
T0 |
contagion |
Emotional Contagion Detection |
heavy |
emotion |
T1 |
emotion |
Emotion-associated vocabulary (NRC lexicon) |
medium |
None |
T1 |
contextual_emotion |
Contextual emotion (broad classifier, experimental) |
heavy |
None |
T2 |
fine_grained_emotion |
Fine-grained multi-label emotion (experimental) |
heavy |
None |
T2 |
entity_sentiment |
Entity-based Sentiment Analysis |
heavy |
ner, sentiment |
T1 |
affect_tension |
Emotion + Sentiment mismatch and tension indices |
medium |
emotion, sentiment |
T1 |
interactions |
Speaker Interaction Analysis |
medium |
None |
T0 |
ner |
Named Entity Recognition |
medium |
None |
T1 |
names |
People mentioned (PERSON entities catalog) |
medium |
ner |
T1 |
semantic_similarity |
Semantic similarity (batched embeddings, vectorized similarity) |
heavy |
None |
T1 |
sentiment |
Sentiment Analysis |
medium |
None |
T1 |
epistemic_markers |
Hedging / certainty / epistemic markers |
light |
None |
T0 |
keyphrases |
Keyphrase ranking (noun chunks / YAKE / KeyBERT) |
medium |
insight_eligibility |
T1 |
stats |
Statistical Analysis |
light |
None |
T0 |
topic_modeling |
Topic Modeling |
heavy |
insight_eligibility |
T2 |
bertopic |
BERTopic topic modeling (optional [bertopic]/[full] stack — see docs/dev/bertopic_optional_module.md) |
heavy |
insight_eligibility |
T2 |
transcript_output |
Generate human readable transcripts |
light |
None |
T0 |
simplified_transcript |
Simplified transcript (tics, agreements, repetitions removed) |
light |
None |
T0 |
understandability |
Understandability Analysis |
medium |
None |
T0 |
lexical_diversity |
Lexical diversity metrics (TTR, MTLD, hapax rate) |
light |
None |
T0 |
wordclouds |
Word Cloud Generation |
light |
insight_eligibility |
T1 |
tics |
Verbal Tics Analysis |
light |
None |
T0 |
transcript_quality |
ASR Confidence |
light |
None |
T0 |
insight_eligibility |
Shared content-vs-style insight eligibility pipeline |
light |
tics |
T0 |
temporal_dynamics |
Temporal Dynamics Analysis |
medium |
None |
T1 |
qa_analysis |
Question-Answer Pairing and Response Quality |
medium |
acts |
T1 |
pauses |
Silence and Timing Analysis |
light |
None |
T0 |
echoes |
Quote/Echo/Paraphrase Detection |
medium |
None |
T1 |
politeness |
Politeness / formality / directiveness markers |
light |
None |
T0 |
momentum |
Stall/Flow Index Analysis |
medium |
pauses |
T0 |
topic_shift |
Topic-shift chapter segmentation |
medium |
None |
T0 |
moments |
Ranked Moments Worth Revisiting |
light |
momentum |
T0 |
highlights |
Highlights and conflict moments (quote-forward) |
light |
insight_eligibility |
T0 |
summary |
Executive brief summary derived from highlights |
light |
highlights |
T0 |
narrative_summary |
Grounded executive narrative from deterministic summary (LLM) |
medium |
summary |
T2 |
llm_summary |
Abstractive transcript summary via local LLM |
medium |
None |
T2 |
llm_speaker_summary |
Abstractive per-speaker summaries via local LLM |
medium |
None |
T2 |
llm_action_items |
Extract structured action items via local LLM |
medium |
None |
T2 |
llm_custom_qa |
Answer custom questions against the transcript via local LLM |
medium |
None |
T2 |
chart_descriptions |
Per-chart LLM narratives (finalize-phase; after all charts) |
medium |
None |
T2 |
insights |
Content-first insights layer separated from style markers |
light |
insight_eligibility, highlights |
T0 |
voice_features |
Voice feature extraction and caching |
heavy |
None |
T0 |
voice_mismatch |
Tone–Text mismatch detection (sarcasm/discord moments) |
medium |
voice_features |
T0 |
voice_tension |
Conversation tension curve from voice |
medium |
voice_features |
T0 |
voice_fingerprint |
Per-speaker voice fingerprint baseline and drift |
medium |
voice_features |
T0 |
prosody_dashboard |
Prosody dashboard charts from voice features |
medium |
voice_features |
T0 |
voice_charts_core |
Voice charts core: pauses + rhythm indices |
medium |
voice_features |
T0 |
voice_contours |
Voice contours (slow; needs audio decode + pitch tracking) |
medium |
voice_features |
T0 |
Category Definitions¶
light: Fast, minimal computation (< 1 second per transcript)
medium: Moderate computation, may use ML models (1-10 seconds)
heavy: Intensive computation, large models (10+ seconds)
Determinism Tiers¶
T0: Fully deterministic - same input always produces same output
T1: Mostly deterministic - minor variations possible (e.g., floating point)
T2: Non-deterministic - output depends on model initialization or randomness