{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-space-openai--whisper","slug":"openai--whisper","name":"whisper","type":"model","url":"https://huggingface.co/spaces/openai/whisper","page_url":"https://unfragile.ai/openai--whisper","categories":["voice-audio"],"tags":["gradio","region:us"],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-space-openai--whisper__cap_0","uri":"capability://data.processing.analysis.multilingual.speech.to.text.transcription.with.automatic.language.detection","name":"multilingual speech-to-text transcription with automatic language detection","description":"Converts audio input (WAV, MP3, M4A, FLAC, OGG) into text transcriptions using a Transformer-based encoder-decoder architecture trained on 680,000 hours of multilingual audio data. The model automatically detects the source language without explicit specification, then transcribes across 99 languages using a unified tokenizer. Inference runs via ONNX or PyTorch backends, with the Gradio interface handling audio upload, streaming, and real-time processing on HuggingFace Spaces infrastructure.","intents":["I need to transcribe a recorded meeting or podcast in multiple languages without manually specifying the language","I want to extract text from audio files for accessibility, archival, or content repurposing","I need to build a speech-to-text pipeline that handles mixed-language or code-switched audio without preprocessing"],"best_for":["content creators and journalists processing multilingual interviews","accessibility teams adding captions to video/audio content","developers prototyping speech-enabled applications without managing model infrastructure"],"limitations":["Accuracy degrades on heavily accented speech, background noise, or domain-specific terminology (medical, legal jargon)","No real-time streaming transcription in the Spaces demo — requires full audio upload before processing","Latency scales with audio duration; 1-hour file may take 2-5 minutes depending on Spaces resource availability","No speaker diarization or speaker identification — treats all audio as a single continuous stream","Punctuation and capitalization are inferred heuristically, not guaranteed to match original intent"],"requires":["Audio file in supported format (WAV, MP3, M4A, FLAC, OGG)","Internet connection to access HuggingFace Spaces or local Whisper installation (Python 3.8+)","GPU recommended for inference speed; CPU inference possible but slow (10-30x real-time for long audio)"],"input_types":["audio files (WAV, MP3, M4A, FLAC, OGG)","audio duration up to ~30 minutes per upload in Spaces demo"],"output_types":["plain text transcription","optional: JSON with timestamps and confidence scores (via API/local use)"],"categories":["data-processing-analysis","speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-openai--whisper__cap_1","uri":"capability://data.processing.analysis.audio.format.normalization.and.preprocessing","name":"audio format normalization and preprocessing","description":"Automatically handles diverse audio input formats (MP3, M4A, FLAC, OGG, WAV) by normalizing to a standard 16kHz mono PCM stream before feeding to the Whisper model. The Gradio interface abstracts format detection and conversion using librosa or ffmpeg backends, transparently converting compressed or multi-channel audio without user intervention. This preprocessing ensures consistent model input regardless of source format or encoding.","intents":["I have audio in various formats and don't want to manually convert them before transcription","I need to ensure audio preprocessing doesn't introduce artifacts or quality loss","I want to handle both high-quality studio recordings and compressed mobile phone audio uniformly"],"best_for":["teams processing heterogeneous audio sources (podcasts, interviews, user-generated content)","developers building audio pipelines who want format agnosticism","non-technical users uploading audio without understanding codec details"],"limitations":["Resampling to 16kHz may lose information from high-fidelity audio (>48kHz) — not suitable for music analysis","Mono conversion discards stereo spatial information; stereo source material is downmixed, losing channel separation","Very large files (>500MB) may timeout or fail on Spaces due to upload/processing limits","No explicit control over preprocessing parameters (e.g., normalization level, noise gate) — fixed pipeline"],"requires":["Audio file in MP3, M4A, FLAC, OGG, or WAV format","ffmpeg or librosa installed (automatic in Spaces environment)"],"input_types":["audio files in MP3, M4A, FLAC, OGG, WAV formats","mono or multi-channel audio","sample rates from 8kHz to 48kHz+"],"output_types":["normalized 16kHz mono PCM audio stream (internal, not exposed to user)"],"categories":["data-processing-analysis","audio-preprocessing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-openai--whisper__cap_2","uri":"capability://data.processing.analysis.zero.shot.language.identification.from.audio","name":"zero-shot language identification from audio","description":"Identifies the spoken language in audio without explicit user specification by using a language classification head trained as part of the Whisper model. The encoder processes the audio spectrogram and outputs language probabilities across 99 supported languages; the model selects the highest-confidence language and uses language-specific tokens to guide transcription. This enables single-pass processing without requiring separate language detection preprocessing.","intents":["I don't know the language of the audio I'm transcribing and want automatic detection","I need to process mixed-language or code-switched audio and identify dominant languages","I want to avoid manual language selection overhead in a batch processing pipeline"],"best_for":["multilingual content platforms processing user-uploaded audio","news organizations handling international feeds without metadata","developers building language-agnostic transcription services"],"limitations":["Accuracy drops on short audio clips (<5 seconds) or heavily accented speech","Misidentifies languages with similar phonetics (e.g., Spanish vs. Portuguese, Dutch vs. German) at ~5-10% error rate","No confidence threshold control — always selects highest-probability language even if confidence is low","Cannot reliably detect code-switching (mixing multiple languages in single utterance) — treats as single language","Requires sufficient speech content; silence or music-only audio may produce random language predictions"],"requires":["Audio file with at least 5-10 seconds of clear speech for reliable detection","One of 99 supported languages present in audio"],"input_types":["audio files in supported formats"],"output_types":["language code (ISO 639-1 or 639-3 format)","optional: confidence score (0-1) for detected language"],"categories":["data-processing-analysis","language-detection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-openai--whisper__cap_3","uri":"capability://automation.workflow.web.based.interactive.transcription.interface.with.real.time.feedback","name":"web-based interactive transcription interface with real-time feedback","description":"Provides a Gradio-based web UI hosted on HuggingFace Spaces enabling users to upload audio files, trigger transcription, and view results in a browser without local setup. The interface handles file upload, displays transcription progress, and streams results back to the client. Gradio abstracts HTTP request handling, file management, and GPU resource allocation, allowing stateless inference on shared Spaces infrastructure with automatic scaling and timeout management.","intents":["I want to transcribe audio without installing software or managing dependencies","I need a shareable link to a transcription tool for non-technical collaborators","I want to test Whisper's capabilities on my own audio before building a custom integration"],"best_for":["non-technical users and content creators","teams evaluating Whisper before integration","rapid prototyping and proof-of-concept validation"],"limitations":["No persistent storage — transcriptions are not saved after session ends","Single-user inference queue — concurrent uploads may experience delays during peak usage","No batch processing interface — requires uploading files one at a time","Limited customization — cannot adjust model parameters (e.g., language override, temperature) from UI","Spaces resource limits may cause timeouts for very long audio files (>30 minutes)","No API authentication — public endpoint accessible to anyone with the link"],"requires":["Web browser with JavaScript enabled","Internet connection","Audio file <500MB (typical Spaces upload limit)"],"input_types":["audio files uploaded via browser file picker"],"output_types":["plain text transcription displayed in browser","optional: downloadable text file"],"categories":["automation-workflow","web-interface"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-openai--whisper__cap_4","uri":"capability://automation.workflow.batch.audio.transcription.via.api.local.self.hosted","name":"batch audio transcription via api (local/self-hosted)","description":"Enables programmatic transcription of multiple audio files by importing the Whisper Python library and calling the transcribe() function in a loop or parallel batch. The local implementation uses PyTorch or ONNX backends, loading the model once and reusing it across files to amortize startup overhead. Developers can control model size (tiny, base, small, medium, large), language override, and output format (JSON with timestamps, plain text, SRT subtitles).","intents":["I need to transcribe hundreds of audio files efficiently without per-file API calls","I want to keep audio data on-premises for privacy and avoid cloud API costs","I need to integrate transcription into a data pipeline with custom error handling and retry logic"],"best_for":["teams processing large audio archives (podcasts, call recordings, surveillance)","organizations with privacy/compliance requirements preventing cloud API use","developers building production transcription services with cost optimization"],"limitations":["Requires GPU for reasonable throughput; CPU inference is 10-30x slower than real-time","Model weights are large (1.5GB for 'large' variant) — requires sufficient disk space and initial download time","No built-in distributed processing — batching requires manual parallelization across processes/machines","Memory overhead scales with model size; 'large' model requires ~6GB VRAM, 'tiny' requires ~1GB","No streaming/incremental output — must wait for full audio processing before results available","Requires Python 3.8+ and PyTorch/ONNX setup — higher barrier than web UI"],"requires":["Python 3.8+","PyTorch or ONNX runtime installed","GPU recommended (NVIDIA CUDA 11.8+, or Apple Silicon with MPS support)","Disk space for model weights (1.5GB for 'large', 140MB for 'tiny')","Audio files in supported formats"],"input_types":["audio file paths (local or remote URLs)","audio formats: WAV, MP3, M4A, FLAC, OGG"],"output_types":["JSON with transcription and timestamps","plain text transcription","SRT subtitle format","VTT subtitle format"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-openai--whisper__cap_5","uri":"capability://automation.workflow.model.size.selection.for.accuracy.latency.tradeoff","name":"model size selection for accuracy-latency tradeoff","description":"Provides five pre-trained model variants (tiny, base, small, medium, large) with different parameter counts (39M to 1.5B) allowing developers to select based on accuracy requirements and computational constraints. Smaller models (tiny, base) run faster on CPU and mobile devices but sacrifice transcription accuracy; larger models (medium, large) achieve higher accuracy but require GPU and more memory. The model selection is exposed via the Python API (whisper.load_model('base')) and can be configured in the Spaces demo via environment variables.","intents":["I need fast transcription on a laptop or mobile device and can tolerate lower accuracy","I want the highest accuracy for critical transcriptions (legal, medical) and have GPU resources","I need to optimize cost-per-transcription by choosing the smallest model that meets my accuracy threshold"],"best_for":["developers optimizing for edge deployment (mobile, embedded devices)","teams with heterogeneous hardware (some GPU, some CPU-only machines)","cost-conscious operations processing high-volume audio with varying quality requirements"],"limitations":["No continuous spectrum of model sizes — only 5 discrete options; cannot fine-tune intermediate sizes","Accuracy improvements diminish with model size — 'large' vs. 'medium' may only improve WER by 1-2%","Larger models have longer startup time (model loading) — not suitable for single-file, low-latency use cases","No automatic model selection based on audio characteristics — requires manual specification","Tiny/base models struggle with accented speech, background noise, and domain-specific terminology"],"requires":["Python 3.8+ (for API) or web browser (for Spaces demo)","Disk space: 140MB (tiny) to 1.5GB (large)","RAM: 1GB (tiny) to 6GB (large)","GPU optional but recommended for models larger than 'base'"],"input_types":["model size parameter: 'tiny', 'base', 'small', 'medium', 'large'"],"output_types":["loaded model object ready for transcription"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-openai--whisper__cap_6","uri":"capability://data.processing.analysis.timestamp.aware.transcription.with.word.level.timing","name":"timestamp-aware transcription with word-level timing","description":"Generates transcription output with precise timestamps for each word or segment, enabling synchronization with video, subtitle generation, or audio-text alignment. The model outputs segment-level timestamps (start/end times in seconds) which can be further refined to word-level granularity via post-processing. The JSON output format includes timing information, allowing developers to build interactive transcripts, searchable video players, or automated subtitle tracks.","intents":["I need to generate SRT or VTT subtitle files with accurate timing for video content","I want to build an interactive transcript where users can click to jump to specific moments in audio","I need to align transcribed text with video frames for accessibility or content analysis"],"best_for":["video production and post-production workflows","accessibility teams creating captions and transcripts","developers building searchable, time-indexed audio/video platforms"],"limitations":["Segment-level timestamps are accurate to ~0.5-1 second; word-level timing requires additional post-processing and may be less reliable","Timing accuracy degrades with background noise, music, or overlapping speech","No speaker-specific timing — cannot distinguish when different speakers begin/end","Timestamps are relative to audio start; no absolute time mapping (e.g., wall-clock time for live broadcasts)","SRT/VTT generation requires external formatting — Whisper outputs JSON; subtitle file generation is developer responsibility"],"requires":["Audio file with clear speech segments","JSON output format (default in Python API)","Optional: subtitle formatting library (e.g., pysrt, webvtt-py) for SRT/VTT generation"],"input_types":["audio files in supported formats"],"output_types":["JSON with segment-level timestamps (start, end, text)","SRT subtitle format (via post-processing)","VTT subtitle format (via post-processing)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":21,"verified":false,"data_access_risk":"low","permissions":["Audio file in supported format (WAV, MP3, M4A, FLAC, OGG)","Internet connection to access HuggingFace Spaces or local Whisper installation (Python 3.8+)","GPU recommended for inference speed; CPU inference possible but slow (10-30x real-time for long audio)","Audio file in MP3, M4A, FLAC, OGG, or WAV format","ffmpeg or librosa installed (automatic in Spaces environment)","Audio file with at least 5-10 seconds of clear speech for reliable detection","One of 99 supported languages present in audio","Web browser with JavaScript enabled","Internet connection","Audio file <500MB (typical Spaces upload limit)"],"failure_modes":["Accuracy degrades on heavily accented speech, background noise, or domain-specific terminology (medical, legal jargon)","No real-time streaming transcription in the Spaces demo — requires full audio upload before processing","Latency scales with audio duration; 1-hour file may take 2-5 minutes depending on Spaces resource availability","No speaker diarization or speaker identification — treats all audio as a single continuous stream","Punctuation and capitalization are inferred heuristically, not guaranteed to match original intent","Resampling to 16kHz may lose information from high-fidelity audio (>48kHz) — not suitable for music analysis","Mono conversion discards stereo spatial information; stereo source material is downmixed, losing channel separation","Very large files (>500MB) may timeout or fail on Spaces due to upload/processing limits","No explicit control over preprocessing parameters (e.g., normalization level, noise gate) — fixed pipeline","Accuracy drops on short audio clips (<5 seconds) or heavily accented speech","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=openai--whisper","compare_url":"https://unfragile.ai/compare?artifact=openai--whisper"}},"signature":"FLjQms0ajinUeDr4iF15Oqoah9agVv0+iNURXfVOifjdeHPCQRyAfGQ7a4b7zmbnIF/LoA8x6lbxb/pNTy3JAw==","signedAt":"2026-06-22T05:38:14.263Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai--whisper","artifact":"https://unfragile.ai/openai--whisper","verify":"https://unfragile.ai/api/v1/verify?slug=openai--whisper","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"}}