{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"assemblyai-api","slug":"assemblyai-api","name":"AssemblyAI API","type":"api","url":"https://assemblyai.com","page_url":"https://unfragile.ai/assemblyai-api","categories":["voice-audio"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":"$0.00250/min"},"status":"active","verified":false},"capabilities":[{"id":"assemblyai-api__cap_0","uri":"capability://data.processing.analysis.universal.3.pro.multilingual.speech.to.text.transcription.with.context.aware.prompting","name":"universal-3 pro multilingual speech-to-text transcription with context-aware prompting","description":"Converts pre-recorded audio to text using AssemblyAI's Universal-3 Pro model, trained on 12.5+ million hours of audio data. Supports context-aware prompting via plain-language instructions and keyterms (up to 1000 words/phrases, max 6 words per phrase) to control transcription behavior. Provides word-level timestamps, speaker role identification, code-switching support, and verbatim mode. Processes audio asynchronously via REST API with per-hour-of-audio billing ($0.21/hr for Universal-3 Pro, $0.15/hr for legacy Universal-2 supporting 99 languages).","intents":["I need to transcribe recorded meetings, interviews, or podcasts with high accuracy across multiple languages","I want to customize transcription output by providing domain-specific terminology or keyterms that should be recognized","I need word-level timing information to sync transcripts with video or build interactive playback experiences","I need to identify which speaker said what without manual annotation"],"best_for":["Teams building transcription features into SaaS products (meeting recorders, podcast platforms, accessibility tools)","Enterprises processing multilingual audio content (customer support recordings, international conferences)","Developers needing high-accuracy transcription with domain-specific vocabulary control"],"limitations":["Maximum audio duration and file size limits not documented in available material","Supported audio formats not specified in provided documentation","Asynchronous processing only for pre-recorded audio — real-time transcription requires Voice Agent API at higher cost ($4.50/hr vs $0.21/hr)","Keyterms prompting limited to 1000 total words/phrases with 6-word maximum per phrase","Universal-3 Pro language support limited to English, Spanish, German, French, Italian, Portuguese (expanding); legacy Universal-2 supports 99 languages but is less accurate"],"requires":["AssemblyAI API key (obtained from dashboard after free tier signup)","Python SDK or JavaScript SDK, or direct HTTP client for REST API calls","Audio file accessible via URL or local file upload capability","$50 free credits minimum (no credit card required for free tier)"],"input_types":["audio file (format unspecified in documentation)","audio URL","keyterms list (plain text, up to 1000 words/phrases)","prompting instructions (plain language, beta feature)"],"output_types":["JSON transcript with word-level timestamps","speaker role labels","confidence scores per word","detected entities (person names, company names, email addresses, dates, locations)"],"categories":["data-processing-analysis","speech-to-text"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai-api__cap_1","uri":"capability://data.processing.analysis.real.time.streaming.speech.to.text.transcription.with.speaker.role.identification","name":"real-time streaming speech-to-text transcription with speaker role identification","description":"Provides real-time transcription of live audio streams using Universal-3 Pro model via WebSocket-based streaming API. Supports speaker role identification (by name or role, not generic diarization labels) and is built on AssemblyAI's proprietary Voice AI stack optimized for production voice agents. Processes audio with sub-second latency for interactive applications like live call transcription, voice agent interactions, and real-time meeting captions. Billed at $4.50/hr of audio processed.","intents":["I need to transcribe live phone calls or video meetings in real-time with minimal latency","I want to build a voice agent that understands and responds to user speech during active conversation","I need to identify which participant (by name or role) is speaking in a live multi-party conversation","I want to provide real-time captions or transcripts to meeting participants as they speak"],"best_for":["Teams building voice agent platforms or conversational AI applications","Contact centers and customer support platforms requiring live call transcription","Meeting software providers adding real-time captioning features","Live event platforms (webinars, conferences) needing instant transcription"],"limitations":["Significantly higher cost than pre-recorded transcription ($4.50/hr vs $0.21/hr for Universal-3 Pro)","Latency profile and SLA not documented in available material","Speaker role identification requires explicit configuration; generic speaker diarization not available in streaming mode","Real-time processing may have higher error rates than batch processing due to lack of full audio context","WebSocket connection management and reconnection logic required on client side"],"requires":["AssemblyAI API key with Voice Agent API access enabled","WebSocket client library (Python SDK or JavaScript SDK provided by AssemblyAI)","Live audio stream source (microphone, phone line, video conference API)","Network connectivity with low-latency WebSocket support"],"input_types":["audio stream (real-time PCM or compressed audio via WebSocket)","speaker role configuration (optional, for role-based identification)"],"output_types":["partial transcript updates (interim results during speech)","final transcript segments (after speech ends)","speaker role labels","confidence scores"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai-api__cap_10","uri":"capability://data.processing.analysis.custom.spelling.and.keyterms.prompting.with.vocabulary.control","name":"custom spelling and keyterms prompting with vocabulary control","description":"Enables customization of transcription output by providing domain-specific terminology, custom spellings, or keyterms that should be recognized and preserved in the transcript. Supports up to 1000 words/phrases with a maximum of 6 words per phrase. Implemented as a prompting feature that influences the transcription model's output without requiring model fine-tuning. Billed at $0.05/hr of audio processed for Universal-3 Pro (included in base price) and $0.05/hr for Universal-2. Enables accurate transcription of specialized vocabulary, proper nouns, product names, and domain-specific terminology.","intents":["I need to ensure product names, company names, or brand terminology are spelled correctly in transcripts","I want to transcribe technical jargon or specialized vocabulary from my industry without manual correction","I need to preserve proper nouns and custom spellings for legal or compliance documentation","I want to improve transcription accuracy for domain-specific language without fine-tuning a custom model"],"best_for":["Technical and specialized industries (software, medical, legal) with domain-specific vocabulary","Companies with proprietary product names or terminology requiring consistent spelling","Legal and compliance teams documenting specialized terms","Multilingual organizations with transliterated names or custom spellings"],"limitations":["Limited to 1000 total words/phrases with maximum 6 words per phrase — large vocabularies require multiple API calls or custom model training","Keyterms prompting is a beta feature for Universal-3 Pro; stability and accuracy not guaranteed","No support for context-dependent spelling (e.g., 'read' as past tense vs present tense)","Prompting effectiveness depends on transcription model confidence; low-confidence audio may ignore keyterms","No feedback loop to improve keyterms list based on transcription results"],"requires":["AssemblyAI API key","Pre-recorded audio file","Base transcription model (Universal-3 Pro or Universal-2)","Keyterms list (up to 1000 words/phrases, max 6 words per phrase)","Additional $0.05/hr billing for keyterms prompting (included with Universal-3 Pro)"],"input_types":["audio file","keyterms list (plain text, comma-separated or JSON array)","optional: context or domain hints"],"output_types":["transcript with keyterms preserved and spelled correctly","word-level timestamps","confidence scores"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai-api__cap_11","uri":"capability://planning.reasoning.lemur.llm.integration.for.audio.native.ai.tasks","name":"lemur llm integration for audio-native ai tasks","description":"Applies large language models (LLMs) directly to audio data via AssemblyAI's LeMUR (Language Model on Embedded Representations) framework, enabling AI-powered tasks like summarization, question-answering, entity extraction, and custom analysis without requiring separate transcript processing. Processes audio through the transcription pipeline and applies LLM reasoning directly on the transcript representation. Specific LLM models supported, pricing, and integration details not documented in available material. Enables end-to-end audio intelligence workflows without chaining multiple services.","intents":["I need to ask questions about audio content and get answers without manually reading transcripts","I want to apply custom AI analysis to audio data (e.g., extract action items, identify risks, analyze sentiment)","I need to generate summaries, reports, or insights from audio in a single API call","I want to build audio-native AI applications without managing separate transcription and LLM services"],"best_for":["Teams building audio intelligence platforms with custom AI analysis","Enterprises applying LLM reasoning to large volumes of recorded conversations","Developers building audio-native AI agents or chatbots","Organizations requiring end-to-end audio processing without service chaining"],"limitations":["LLM models supported, pricing, and technical implementation details not documented in available material","Integration with specific LLM providers (OpenAI, Anthropic, etc.) not specified","Latency and performance characteristics not documented","No information on context window limits or maximum audio duration","Requires understanding of LLM prompting and prompt engineering for custom tasks"],"requires":["AssemblyAI API key with LeMUR access enabled","Pre-recorded audio file","Base transcription model (Universal-3 Pro or Universal-2)","LLM API key or credentials (if using external LLM providers)","Prompt or task definition for LLM analysis"],"input_types":["audio file","LLM prompt or task definition","optional: context or system instructions"],"output_types":["LLM-generated response (text, structured data, or custom format)","source transcript segments (if citation enabled)","confidence scores or reasoning traces (if available)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai-api__cap_12","uri":"capability://data.processing.analysis.verbatim.transcription.mode.with.filler.word.preservation","name":"verbatim transcription mode with filler word preservation","description":"Transcription mode that preserves filler words, false starts, and non-standard speech patterns exactly as spoken, without normalization or cleanup. Implemented as a transcription parameter that disables automatic filler word removal and speech normalization, returning a verbatim record of the audio content. Useful for linguistic analysis, legal documentation, or accessibility applications requiring exact speech representation. Included in base transcription cost (no additional billing).","intents":["I need a verbatim record of spoken content for legal or compliance documentation","I want to analyze speech patterns and linguistic features including filler words and false starts","I need to generate accessible transcripts that accurately represent how someone spoke","I want to preserve the exact words spoken for linguistic research or speech analysis"],"best_for":["Legal and compliance teams requiring verbatim records of depositions or interviews","Linguistic research and speech analysis applications","Accessibility services providing accurate captions for deaf and hard-of-hearing users","Forensic and investigative applications requiring exact speech documentation"],"limitations":["Verbatim mode may result in less readable transcripts with filler words and false starts","No automatic cleanup or normalization; downstream processing may be required for readability","Verbatim transcripts may be longer and harder to search or index than cleaned transcripts","No option to selectively preserve certain filler words while removing others"],"requires":["AssemblyAI API key","Pre-recorded audio file","Base transcription model (Universal-3 Pro or Universal-2)","Verbatim mode parameter enabled in API request"],"input_types":["audio file"],"output_types":["verbatim transcript with filler words and false starts preserved","word-level timestamps","confidence scores"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai-api__cap_13","uri":"capability://data.processing.analysis.code.switching.support.for.multilingual.audio","name":"code-switching support for multilingual audio","description":"Handles audio containing multiple languages mixed within a single conversation (code-switching), accurately transcribing each language segment and optionally identifying language boundaries. Implemented as a native feature of Universal-3 Pro that detects language switches and transcribes each segment in the appropriate language. Enables accurate transcription of multilingual conversations without requiring separate language-specific models or manual language selection. Specific language pair support and language detection accuracy not documented in available material.","intents":["I need to transcribe conversations where speakers mix multiple languages (e.g., English and Spanish)","I want to identify which language is spoken in each part of a multilingual conversation","I need to transcribe immigrant communities or multilingual families without manual language switching","I want to analyze code-switching patterns in multilingual conversations for linguistic research"],"best_for":["Multilingual organizations and international teams with code-switching conversations","Immigrant and refugee services processing multilingual interviews","Linguistic research studying code-switching patterns","Global customer support platforms handling multilingual calls"],"limitations":["Code-switching support is a feature of Universal-3 Pro only; not available in Universal-2","Specific language pairs supported and language detection accuracy not documented in available material","Accuracy may degrade with rapid language switching or unfamiliar language combinations","No support for dialect-specific variations or regional language variants","Language boundary detection may not be 100% accurate, especially with similar languages"],"requires":["AssemblyAI API key","Pre-recorded audio file with code-switching","Universal-3 Pro model (code-switching not available in Universal-2)","Languages involved in code-switching must be within Universal-3 Pro's supported languages"],"input_types":["audio file with multiple languages mixed"],"output_types":["transcript with each language segment transcribed correctly","language labels per segment (if language detection enabled)","language boundaries and switch points","confidence scores per language segment"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai-api__cap_14","uri":"capability://data.processing.analysis.word.level.timestamps.and.confidence.scores.for.transcript.synchronization","name":"word-level timestamps and confidence scores for transcript synchronization","description":"Provides precise timing information for each word in the transcript (start and end timestamps) along with per-word confidence scores indicating transcription accuracy. Implemented as a native feature of the transcription output that returns word-level metadata for synchronization with audio/video playback, interactive transcript building, or quality analysis. Enables downstream applications like interactive transcripts, video captions, and transcript-based search with playback seeking.","intents":["I need to sync transcripts with video or audio playback for interactive viewing","I want to build searchable transcripts where clicking a word jumps to that point in the audio","I need to identify low-confidence words for quality assurance or manual review","I want to generate accurate captions with precise timing for video accessibility"],"best_for":["Video and podcast platforms building interactive transcripts","Accessibility services generating captions with precise timing","Quality assurance teams identifying transcription errors via confidence scores","Meeting and video conferencing platforms adding transcript search with playback seeking"],"limitations":["Timestamp accuracy depends on audio quality and speech clarity; poor audio may result in imprecise timing","Confidence scores are model-generated estimates; they don't guarantee actual accuracy","Word-level timestamps may have slight timing drift for very long audio files","No support for sub-word timing (phoneme-level) for advanced speech analysis"],"requires":["AssemblyAI API key","Pre-recorded audio file","Base transcription model (Universal-3 Pro or Universal-2)","Downstream system to process and display word-level metadata"],"input_types":["audio file"],"output_types":["transcript with word-level start and end timestamps (millisecond precision)","per-word confidence scores (0-1 scale)","word boundaries and positions"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai-api__cap_15","uri":"capability://data.processing.analysis.word.level.timestamps.and.temporal.alignment","name":"word-level timestamps and temporal alignment","description":"Returns precise word-level timing information for each word in the transcript, enabling applications to synchronize text with audio playback, highlight words as they're spoken, or extract segments by time range. Timestamps are returned in milliseconds with start and end times per word.","intents":["I want to build a player that highlights transcript words as audio plays","I need to extract specific time ranges from audio based on transcript content","I want to align subtitles or captions with audio for video players"],"best_for":["video and podcast platforms building interactive transcripts","accessibility tools creating synchronized captions","research tools analyzing speech timing and prosody"],"limitations":["Timestamp accuracy (millisecond precision) not verified","Behavior on overlapping speech or speaker transitions not documented","Timestamp format and JSON structure not specified","No confidence scores per word timestamp"],"requires":["AssemblyAI API key","Audio file for transcription"],"input_types":["audio file or stream"],"output_types":["JSON transcript with word-level timing (start_ms, end_ms per word)","optional: confidence scores per word"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai-api__cap_2","uri":"capability://data.processing.analysis.speaker.diarization.with.segment.level.speaker.labels","name":"speaker diarization with segment-level speaker labels","description":"Segments transcript by speaker and assigns speaker labels to each segment, enabling identification of who said what in multi-speaker audio. Implemented as an add-on feature to both Universal-3 Pro and Universal-2 models, processing audio asynchronously and returning speaker-labeled segments in the transcript JSON response. Billed at $0.02/hr of audio processed (in addition to base transcription cost). Does not require pre-configuration of speaker count or identities.","intents":["I need to identify which speaker said each part of a multi-speaker recording (meeting, interview, podcast)","I want to generate speaker-labeled transcripts for accessibility or compliance documentation","I need to extract quotes attributed to specific speakers from recorded conversations","I want to analyze speaking patterns or turn-taking in group conversations"],"best_for":["Meeting transcription services and meeting intelligence platforms","Podcast production and editing tools","Legal and compliance teams processing recorded depositions or interviews","Accessibility services providing speaker-labeled captions"],"limitations":["Speaker diarization is a separate add-on feature with additional cost ($0.02/hr on top of transcription cost)","Technical implementation details (clustering algorithm, speaker count detection) not documented","No speaker identity matching — labels are generic (Speaker 1, Speaker 2, etc.) unless combined with streaming API's speaker role identification","Accuracy degrades with many speakers (>5) or overlapping speech","Requires asynchronous processing; not available in real-time streaming mode without additional configuration"],"requires":["AssemblyAI API key with speaker diarization feature enabled","Pre-recorded audio file (not real-time streaming)","Base transcription model (Universal-3 Pro or Universal-2)","Additional $0.02/hr billing allocation for diarization feature"],"input_types":["audio file with multiple speakers","optional: speaker count hint (if known)"],"output_types":["transcript JSON with speaker labels per segment","speaker change boundaries","confidence scores for speaker assignments"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai-api__cap_3","uri":"capability://safety.moderation.pii.redaction.with.entity.detection.and.masking","name":"pii redaction with entity detection and masking","description":"Automatically detects and redacts personally identifiable information (PII) from transcripts, including person names, company names, email addresses, dates, and locations. Implemented as a speech understanding add-on feature that processes the transcript output and masks or removes sensitive entities. Returns redacted transcript with optional entity metadata for compliance and privacy workflows. Specific masking strategy (replacement tokens, hashing, removal) not documented in available material.","intents":["I need to remove sensitive personal information from customer support call transcripts before sharing with analytics teams","I want to comply with GDPR/CCPA by redacting PII from recorded conversations before storage or processing","I need to generate shareable transcripts from interviews or user research sessions without exposing participant identities","I want to audit which PII was detected in a recording for compliance reporting"],"best_for":["Healthcare and financial services companies processing regulated audio (HIPAA, PCI-DSS compliance)","Legal and compliance teams managing recorded conversations","Customer support platforms handling sensitive customer data","User research and UX teams protecting participant privacy"],"limitations":["Technical implementation details (entity recognition model, masking strategy) not documented in available material","Entity types supported limited to: person names, company names, email addresses, dates, locations — no custom entity types","Redaction accuracy depends on transcription accuracy; errors in transcription may result in missed or false-positive PII detections","No granular control over which entity types to redact (all-or-nothing feature)","Pricing for PII redaction feature not specified in available documentation"],"requires":["AssemblyAI API key with speech understanding features enabled","Pre-recorded audio file","Base transcription model (Universal-3 Pro or Universal-2)","Compliance framework or policy defining which entity types require redaction"],"input_types":["audio file with potential PII","optional: entity type filter (if granular control available)"],"output_types":["redacted transcript (PII masked or removed)","entity metadata (detected PII with locations and types)","redaction confidence scores"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai-api__cap_4","uri":"capability://safety.moderation.content.moderation.with.policy.violation.detection","name":"content moderation with policy violation detection","description":"Analyzes transcript content for policy violations, inappropriate language, or flagged content categories. Implemented as a speech understanding add-on feature that processes the transcript and returns moderation scores or flags for content categories. Specific moderation categories, confidence thresholds, and flagging logic not documented in available material. Enables content filtering workflows for platforms with community guidelines or compliance requirements.","intents":["I need to flag user-generated audio content that violates platform community guidelines before publishing","I want to identify inappropriate language or harmful content in customer support calls for quality assurance","I need to audit recorded conversations for compliance with content policies in regulated industries","I want to automatically quarantine or review flagged content before it reaches end users"],"best_for":["Social media and user-generated content platforms with moderation requirements","Customer support platforms monitoring call quality and compliance","Podcast and audio streaming platforms enforcing content policies","Enterprises with strict content governance policies"],"limitations":["Moderation categories, confidence thresholds, and flagging criteria not documented in available material","Accuracy depends on transcription quality; transcription errors may result in false positives or false negatives","No granular control over moderation sensitivity or custom policy rules","Pricing for content moderation feature not specified in available documentation","May not detect context-dependent violations (sarcasm, irony, cultural references) that require semantic understanding"],"requires":["AssemblyAI API key with speech understanding features enabled","Pre-recorded audio file","Base transcription model (Universal-3 Pro or Universal-2)","Content policy framework defining what constitutes a violation"],"input_types":["audio file with potential policy violations","optional: moderation policy configuration (if customizable)"],"output_types":["moderation flags (flagged/not flagged)","violation categories detected","confidence scores per category","flagged text segments with timestamps"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai-api__cap_5","uri":"capability://text.generation.language.automatic.transcript.summarization.with.key.point.extraction","name":"automatic transcript summarization with key point extraction","description":"Generates abstractive summaries of transcribed audio content, extracting key points, main topics, and action items from the full transcript. Implemented as a speech understanding add-on feature that processes the transcript and returns a structured summary. Specific summarization algorithm (extractive vs abstractive), summary length control, and key point extraction logic not documented in available material. Enables rapid content review and knowledge extraction from long-form audio.","intents":["I need to quickly understand the main points from a long meeting or interview without reading the full transcript","I want to extract action items and decisions from recorded meetings for follow-up","I need to generate executive summaries of customer support calls or user research sessions","I want to identify key topics discussed in podcasts or webinars for content indexing"],"best_for":["Meeting intelligence and productivity platforms","Customer support platforms analyzing call outcomes","Research and user testing platforms processing interview recordings","Content platforms (podcasts, webinars) requiring rapid content summarization"],"limitations":["Summarization algorithm type (extractive vs abstractive) not documented in available material","No control over summary length, detail level, or focus areas","Accuracy depends on transcription quality; transcription errors propagate to summary","May miss context-dependent insights or nuanced discussion points","Pricing for summarization feature not specified in available documentation","No support for multi-language summarization (summary language not documented)"],"requires":["AssemblyAI API key with speech understanding features enabled","Pre-recorded audio file","Base transcription model (Universal-3 Pro or Universal-2)","Minimum audio duration (threshold not documented)"],"input_types":["audio file","optional: summary focus or topic hints (if supported)"],"output_types":["summary text (length and format unspecified)","key points list","action items (if detected)","main topics or themes"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai-api__cap_6","uri":"capability://data.processing.analysis.sentiment.analysis.with.emotion.detection.per.speaker.segment","name":"sentiment analysis with emotion detection per speaker segment","description":"Analyzes the emotional tone and sentiment of transcript segments, detecting positive, negative, or neutral sentiment and optionally emotion categories (confidence, frustration, satisfaction, etc.). Implemented as a speech understanding add-on feature that processes the transcript and returns sentiment scores per segment or speaker. Specific emotion categories, scoring methodology, and segment granularity not documented in available material. Enables sentiment-driven insights from customer interactions, user research, and team communications.","intents":["I need to identify customer satisfaction or frustration levels from support call recordings","I want to analyze team morale or sentiment from recorded meetings or all-hands sessions","I need to detect emotional reactions to product features in user research interviews","I want to flag calls with negative sentiment for quality assurance or escalation review"],"best_for":["Customer support platforms analyzing call sentiment for quality and satisfaction metrics","User research and product teams understanding emotional reactions to features","HR and internal communications teams monitoring team sentiment","Sales teams analyzing customer engagement and deal health from call recordings"],"limitations":["Emotion categories and sentiment scale (binary, ternary, continuous) not documented in available material","Accuracy depends on transcription quality; transcription errors affect sentiment detection","May struggle with sarcasm, irony, or context-dependent sentiment (e.g., 'That's just great' said sarcastically)","No support for mixed or conflicting sentiments within a single segment","Pricing for sentiment analysis feature not specified in available documentation","Segment granularity (per-sentence, per-speaker-turn, per-topic) not documented"],"requires":["AssemblyAI API key with speech understanding features enabled","Pre-recorded audio file with clear speech","Base transcription model (Universal-3 Pro or Universal-2)","Optional: speaker diarization for per-speaker sentiment analysis"],"input_types":["audio file","optional: sentiment focus or emotion categories to detect (if customizable)"],"output_types":["sentiment score per segment (scale unspecified)","sentiment label (positive/negative/neutral or emotion category)","confidence scores","per-speaker sentiment aggregation (if diarization enabled)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai-api__cap_7","uri":"capability://data.processing.analysis.medical.optimized.transcription.with.healthcare.terminology","name":"medical-optimized transcription with healthcare terminology","description":"Specialized transcription mode optimized for medical and healthcare conversations, with enhanced recognition of medical terminology, drug names, anatomical terms, and healthcare-specific vocabulary. Implemented as an add-on feature to both Universal-3 Pro and Universal-2 models, processing audio asynchronously and returning transcripts with improved accuracy for medical content. Billed at $0.15/hr of audio processed (in addition to base transcription cost). Enables compliance with healthcare documentation standards (HIPAA, medical record requirements).","intents":["I need to transcribe doctor-patient conversations or clinical notes with accurate medical terminology","I want to generate HIPAA-compliant medical records from recorded consultations or procedures","I need to transcribe medical conferences, lectures, or training sessions with specialized vocabulary","I want to improve transcription accuracy for healthcare-specific language in patient interactions"],"best_for":["Healthcare providers and telemedicine platforms generating clinical documentation","Medical transcription services and medical records management companies","Healthcare compliance and quality assurance teams","Medical research and clinical trial platforms processing recorded interviews"],"limitations":["Medical Mode is an add-on feature with additional cost ($0.15/hr on top of base transcription cost)","Specific medical terminology database or vocabulary list not documented in available material","Accuracy improvements over standard transcription not quantified in available documentation","No support for specialized medical subspecialties (e.g., cardiology-specific terminology) — general medical vocabulary only","Requires asynchronous processing; not available in real-time streaming mode","PII redaction (patient names, medical record numbers) requires separate PII redaction feature"],"requires":["AssemblyAI API key with Medical Mode feature enabled","Pre-recorded audio file from healthcare setting","Base transcription model (Universal-3 Pro or Universal-2)","Additional $0.15/hr billing allocation for Medical Mode feature","HIPAA Business Associate Agreement (if handling protected health information)"],"input_types":["audio file from medical conversation (doctor-patient, clinical notes, medical lectures)","optional: medical specialty hint (if supported)"],"output_types":["transcript with medical terminology preserved","word-level timestamps","confidence scores","optional: PII redaction (if enabled separately)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai-api__cap_8","uri":"capability://data.processing.analysis.entity.extraction.with.named.entity.recognition.ner","name":"entity extraction with named entity recognition (ner)","description":"Automatically detects and extracts named entities from transcripts, including person names, company names, email addresses, dates, and locations. Implemented as a native feature of the transcription output that identifies entity boundaries and types without requiring separate NLP processing. Returns entity metadata with positions in the transcript for downstream processing, indexing, or knowledge base construction. Enables rapid information extraction from unstructured audio content.","intents":["I need to extract contact information (names, emails, companies) from sales calls or customer interactions","I want to identify all mentioned companies, products, or people in recorded meetings for CRM integration","I need to build a knowledge base of entities mentioned in podcasts or interviews","I want to automatically populate contact fields from call transcripts without manual data entry"],"best_for":["Sales and CRM platforms extracting contact information from calls","Customer support platforms identifying customer and company mentions","Knowledge management and research platforms building entity indexes","Meeting intelligence platforms extracting attendee and company mentions"],"limitations":["Entity types limited to: person names, company names, email addresses, dates, locations — no custom entity types","Entity extraction accuracy depends on transcription accuracy; transcription errors result in missed or incorrect entities","No entity disambiguation (e.g., 'Apple' as company vs fruit); context-dependent entity resolution not supported","No entity linking to external knowledge bases (e.g., company databases, person profiles)","Entity extraction is included in base transcription cost (no separate billing), but technical implementation details not documented"],"requires":["AssemblyAI API key","Pre-recorded audio file","Base transcription model (Universal-3 Pro or Universal-2)","Downstream system to process entity metadata (CRM, knowledge base, etc.)"],"input_types":["audio file with potential named entities"],"output_types":["entity list with type, text, and position in transcript","entity confidence scores","entity boundaries (start/end character positions)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai-api__cap_9","uri":"capability://data.processing.analysis.filler.word.detection.and.removal","name":"filler word detection and removal","description":"Identifies filler words and non-speech sounds (um, uh, ah, like, you know, etc.) in transcripts and optionally removes or flags them. Implemented as a native feature of the transcription output that detects filler words at the word level and returns them with position metadata. Enables transcript cleanup for professional documentation, presentation materials, or speech analysis. Specific filler word list and detection methodology not documented in available material.","intents":["I need to clean up transcripts for professional documentation by removing filler words","I want to analyze speaking patterns and filler word frequency for speech coaching or presentation training","I need to generate polished transcripts for publishing or sharing without manual editing","I want to identify speakers with high filler word usage for quality assurance or training"],"best_for":["Professional transcription services and legal/compliance documentation","Speech coaching and presentation training platforms","Podcast production and audio editing tools","Meeting intelligence platforms generating executive summaries"],"limitations":["Specific filler word list and detection algorithm not documented in available material","Filler word detection accuracy depends on transcription quality and audio clarity","No support for language-specific filler words beyond English","No granular control over which filler words to detect or remove","Filler word detection is included in base transcription cost (no separate billing)"],"requires":["AssemblyAI API key","Pre-recorded audio file with clear speech","Base transcription model (Universal-3 Pro or Universal-2)"],"input_types":["audio file with potential filler words"],"output_types":["transcript with filler words marked or removed","filler word list with positions and frequencies","filler word statistics per speaker (if diarization enabled)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai-api__headline","uri":"capability://voice.audio.ai.speech.to.text.api.with.advanced.features","name":"ai speech-to-text api with advanced features","description":"A powerful AI speech-to-text API that offers real-time transcription, speaker labels, content moderation, PII redaction, and sentiment analysis, making it ideal for developers looking to integrate audio processing into their applications.","intents":["best speech-to-text API","speech-to-text API for real-time transcription","AI transcription service with speaker identification","API for audio content moderation","best API for audio sentiment analysis"],"best_for":["real-time audio transcription","audio content analysis"],"limitations":[],"requires":[],"input_types":["audio files","audio streams"],"output_types":["transcriptions","metadata"],"categories":["voice-audio"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["AssemblyAI API key (obtained from dashboard after free tier signup)","Python SDK or JavaScript SDK, or direct HTTP client for REST API calls","Audio file accessible via URL or local file upload capability","$50 free credits minimum (no credit card required for free tier)","AssemblyAI API key with Voice Agent API access enabled","WebSocket client library (Python SDK or JavaScript SDK provided by AssemblyAI)","Live audio stream source (microphone, phone line, video conference API)","Network connectivity with low-latency WebSocket support","AssemblyAI API key","Pre-recorded audio file"],"failure_modes":["Maximum audio duration and file size limits not documented in available material","Supported audio formats not specified in provided documentation","Asynchronous processing only for pre-recorded audio — real-time transcription requires Voice Agent API at higher cost ($4.50/hr vs $0.21/hr)","Keyterms prompting limited to 1000 total words/phrases with 6-word maximum per phrase","Universal-3 Pro language support limited to English, Spanish, German, French, Italian, Portuguese (expanding); legacy Universal-2 supports 99 languages but is less accurate","Significantly higher cost than pre-recorded transcription ($4.50/hr vs $0.21/hr for Universal-3 Pro)","Latency profile and SLA not documented in available material","Speaker role identification requires explicit configuration; generic speaker diarization not available in streaming mode","Real-time processing may have higher error rates than batch processing due to lack of full audio context","WebSocket connection management and reconnection logic required on client side","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:19.836Z","last_scraped_at":null,"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=assemblyai-api","compare_url":"https://unfragile.ai/compare?artifact=assemblyai-api"}},"signature":"2B7RJstR6g0Gl2mg39yfTyqjUuuDzG9HQoi1YyliUgNaPcu3uRBmkZ+K/T/ThxZYa0puI0i0/exetYVkHQq2Aw==","signedAt":"2026-06-20T14:36:53.361Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/assemblyai-api","artifact":"https://unfragile.ai/assemblyai-api","verify":"https://unfragile.ai/api/v1/verify?slug=assemblyai-api","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"}}