{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_lugs","slug":"lugs","name":"Lugs","type":"product","url":"https://lugs.ai","page_url":"https://unfragile.ai/lugs","categories":["voice-audio"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_lugs__cap_0","uri":"capability://data.processing.analysis.dual.source.audio.capture.and.transcription","name":"dual-source audio capture and transcription","description":"Simultaneously captures audio from system output (speakers/application audio) and microphone input using OS-level audio routing APIs, then routes both streams through a local or hybrid transcription engine. This dual-stream architecture enables comprehensive captioning of both incoming speech and computer-generated audio without requiring separate recording applications or manual audio mixing.","intents":["I need to caption both what I'm saying and what's playing from my screen in real-time","I want to transcribe video calls where I need captions for both participants and system notifications","I'm creating accessibility-focused content and need simultaneous captions for multiple audio sources"],"best_for":["Content creators producing videos with mixed audio sources","Accessibility advocates building inclusive workflows","Researchers conducting interviews with system audio context"],"limitations":["Dual-stream processing increases CPU overhead compared to single-source transcription","Audio routing APIs differ significantly between Windows/macOS/Linux, limiting cross-platform consistency","Real-time sync between microphone and system audio streams may drift under high system load"],"requires":["Windows 10+ or macOS 10.14+ or Linux with PulseAudio/ALSA","Microphone hardware with proper OS-level driver support","Minimum 4GB RAM for concurrent stream processing"],"input_types":["audio stream (system output)","audio stream (microphone input)"],"output_types":["text (real-time captions)","text (full transcript)"],"categories":["data-processing-analysis","accessibility"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_lugs__cap_1","uri":"capability://data.processing.analysis.local.first.real.time.transcription.engine","name":"local-first real-time transcription engine","description":"Processes audio streams through an on-device transcription model (likely Whisper or similar) that runs locally without sending audio to cloud servers, enabling sub-second latency for caption generation while maintaining privacy. The local architecture trades off some accuracy potential for immediate responsiveness and eliminates network dependency.","intents":["I need captions to appear instantly without waiting for cloud API round-trips","I want to transcribe sensitive or confidential audio without uploading to external servers","I'm working offline or in environments with unreliable internet connectivity"],"best_for":["Privacy-conscious users handling confidential content","Teams in low-bandwidth or offline environments","Developers building accessibility features requiring sub-500ms latency"],"limitations":["Local model accuracy typically 5-15% lower than cloud-based alternatives (Rev, Google Cloud Speech-to-Text) due to smaller model size constraints","GPU acceleration required for real-time performance; CPU-only processing introduces 2-5 second latency per audio chunk","Model updates require manual application updates rather than automatic cloud-side improvements","Limited language support compared to enterprise transcription services (likely 10-20 languages vs 100+)"],"requires":["GPU with CUDA 11.0+ or Metal support (macOS) for real-time performance","Minimum 8GB RAM for model inference","500MB-2GB disk space for model weights"],"input_types":["audio stream (PCM, 16kHz sample rate)"],"output_types":["text (word-level timestamps)","text (confidence scores per word)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_lugs__cap_2","uri":"capability://automation.workflow.system.level.caption.overlay.and.display","name":"system-level caption overlay and display","description":"Renders real-time captions as a system-level overlay that persists across all applications and windows, using native OS graphics APIs (DirectX on Windows, Metal on macOS) to ensure captions remain visible regardless of active application. The overlay system includes positioning, styling, and transparency controls to minimize visual obstruction while maintaining readability.","intents":["I want captions to appear on top of any application I'm using without switching windows","I need to customize caption appearance (size, color, position) to match my accessibility needs","I'm watching videos or attending meetings and need persistent captions across different apps"],"best_for":["Users with hearing impairments requiring persistent visual feedback","Content creators monitoring captions while recording or streaming","Accessibility teams standardizing caption display across organizational workflows"],"limitations":["System-level overlay may conflict with full-screen exclusive mode applications (some games, video players)","Rendering overhead adds 50-150ms to frame composition on lower-end GPUs","Caption positioning logic must account for multi-monitor setups, which adds complexity and potential edge cases","Some applications with custom rendering pipelines may not respect overlay z-order"],"requires":["Windows 10+ with DirectX 11 or macOS 10.14+ with Metal support","Administrator/elevated privileges for system-level overlay injection","GPU with dedicated VRAM (2GB minimum) for overlay rendering"],"input_types":["text (transcribed captions)","metadata (timestamps, speaker identification)"],"output_types":["visual overlay (rendered captions on screen)"],"categories":["automation-workflow","accessibility"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_lugs__cap_3","uri":"capability://data.processing.analysis.speaker.identification.and.diarization","name":"speaker identification and diarization","description":"Analyzes audio characteristics (pitch, timbre, speech patterns) to distinguish between different speakers in real-time, labeling transcript segments with speaker identifiers or names. The diarization engine uses voice embedding models to cluster similar voices and track speaker continuity across conversation segments, enabling multi-speaker transcripts without manual annotation.","intents":["I'm transcribing a meeting with multiple participants and need to know who said what","I want to generate interview transcripts with clear speaker attribution","I'm creating accessible content from multi-speaker audio and need automatic speaker labels"],"best_for":["Researchers conducting multi-participant interviews","Meeting organizers generating accessible transcripts","Content creators producing podcasts or panel discussions"],"limitations":["Speaker diarization accuracy degrades with overlapping speech (2+ speakers talking simultaneously), typically achieving 70-85% accuracy vs 95%+ for single-speaker segments","Requires minimum 10-15 seconds of speech per speaker for reliable voice embedding; short interjections may be misattributed","Cannot identify speakers by name without pre-enrollment or external speaker database integration","Performs poorly with heavy accents, speech impediments, or non-native speakers due to training data bias"],"requires":["Audio with clear speaker separation (SNR > 15dB recommended)","Minimum 30 seconds of total audio for reliable clustering","GPU acceleration for real-time diarization (CPU-only adds 3-5 second latency)"],"input_types":["audio stream (multi-speaker)"],"output_types":["text (transcript with speaker labels)","structured data (speaker segments with timestamps)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_lugs__cap_4","uri":"capability://data.processing.analysis.transcript.export.and.format.conversion","name":"transcript export and format conversion","description":"Converts real-time transcription output into multiple standard formats (SRT, VTT, JSON, plain text) with configurable metadata (timestamps, speaker labels, confidence scores). The export pipeline includes options for transcript segmentation (by speaker, by time interval, by sentence) and can generate both human-readable and machine-parseable outputs for downstream processing.","intents":["I need to export captions as SRT files for video editing software","I want to save transcripts in JSON format for programmatic processing","I'm generating VTT subtitles for web video players"],"best_for":["Video editors integrating captions into post-production workflows","Developers building transcript processing pipelines","Content creators distributing captions across multiple platforms"],"limitations":["SRT/VTT format limitations (no speaker labels, limited styling) require lossy conversion from full transcript metadata","Timestamp accuracy depends on upstream transcription engine; drift in real-time processing propagates to exported files","Large transcripts (>2 hours) may require chunking for compatibility with some video editing software","Custom format extensions (e.g., speaker confidence scores) not supported by standard subtitle formats"],"requires":["Completed or in-progress transcript with timestamp data","Write permissions to output directory"],"input_types":["structured data (transcript with timestamps, speaker labels, confidence scores)"],"output_types":["text (SRT subtitle format)","text (WebVTT subtitle format)","text (plain text transcript)","structured data (JSON with full metadata)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_lugs__cap_5","uri":"capability://data.processing.analysis.audio.quality.monitoring.and.noise.detection","name":"audio quality monitoring and noise detection","description":"Continuously analyzes incoming audio streams to detect signal-to-noise ratio (SNR), clipping, background noise patterns, and audio codec issues in real-time. The monitoring system provides visual/textual feedback on audio quality and can trigger automatic gain adjustment or noise suppression to maintain transcription accuracy, with configurable thresholds for different use cases.","intents":["I want to know if my microphone audio quality is good enough for accurate transcription","I need to detect and suppress background noise before it reaches the transcription engine","I'm monitoring audio health during a live stream or meeting to catch technical issues early"],"best_for":["Content creators ensuring broadcast-quality audio","Accessibility teams troubleshooting transcription accuracy issues","Remote workers optimizing microphone setup"],"limitations":["Noise detection heuristics may misclassify speech in noisy environments as background noise, leading to false positives","Real-time noise suppression (if implemented) introduces 100-300ms latency and may remove legitimate speech components","SNR calculation requires baseline noise profile, which takes 5-10 seconds to establish at application startup","Cannot distinguish between different noise types (fan noise vs traffic vs speech) without specialized acoustic models"],"requires":["Continuous audio stream input","Minimum 2-3 seconds of audio for baseline noise characterization"],"input_types":["audio stream (raw PCM)"],"output_types":["structured data (SNR, noise level, clipping detection)","text (quality warnings/alerts)","audio stream (optionally noise-suppressed)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_lugs__cap_6","uri":"capability://automation.workflow.keyboard.shortcut.and.hotkey.customization","name":"keyboard shortcut and hotkey customization","description":"Allows users to define custom keyboard shortcuts for common transcription operations (start/stop recording, pause/resume, export, toggle overlay visibility) with conflict detection against system and application hotkeys. The hotkey system uses OS-level keyboard hooks to capture shortcuts globally, even when the application window is not in focus, enabling hands-free control during active transcription.","intents":["I want to start/stop transcription without switching windows using a custom hotkey","I need to pause transcription during sensitive conversations without touching the mouse","I'm setting up accessibility controls for users who cannot use the GUI"],"best_for":["Power users optimizing transcription workflows","Accessibility advocates building keyboard-only interfaces","Teams standardizing hotkey configurations across organizations"],"limitations":["Global hotkey hooks require elevated privileges on Windows/macOS, which may trigger security warnings","Hotkey conflicts with system shortcuts (e.g., Windows key combinations) may be unresolvable without OS-level configuration","Some applications (games, full-screen video players) may intercept hotkeys before Lugs receives them","Hotkey customization is per-user; no built-in mechanism for sharing configurations across team members"],"requires":["Administrator/elevated privileges for global hotkey registration","Windows 10+ or macOS 10.14+ with native hotkey API support"],"input_types":["keyboard input (hotkey combinations)"],"output_types":["action (start/stop/pause transcription, export, toggle overlay)"],"categories":["automation-workflow","accessibility"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_lugs__cap_7","uri":"capability://search.retrieval.transcript.search.and.indexing","name":"transcript search and indexing","description":"Indexes completed transcripts using full-text search with support for speaker filtering, timestamp-based range queries, and confidence score thresholds. The search engine enables users to quickly locate specific phrases or speakers within large transcripts without manual scrolling, with results linked back to original timestamps for playback or export.","intents":["I need to find a specific phrase mentioned in a 2-hour meeting transcript","I want to extract all segments where a particular speaker contributed","I'm searching for low-confidence transcription segments that may need manual review"],"best_for":["Researchers analyzing large interview or meeting transcripts","Content creators extracting highlights from long recordings","Accessibility teams auditing transcription quality"],"limitations":["Search indexing adds latency to transcript completion; large transcripts (>1 hour) may require 10-30 seconds to index","Full-text search does not support fuzzy matching or phonetic search, limiting ability to find misheard words","Timestamp-based range queries require accurate upstream timestamp data; drift in real-time processing propagates to search results","Search index stored locally; no built-in mechanism for searching across multiple transcript files or shared transcript databases"],"requires":["Completed transcript with full metadata (timestamps, speaker labels, confidence scores)","Minimum 50MB free disk space for search index (scales with transcript volume)"],"input_types":["text (search query)","structured data (filter criteria: speaker, timestamp range, confidence threshold)"],"output_types":["structured data (search results with timestamps and context snippets)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_lugs__cap_8","uri":"capability://data.processing.analysis.multi.language.transcription.with.automatic.language.detection","name":"multi-language transcription with automatic language detection","description":"Detects the language of incoming audio automatically and switches transcription models in real-time to match detected language, supporting a curated set of languages (likely 10-20 based on local model constraints). The language detection uses audio feature analysis to identify language within the first few seconds of speech, enabling seamless transcription of multilingual conversations.","intents":["I'm transcribing a conversation that switches between English and Spanish","I want automatic language detection without manually selecting the language upfront","I'm working with international teams and need transcription in multiple languages"],"best_for":["International teams with multilingual conversations","Researchers studying code-switching or multilingual speech","Content creators producing global content"],"limitations":["Automatic language detection accuracy drops to 70-80% when audio contains heavy accents or code-switching (mixing languages within sentences)","Language switching mid-conversation may cause transcription errors during the transition period (first 2-3 seconds after switch)","Limited language support (likely 10-20 languages) compared to cloud services like Google Cloud Speech-to-Text (100+ languages)","Transcription accuracy varies significantly by language; languages with less training data (e.g., low-resource languages) achieve 60-75% accuracy vs 95%+ for English"],"requires":["Audio with clear language identification (SNR > 10dB recommended)","Minimum 2-3 seconds of speech for reliable language detection","GPU with sufficient VRAM to load multiple language models (8GB+ recommended)"],"input_types":["audio stream (multilingual)"],"output_types":["text (transcript with language labels per segment)","structured data (detected language with confidence score)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_lugs__cap_9","uri":"capability://text.generation.language.transcript.editing.and.correction.interface","name":"transcript editing and correction interface","description":"Provides a text editor interface for manual correction of transcription errors with word-level timestamp preservation and speaker label editing. The editor includes undo/redo functionality, batch find-and-replace for systematic corrections, and exports corrected transcripts while maintaining alignment with original audio timestamps for caption synchronization.","intents":["I need to fix transcription errors before exporting captions for video","I want to correct speaker labels that were misidentified by the diarization engine","I'm doing batch corrections on repeated transcription errors (e.g., proper nouns)"],"best_for":["Content creators ensuring caption accuracy before publication","Accessibility teams auditing and correcting transcripts","Researchers preparing interview transcripts for analysis"],"limitations":["Manual editing breaks real-time workflow; corrections must be made after transcription completes","Timestamp preservation requires careful implementation; editing text length changes may desynchronize timestamps with audio","Batch find-and-replace without context awareness may introduce errors (e.g., replacing 'bank' in both 'river bank' and 'financial bank' with different corrections)","No collaborative editing support; multiple users cannot simultaneously edit the same transcript"],"requires":["Completed transcript with word-level timestamps","Minimum 100MB free disk space for undo/redo history"],"input_types":["text (transcript)","structured data (word-level timestamps, speaker labels)"],"output_types":["text (corrected transcript)","structured data (corrected transcript with preserved timestamps)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Windows 10+ or macOS 10.14+ or Linux with PulseAudio/ALSA","Microphone hardware with proper OS-level driver support","Minimum 4GB RAM for concurrent stream processing","GPU with CUDA 11.0+ or Metal support (macOS) for real-time performance","Minimum 8GB RAM for model inference","500MB-2GB disk space for model weights","Windows 10+ with DirectX 11 or macOS 10.14+ with Metal support","Administrator/elevated privileges for system-level overlay injection","GPU with dedicated VRAM (2GB minimum) for overlay rendering","Audio with clear speaker separation (SNR > 15dB recommended)"],"failure_modes":["Dual-stream processing increases CPU overhead compared to single-source transcription","Audio routing APIs differ significantly between Windows/macOS/Linux, limiting cross-platform consistency","Real-time sync between microphone and system audio streams may drift under high system load","Local model accuracy typically 5-15% lower than cloud-based alternatives (Rev, Google Cloud Speech-to-Text) due to smaller model size constraints","GPU acceleration required for real-time performance; CPU-only processing introduces 2-5 second latency per audio chunk","Model updates require manual application updates rather than automatic cloud-side improvements","Limited language support compared to enterprise transcription services (likely 10-20 languages vs 100+)","System-level overlay may conflict with full-screen exclusive mode applications (some games, video players)","Rendering overhead adds 50-150ms to frame composition on lower-end GPUs","Caption positioning logic must account for multi-monitor setups, which adds complexity and potential edge cases","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:31.447Z","last_scraped_at":"2026-04-05T13:23:42.560Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=lugs","compare_url":"https://unfragile.ai/compare?artifact=lugs"}},"signature":"8oNWCFPPkaQ2RXVEQnF0P15Zi67oWrmQEuQQJSYfhzdYn2pEf3puBOWL3PayQYgRyqFVhyvgwzJ7GVgqWFbbBA==","signedAt":"2026-06-23T08:20:55.255Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/lugs","artifact":"https://unfragile.ai/lugs","verify":"https://unfragile.ai/api/v1/verify?slug=lugs","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}