{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_scribewave","slug":"scribewave","name":"Scribewave","type":"product","url":"https://scribewave.com","page_url":"https://unfragile.ai/scribewave","categories":["text-writing"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_scribewave__cap_0","uri":"capability://text.generation.language.real.time.speech.to.text.transcription.with.minimal.latency","name":"real-time speech-to-text transcription with minimal latency","description":"Converts live audio streams into text with sub-second latency suitable for synchronous meeting transcription and live lecture capture. The system processes audio chunks through a streaming inference pipeline that buffers and processes audio frames incrementally rather than waiting for complete utterances, enabling near-instantaneous text output as speakers talk. Architecture likely uses a streaming ASR (Automatic Speech Recognition) model with frame-level processing and confidence scoring to balance accuracy against latency.","intents":["I need to transcribe live Zoom/Teams meetings in real-time without post-processing delays","I want to capture lecture audio as it happens and have searchable text immediately available","I need to monitor what's being said in a live stream and react to content in real-time"],"best_for":["solopreneurs conducting client calls who need instant transcripts","educators recording lectures live for accessibility","podcast hosts streaming live episodes who want simultaneous captions"],"limitations":["Real-time latency introduces ~500-1500ms delay before text appears, making true synchronous captioning challenging","Streaming models typically have lower accuracy than batch-processed models due to lack of full-utterance context","Network jitter and packet loss directly impact transcription quality and latency in unstable connections"],"requires":["Stable internet connection with minimum 1 Mbps upload bandwidth","Audio input device with 16kHz+ sample rate","Browser with WebRTC or native app with audio capture permissions"],"input_types":["audio stream (WAV, PCM, Opus)","microphone input (browser or native)","VoIP call audio (via API integration)"],"output_types":["text stream (incremental, word-by-word)","structured transcript with timestamps","confidence scores per word segment"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_scribewave__cap_1","uri":"capability://text.generation.language.multilingual.transcription.across.99.languages.with.dialect.recognition","name":"multilingual transcription across 99+ languages with dialect recognition","description":"Detects and transcribes audio in 99+ languages and regional dialects using a language-agnostic acoustic model combined with language-specific language models. The system likely uses a universal phoneme inventory or multilingual embedding space to handle phonetic variation across languages, then applies language identification on audio chunks to route to appropriate language models. Dialect recognition suggests fine-grained language variant detection (e.g., Brazilian Portuguese vs European Portuguese) through acoustic and lexical feature analysis.","intents":["I need to transcribe international team meetings where participants speak different languages","I want to capture podcasts or interviews with multilingual guests without manual language switching","I need to process user-generated content from global audiences in their native languages"],"best_for":["international teams and distributed companies with multilingual workforces","content creators serving global audiences","research institutions processing multilingual corpora"],"limitations":["Accuracy varies significantly by language — high-resource languages (English, Spanish, Mandarin) achieve 85-95% WER while low-resource languages may drop to 60-75%","Dialect recognition requires sufficient audio samples to distinguish variants; short utterances may be misclassified","Code-switching (mixing languages mid-sentence) is not explicitly handled and typically produces degraded output"],"requires":["Audio with clear language separation or single-language segments for best results","Minimum 3-5 seconds of audio per language for reliable language identification"],"input_types":["audio files (MP3, WAV, M4A, OGG)","audio streams in any language","mixed-language audio (with degraded accuracy)"],"output_types":["transcribed text in source language","language and dialect metadata per segment","confidence scores for language identification"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_scribewave__cap_2","uri":"capability://data.processing.analysis.batch.audio.file.transcription.with.format.conversion","name":"batch audio file transcription with format conversion","description":"Processes pre-recorded audio files in multiple formats (MP3, WAV, M4A, OGG) through an offline transcription pipeline that optimizes for accuracy over speed by using full-utterance context and language models. The system likely queues files, extracts audio from containers, resamples to optimal model input (typically 16kHz mono), runs inference with full-context language modeling, and outputs structured transcripts with timing information. Batch processing enables model optimizations like beam search and n-gram rescoring that are too expensive for real-time.","intents":["I have a library of podcast episodes or interview recordings I need transcribed for archival and search","I want to convert video files to transcripts for accessibility and SEO purposes","I need to process recorded lectures or training materials in bulk without manual intervention"],"best_for":["content creators with backlogs of recorded material","organizations doing compliance recording transcription","researchers processing audio corpora"],"limitations":["Processing time scales with audio duration; a 1-hour file typically takes 5-15 minutes depending on language and model complexity","No real-time feedback during processing — users must wait for complete transcription before reviewing","File size limits typically 500MB-2GB depending on tier, requiring pre-splitting of very long recordings"],"requires":["Audio file in supported format (MP3, WAV, M4A, OGG, FLAC)","File size under platform limits (typically 500MB-2GB)","Sufficient account storage quota"],"input_types":["audio files (MP3, WAV, M4A, OGG, FLAC)","video files with audio tracks (MP4, MOV, WebM)","URLs pointing to audio/video files"],"output_types":["plain text transcript","SRT/VTT subtitle files","JSON with word-level timing and confidence","searchable transcript with chapter markers"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_scribewave__cap_3","uri":"capability://data.processing.analysis.basic.speaker.diarization.with.limited.multi.participant.separation","name":"basic speaker diarization with limited multi-participant separation","description":"Attempts to identify and separate different speakers in multi-participant audio by clustering voice embeddings and assigning speaker labels to transcript segments. The implementation likely uses speaker embedding extraction (e.g., x-vector or speaker-focused embeddings) combined with clustering algorithms (k-means, agglomerative clustering) to group similar voices. However, the editorial note indicates this is limited compared to enterprise alternatives, suggesting it may not handle overlapping speech, speaker changes mid-utterance, or accurately distinguish similar voices.","intents":["I need to know who said what in a meeting transcript without manually annotating speakers","I want to separate interviewer and interviewee audio for editing purposes","I need to identify when different team members speak in a group call recording"],"best_for":["small teams (2-4 participants) with distinct voices","podcast interviews with clear host/guest separation","meeting recordings where speaker changes are infrequent"],"limitations":["Accuracy degrades significantly with >4 participants or similar-sounding voices (e.g., same gender, similar age)","Cannot reliably handle overlapping speech — assigns speech to single speaker even when multiple people talk simultaneously","Requires minimum 30-60 seconds of speech per speaker for reliable embedding extraction; short interjections may be misattributed","No speaker name mapping — outputs generic 'Speaker 1', 'Speaker 2' labels without automatic identification"],"requires":["Audio with clear speaker separation (minimal background noise)","At least 2-3 distinct speakers with sufficient speech duration","Mono or stereo audio (not multi-channel)"],"input_types":["audio files with multiple speakers","meeting recordings (Zoom, Teams, etc.)","interview or podcast audio"],"output_types":["transcript with speaker labels (Speaker 1, Speaker 2, etc.)","speaker segments with timing boundaries","speaker embedding confidence scores"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_scribewave__cap_4","uri":"capability://text.generation.language.transcript.editing.and.formatting.interface","name":"transcript editing and formatting interface","description":"Provides a web-based editor for reviewing, correcting, and formatting transcripts with basic text editing capabilities, timestamp adjustment, and export options. The interface likely allows inline editing of text, manual speaker label correction, and timestamp fine-tuning through a timeline scrubber or manual entry. Export functionality probably supports multiple formats (TXT, SRT, VTT, DOCX) with configurable formatting options.","intents":["I need to fix transcription errors and speaker labels before publishing or archiving","I want to format transcripts for different use cases (subtitles, blog posts, legal documents)","I need to adjust timestamps that are slightly off due to audio processing delays"],"best_for":["content creators doing final QA on transcripts before publication","teams needing to correct sensitive or technical terminology","accessibility specialists preparing captions for video"],"limitations":["Editorial summary notes lack of polish and collaborative features — no real-time multi-user editing or comment threads","No built-in spell-check or grammar correction; manual review required","Limited formatting options compared to dedicated caption editors like Kapwing or Descript","No audio playback sync during editing — users must manually verify corrections against audio"],"requires":["Completed transcript from Scribewave","Web browser with JavaScript enabled","Basic text editing skills"],"input_types":["Scribewave transcript JSON or plain text","manual text input for corrections"],"output_types":["plain text (.txt)","subtitle formats (.srt, .vtt)","document formats (.docx, .pdf)","JSON with metadata"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_scribewave__cap_5","uri":"capability://automation.workflow.tiered.pricing.with.per.minute.transcription.allowance","name":"tiered pricing with per-minute transcription allowance","description":"Implements a subscription model with fixed monthly allowances of transcription minutes rather than pay-per-minute overage fees. Users select a tier (e.g., 10 hours/month, 50 hours/month, unlimited) and can transcribe up to that limit without additional charges. This model contrasts with competitors like Otter.ai that charge per-minute overages, making costs more predictable for heavy users.","intents":["I need predictable monthly transcription costs without surprise overage charges","I want to budget for transcription as a fixed line item rather than variable expense","I need unlimited transcription capacity for my content production workflow"],"best_for":["solopreneurs and small teams with consistent monthly transcription needs","podcasters with regular episode schedules","organizations doing compliance recording transcription"],"limitations":["Unused minutes typically don't roll over to next month — encourages over-purchasing or under-utilization","Lower tiers may be insufficient for heavy users, forcing upgrade to higher-cost plans","No granular pricing for different languages or features — all transcription counts equally toward minute limit"],"requires":["Active subscription to Scribewave","Credit card or payment method on file"],"input_types":["subscription tier selection","audio files for transcription"],"output_types":["transcription minute allowance","usage tracking dashboard","billing invoice"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_scribewave__cap_6","uri":"capability://data.processing.analysis.audio.quality.enhancement.and.noise.reduction","name":"audio quality enhancement and noise reduction","description":"Applies preprocessing to audio before transcription to reduce background noise, normalize volume levels, and enhance speech clarity. The system likely uses spectral subtraction, noise gating, or deep learning-based denoising models to suppress non-speech audio while preserving speech intelligibility. This preprocessing step improves downstream transcription accuracy by reducing acoustic variability.","intents":["I have noisy meeting recordings from home offices and need cleaner transcripts","I want to improve transcription accuracy for audio recorded in loud environments","I need to normalize volume levels across multiple speakers in a recording"],"best_for":["remote workers recording calls with background noise","podcasters recording in non-studio environments","researchers processing real-world audio corpora"],"limitations":["Aggressive noise reduction can remove speech components that sound similar to noise, reducing intelligibility","Cannot recover speech obscured by loud background noise — only reduces noise, not eliminates it","Processing adds 10-30% latency to transcription pipeline","May introduce artifacts (e.g., metallic sounds) in heavily processed audio"],"requires":["Audio with identifiable speech and background noise separation","Minimum SNR (Signal-to-Noise Ratio) of ~5dB for effective enhancement"],"input_types":["noisy audio files","meeting recordings with background noise","podcast audio with room tone"],"output_types":["enhanced audio (optional export)","improved transcription accuracy","noise reduction metadata"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_scribewave__cap_7","uri":"capability://search.retrieval.transcript.search.and.indexing","name":"transcript search and indexing","description":"Indexes transcribed text to enable full-text search across transcripts, allowing users to find specific words, phrases, or topics within their transcript library. The system likely builds inverted indices on transcript text and metadata (speaker, timestamp, language) to support fast keyword queries. Search results return matching segments with context and timestamps for quick navigation to relevant portions of audio.","intents":["I need to find when a specific topic was discussed in a 2-hour meeting recording","I want to search across all my podcast transcripts for mentions of a particular guest or product","I need to locate compliance-relevant statements in recorded calls for audit purposes"],"best_for":["content creators managing large transcript libraries","organizations doing compliance and legal review","researchers analyzing interview or podcast corpora"],"limitations":["Search is keyword-based, not semantic — cannot find conceptually similar content without exact phrase matches","Indexing latency may delay search availability for newly transcribed files (typically 1-5 minutes)","No advanced query syntax (boolean operators, regex) — basic keyword search only","Search results limited to exact matches; typos or transcription errors prevent finding relevant content"],"requires":["Completed transcripts indexed in Scribewave system","Search query in supported language"],"input_types":["search keywords or phrases","optional filters (speaker, date range, language)"],"output_types":["matching transcript segments with timestamps","context snippets (surrounding text)","relevance ranking"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"low","permissions":["Stable internet connection with minimum 1 Mbps upload bandwidth","Audio input device with 16kHz+ sample rate","Browser with WebRTC or native app with audio capture permissions","Audio with clear language separation or single-language segments for best results","Minimum 3-5 seconds of audio per language for reliable language identification","Audio file in supported format (MP3, WAV, M4A, OGG, FLAC)","File size under platform limits (typically 500MB-2GB)","Sufficient account storage quota","Audio with clear speaker separation (minimal background noise)","At least 2-3 distinct speakers with sufficient speech duration"],"failure_modes":["Real-time latency introduces ~500-1500ms delay before text appears, making true synchronous captioning challenging","Streaming models typically have lower accuracy than batch-processed models due to lack of full-utterance context","Network jitter and packet loss directly impact transcription quality and latency in unstable connections","Accuracy varies significantly by language — high-resource languages (English, Spanish, Mandarin) achieve 85-95% WER while low-resource languages may drop to 60-75%","Dialect recognition requires sufficient audio samples to distinguish variants; short utterances may be misclassified","Code-switching (mixing languages mid-sentence) is not explicitly handled and typically produces degraded output","Processing time scales with audio duration; a 1-hour file typically takes 5-15 minutes depending on language and model complexity","No real-time feedback during processing — users must wait for complete transcription before reviewing","File size limits typically 500MB-2GB depending on tier, requiring pre-splitting of very long recordings","Accuracy degrades significantly with >4 participants or similar-sounding voices (e.g., same gender, similar age)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.7300000000000001,"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":"2026-05-24T12:16:33.095Z","last_scraped_at":"2026-04-05T13:23:42.551Z","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=scribewave","compare_url":"https://unfragile.ai/compare?artifact=scribewave"}},"signature":"9ZXgX6juxGyS07XDtDQwQpj0EimR/wE+pXXTEBwzYcLwVYS3vwMlziB5227fqvxb0qZpRyhAQzXgsWsLCEV6AQ==","signedAt":"2026-06-21T15:30:01.825Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/scribewave","artifact":"https://unfragile.ai/scribewave","verify":"https://unfragile.ai/api/v1/verify?slug=scribewave","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"}}