{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-openai-gpt-audio","slug":"openai-gpt-audio","name":"OpenAI: GPT Audio","type":"model","url":"https://openrouter.ai/models/openai~gpt-audio","page_url":"https://unfragile.ai/openai-gpt-audio","categories":["voice-audio"],"tags":["openai","api-access","text","audio"],"pricing":{"model":"paid","free":false,"starting_price":"$2.50e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-openai-gpt-audio__cap_0","uri":"capability://text.generation.language.text.to.speech.synthesis.with.voice.consistency","name":"text-to-speech synthesis with voice consistency","description":"Converts input text to natural-sounding audio output using an upgraded neural decoder architecture that maintains consistent voice characteristics across multiple utterances. The model applies voice embedding techniques to preserve speaker identity and prosody patterns, enabling multi-turn conversations with stable vocal properties. Supports streaming output for real-time audio generation without waiting for full synthesis completion.","intents":["Generate natural-sounding voiceovers for video content while maintaining consistent narrator voice across multiple clips","Create accessible audio versions of written content with stable voice characteristics for long-form documents","Build voice-enabled chatbots and conversational agents that sound consistent across multiple API calls","Synthesize multilingual audio content with speaker identity preservation across language switches"],"best_for":["Product teams building accessibility features into web and mobile applications","Content creators and publishers automating voiceover production at scale","AI application developers building voice-first interfaces and conversational UIs","Enterprises requiring consistent branded voice across customer-facing audio systems"],"limitations":["Voice consistency degrades with extreme emotional range or highly stylized speech patterns not present in training data","Latency varies with text length; typical synthesis takes 2-5 seconds for 100-word passages depending on voice selection","Limited to predefined voice profiles; custom voice cloning from user samples not supported in this release","Audio quality capped at 24kHz sample rate; high-fidelity 48kHz output not available","No built-in support for SSML markup or fine-grained prosody control; only basic text input accepted"],"requires":["OpenAI API key with audio model access enabled","HTTP/2 or HTTP/1.1 client supporting streaming responses","Audio playback capability or file storage for generated MP3/WAV output","Text input maximum 4096 characters per request"],"input_types":["plain text (UTF-8 encoded)","text with basic punctuation for prosody hints"],"output_types":["MP3 audio file (default, lossy compression)","WAV audio file (lossless PCM, higher bandwidth)","streaming audio chunks (for real-time playback)"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-audio__cap_1","uri":"capability://data.processing.analysis.speech.to.text.transcription.with.speaker.diarization","name":"speech-to-text transcription with speaker diarization","description":"Transcribes audio input to text using a Whisper-based architecture enhanced with speaker diarization capabilities that identify and label different speakers in multi-speaker audio. The model processes audio frames through a sequence-to-sequence transformer decoder that outputs both transcribed text and speaker turn boundaries, enabling conversation analysis and meeting minutes generation. Supports variable audio lengths up to 25MB and multiple audio formats through unified preprocessing pipeline.","intents":["Transcribe multi-speaker meetings and interviews while identifying which speaker said what","Convert podcast episodes to searchable text with speaker attribution for content discovery","Generate meeting notes and summaries with speaker labels for team collaboration platforms","Build voice-enabled search over audio archives by transcribing and indexing speaker segments"],"best_for":["Enterprise teams managing recorded meetings and conference calls","Podcast networks and audio content platforms requiring searchable transcripts","Legal and compliance teams documenting depositions and interviews with speaker attribution","Accessibility teams adding captions and transcripts to video content"],"limitations":["Speaker diarization accuracy degrades with more than 5 simultaneous speakers or heavy background noise (SNR < 10dB)","Latency scales linearly with audio duration; 1-hour audio takes ~30-60 seconds to process depending on speaker count","No speaker identification by name; only generic speaker labels (Speaker 1, Speaker 2) without voice recognition","Punctuation and capitalization are model-inferred and may be inaccurate for technical jargon or proper nouns","Audio preprocessing removes some background context; music or ambient sound may be partially transcribed as artifacts"],"requires":["OpenAI API key with audio model access","Audio file in MP3, MP4, MPEG, MPGA, M4A, WAV, or WebM format","Audio file size maximum 25MB","Audio duration maximum 25 hours per request"],"input_types":["MP3 audio files","MP4/MPEG video files (audio extracted)","WAV/FLAC lossless audio","WebM audio streams","M4A/AAC encoded audio"],"output_types":["JSON with transcribed text and speaker labels","VTT subtitle format with speaker attribution","plain text with [Speaker N] markers","structured segments with timestamps and speaker boundaries"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-audio__cap_2","uri":"capability://text.generation.language.audio.to.audio.translation.with.voice.preservation","name":"audio-to-audio translation with voice preservation","description":"Translates spoken audio from one language to another while preserving the original speaker's voice characteristics, accent patterns, and emotional tone. The system chains speech-to-text transcription, text translation, and voice-preserving TTS synthesis, using speaker embedding extraction from the source audio to guide the target language synthesis. Supports 99+ language pairs with automatic language detection on input audio.","intents":["Localize video content for international audiences while maintaining original speaker voice and performance","Enable real-time multilingual communication in video conferencing by translating and re-synthesizing audio on-the-fly","Create dubbed versions of films and documentaries with voice consistency matching original performances","Build language-agnostic voice assistants that respond in the user's native language with consistent vocal identity"],"best_for":["Media production companies creating multilingual content at scale","Global SaaS platforms adding real-time translation to video calls","Educational content creators localizing lectures and tutorials for international students","Entertainment platforms dubbing films and series for regional markets"],"limitations":["Voice preservation quality degrades for languages with significantly different phonetic inventories (e.g., tonal languages to non-tonal)","Processing latency is cumulative: 10-15 seconds for 1-minute audio due to sequential transcription → translation → synthesis pipeline","Emotional nuance and sarcasm may be lost in translation, requiring manual review for high-stakes content","Accent preservation is approximate; strong regional accents may be normalized toward standard pronunciation in target language","Requires separate API calls for each language pair; no batch translation of multiple language targets in single request"],"requires":["OpenAI API key with audio model access","Source audio file in supported format (MP3, WAV, M4A, etc.)","Target language code (ISO 639-1 or full language name)","Audio file size maximum 25MB"],"input_types":["audio files in MP3, WAV, M4A, MPEG, WebM formats","language code or auto-detect flag"],"output_types":["translated audio file with original speaker voice","JSON with transcription, translation, and audio URL","streaming audio chunks for real-time playback"],"categories":["text-generation-language","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-audio__cap_3","uri":"capability://safety.moderation.audio.content.moderation.and.safety.filtering","name":"audio content moderation and safety filtering","description":"Analyzes audio input to detect and flag harmful content including hate speech, explicit language, violence references, and policy violations using a fine-tuned classifier trained on moderation guidelines. The system transcribes audio, applies multi-modal safety checks (combining acoustic features and semantic content), and returns confidence scores for each violation category. Supports custom policy definitions and threshold tuning for different use cases.","intents":["Moderate user-generated audio content in community platforms before publishing","Screen podcast and streaming audio for advertiser-friendly content compliance","Detect policy violations in customer support recordings for quality assurance","Filter harmful content in voice chat applications and gaming platforms"],"best_for":["Content moderation teams managing large volumes of user-generated audio","Streaming platforms and podcast networks ensuring brand safety","Customer service organizations monitoring call quality and compliance","Gaming and social platforms protecting community standards"],"limitations":["Moderation accuracy varies by language; English achieves ~92% precision, non-English languages 75-85% due to training data imbalance","Context-dependent violations (e.g., quoting harmful speech in educational context) may be flagged as violations without nuance","Sarcasm, irony, and cultural references may be misclassified; requires manual review for edge cases","Acoustic features alone cannot detect some violations (e.g., dog-whistle terminology); relies on transcription accuracy","No real-time streaming moderation; full audio must be processed before results returned"],"requires":["OpenAI API key with audio model access","Audio file in supported format","Moderation policy configuration (default or custom categories)","Audio file size maximum 25MB"],"input_types":["audio files in MP3, WAV, M4A, MPEG, WebM formats","custom moderation policy JSON (optional)"],"output_types":["JSON with violation flags and confidence scores per category","transcription with flagged segments highlighted","moderation report with severity levels and recommended actions"],"categories":["safety-moderation","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-audio__cap_4","uri":"capability://data.processing.analysis.audio.emotion.and.sentiment.analysis","name":"audio emotion and sentiment analysis","description":"Analyzes audio input to detect speaker emotional state, sentiment polarity, and engagement level using acoustic feature extraction combined with semantic content analysis. The system extracts prosodic features (pitch, tempo, energy), voice quality markers (breathiness, tension), and transcribed text sentiment, then fuses these signals through a multi-modal classifier to output emotion labels and confidence scores. Supports fine-grained emotion categories (joy, anger, frustration, confusion, etc.) and speaker engagement metrics.","intents":["Analyze customer support calls to identify frustrated or angry customers for escalation routing","Measure speaker engagement and emotional response in educational videos or training content","Monitor employee well-being in workplace communications by detecting stress or burnout indicators","Evaluate presenter performance in pitches and presentations by analyzing emotional delivery and audience engagement"],"best_for":["Customer experience teams optimizing support workflows based on emotional signals","Educational platforms measuring student engagement and learning outcomes","HR and workplace wellness programs monitoring employee sentiment trends","Sales and pitch coaching platforms providing feedback on emotional delivery"],"limitations":["Emotion detection accuracy varies significantly by language, accent, and cultural expression norms (70-85% F1 score)","Acoustic features alone cannot distinguish genuine emotion from acted performance; requires transcription context","Background noise, audio compression, and microphone quality significantly degrade prosodic feature extraction","Individual emotional expression varies widely; model may misclassify atypical speakers or neurodivergent communication patterns","No speaker-specific baseline; cannot distinguish between speaker's baseline emotional state and situational changes"],"requires":["OpenAI API key with audio model access","Audio file in supported format with clear speech","Minimum audio duration 3 seconds for reliable emotion detection","Audio file size maximum 25MB"],"input_types":["audio files in MP3, WAV, M4A, MPEG, WebM formats","optional speaker baseline or context metadata"],"output_types":["JSON with emotion labels and confidence scores per segment","engagement metrics (0-100 scale) with temporal breakdown","sentiment polarity (positive/neutral/negative) with intensity","prosodic feature breakdown (pitch range, speaking rate, energy variation)"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-audio__cap_5","uri":"capability://automation.workflow.real.time.audio.streaming.with.low.latency.processing","name":"real-time audio streaming with low-latency processing","description":"Processes continuous audio streams with sub-second latency using a streaming decoder architecture that processes audio frames incrementally without buffering entire audio files. The system maintains state across frame boundaries to preserve context for speaker diarization and emotion detection, enabling live transcription, translation, and moderation of audio feeds. Supports WebSocket connections for bidirectional streaming and automatic reconnection with state recovery.","intents":["Enable live captioning and transcription in video conferences and streaming broadcasts","Provide real-time translation in multilingual meetings without noticeable latency","Monitor live audio streams for policy violations and content safety in real-time","Build voice-first applications with immediate audio feedback and responsiveness"],"best_for":["Video conferencing platforms adding live captioning and translation features","Live streaming and broadcast platforms requiring real-time moderation","Voice assistant and conversational AI applications requiring immediate responsiveness","Accessibility teams implementing live captioning for events and broadcasts"],"limitations":["Streaming latency is 500-1500ms depending on frame size and network conditions; not suitable for sub-100ms real-time applications","Speaker diarization accuracy degrades in streaming mode due to limited lookahead context; requires post-processing for final accuracy","Requires persistent connection; network interruptions cause transcription gaps that cannot be retroactively filled","Memory usage scales with number of concurrent streams; typical deployment supports 100-500 concurrent streams per instance","Emotion and sentiment analysis less reliable in streaming mode due to incomplete utterance context"],"requires":["OpenAI API key with audio streaming access","WebSocket client supporting binary frames","Audio input at 16kHz or 24kHz sample rate (resampling required for other rates)","Network bandwidth minimum 64kbps for audio + metadata"],"input_types":["raw PCM audio frames (16-bit signed integer)","Opus-encoded audio frames","µ-law or A-law encoded audio"],"output_types":["streaming JSON with partial transcriptions and confidence scores","real-time speaker labels and diarization updates","live emotion/sentiment scores with temporal resolution","moderation flags with immediate action recommendations"],"categories":["automation-workflow","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"low","permissions":["OpenAI API key with audio model access enabled","HTTP/2 or HTTP/1.1 client supporting streaming responses","Audio playback capability or file storage for generated MP3/WAV output","Text input maximum 4096 characters per request","OpenAI API key with audio model access","Audio file in MP3, MP4, MPEG, MPGA, M4A, WAV, or WebM format","Audio file size maximum 25MB","Audio duration maximum 25 hours per request","Source audio file in supported format (MP3, WAV, M4A, etc.)","Target language code (ISO 639-1 or full language name)"],"failure_modes":["Voice consistency degrades with extreme emotional range or highly stylized speech patterns not present in training data","Latency varies with text length; typical synthesis takes 2-5 seconds for 100-word passages depending on voice selection","Limited to predefined voice profiles; custom voice cloning from user samples not supported in this release","Audio quality capped at 24kHz sample rate; high-fidelity 48kHz output not available","No built-in support for SSML markup or fine-grained prosody control; only basic text input accepted","Speaker diarization accuracy degrades with more than 5 simultaneous speakers or heavy background noise (SNR < 10dB)","Latency scales linearly with audio duration; 1-hour audio takes ~30-60 seconds to process depending on speaker count","No speaker identification by name; only generic speaker labels (Speaker 1, Speaker 2) without voice recognition","Punctuation and capitalization are model-inferred and may be inaccurate for technical jargon or proper nouns","Audio preprocessing removes some background context; music or ambient sound may be partially transcribed as artifacts","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.37,"ecosystem":0.27,"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":"2026-05-24T12:16:24.485Z","last_scraped_at":"2026-05-03T15:20:45.776Z","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=openai-gpt-audio","compare_url":"https://unfragile.ai/compare?artifact=openai-gpt-audio"}},"signature":"8+NkVMQ4Va4Md7cSWUDihKrRBnS9SlcGh4AvknxfXsCGyTUdJcxUtXBu5rTX1ukBeREjx4Dp4WAV8/1PzICdCw==","signedAt":"2026-06-20T03:03:45.428Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-gpt-audio","artifact":"https://unfragile.ai/openai-gpt-audio","verify":"https://unfragile.ai/api/v1/verify?slug=openai-gpt-audio","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"}}