{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-argmaxinc--whisperkit-coreml","slug":"argmaxinc--whisperkit-coreml","name":"whisperkit-coreml","type":"model","url":"https://huggingface.co/argmaxinc/whisperkit-coreml","page_url":"https://unfragile.ai/argmaxinc--whisperkit-coreml","categories":["voice-audio"],"tags":["whisperkit","coreml","whisper","asr","quantized","automatic-speech-recognition","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-argmaxinc--whisperkit-coreml__cap_0","uri":"capability://code.generation.editing.quantized.coreml.speech.recognition.inference","name":"quantized-coreml-speech-recognition-inference","description":"Executes Whisper automatic speech recognition on Apple devices using Core ML quantized models, converting audio waveforms to text through a compiled, device-optimized neural network that runs locally without cloud connectivity. The quantization reduces model size from ~3GB to ~500MB-1.5GB per variant while maintaining accuracy through post-training quantization techniques, enabling on-device inference on iPhone, iPad, and Mac with hardware acceleration via Neural Engine or GPU.","intents":["I need to transcribe audio on iOS/macOS without sending data to cloud servers","I want to reduce model size for on-device deployment while maintaining speech recognition accuracy","I need real-time or near-real-time speech-to-text for accessibility or voice command features","I'm building a privacy-first voice application that cannot rely on network connectivity"],"best_for":["iOS/macOS developers building privacy-preserving voice features","teams deploying speech recognition in offline-first or regulated environments","mobile app developers targeting iPhone 11+ or M1+ Macs with Neural Engine support","accessibility engineers implementing voice control without cloud dependencies"],"limitations":["Inference latency varies by device: ~2-5 seconds on iPhone 13, ~500ms on M1 Mac depending on audio length and model variant","Quantization introduces 1-3% accuracy degradation vs full-precision models on out-of-domain audio","Core ML runtime requires iOS 15.1+ or macOS 12.0+; no support for older OS versions","Model variants limited to Whisper base, small, medium sizes; large/turbo variants exceed device memory constraints","No streaming/chunked inference — requires complete audio file or buffered audio segment before processing","Multilingual support depends on training data; performance degrades on low-resource languages outside Whisper's 99-language training set"],"requires":["iOS 15.1+ or macOS 12.0+ with Core ML framework","Apple device with Neural Engine (iPhone 11+, iPad Air 3+, M1+ Mac) or GPU for acceleration","Audio input as WAV, MP3, or PCM format at 16kHz sample rate","Swift or Objective-C integration layer; no Python runtime on device","~500MB-1.5GB free storage per model variant"],"input_types":["audio/wav","audio/mp3","audio/pcm (raw PCM at 16kHz)","audio/aac"],"output_types":["text/plain (transcribed text)","application/json (with optional token-level timing and confidence scores)"],"categories":["code-generation-editing","speech-recognition","on-device-ml"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-argmaxinc--whisperkit-coreml__cap_1","uri":"capability://text.generation.language.multilingual.speech.transcription.with.language.detection","name":"multilingual-speech-transcription-with-language-detection","description":"Automatically detects the spoken language from audio input and transcribes speech across 99 languages using Whisper's multilingual encoder-decoder architecture, without requiring explicit language specification. The model internally learns language-specific acoustic and linguistic patterns during training, enabling zero-shot language identification and cross-lingual transfer for low-resource languages through a shared embedding space.","intents":["I need to transcribe audio in unknown languages without pre-specifying the language","I'm building a global voice application that handles code-switching or mixed-language audio","I want language detection as a byproduct of transcription without separate inference passes","I need consistent transcription quality across 99 languages with a single model"],"best_for":["international teams building voice products for global markets","accessibility platforms supporting multilingual users","content platforms ingesting user-generated audio in unknown languages","research teams studying low-resource language ASR"],"limitations":["Language detection confidence is implicit; no explicit confidence scores returned for detected language","Performance degrades on code-switched audio (mixing multiple languages in single utterance) — typically 5-15% WER increase","Accented speech or non-native speakers reduce accuracy by 2-8% depending on language pair and accent","Rare languages (e.g., Icelandic, Swahili) have lower accuracy due to limited training data representation","No language-specific fine-tuning capability in quantized Core ML variant — must use full model for domain adaptation"],"requires":["Audio sample with sufficient duration (>2 seconds recommended) for reliable language detection","Clear audio without heavy background noise; SNR >15dB optimal","iOS 15.1+ or macOS 12.0+ with Core ML framework"],"input_types":["audio/wav","audio/mp3","audio/pcm"],"output_types":["text/plain (transcribed text in detected language)","application/json (with detected language code per ISO 639-1)"],"categories":["text-generation-language","speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-argmaxinc--whisperkit-coreml__cap_2","uri":"capability://data.processing.analysis.timestamp.aligned.word.level.transcription","name":"timestamp-aligned-word-level-transcription","description":"Generates transcribed text with frame-level timing information, enabling alignment of each word or token to its corresponding audio timestamp (typically 20ms frame granularity). This is achieved through Whisper's decoder attention weights and frame-to-token alignment, allowing downstream applications to synchronize captions, highlight spoken words, or enable seek-to-word functionality in media players.","intents":["I need to generate subtitle files (SRT, VTT) with precise word-level timing from audio","I want to highlight words in real-time as they're spoken during playback","I need to enable 'search and jump to timestamp' functionality in voice recordings","I'm building accessibility features like live captions with word-level synchronization"],"best_for":["video/podcast platforms building native caption systems","accessibility engineers implementing live caption displays","media editing tools requiring frame-accurate transcription alignment","voice search applications needing timestamp-indexed audio"],"limitations":["Timestamp accuracy is ±100-200ms due to frame-level granularity and attention weight interpretation — not suitable for frame-accurate video sync","Word boundaries may be ambiguous in languages without clear word segmentation (e.g., Chinese, Japanese) — requires post-processing tokenization","Timing alignment degrades with background noise, music, or overlapping speech — accuracy drops 5-10% per 5dB SNR decrease","No sub-word or character-level timing in Core ML variant — only token-level alignment available","Streaming inference not supported — timing requires full audio context for attention computation"],"requires":["Complete audio file or pre-buffered audio segment (no streaming)","Audio at 16kHz sample rate for optimal alignment","iOS 15.1+ or macOS 12.0+"],"input_types":["audio/wav","audio/mp3","audio/pcm"],"output_types":["application/json (with word tokens and timestamps)","text/vtt (WebVTT subtitle format)","text/srt (SRT subtitle format)"],"categories":["data-processing-analysis","speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-argmaxinc--whisperkit-coreml__cap_3","uri":"capability://data.processing.analysis.model.variant.selection.for.accuracy.latency.tradeoff","name":"model-variant-selection-for-accuracy-latency-tradeoff","description":"Provides multiple quantized Whisper model variants (tiny, base, small, medium) with different parameter counts and accuracy profiles, allowing developers to select based on target device capabilities and latency requirements. Each variant is pre-quantized to INT8 or FP16 and compiled to Core ML, with documented accuracy (WER) and inference time benchmarks across device classes (iPhone, iPad, Mac).","intents":["I need to choose between model size and transcription accuracy for my target device","I want to know the exact latency and memory footprint before deploying to production","I need to support older iPhones with limited RAM — which model should I use?","I'm optimizing for battery life — what's the smallest model that meets my accuracy threshold?"],"best_for":["mobile developers with strict latency/memory budgets","teams supporting diverse device ecosystems (iPhone SE to iPhone 15 Pro)","battery-constrained applications (hearing aids, wearables)","developers prototyping before committing to a specific model size"],"limitations":["Accuracy degrades predictably with model size: tiny ~15% WER, base ~5% WER, small ~3% WER on English test sets — but degradation is non-linear across languages","Latency benchmarks are device-specific and vary by iOS version; published benchmarks may not match production performance","No dynamic model switching at runtime — must bundle multiple models if supporting fallback behavior","Larger models (small, medium) may exceed available RAM on iPhone 11 or older iPad models during inference","Quantization artifacts more pronounced in tiny variant — may produce hallucinations on ambiguous audio"],"requires":["Device with sufficient RAM: tiny ~200MB, base ~400MB, small ~800MB, medium ~1.5GB","Storage for model weights: tiny ~40MB, base ~140MB, small ~500MB, medium ~1.5GB","iOS 15.1+ or macOS 12.0+"],"input_types":["audio/wav","audio/mp3","audio/pcm"],"output_types":["text/plain (transcribed text)","application/json (with model metadata and inference metrics)"],"categories":["data-processing-analysis","speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-argmaxinc--whisperkit-coreml__cap_4","uri":"capability://automation.workflow.batch.audio.transcription.with.preprocessing","name":"batch-audio-transcription-with-preprocessing","description":"Processes multiple audio files sequentially or in batches through the Core ML model, with optional preprocessing steps including audio normalization, silence trimming, and format conversion. The preprocessing pipeline handles common audio issues (clipping, DC offset, variable sample rates) before feeding to the ASR model, improving transcription quality on real-world recordings.","intents":["I have 100+ audio files to transcribe — how do I process them efficiently on device?","My audio files are in various formats and sample rates — can I normalize them automatically?","I want to remove silence and trim long recordings before transcription to save compute","I need to batch-process audio from a voice memo app or call recording service"],"best_for":["iOS apps with voice memo or note-taking features","podcast/audio content platforms processing user uploads","call recording applications requiring post-call transcription","research tools analyzing audio corpora on device"],"limitations":["Batch processing is sequential, not parallel — no GPU/Neural Engine batching in Core ML; each file waits for previous to complete","Preprocessing adds 100-500ms per file depending on audio length and format conversion complexity","Silence trimming may remove intentional pauses in speech — no tunable threshold in standard implementation","Audio normalization uses simple peak-based scaling; may not handle dynamic range well for music or heavily compressed audio","No streaming/chunked processing — entire audio file must be loaded into memory before inference","Format conversion (MP3 → PCM) requires AVFoundation; adds dependency and potential licensing concerns"],"requires":["iOS 15.1+ or macOS 12.0+ with AVFoundation framework","Sufficient RAM to buffer largest audio file + model weights","Audio files in WAV, MP3, AAC, or PCM format"],"input_types":["audio/wav","audio/mp3","audio/aac","audio/pcm"],"output_types":["application/json (array of transcription results with per-file metadata)","text/csv (batch results in tabular format)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-argmaxinc--whisperkit-coreml__cap_5","uri":"capability://automation.workflow.streaming.audio.buffering.with.partial.transcription","name":"streaming-audio-buffering-with-partial-transcription","description":"Accepts audio input in streaming chunks (e.g., from microphone or network stream) and buffers them into fixed-size segments, transcribing each segment independently while maintaining context across segments through a sliding window approach. This enables near-real-time transcription feedback without waiting for complete audio, though with latency of 1-2 segments (typically 1-2 seconds).","intents":["I want to show live transcription as the user speaks into the microphone","I'm receiving audio from a network stream and need incremental transcription results","I need to balance latency vs accuracy — transcribe every N seconds rather than waiting for silence","I'm building a voice assistant that needs to respond quickly to user speech"],"best_for":["real-time voice assistant applications","live caption/subtitle generation for video calls","accessibility applications providing live speech feedback","voice command interfaces requiring low-latency response"],"limitations":["Segment boundaries may split words or sentences — requires post-processing to merge and clean up partial results","Context loss at segment boundaries reduces accuracy by 1-3% compared to full-audio transcription","Latency is at least 1 segment duration (typically 1-2 seconds) — not suitable for sub-second response requirements","No true streaming inference; each segment is processed independently — no attention over future context","Microphone input requires AVAudioEngine setup; adds complexity vs file-based inference","Memory usage grows with buffer size — larger buffers improve accuracy but increase latency"],"requires":["iOS 15.1+ or macOS 12.0+ with AVAudioEngine framework","Microphone permission (iOS) or audio input device (macOS)","Audio buffer size typically 16k-32k samples (1-2 seconds at 16kHz)","Real-time thread management for audio capture"],"input_types":["audio/pcm (streaming chunks from microphone or network)","audio/raw (raw PCM samples)"],"output_types":["application/json (streaming transcription results with segment boundaries)","text/plain (incremental text updates)"],"categories":["automation-workflow","speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-argmaxinc--whisperkit-coreml__headline","uri":"capability://voice.audio.automatic.speech.recognition.model","name":"automatic speech recognition model","description":"Whisperkit-coreml is an advanced automatic speech recognition model designed for high accuracy and efficiency, making it ideal for developers looking to integrate speech-to-text capabilities into their applications.","intents":["best automatic speech recognition model","automatic speech recognition for mobile apps","top ASR solutions for real-time transcription","automatic speech recognition for voice commands","best ASR model for developers"],"best_for":["mobile applications","real-time transcription","voice command integration"],"limitations":[],"requires":[],"input_types":["audio"],"output_types":["text"],"categories":["voice-audio"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":54,"verified":false,"data_access_risk":"high","permissions":["iOS 15.1+ or macOS 12.0+ with Core ML framework","Apple device with Neural Engine (iPhone 11+, iPad Air 3+, M1+ Mac) or GPU for acceleration","Audio input as WAV, MP3, or PCM format at 16kHz sample rate","Swift or Objective-C integration layer; no Python runtime on device","~500MB-1.5GB free storage per model variant","Audio sample with sufficient duration (>2 seconds recommended) for reliable language detection","Clear audio without heavy background noise; SNR >15dB optimal","Complete audio file or pre-buffered audio segment (no streaming)","Audio at 16kHz sample rate for optimal alignment","iOS 15.1+ or macOS 12.0+"],"failure_modes":["Inference latency varies by device: ~2-5 seconds on iPhone 13, ~500ms on M1 Mac depending on audio length and model variant","Quantization introduces 1-3% accuracy degradation vs full-precision models on out-of-domain audio","Core ML runtime requires iOS 15.1+ or macOS 12.0+; no support for older OS versions","Model variants limited to Whisper base, small, medium sizes; large/turbo variants exceed device memory constraints","No streaming/chunked inference — requires complete audio file or buffered audio segment before processing","Multilingual support depends on training data; performance degrades on low-resource languages outside Whisper's 99-language training set","Language detection confidence is implicit; no explicit confidence scores returned for detected language","Performance degrades on code-switched audio (mixing multiple languages in single utterance) — typically 5-15% WER increase","Accented speech or non-native speakers reduce accuracy by 2-8% depending on language pair and accent","Rare languages (e.g., Icelandic, Swahili) have lower accuracy due to limited training data representation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8768047790650467,"quality":0.37,"ecosystem":0.5000000000000001,"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":"2026-05-24T12:16:22.764Z","last_scraped_at":"2026-05-03T14:22:52.900Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":9996670,"model_likes":172}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=argmaxinc--whisperkit-coreml","compare_url":"https://unfragile.ai/compare?artifact=argmaxinc--whisperkit-coreml"}},"signature":"3s8zgWL69TpOMrZmgtJhSz407iUm4z8k9eG57Cu325Gdegdi6Vx1iUH1u1UrQ0u8oEC1AQ1ZyFcCBt7DevKeBQ==","signedAt":"2026-06-22T05:31:14.028Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/argmaxinc--whisperkit-coreml","artifact":"https://unfragile.ai/argmaxinc--whisperkit-coreml","verify":"https://unfragile.ai/api/v1/verify?slug=argmaxinc--whisperkit-coreml","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"}}