{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"whisper-large-v3","slug":"whisper-large-v3","name":"Whisper Large v3","type":"model","url":"https://github.com/openai/whisper","page_url":"https://unfragile.ai/whisper-large-v3","categories":["voice-audio"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"whisper-large-v3__cap_0","uri":"capability://data.processing.analysis.multilingual.speech.to.text.transcription.with.language.specific.optimization","name":"multilingual speech-to-text transcription with language-specific optimization","description":"Transcribes audio in 98 languages to text in the original language using a Transformer sequence-to-sequence architecture trained on 680,000 hours of diverse internet audio. The system uses mel spectrogram feature extraction via FFmpeg integration, processes audio through an AudioEncoder that generates embeddings, then applies an autoregressive TextDecoder with task-specific tokens to produce language-native transcriptions. Language-specific models (e.g., tiny.en, base.en) optimize for English-only workloads with reduced parameter count.","intents":["I need to transcribe audio files in multiple languages without building separate pipelines for each language","I want to reduce model size and latency for English-only transcription tasks","I need to handle diverse audio sources (podcasts, meetings, internet audio) with robust accuracy across quality variations"],"best_for":["developers building multilingual voice applications and transcription services","teams processing diverse audio datasets with mixed language content","resource-constrained environments requiring English-only optimization"],"limitations":["English has highest accuracy (65% of training data) — non-English languages show degraded performance, especially low-resource languages","Fixed 30-second audio segment processing requires sliding window for longer audio, adding latency and potential context loss at boundaries","Mel spectrogram conversion via FFmpeg adds system dependency and ~100-200ms overhead per file","No fine-tuning support in base release — accuracy cannot be improved for domain-specific vocabularies or accents"],"requires":["Python 3.8-3.11","PyTorch (CPU or CUDA-enabled)","FFmpeg system-level installation","1-10 GB VRAM depending on model size (tiny=1GB, large=10GB)"],"input_types":["audio files (MP3, WAV, M4A, FLAC, OGG via FFmpeg)","audio byte streams","file paths as strings"],"output_types":["JSON with transcribed text and metadata","plain text transcription","structured segments with timestamps"],"categories":["data-processing-analysis","speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"whisper-large-v3__cap_1","uri":"capability://data.processing.analysis.speech.to.english.translation.with.direct.audio.to.text.conversion","name":"speech-to-english translation with direct audio-to-text conversion","description":"Translates non-English speech directly to English text in a single forward pass using the same Transformer architecture as transcription, but with a translation task token prepended to the decoder input. The model learns to skip intermediate transcription and generate English output directly from audio embeddings, avoiding cascading errors from intermediate transcription steps. Supports 98 source languages translating to English only.","intents":["I need to translate foreign-language audio to English without intermediate transcription steps","I want to avoid cascading errors from separate transcription-then-translation pipelines","I need to process multilingual meetings or content and output English for downstream analysis"],"best_for":["international teams processing multilingual audio content","content localization pipelines requiring English output","real-time translation applications where intermediate transcription adds latency"],"limitations":["Translation output is English-only — cannot translate to other target languages","Turbo model variant (809M parameters) is NOT trained for translation tasks, only transcription","Translation accuracy depends on source language representation in training data — underrepresented languages show lower quality","No speaker diarization or source language confidence scores in output"],"requires":["Python 3.8-3.11","PyTorch with CUDA for reasonable latency (CPU inference ~30-60s per minute of audio)","FFmpeg for audio preprocessing","Model checkpoint (large or medium recommended for translation quality)"],"input_types":["audio files in non-English languages","audio byte streams","file paths"],"output_types":["English text translation","JSON with translated segments and timing metadata"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"whisper-large-v3__cap_10","uri":"capability://data.processing.analysis.robust.audio.preprocessing.with.silence.padding.and.trimming","name":"robust audio preprocessing with silence padding and trimming","description":"Normalizes variable-length audio to exactly 30 seconds via `whisper.pad_or_trim()`: audio shorter than 30 seconds is padded with silence (zeros) to reach 30 seconds, audio longer than 30 seconds is trimmed to first 30 seconds. This ensures consistent input shape (80×3000 mel spectrogram) for the model, avoiding shape mismatches and enabling batch processing. Padding strategy is simple zero-padding rather than sophisticated techniques like repetition or interpolation.","intents":["I need to handle variable-length audio without manual preprocessing","I want to ensure consistent input shapes for batch processing","I need to understand how Whisper handles short audio clips"],"best_for":["batch transcription pipelines with heterogeneous audio lengths","applications processing short audio clips (voice commands, notifications)","systems requiring deterministic input shapes for optimization"],"limitations":["Simple zero-padding for short audio may introduce artifacts at boundaries — silence padding is acoustically unnatural","Trimming to 30 seconds loses information from longer audio — requires sliding window for full transcription","No adaptive padding strategy — cannot adjust padding based on audio content (e.g., pad to nearest speech boundary)","Padding adds latency for short audio — 5-second clip is padded to 30 seconds, 6x increase in processing time"],"requires":["Python 3.8-3.11","NumPy","Audio array input"],"input_types":["audio array (NumPy array or tensor)","target length (default 30 seconds = 480000 samples at 16kHz)"],"output_types":["normalized audio array of shape (480000,) or (80, 3000) mel spectrogram"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"whisper-large-v3__cap_11","uri":"capability://data.processing.analysis.structured.result.formatting.with.metadata.and.confidence.information","name":"structured result formatting with metadata and confidence information","description":"Returns transcription results as structured JSON objects containing: transcribed text, language code, duration, segments (with timing and text), and optional confidence metrics. The `model.transcribe()` API returns a dictionary with keys like 'text' (full transcript), 'language' (detected language), 'segments' (list of segment objects with start/end times and text). This structured format enables downstream processing (subtitle generation, database storage, API responses) without string parsing.","intents":["I need to extract transcription results in a structured format for downstream processing","I want to access segment-level metadata (timing, language) alongside full transcript","I need to generate subtitles or store results in a database with structured fields"],"best_for":["applications building on Whisper output (subtitle generators, search indexing)","data pipelines requiring structured input for ETL","APIs wrapping Whisper that need to return JSON responses"],"limitations":["No confidence scores per segment or word — only language detection confidence available","Segment boundaries are fixed at 30-second intervals — not aligned to natural speech boundaries (sentences, pauses)","No speaker diarization or speaker labels in output — cannot distinguish multiple speakers","Metadata is minimal — no information about audio quality, noise level, or transcription difficulty"],"requires":["Python 3.8-3.11","Whisper model","JSON serialization library (built-in)"],"input_types":["transcription results from model.transcribe()"],"output_types":["JSON dictionary with text, language, segments, duration","Python dict object"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"whisper-large-v3__cap_2","uri":"capability://data.processing.analysis.automatic.language.identification.from.audio.with.98.language.support","name":"automatic language identification from audio with 98-language support","description":"Detects the spoken language in audio by processing mel spectrograms through the AudioEncoder and using a language classification head that outputs probability distributions over 98 supported languages. The model leverages 680K hours of multilingual training data to recognize language characteristics from acoustic features alone, without requiring transcription. Language detection occurs as a preliminary step in the transcription pipeline and can be called independently via the language detection task token.","intents":["I need to automatically detect which language is spoken in audio files before routing to language-specific processing","I want to filter or categorize audio datasets by language without manual labeling","I need to validate that audio content matches expected language before transcription"],"best_for":["multilingual content management systems requiring automatic language routing","audio dataset curation and quality assurance workflows","voice applications that adapt behavior based on detected language"],"limitations":["Language detection accuracy varies significantly by language — high-resource languages (English, Spanish, Mandarin) >95% accuracy, low-resource languages <70%","Requires minimum audio duration (~5-10 seconds) for reliable detection — very short clips may be misclassified","Cannot distinguish between language variants (e.g., Mandarin vs Cantonese, Brazilian vs European Portuguese) — outputs only primary language","Confidence scores not exposed in standard API — only language ID returned, not probability distribution"],"requires":["Python 3.8-3.11","PyTorch","FFmpeg","Any Whisper model size (tiny sufficient for language detection)"],"input_types":["audio files","audio byte streams"],"output_types":["language code (ISO 639-1 or 639-3)","language name string"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"whisper-large-v3__cap_3","uri":"capability://data.processing.analysis.multi.size.model.selection.with.speed.accuracy.tradeoff.optimization","name":"multi-size model selection with speed-accuracy tradeoff optimization","description":"Provides six model variants (tiny 39M, base 74M, small 244M, medium 769M, large 1550M, turbo 809M) with different parameter counts, VRAM requirements (1-10GB), and inference speeds (10x-1x relative to large). Each size trades accuracy for speed — tiny runs ~10x faster but with ~5-10% lower WER (word error rate), while large provides best accuracy at 10GB VRAM cost. Turbo variant (809M params) optimizes large-v3 for 8x speedup with minimal accuracy loss but lacks translation support.","intents":["I need to choose the right model size for my hardware constraints and latency requirements","I want to run Whisper on edge devices or resource-constrained environments","I need to balance transcription accuracy against inference speed for real-time applications"],"best_for":["developers deploying Whisper on heterogeneous hardware (mobile, edge, cloud)","real-time transcription applications with strict latency budgets","batch processing pipelines where throughput matters more than per-file accuracy"],"limitations":["Turbo model (809M) does NOT support translation tasks — only transcription and language detection","No continuous model size spectrum — must choose from 6 discrete sizes, no fine-grained speed-accuracy control","Accuracy degradation is non-linear across sizes — tiny shows ~10% WER increase vs large, but base shows only ~3-5% increase","VRAM requirements are per-model, not cumulative — cannot run multiple sizes simultaneously on single GPU without model swapping overhead"],"requires":["Python 3.8-3.11","PyTorch","VRAM: 1GB minimum (tiny), 10GB for large model","CPU fallback available but ~30-60x slower than GPU"],"input_types":["model size identifier string (tiny, base, small, medium, large, turbo)"],"output_types":["loaded PyTorch model object","model metadata (parameter count, VRAM requirement)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"whisper-large-v3__cap_4","uri":"capability://data.processing.analysis.sliding.window.transcription.for.audio.longer.than.30.seconds","name":"sliding-window transcription for audio longer than 30 seconds","description":"Automatically segments audio longer than 30 seconds into overlapping windows, processes each window independently through the transcription pipeline, and merges results with overlap handling to produce seamless full-length transcripts. The system uses `whisper.pad_or_trim()` to normalize each segment to exactly 30 seconds (padding with silence if needed), then applies the decoder to each segment and concatenates outputs while managing word-level boundaries and timestamp continuity across segment edges.","intents":["I need to transcribe audio files longer than 30 seconds without manual segmentation","I want to process long-form audio (podcasts, lectures, meetings) in a single API call","I need to maintain accurate word-level timestamps across segment boundaries"],"best_for":["podcast and audiobook transcription services","meeting recording processing pipelines","long-form content analysis workflows"],"limitations":["Segment boundaries at 30-second marks may cut off words mid-utterance, requiring post-processing to fix broken words at boundaries","Overlap handling can introduce duplicate text if context window is too small — requires deduplication logic","Timestamp accuracy degrades at segment boundaries due to independent processing — word-level timing may be off by 100-500ms at edges","No speaker diarization across segments — cannot track speaker changes at boundaries"],"requires":["Python 3.8-3.11","PyTorch","FFmpeg","Audio file or stream input"],"input_types":["audio files of any length","audio byte streams"],"output_types":["full-length transcript with merged segments","segment-level metadata with timing"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"whisper-large-v3__cap_5","uri":"capability://data.processing.analysis.word.level.timestamp.generation.with.millisecond.precision","name":"word-level timestamp generation with millisecond precision","description":"Generates precise word-level timestamps (start and end times in milliseconds) for each word in the transcript by leveraging the decoder's attention weights and token alignment information. The system maps output tokens back to audio frames using the attention mechanism, then converts frame indices to millisecond timestamps based on the mel spectrogram hop length (20ms per frame). Timestamps are returned as part of the structured output alongside transcribed text.","intents":["I need to synchronize transcribed text with video or audio playback at word-level granularity","I want to identify exactly when specific words were spoken for content analysis or editing","I need to generate subtitle files with precise word timing for video platforms"],"best_for":["video subtitle generation and synchronization","interactive transcript applications with word-level seeking","audio editing and content analysis workflows"],"limitations":["Timestamp accuracy is ±100-200ms due to mel spectrogram frame quantization (20ms frames) and attention weight ambiguity","Timestamps may be inaccurate for very short words (<100ms duration) or overlapping speech","Sliding window segmentation introduces timestamp discontinuities at segment boundaries — timestamps reset per segment and must be adjusted during merging","No confidence scores per timestamp — cannot distinguish high-confidence from low-confidence word boundaries"],"requires":["Python 3.8-3.11","PyTorch","FFmpeg","Whisper model with timestamp support enabled"],"input_types":["audio files","audio byte streams"],"output_types":["JSON with word-level objects containing text, start_time_ms, end_time_ms","VTT/SRT subtitle format with timestamps"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"whisper-large-v3__cap_6","uri":"capability://data.processing.analysis.mel.spectrogram.feature.extraction.with.ffmpeg.audio.preprocessing","name":"mel spectrogram feature extraction with ffmpeg audio preprocessing","description":"Converts raw audio files in multiple formats (MP3, WAV, M4A, FLAC, OGG) to mel spectrograms via a two-stage pipeline: (1) FFmpeg decodes audio to 16kHz mono PCM, (2) `whisper.log_mel_spectrogram()` applies a mel-scale filterbank (80 mel bins) and log compression to produce 80×3000 feature matrices (30 seconds at 100Hz frame rate). The mel spectrogram is the input to the AudioEncoder, making this preprocessing critical for model accuracy.","intents":["I need to handle diverse audio formats without manual format conversion","I want to normalize audio preprocessing across different input sources","I need to understand what audio features the model actually processes"],"best_for":["audio pipeline developers building preprocessing layers","researchers analyzing Whisper's acoustic feature space","systems integrating Whisper with heterogeneous audio sources"],"limitations":["FFmpeg dependency adds system-level installation requirement and ~100-200ms overhead per file","Fixed 16kHz sample rate and mono conversion may lose information from high-quality stereo sources","Mel spectrogram compression (log scale) loses dynamic range information — very quiet or very loud audio may be poorly represented","80 mel bins is fixed — no option to adjust frequency resolution for domain-specific audio (e.g., music vs speech)"],"requires":["FFmpeg system-level installation (libavcodec, libavformat)","Python 3.8-3.11","NumPy for spectrogram computation"],"input_types":["audio files (MP3, WAV, M4A, FLAC, OGG, etc.)","audio byte streams","raw PCM audio arrays"],"output_types":["NumPy array of shape (80, 3000) representing mel spectrogram","log-scaled mel spectrogram values"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"whisper-large-v3__cap_7","uri":"capability://planning.reasoning.low.level.decoding.with.configurable.inference.strategies","name":"low-level decoding with configurable inference strategies","description":"Provides `whisper.decode()` function accepting `DecodingOptions` object to control inference behavior: beam search width, temperature for sampling, language/task specification, no-speech threshold, and initial prompt. The decoder implements autoregressive token generation with optional beam search (width 1-5) for exploring multiple hypotheses, temperature-based sampling for diversity, and early stopping when no-speech probability exceeds threshold. Developers can tune these parameters per-file for accuracy-latency tradeoffs.","intents":["I need fine-grained control over transcription inference behavior for specific audio characteristics","I want to use beam search for higher accuracy on critical transcriptions","I need to set language/task hints to guide the decoder toward expected output"],"best_for":["advanced developers building custom transcription pipelines","researchers experimenting with decoding strategies","applications requiring per-file inference tuning"],"limitations":["Beam search width >1 increases latency linearly (width=5 is ~5x slower than greedy) — tradeoff between accuracy and speed","Temperature sampling introduces non-determinism — same audio produces different transcripts on different runs, problematic for reproducibility","Initial prompt feature requires manual specification — no automatic prompt generation from context","No built-in constraint decoding (e.g., force output to match vocabulary list) — requires post-processing filtering"],"requires":["Python 3.8-3.11","PyTorch","Loaded Whisper model","Mel spectrogram input (from `whisper.log_mel_spectrogram()`)"],"input_types":["mel spectrogram tensor","DecodingOptions configuration object"],"output_types":["DecodingResult object with text and token sequences","confidence scores per token"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"whisper-large-v3__cap_8","uri":"capability://automation.workflow.cuda.acceleration.with.automatic.device.management","name":"cuda acceleration with automatic device management","description":"Automatically detects CUDA-capable GPU and moves model weights and inference computations to GPU for 30-60x speedup vs CPU. The system uses PyTorch's device management to handle GPU memory allocation, model loading to GPU via `.to('cuda')`, and batch processing of mel spectrograms on GPU. Falls back to CPU if CUDA unavailable, with transparent API (developers don't explicitly specify device).","intents":["I need to accelerate Whisper inference on GPU without managing device placement manually","I want to process large audio batches efficiently using GPU parallelism","I need to understand GPU memory requirements for different model sizes"],"best_for":["developers deploying Whisper on GPU-equipped servers or cloud instances","batch transcription pipelines processing hundreds of files","real-time transcription applications requiring <1s latency per file"],"limitations":["GPU memory is bottleneck — large model (1550M params) requires 10GB VRAM, limiting batch size to 1-2 files on typical GPUs","GPU transfer overhead (~50-100ms) makes GPU slower than CPU for very short audio (<5 seconds)","No multi-GPU support in base implementation — cannot parallelize across multiple GPUs","CUDA version compatibility issues — requires PyTorch built with matching CUDA version (11.8, 12.1, etc.)"],"requires":["NVIDIA GPU with CUDA Compute Capability 3.5+ (Tesla K40 or newer)","CUDA Toolkit 11.8+ installed","PyTorch built with CUDA support","cuDNN library for optimized operations"],"input_types":["mel spectrogram tensors","model object"],"output_types":["transcription results (same as CPU, but faster)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"whisper-large-v3__cap_9","uri":"capability://planning.reasoning.task.specific.token.injection.for.unified.multitask.inference","name":"task-specific token injection for unified multitask inference","description":"Uses special task tokens prepended to decoder input to control model behavior: `<|transcribe|>` for speech-to-text in original language, `<|translate|>` for speech-to-English translation, and language tokens (e.g., `<|en|>`, `<|es|>`) to specify source language. The same model weights handle all three tasks (transcription, translation, language detection) by conditioning on these tokens, avoiding separate model checkpoints and enabling task switching without model reloading.","intents":["I need to switch between transcription and translation tasks without reloading the model","I want to specify source language to improve accuracy on multilingual audio","I need to understand how Whisper handles multiple tasks with a single model"],"best_for":["applications supporting multiple speech tasks (transcribe + translate)","developers building flexible speech processing pipelines","researchers studying multitask learning in speech models"],"limitations":["Task tokens are not exposed in high-level API — only accessible via low-level `decode()` with custom DecodingOptions","Language token specification is optional but improves accuracy — no automatic language selection if token omitted","Turbo model does NOT support translation task token — attempting translation with turbo model fails silently or produces garbage","No task confidence scores — model commits to task without indicating uncertainty"],"requires":["Python 3.8-3.11","PyTorch","Understanding of Whisper tokenization (task tokens are part of vocabulary)"],"input_types":["task token string (transcribe, translate)","language token string (en, es, fr, etc.)"],"output_types":["task-specific output (transcription or translation)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"whisper-large-v3__headline","uri":"capability://voice.audio.multilingual.automatic.speech.recognition.model","name":"multilingual automatic speech recognition model","description":"OpenAI's Whisper Large v3 is a state-of-the-art automatic speech recognition model that supports over 100 languages, providing high accuracy for transcription and translation in various audio processing applications.","intents":["best multilingual speech recognition model","automatic speech recognition for audio transcription","speech translation tool for voice applications","top ASR model for diverse languages","AI model for transcribing and translating speech"],"best_for":["developers needing multilingual support","applications requiring high transcription accuracy"],"limitations":["performance varies by language","requires significant computational resources"],"requires":["Python","PyTorch","FFmpeg"],"input_types":["audio files"],"output_types":["text transcription","translated text"],"categories":["voice-audio"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.8-3.11","PyTorch (CPU or CUDA-enabled)","FFmpeg system-level installation","1-10 GB VRAM depending on model size (tiny=1GB, large=10GB)","PyTorch with CUDA for reasonable latency (CPU inference ~30-60s per minute of audio)","FFmpeg for audio preprocessing","Model checkpoint (large or medium recommended for translation quality)","NumPy","Audio array input","Whisper model"],"failure_modes":["English has highest accuracy (65% of training data) — non-English languages show degraded performance, especially low-resource languages","Fixed 30-second audio segment processing requires sliding window for longer audio, adding latency and potential context loss at boundaries","Mel spectrogram conversion via FFmpeg adds system dependency and ~100-200ms overhead per file","No fine-tuning support in base release — accuracy cannot be improved for domain-specific vocabularies or accents","Translation output is English-only — cannot translate to other target languages","Turbo model variant (809M parameters) is NOT trained for translation tasks, only transcription","Translation accuracy depends on source language representation in training data — underrepresented languages show lower quality","No speaker diarization or source language confidence scores in output","Simple zero-padding for short audio may introduce artifacts at boundaries — silence padding is acoustically unnatural","Trimming to 30 seconds loses information from longer audio — requires sliding window for full transcription","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:05.297Z","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=whisper-large-v3","compare_url":"https://unfragile.ai/compare?artifact=whisper-large-v3"}},"signature":"yAFGgHwsooer43WjpqoyBpj5dAUOsOxYIHhEtlqu18Xu7nlFmFHviSnxOB3bOMiIRd81SdFQ5Sz9sf1ffLWoBQ==","signedAt":"2026-06-23T10:29:54.845Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/whisper-large-v3","artifact":"https://unfragile.ai/whisper-large-v3","verify":"https://unfragile.ai/api/v1/verify?slug=whisper-large-v3","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"}}