{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-space-xenova--whisper-web","slug":"xenova--whisper-web","name":"whisper-web","type":"model","url":"https://huggingface.co/spaces/Xenova/whisper-web","page_url":"https://unfragile.ai/xenova--whisper-web","categories":["voice-audio"],"tags":["static","region:us"],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-space-xenova--whisper-web__cap_0","uri":"capability://code.generation.editing.browser.based.speech.to.text.transcription","name":"browser-based speech-to-text transcription","description":"Runs OpenAI's Whisper model directly in the browser using ONNX Runtime Web, eliminating server-side processing and enabling offline transcription. The model executes client-side via WebAssembly, converting audio input streams to text without transmitting audio data to external servers. Supports multiple audio formats and languages through Whisper's multilingual capabilities.","intents":["transcribe audio files locally without sending data to cloud services","build privacy-preserving voice-to-text applications that work offline","integrate speech recognition into web apps without backend infrastructure","process multiple audio formats in-browser with minimal latency"],"best_for":["privacy-conscious developers building web applications","teams needing HIPAA/GDPR-compliant transcription without cloud dependencies","frontend engineers prototyping voice features without backend setup","users in regions with limited cloud service access"],"limitations":["Model inference speed depends on client device CPU/GPU capabilities — can be 5-30x slower than server-side on consumer hardware","Initial model download (1-3GB depending on model size) required on first use, with no built-in caching strategy across sessions","Browser memory constraints limit processing of very long audio files (>30 minutes) without chunking","No GPU acceleration in most browsers — relies on CPU or WebGL fallbacks, significantly slower than CUDA/Metal alternatives","Requires modern browser with WebAssembly support (Chrome 57+, Firefox 52+, Safari 14.1+)"],"requires":["Modern web browser with WebAssembly support","Minimum 2GB free RAM for model loading","Audio input device or file upload capability","JavaScript enabled","Internet connection for initial model download from HuggingFace"],"input_types":["audio/wav","audio/mp3","audio/ogg","audio/webm","audio/flac","microphone stream (MediaRecorder API)"],"output_types":["plain text transcription","JSON with timestamps and confidence scores","SRT/VTT subtitle format"],"categories":["code-generation-editing","speech-recognition","browser-native"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-xenova--whisper-web__cap_1","uri":"capability://text.generation.language.multilingual.speech.recognition.with.language.auto.detection","name":"multilingual speech recognition with language auto-detection","description":"Leverages Whisper's built-in multilingual capabilities to automatically detect and transcribe speech in 99+ languages without explicit language selection. The model uses a language identification token at the beginning of the decoding sequence to determine the source language, then applies language-specific acoustic and linguistic patterns for accurate transcription.","intents":["transcribe audio in unknown languages without manual language selection","build international applications that handle mixed-language content","process multilingual datasets without preprocessing language labels","support global users without requiring language preference configuration"],"best_for":["international SaaS platforms serving diverse language communities","content creators working with multilingual media","research teams analyzing global audio datasets","accessibility tools for non-English speakers"],"limitations":["Language detection accuracy degrades for short audio clips (<5 seconds) or heavily accented speech","Some low-resource languages (e.g., minority regional dialects) have lower accuracy than major languages","Code-switching (mixing multiple languages in single utterance) may produce inconsistent results","No explicit language confidence scores returned — only detected language label"],"requires":["Whisper model (any size: tiny, base, small, medium, large)","Audio sample with sufficient duration (10+ seconds recommended for reliable detection)","Browser with WebAssembly support"],"input_types":["audio/wav","audio/mp3","audio/ogg","audio/webm","audio/flac","raw PCM audio stream"],"output_types":["detected language code (ISO 639-1 format)","transcribed text in detected language","language confidence metadata"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-xenova--whisper-web__cap_2","uri":"capability://automation.workflow.real.time.audio.streaming.transcription","name":"real-time audio streaming transcription","description":"Processes continuous audio streams from microphone or media sources using the MediaRecorder API and chunked processing, enabling live transcription with minimal latency. Audio is buffered in small chunks (typically 30-60 second segments), processed incrementally through the Whisper model, and streamed results back to the UI as they become available.","intents":["transcribe live meetings or presentations in real-time","build voice-controlled applications with immediate feedback","create live captioning for accessibility without server infrastructure","enable interactive voice interfaces with sub-second response times"],"best_for":["accessibility teams building live captioning tools","meeting software developers adding transcription features","voice assistant developers requiring client-side processing","content creators needing real-time subtitles"],"limitations":["Latency varies significantly based on device CPU — typically 2-10 seconds behind real-time on consumer hardware","Chunking strategy may split words/sentences at boundaries, requiring post-processing for coherence","No context preservation between chunks — each segment transcribed independently, losing discourse continuity","Memory usage grows with longer sessions due to accumulated audio buffers without automatic cleanup","Browser tab must remain in focus; background processing may be throttled by browser power management"],"requires":["Browser with MediaRecorder API support (Chrome 49+, Firefox 25+, Safari 14.1+)","Microphone permissions granted by user","Minimum 2GB RAM for model + streaming buffers","Modern CPU (Intel i5/AMD Ryzen 5 equivalent or better for acceptable latency)"],"input_types":["microphone stream (getUserMedia API)","audio element stream (HTMLMediaElement)","WebRTC peer connection audio"],"output_types":["streaming text transcription","partial/interim results with confidence","final transcription with timestamps","WebVTT subtitle stream"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-xenova--whisper-web__cap_3","uri":"capability://automation.workflow.model.size.selection.and.optimization.for.device.constraints","name":"model size selection and optimization for device constraints","description":"Provides multiple Whisper model variants (tiny, base, small, medium, large) with different parameter counts and accuracy/speed tradeoffs, allowing users to select based on device capabilities. The framework automatically handles model downloading, quantization, and memory management to fit within browser constraints while maintaining transcription quality.","intents":["run transcription on low-end devices with limited RAM and CPU","optimize for speed vs accuracy based on use case requirements","reduce initial model download size for faster first-use experience","balance inference latency against transcription quality"],"best_for":["developers targeting diverse device ecosystems (mobile, tablets, older laptops)","teams with bandwidth constraints in emerging markets","applications requiring sub-second latency on consumer hardware","resource-constrained environments (embedded systems, IoT devices)"],"limitations":["Smaller models (tiny, base) have noticeably lower accuracy on accented speech and technical terminology","Model selection is manual — no automatic device profiling to recommend optimal size","Quantized models may lose 1-3% accuracy compared to full-precision variants","No incremental model loading — entire selected model must be downloaded before use","Large model (1.5GB+) may exceed browser memory limits on devices with <4GB RAM"],"requires":["Browser with sufficient RAM for selected model (tiny: 400MB, base: 800MB, small: 1.5GB, medium: 3GB, large: 3GB)","Sufficient disk space for model cache (varies by model size)","IndexedDB or similar persistent storage for model caching across sessions"],"input_types":["model selection parameter (enum: tiny, base, small, medium, large)","device capability hints (optional)"],"output_types":["loaded model instance","model metadata (size, parameters, accuracy metrics)","performance benchmarks (inference time, memory usage)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-xenova--whisper-web__cap_4","uri":"capability://data.processing.analysis.audio.format.conversion.and.preprocessing","name":"audio format conversion and preprocessing","description":"Automatically handles multiple audio input formats (MP3, WAV, OGG, WebM, FLAC) by decoding them to PCM audio using Web Audio API or ffmpeg.wasm, normalizing sample rates and bit depths to Whisper's expected input format (16kHz mono PCM). Includes audio resampling, silence trimming, and volume normalization to improve transcription accuracy.","intents":["process audio files in any common format without manual conversion","normalize audio quality to improve transcription accuracy","handle audio from diverse sources (recordings, streaming, user uploads)","reduce preprocessing steps in transcription pipelines"],"best_for":["content management systems accepting user-uploaded audio","media processing pipelines handling heterogeneous audio sources","accessibility tools processing archived audio in legacy formats","research applications analyzing diverse audio datasets"],"limitations":["Web Audio API resampling quality is lower than offline tools (libsamplerate) — may introduce artifacts for high-quality audio","ffmpeg.wasm adds 5-15MB to bundle size and requires additional download","Silence trimming uses simple amplitude thresholding — may incorrectly trim quiet speech or music","No support for compressed formats requiring proprietary codecs (AAC, ALAC without browser native support)","Processing very large files (>500MB) may cause browser memory exhaustion or timeout"],"requires":["Web Audio API support (all modern browsers)","For advanced formats: ffmpeg.wasm library (optional, adds bundle size)","Sufficient browser memory for audio buffering (1-2x file size)"],"input_types":["audio/wav","audio/mpeg (MP3)","audio/ogg","audio/webm","audio/flac","audio/aac (browser-dependent)","raw audio file blob"],"output_types":["normalized PCM audio (16kHz, mono, 16-bit)","audio metadata (original format, duration, sample rate)","preprocessed audio buffer ready for Whisper inference"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-xenova--whisper-web__cap_5","uri":"capability://data.processing.analysis.timestamp.and.segment.level.transcription.output","name":"timestamp and segment-level transcription output","description":"Generates transcription output with word-level and segment-level timestamps, enabling precise synchronization with video/audio playback and subtitle generation. The Whisper model outputs token-level timing information which is aggregated into word and sentence boundaries, allowing downstream applications to map transcribed text back to specific audio positions.","intents":["generate SRT/VTT subtitle files with accurate timing for video players","enable word-level highlighting synchronized with audio playback","create searchable transcripts with temporal anchors for navigation","build interactive transcription interfaces with click-to-seek functionality"],"best_for":["video platform developers adding subtitle generation","accessibility teams building synchronized captions","podcast/audio content platforms enabling searchable transcripts","educational technology tools for lecture transcription"],"limitations":["Timestamp accuracy degrades with background noise or overlapping speech — can drift 100-500ms over long segments","Word-level timestamps not available for all languages equally — some languages have coarser granularity","Segment boundaries determined by Whisper's internal tokenization — may not align with natural sentence breaks","No speaker diarization — cannot distinguish between multiple speakers or assign timestamps to specific speakers","Timestamps are relative to audio start — require manual offset adjustment if audio has leading silence"],"requires":["Whisper model with timestamp token support (all standard variants)","Audio with clear speech and minimal background noise for accurate timing","Subtitle generation library (optional, for SRT/VTT formatting)"],"input_types":["audio file or stream","transcription output from Whisper model"],"output_types":["JSON with word-level timestamps","SRT subtitle format","VTT subtitle format","JSON with segment-level timing and confidence scores"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-xenova--whisper-web__cap_6","uri":"capability://automation.workflow.offline.first.application.with.progressive.enhancement","name":"offline-first application with progressive enhancement","description":"Implements a fully functional offline-first architecture where the Whisper model and all dependencies are cached locally after first download, enabling transcription without internet connectivity. Uses service workers and IndexedDB to persist model weights and application state, with graceful degradation if network becomes unavailable during operation.","intents":["transcribe audio in environments with unreliable or no internet connectivity","reduce bandwidth usage for repeated transcription tasks","enable privacy-preserving transcription without cloud dependencies","build resilient applications that function during network outages"],"best_for":["field researchers and journalists in remote areas with limited connectivity","organizations with strict data residency requirements (healthcare, government)","developers building offline-capable web applications","users in regions with expensive or unreliable internet service"],"limitations":["Initial model download requires internet connection — first-use experience requires 1-3GB download depending on model size","IndexedDB storage quota varies by browser (typically 50MB-1GB) — may require user permission to exceed default limits","Service worker caching strategy must be manually configured — no automatic cache invalidation when models are updated","Offline mode provides no automatic updates to Whisper model — users must manually clear cache to get newer versions","Browser storage persistence not guaranteed — users can clear cache at any time, requiring re-download"],"requires":["Browser with Service Worker support (Chrome 40+, Firefox 44+, Safari 11.1+)","IndexedDB support for persistent model storage","Sufficient persistent storage quota (2-3GB for large models)","Internet connection for initial model download"],"input_types":["audio file or microphone stream","cache control parameters (force refresh, use cached model)"],"output_types":["transcription results","cache status metadata","offline availability indicators"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":21,"verified":false,"data_access_risk":"low","permissions":["Modern web browser with WebAssembly support","Minimum 2GB free RAM for model loading","Audio input device or file upload capability","JavaScript enabled","Internet connection for initial model download from HuggingFace","Whisper model (any size: tiny, base, small, medium, large)","Audio sample with sufficient duration (10+ seconds recommended for reliable detection)","Browser with WebAssembly support","Browser with MediaRecorder API support (Chrome 49+, Firefox 25+, Safari 14.1+)","Microphone permissions granted by user"],"failure_modes":["Model inference speed depends on client device CPU/GPU capabilities — can be 5-30x slower than server-side on consumer hardware","Initial model download (1-3GB depending on model size) required on first use, with no built-in caching strategy across sessions","Browser memory constraints limit processing of very long audio files (>30 minutes) without chunking","No GPU acceleration in most browsers — relies on CPU or WebGL fallbacks, significantly slower than CUDA/Metal alternatives","Requires modern browser with WebAssembly support (Chrome 57+, Firefox 52+, Safari 14.1+)","Language detection accuracy degrades for short audio clips (<5 seconds) or heavily accented speech","Some low-resource languages (e.g., minority regional dialects) have lower accuracy than major languages","Code-switching (mixing multiple languages in single utterance) may produce inconsistent results","No explicit language confidence scores returned — only detected language label","Latency varies significantly based on device CPU — typically 2-10 seconds behind real-time on consumer hardware","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.24,"ecosystem":0.36,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.325Z","last_scraped_at":"2026-05-03T14:22:48.012Z","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=xenova--whisper-web","compare_url":"https://unfragile.ai/compare?artifact=xenova--whisper-web"}},"signature":"sP935sUE6TmnHtVPMqYux9fFgIbSI6qyAn4iofiKIKSuAKsKyeOmCv2tJ5qrck+zrgVMCkkYaWNbJ8gyIEYACA==","signedAt":"2026-06-21T19:46:54.024Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/xenova--whisper-web","artifact":"https://unfragile.ai/xenova--whisper-web","verify":"https://unfragile.ai/api/v1/verify?slug=xenova--whisper-web","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"}}