{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-vibe-transcribe","slug":"vibe-transcribe","name":"Vibe Transcribe","type":"webapp","url":"https://thewh1teagle.github.io/vibe/","page_url":"https://unfragile.ai/vibe-transcribe","categories":["automation"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-vibe-transcribe__cap_0","uri":"capability://data.processing.analysis.local.audio.video.transcription.with.offline.inference","name":"local-audio-video-transcription-with-offline-inference","description":"Performs speech-to-text transcription on audio and video files using local machine learning models (likely Whisper or similar) that run entirely on-device without cloud API calls. The system handles multiple audio formats and video containers, extracting audio streams and processing them through a local inference pipeline that maintains privacy and eliminates per-minute API costs.","intents":["I need to transcribe sensitive audio/video files without sending them to cloud services","I want to batch-transcribe hundreds of media files without incurring per-minute API costs","I need transcription to work offline or in air-gapped environments","I want to control the transcription model and parameters locally"],"best_for":["privacy-conscious teams handling confidential recordings","researchers processing large media datasets","developers building transcription features into offline-first applications","organizations with strict data residency requirements"],"limitations":["Local inference is slower than cloud APIs — typical processing at 0.5-2x realtime speed depending on hardware","Requires significant disk space for model weights (Whisper models range 140MB-3GB)","Quality and language support depend on the bundled model; no fine-tuning capability exposed","GPU acceleration optional but recommended; CPU-only transcription is very slow for long files"],"requires":["Python 3.8+ or Node.js 16+ (depending on implementation)","2-8GB RAM minimum (4GB+ recommended)","GPU with CUDA/Metal support optional but strongly recommended","Disk space for model weights (500MB-3GB depending on model size)"],"input_types":["audio files (MP3, WAV, FLAC, OGG, M4A, AAC)","video files (MP4, MKV, WebM, MOV, AVI)"],"output_types":["plain text transcription","timestamped transcript (SRT, VTT, JSON with timecodes)","structured JSON with confidence scores"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-vibe-transcribe__cap_1","uri":"capability://data.processing.analysis.multi.format.audio.video.extraction.and.normalization","name":"multi-format-audio-video-extraction-and-normalization","description":"Automatically detects and extracts audio streams from diverse video container formats (MP4, MKV, WebM, etc.) and normalizes audio to a standard format for downstream transcription processing. Uses container-aware parsing (likely FFmpeg or libav) to handle codec detection, stream selection, and format conversion without manual user configuration.","intents":["I have video files in mixed formats and need to extract audio consistently","I want to transcribe video without manually converting to audio first","I need to handle multiple audio tracks and select the right one automatically","I want to normalize audio sample rates and bit depths before transcription"],"best_for":["content creators processing video libraries with mixed codecs","researchers working with heterogeneous media collections","automation engineers building transcription pipelines"],"limitations":["Codec support depends on underlying FFmpeg/libav build; some proprietary codecs may not be available","Multi-track audio selection is automatic (usually first track) — no UI for manual selection in basic mode","Extraction adds 10-30% overhead to total processing time","Very large video files (>4GB) may require streaming extraction to avoid memory exhaustion"],"requires":["FFmpeg or libav installed and in system PATH","Support for H.264, VP8, VP9, AV1 video codecs (others depend on FFmpeg build)"],"input_types":["video containers (MP4, MKV, WebM, MOV, AVI, FLV, WMV)","audio files (passed through without extraction)"],"output_types":["normalized WAV or PCM audio stream","standardized sample rate (typically 16kHz for speech models)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-vibe-transcribe__cap_10","uri":"capability://tool.use.integration.api.server.for.programmatic.transcription.access","name":"api-server-for-programmatic-transcription-access","description":"Exposes transcription functionality via HTTP REST API, allowing external applications to submit files for transcription and retrieve results. Supports asynchronous job submission, polling for status, and webhook callbacks for result notification. Likely uses a lightweight HTTP framework (Flask, FastAPI) with job queue integration.","intents":["I want to integrate transcription into my web application","I need to submit transcription jobs from a remote client","I want webhook notifications when transcription completes","I need to build a transcription service for multiple users"],"best_for":["developers building transcription features into applications","teams running transcription as a shared service","organizations integrating with existing workflows via APIs"],"limitations":["API adds latency compared to direct library usage (50-200ms per request)","No built-in authentication — requires external auth layer for multi-user scenarios","File upload size limits depend on HTTP server configuration (typically 1-4GB)","Concurrent request handling limited by available hardware and worker processes"],"requires":["HTTP server (Flask, FastAPI, Express, etc.)","API documentation (likely OpenAPI/Swagger)","Job queue for async processing (optional but recommended)"],"input_types":["HTTP multipart file upload","JSON request body with file URL or base64-encoded audio"],"output_types":["JSON response with job ID and status","JSON transcription result with timing and metadata","webhook POST to client-provided URL on completion"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-vibe-transcribe__cap_2","uri":"capability://automation.workflow.batch.transcription.with.progress.tracking","name":"batch-transcription-with-progress-tracking","description":"Processes multiple audio/video files sequentially or in parallel with real-time progress reporting, queue management, and error handling. Tracks transcription status per file, allows pause/resume, and provides detailed logs of successes and failures without requiring manual orchestration or external job queue systems.","intents":["I need to transcribe 100+ files and want to see progress without polling","I want to pause a batch job and resume it later without losing progress","I need detailed error logs when transcription fails on specific files","I want to process files in parallel to use multi-core hardware efficiently"],"best_for":["teams processing large media archives","content creators with recurring transcription workflows","data engineers building ETL pipelines for media processing"],"limitations":["Parallel processing is limited by available GPU/CPU — too many concurrent jobs cause memory exhaustion or thrashing","No distributed processing across multiple machines — all work happens on a single device","Queue state is not persisted by default — restart loses progress unless explicitly saved","Progress tracking adds ~5-10% overhead due to status update I/O"],"requires":["Sufficient RAM for concurrent model instances (2-4GB per parallel job)","File system with reasonable I/O performance (network drives may bottleneck)"],"input_types":["file paths (local or network-accessible)","batch configuration (JSON or YAML with file lists and options)"],"output_types":["per-file transcription results","batch summary report (success count, failure count, total duration)","detailed error logs with file-specific diagnostics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-vibe-transcribe__cap_3","uri":"capability://data.processing.analysis.timestamp.aware.transcription.output.formatting","name":"timestamp-aware-transcription-output-formatting","description":"Generates transcriptions with precise word-level or sentence-level timestamps, supporting multiple output formats (SRT, VTT, JSON) for subtitle generation and media synchronization. Preserves timing information from the speech model's output and formats it according to standard subtitle specifications or custom JSON schemas.","intents":["I need SRT/VTT subtitles for video with accurate timing","I want to programmatically access transcription with timestamps for custom processing","I need to sync transcription back to video for editing or analysis","I want word-level timing for karaoke or interactive transcript features"],"best_for":["video editors and content creators","developers building interactive transcript UIs","accessibility teams generating subtitles for compliance"],"limitations":["Timestamp accuracy depends on underlying model — typically ±100-500ms error","Word-level timestamps require model support and add processing overhead","SRT/VTT format has limitations (max line length, no styling) — JSON is more flexible but less standardized","Timing can drift on very long files (>2 hours) due to cumulative model errors"],"requires":["Speech model with timestamp output support (Whisper provides segment-level timing)","Output format library (likely built-in or using standard subtitle libraries)"],"input_types":["transcription with timing metadata from speech model"],"output_types":["SRT (SubRip) format","VTT (WebVTT) format","JSON with word-level or segment-level timestamps","custom formats via template system"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-vibe-transcribe__cap_4","uri":"capability://data.processing.analysis.language.detection.and.multi.language.transcription","name":"language-detection-and-multi-language-transcription","description":"Automatically detects the spoken language in audio and selects the appropriate transcription model or language-specific parameters. Supports transcription of multiple languages without requiring users to manually specify language codes, with fallback handling for mixed-language content.","intents":["I have audio in unknown languages and need automatic detection","I want to transcribe multilingual content without preprocessing","I need to handle code-switching (mixing languages) in transcription","I want to transcribe non-English content with high accuracy"],"best_for":["international teams and organizations","content creators with multilingual audiences","researchers working with diverse language datasets"],"limitations":["Language detection is imperfect on short audio clips (<5 seconds) — confidence drops significantly","Not all languages are supported equally — some have lower accuracy than English","Code-switching (language mixing) may confuse detection and reduce transcription quality","Detection adds 2-5 seconds latency before transcription begins"],"requires":["Language detection model (likely built into Whisper or separate lightweight model)","Multi-language model support (Whisper supports 99+ languages)"],"input_types":["audio in any supported language"],"output_types":["detected language code (ISO 639-1 or 639-3)","transcription in detected language","confidence score for language detection"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-vibe-transcribe__cap_5","uri":"capability://data.processing.analysis.speaker.diarization.and.speaker.attribution","name":"speaker-diarization-and-speaker-attribution","description":"Identifies and separates different speakers in audio, attributing transcribed segments to specific speakers with labels (Speaker 1, Speaker 2, etc.). Uses voice activity detection and speaker embedding models to cluster and distinguish speakers without requiring speaker enrollment or training data.","intents":["I need to transcribe a meeting and know who said what","I want to generate speaker-labeled transcripts for interviews or podcasts","I need to identify when speakers change in multi-speaker audio","I want to extract individual speaker contributions for analysis"],"best_for":["meeting transcription and documentation","podcast and interview processing","research teams analyzing multi-speaker conversations"],"limitations":["Diarization accuracy degrades with >4-5 speakers — confusion increases exponentially","Requires clean audio; heavy background noise causes speaker misidentification","Cannot identify speakers by name without additional speaker enrollment or metadata","Adds 30-60% processing time overhead compared to single-speaker transcription","May struggle with similar voices or rapid speaker switching"],"requires":["Diarization model (e.g., Pyannote, speaker-diarization libraries)","Voice activity detection model","Sufficient audio quality (SNR >10dB recommended)"],"input_types":["multi-speaker audio"],"output_types":["transcription with speaker labels and timestamps","speaker segments with start/end times","speaker embedding vectors (for clustering or comparison)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-vibe-transcribe__cap_6","uri":"capability://automation.workflow.web.ui.for.drag.and.drop.transcription","name":"web-ui-for-drag-and-drop-transcription","description":"Provides a browser-based interface allowing users to drag-and-drop audio/video files for transcription without command-line interaction. The UI handles file upload, progress visualization, and result display, with optional export options. Likely runs a local HTTP server that processes files and streams results back to the browser.","intents":["I want to transcribe files without using the command line","I need a simple UI to upload and process multiple files at once","I want to see transcription results in the browser and copy/export them easily","I want non-technical team members to use transcription without setup"],"best_for":["non-technical users and content creators","teams wanting a shared transcription tool without cloud dependencies","organizations preferring GUI over CLI workflows"],"limitations":["Browser-based UI adds latency for large files due to HTTP overhead","File size limits may apply depending on browser and server configuration (typically 1-4GB practical limit)","No persistent session storage by default — results lost on browser close unless explicitly saved","Requires local HTTP server running — not suitable for pure static hosting"],"requires":["Modern web browser (Chrome, Firefox, Safari, Edge)","Local HTTP server (likely Node.js or Python-based)","Network access to localhost (127.0.0.1)"],"input_types":["file uploads via browser (drag-and-drop or file picker)"],"output_types":["HTML-rendered transcription","downloadable text/SRT/JSON files","copy-to-clipboard functionality"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-vibe-transcribe__cap_7","uri":"capability://data.processing.analysis.configurable.transcription.model.selection.and.parameters","name":"configurable-transcription-model-selection-and-parameters","description":"Allows users to choose between different model sizes (tiny, base, small, medium, large) and configure transcription parameters like language, temperature, and beam search settings. Exposes model-specific options without requiring code changes, enabling trade-offs between speed, accuracy, and resource usage.","intents":["I want to use a smaller model for faster transcription on low-end hardware","I need higher accuracy and can afford longer processing time","I want to fine-tune transcription behavior (e.g., temperature for confidence)","I need to transcribe in a specific language or dialect"],"best_for":["developers optimizing for specific hardware constraints","researchers experimenting with model parameters","teams balancing accuracy vs speed requirements"],"limitations":["Larger models require 4-8GB VRAM; tiny models still need 1-2GB","Parameter tuning requires domain knowledge — no automatic optimization","Model switching requires re-downloading weights (500MB-3GB per model)","Some parameters (e.g., beam search width) have diminishing returns and add latency"],"requires":["Model weights downloaded and cached locally","Configuration file or CLI arguments for parameter specification"],"input_types":["configuration JSON/YAML with model name and parameters","CLI flags for model selection"],"output_types":["transcription using selected model and parameters"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-vibe-transcribe__cap_8","uri":"capability://data.processing.analysis.transcription.result.export.to.multiple.formats","name":"transcription-result-export-to-multiple-formats","description":"Exports transcription results in multiple formats (plain text, SRT, VTT, JSON, Markdown) with customizable formatting and metadata inclusion. Supports batch export of multiple files and template-based formatting for custom output structures.","intents":["I need to export transcripts in SRT format for video editing","I want JSON output for programmatic processing","I need Markdown with timestamps for documentation","I want to batch-export 50 files in different formats"],"best_for":["content creators and video editors","developers integrating transcription into workflows","teams with diverse tool requirements"],"limitations":["Format conversion is lossless only for text — timing and metadata may be lost in plain text export","Custom templates require understanding of template syntax","Large batch exports can be slow (1-2 seconds per file for format conversion)"],"requires":["Template engine for custom formats (likely Jinja2 or similar)"],"input_types":["transcription data with timing and metadata"],"output_types":["plain text (.txt)","SRT subtitles (.srt)","WebVTT subtitles (.vtt)","JSON (.json)","Markdown (.md)","custom formats via templates"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-vibe-transcribe__cap_9","uri":"capability://automation.workflow.gpu.acceleration.with.fallback.to.cpu","name":"gpu-acceleration-with-fallback-to-cpu","description":"Automatically detects GPU availability (CUDA, Metal, ROCm) and uses GPU acceleration when available, with transparent fallback to CPU processing if GPU is unavailable or incompatible. Handles device memory management and batch sizing to prevent out-of-memory errors.","intents":["I want transcription to use my GPU if available, but work on CPU if not","I need automatic memory management to prevent crashes on large files","I want to transcribe on different machines with varying hardware","I need to optimize for available hardware without manual configuration"],"best_for":["users with heterogeneous hardware setups","developers building portable transcription tools","teams with both GPU and CPU-only machines"],"limitations":["GPU detection is framework-specific (PyTorch, TensorFlow, ONNX) — not all frameworks detect all GPU types","Memory management is conservative — may not fully utilize available VRAM","GPU acceleration provides 5-20x speedup depending on model size and hardware","CPU fallback is slow for large models (>1GB) — may take hours for long files"],"requires":["CUDA 11.0+ (for NVIDIA GPUs) or Metal (for Apple Silicon) or ROCm (for AMD)","Appropriate GPU drivers installed","PyTorch or similar framework with GPU support"],"input_types":["audio/video files"],"output_types":["transcription (same regardless of hardware used)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+ or Node.js 16+ (depending on implementation)","2-8GB RAM minimum (4GB+ recommended)","GPU with CUDA/Metal support optional but strongly recommended","Disk space for model weights (500MB-3GB depending on model size)","FFmpeg or libav installed and in system PATH","Support for H.264, VP8, VP9, AV1 video codecs (others depend on FFmpeg build)","HTTP server (Flask, FastAPI, Express, etc.)","API documentation (likely OpenAPI/Swagger)","Job queue for async processing (optional but recommended)","Sufficient RAM for concurrent model instances (2-4GB per parallel job)"],"failure_modes":["Local inference is slower than cloud APIs — typical processing at 0.5-2x realtime speed depending on hardware","Requires significant disk space for model weights (Whisper models range 140MB-3GB)","Quality and language support depend on the bundled model; no fine-tuning capability exposed","GPU acceleration optional but recommended; CPU-only transcription is very slow for long files","Codec support depends on underlying FFmpeg/libav build; some proprietary codecs may not be available","Multi-track audio selection is automatic (usually first track) — no UI for manual selection in basic mode","Extraction adds 10-30% overhead to total processing time","Very large video files (>4GB) may require streaming extraction to avoid memory exhaustion","API adds latency compared to direct library usage (50-200ms per request)","No built-in authentication — requires external auth layer for multi-user scenarios","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"ecosystem":0.25,"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":"2026-06-17T09:51:04.689Z","last_scraped_at":"2026-05-03T14:00:20.516Z","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=vibe-transcribe","compare_url":"https://unfragile.ai/compare?artifact=vibe-transcribe"}},"signature":"2SVzpFDWx/LMl0EjFx6+m8oaPS/1POdP5ccL8a4AXRNxmGcbRaexzJDojnVDGrBdkvEGklFNCHQvryMH2+GkBA==","signedAt":"2026-06-21T01:42:08.458Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/vibe-transcribe","artifact":"https://unfragile.ai/vibe-transcribe","verify":"https://unfragile.ai/api/v1/verify?slug=vibe-transcribe","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"}}