{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-openai--whisper-large-v3","slug":"openai--whisper-large-v3","name":"whisper-large-v3","type":"model","url":"https://huggingface.co/openai/whisper-large-v3","page_url":"https://unfragile.ai/openai--whisper-large-v3","categories":["voice-audio"],"tags":["transformers","pytorch","jax","safetensors","whisper","automatic-speech-recognition","audio","hf-asr-leaderboard","en","zh","de","es","ru","ko","fr","ja","pt","tr","pl","ca"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-openai--whisper-large-v3__cap_0","uri":"capability://data.processing.analysis.multilingual.speech.to.text.transcription","name":"multilingual-speech-to-text-transcription","description":"Converts audio waveforms to text across 99 languages using a transformer-based encoder-decoder architecture trained on 680,000 hours of multilingual audio data from the web. The model uses mel-spectrogram feature extraction with a convolutional stem followed by transformer encoder layers, enabling robust handling of accents, background noise, and technical language without language-specific preprocessing. Inference can run via PyTorch, JAX, or ONNX backends with automatic device placement (CPU/GPU/TPU).","intents":["I need to transcribe audio files in multiple languages without building separate language-specific pipelines","I want to process speech from diverse sources (podcasts, meetings, user-generated content) with a single unified model","I need to handle noisy real-world audio without extensive preprocessing or language detection steps","I want to deploy speech recognition that works across 99 languages with consistent quality"],"best_for":["teams building multilingual voice applications (chatbots, transcription services, accessibility tools)","developers prototyping speech-to-text features without language-specific model management","organizations processing international audio content at scale"],"limitations":["Inference latency ~5-15 seconds per minute of audio on CPU; GPU acceleration required for real-time use cases","No speaker diarization or speaker identification — outputs single continuous transcript without speaker labels","Trained primarily on English-dominant web audio; performance degrades on low-resource languages and highly specialized domains (medical, legal terminology)","Output is raw transcription without punctuation or capitalization; post-processing required for production-grade formatting","Memory footprint ~3GB for large-v3 variant; requires 8GB+ RAM for comfortable inference with batching"],"requires":["Python 3.8+","PyTorch 1.9+ OR JAX 0.3+ OR ONNX Runtime 1.10+","librosa or similar audio loading library for preprocessing","transformers library 4.20+","Audio input: WAV, MP3, FLAC, or raw PCM at 16kHz sample rate (model resamples automatically)"],"input_types":["audio-file (WAV, MP3, FLAC, OGG, M4A)","raw-audio-array (numpy array or torch tensor at 16kHz)","audio-stream (via streaming inference with sliding window)"],"output_types":["text-transcript (raw string)","structured-transcript (with token-level timestamps and confidence scores)","language-detection (detected language code for each segment)"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-large-v3__cap_1","uri":"capability://data.processing.analysis.language.detection.from.audio","name":"language-detection-from-audio","description":"Automatically detects the spoken language from audio segments using the model's internal language classification head, which operates on the transformer encoder's hidden states before decoding. The model outputs a language token (e.g., <|zh|>, <|es|>) as the first token in the sequence, enabling zero-shot language identification without separate language detection models. Supports detection across 99 languages with confidence scores derived from the model's token probability distribution.","intents":["I need to automatically detect which language is being spoken before routing to language-specific processing pipelines","I want to handle mixed-language audio by identifying language boundaries within a single file","I need to filter or categorize audio content by language without manual labeling"],"best_for":["multilingual voice applications requiring automatic language routing","content moderation and categorization systems processing international audio","speech analytics platforms analyzing language distribution in call centers or media"],"limitations":["Language detection accuracy varies significantly by language; low-resource languages (e.g., Icelandic, Swahili) have lower confidence scores","Cannot reliably detect language switches within a single audio segment — treats entire clip as single language","Requires minimum ~2-3 seconds of audio for reliable detection; very short clips may misclassify","No confidence threshold mechanism built-in; developers must manually interpret probability scores to determine detection reliability"],"requires":["Python 3.8+","transformers library 4.20+","Audio input minimum 2-3 seconds at 16kHz sample rate"],"input_types":["audio-file","raw-audio-array"],"output_types":["language-code (ISO 639-1 or custom Whisper language tokens)","confidence-score (probability from model's softmax distribution)"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-large-v3__cap_10","uri":"capability://automation.workflow.fine.tuning.and.domain.adaptation","name":"fine-tuning-and-domain-adaptation","description":"Supports fine-tuning the Whisper model on domain-specific audio data to improve accuracy for specialized use cases (medical, legal, technical, accented speech). The implementation uses standard PyTorch training loops with the model's encoder-decoder weights unfrozen, enabling adaptation to new domains with relatively small labeled datasets (100-1000 hours). Fine-tuning leverages the model's pretrained representations, requiring less data than training from scratch while achieving significant accuracy improvements (5-15% WER reduction) on target domains.","intents":["I need to improve transcription accuracy for a specialized domain (medical, legal, technical) where the base model underperforms","I want to adapt the model to recognize accented or non-native speech with higher accuracy","I need to create a custom speech recognition model for proprietary terminology or domain-specific language"],"best_for":["organizations with domain-specific audio data (medical, legal, technical) and resources for model training","companies building proprietary speech recognition systems with custom terminology","teams addressing systematic errors in base model performance on their target domain"],"limitations":["Requires 100-1000 hours of labeled audio data; smaller datasets may overfit or provide minimal improvement","Fine-tuning is computationally expensive; requires GPU with 16GB+ VRAM and 1-7 days of training depending on dataset size","Risk of catastrophic forgetting; fine-tuning on narrow domains may degrade performance on general-purpose speech","Requires careful hyperparameter tuning (learning rate, batch size, warmup steps); suboptimal settings lead to poor convergence","Fine-tuned models are not compatible with standard Whisper inference pipelines without custom loading code"],"requires":["Python 3.8+","PyTorch 1.9+","GPU with 16GB+ VRAM (A100, V100, or RTX 3090+)","100-1000 hours of labeled audio data in target domain","Training infrastructure (distributed training for large datasets recommended)","Hugging Face transformers library 4.20+"],"input_types":["labeled-audio-dataset (audio files with corresponding transcripts)","dataset-format (WebDataset, HuggingFace Dataset, or custom PyTorch DataLoader)"],"output_types":["fine-tuned-model-weights (PyTorch checkpoint)","training-metrics (loss curves, WER on validation set)","domain-adapted-model (ready for inference)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-large-v3__cap_11","uri":"capability://data.processing.analysis.speaker.aware.transcription.with.diarization.integration","name":"speaker-aware-transcription-with-diarization-integration","description":"Integrates with external speaker diarization systems (e.g., pyannote.audio) to produce speaker-labeled transcripts where each segment is attributed to a specific speaker. The implementation uses diarization output (speaker segments with timestamps) to segment the audio, transcribe each segment independently, and reassemble the transcript with speaker labels. While Whisper itself does not perform diarization, this capability enables end-to-end speaker-aware transcription by combining Whisper with complementary diarization models.","intents":["I need to transcribe multi-speaker audio (meetings, interviews, podcasts) with speaker labels","I want to create speaker-attributed transcripts for meeting minutes or interview analysis","I need to identify who said what in a conversation for accessibility or content analysis"],"best_for":["meeting transcription services requiring speaker identification","interview and podcast transcription with speaker attribution","accessibility tools for multi-speaker content (video captions, audio descriptions)"],"limitations":["Diarization accuracy directly impacts transcript quality; poor diarization (speaker misidentification, missed speakers) propagates to final output","Requires separate diarization model (e.g., pyannote.audio); adds complexity and latency (10-30% overhead)","Diarization models require speaker embeddings and may fail on very short speaker segments (<2 seconds) or overlapping speech","No built-in speaker name mapping; speaker labels are numeric IDs (Speaker 1, Speaker 2) without names unless provided externally","Transcription accuracy may degrade slightly due to speaker segmentation artifacts at diarization boundaries"],"requires":["Python 3.8+","transformers library 4.20+ (for Whisper)","pyannote.audio 2.0+ (for diarization) OR alternative diarization system","Audio input with multiple speakers at 16kHz sample rate"],"input_types":["multi-speaker-audio-file","raw-audio-array","diarization-output (optional; can be computed automatically)"],"output_types":["speaker-attributed-transcript (list of {speaker_id, text, start_time, end_time})","structured-dialogue (JSON with speaker turns and timestamps)","vtt-subtitle-format (with speaker labels)"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-large-v3__cap_12","uri":"capability://automation.workflow.quantization.and.model.compression","name":"quantization-and-model-compression","description":"Supports model quantization (INT8, INT4) and distillation to reduce model size and inference latency, enabling deployment on resource-constrained devices (mobile, edge, embedded systems). The implementation uses PyTorch quantization APIs or ONNX quantization tools to convert the 1.5B-parameter large-v3 model to 8-bit or 4-bit precision, reducing model size from ~3GB to ~750MB-1.5GB with minimal accuracy loss (<1% WER degradation). Quantized models enable real-time inference on CPUs and mobile devices.","intents":["I need to deploy Whisper on mobile devices or edge hardware with limited memory and compute","I want to reduce inference latency for real-time transcription on CPU-only systems","I need to minimize model size for on-device deployment without cloud connectivity"],"best_for":["mobile transcription apps (iOS, Android) with on-device processing","edge computing deployments (IoT devices, embedded systems)","privacy-sensitive applications requiring local-only processing"],"limitations":["Quantization introduces 0.5-2% WER degradation due to precision loss; accuracy impact varies by domain and language","INT4 quantization is more aggressive and may introduce artifacts in low-confidence regions; INT8 is more stable","Quantized models are not compatible with standard Whisper inference code; require custom loading and inference logic","Quantization tools (PyTorch, ONNX) have limited hardware support; quantized models may not run on all devices","Inference latency reduction is modest on modern GPUs (10-20%) but significant on CPUs (2-3x speedup)"],"requires":["Python 3.8+","PyTorch 1.9+ with quantization support OR ONNX Runtime 1.10+","Quantization tools (torch.quantization or ONNX quantization)","Target hardware specification (mobile device, edge device, CPU architecture)"],"input_types":["pretrained-whisper-model (large-v3 checkpoint)","quantization-config (INT8 or INT4 specification)"],"output_types":["quantized-model-weights (reduced-precision checkpoint)","quantization-metadata (precision, scale factors, zero points)","performance-metrics (model size, inference latency, accuracy)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-large-v3__cap_2","uri":"capability://data.processing.analysis.timestamp.aligned.transcription","name":"timestamp-aligned-transcription","description":"Generates token-level timestamps for transcribed text by leveraging the model's attention weights and the decoder's autoregressive token generation sequence. The implementation uses the alignment between input mel-spectrogram frames (12.5ms per frame) and output tokens to compute precise start/end times for each word or subword unit. Timestamps are extracted from the model's internal state during inference without requiring separate alignment models, enabling efficient end-to-end processing.","intents":["I need to know exactly when each word was spoken in the audio for subtitle generation or video synchronization","I want to create searchable transcripts where users can click to jump to specific moments in the audio","I need to align transcripts with video frames for accessibility or content analysis"],"best_for":["video subtitle generation and synchronization workflows","interactive transcript platforms with seek-to-timestamp functionality","accessibility tools for deaf and hard-of-hearing users requiring precise timing"],"limitations":["Timestamp accuracy is ±100-200ms due to mel-spectrogram frame quantization and attention weight ambiguity","Subword tokenization (BPE) produces timestamps for tokens, not words; post-processing required to align with word boundaries","Timestamps degrade in quality for overlapping speech, background noise, or rapid speaker transitions","No built-in confidence scores for timestamp accuracy; developers cannot distinguish high-confidence from low-confidence alignments"],"requires":["Python 3.8+","transformers library 4.20+ with attention output enabled","Audio at 16kHz sample rate","Post-processing logic to convert token-level timestamps to word-level boundaries"],"input_types":["audio-file","raw-audio-array"],"output_types":["structured-transcript-with-timestamps (list of {text, start_time_ms, end_time_ms})","srt-subtitle-format (compatible with video players)"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-large-v3__cap_3","uri":"capability://data.processing.analysis.streaming.audio.transcription","name":"streaming-audio-transcription","description":"Processes audio in real-time or near-real-time using a sliding-window inference approach where the model processes overlapping chunks of audio (typically 30-second windows with 5-second overlap) and stitches transcripts together. The implementation maintains state across chunks to handle word boundaries and context, using the model's encoder-decoder architecture to process each window independently while preserving continuity. Streaming mode trades some accuracy for latency reduction, enabling live transcription with ~2-5 second delay.","intents":["I need to transcribe live audio streams (meetings, podcasts, broadcasts) with minimal latency","I want to provide real-time captions for video calls or live events","I need to process continuous audio without loading entire files into memory"],"best_for":["live transcription services (Zoom, Teams, Google Meet integrations)","real-time captioning for accessibility in live events","continuous audio processing systems with memory constraints"],"limitations":["Streaming mode introduces 2-5 second latency due to sliding-window buffering; not suitable for sub-second response requirements","Accuracy degrades ~1-3% compared to full-file transcription because context window is limited to 30 seconds","Word boundaries at chunk edges may be misaligned; requires post-processing to merge fragmented words across windows","No built-in handling of speaker changes or long pauses; may produce spurious transcriptions during silence","Requires careful tuning of window size and overlap parameters; suboptimal settings cause significant accuracy loss"],"requires":["Python 3.8+","transformers library 4.20+","Audio streaming library (e.g., pyaudio, sounddevice) for real-time input","Buffering mechanism to accumulate audio chunks (typically 30-second windows)","Post-processing logic to handle chunk boundary artifacts"],"input_types":["audio-stream (real-time microphone input or network stream)","chunked-audio-array (pre-segmented audio chunks)"],"output_types":["streaming-transcript (incremental text output as chunks are processed)","partial-transcript (with confidence scores indicating finality of each segment)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-large-v3__cap_4","uri":"capability://automation.workflow.batch.audio.processing.with.batching","name":"batch-audio-processing-with-batching","description":"Processes multiple audio files in parallel using PyTorch's DataLoader or JAX's vmap for vectorized inference, enabling efficient GPU utilization when transcribing large audio collections. The implementation pads variable-length audio inputs to a common length within each batch, processes them through the model simultaneously, and unpacks results. Batching reduces per-sample inference overhead and amortizes model loading costs, achieving 3-5x throughput improvement over sequential processing on GPU hardware.","intents":["I need to transcribe hundreds or thousands of audio files efficiently without sequential processing","I want to maximize GPU utilization when processing large audio archives or datasets","I need to reduce total wall-clock time for batch transcription jobs"],"best_for":["batch transcription services processing large audio archives","data preparation pipelines for speech recognition model training","content indexing systems transcribing media libraries"],"limitations":["Batching requires variable-length audio padding, which wastes computation on padding tokens; efficiency gains diminish with highly variable audio lengths","GPU memory scales linearly with batch size; large batches (>32) may cause out-of-memory errors on consumer GPUs (8-16GB VRAM)","Padding introduces minor accuracy degradation (~0.1-0.5% WER) due to attention artifacts at padding boundaries","Batch processing introduces latency for small jobs; not suitable for single-file transcription where sequential processing is faster","No built-in dynamic batching; developers must manually tune batch size for their hardware"],"requires":["Python 3.8+","PyTorch 1.9+ with DataLoader OR JAX 0.3+ with vmap","GPU with 8GB+ VRAM for batch size >16","Audio files at consistent or resampled sample rate (16kHz)"],"input_types":["list-of-audio-files","audio-dataset (PyTorch Dataset or TensorFlow Dataset interface)"],"output_types":["batch-transcripts (list of transcription results with file mapping)","structured-results (JSON with file paths and transcripts)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-large-v3__cap_5","uri":"capability://data.processing.analysis.audio.preprocessing.and.normalization","name":"audio-preprocessing-and-normalization","description":"Automatically handles audio preprocessing including resampling to 16kHz, mono conversion, normalization, and silence trimming before transcription. The model expects 16kHz mono PCM audio; the implementation uses librosa or torchaudio to convert arbitrary input formats (MP3, FLAC, 48kHz stereo, etc.) to the required specification. Preprocessing is transparent to the user — the model accepts raw audio files and handles format conversion internally, with optional configuration for silence detection and volume normalization.","intents":["I want to transcribe audio files in any format without manually converting to 16kHz mono WAV","I need to handle audio from diverse sources (phone recordings, streaming services, professional equipment) with consistent preprocessing","I want to remove silence and normalize volume before transcription to improve accuracy"],"best_for":["production transcription services accepting user-uploaded audio in arbitrary formats","data pipelines processing heterogeneous audio sources","accessibility tools requiring robust audio handling"],"limitations":["Resampling introduces minor quality loss (~0.1-0.5% WER) due to interpolation artifacts, especially when downsampling from high sample rates","Mono conversion loses spatial information from stereo recordings; stereo-specific content (e.g., separated speakers on L/R channels) may be degraded","Silence trimming is heuristic-based and may incorrectly remove speech with low volume or remove intentional pauses","Volume normalization uses peak or RMS-based scaling; may not handle dynamic range compression well for music or highly variable audio","Processing adds 0.5-2 seconds latency per file depending on format and resampling algorithm"],"requires":["Python 3.8+","librosa 0.9+ OR torchaudio 0.10+","ffmpeg (for MP3, FLAC, and other compressed formats)","Audio input: any format supported by librosa/torchaudio (WAV, MP3, FLAC, OGG, M4A, etc.)"],"input_types":["audio-file (any format)","raw-audio-array (arbitrary sample rate and channels)","audio-stream (with on-the-fly resampling)"],"output_types":["normalized-audio-array (16kHz mono PCM)","preprocessing-metadata (original sample rate, channels, duration)"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-large-v3__cap_6","uri":"capability://data.processing.analysis.vocabulary.constrained.decoding","name":"vocabulary-constrained-decoding","description":"Restricts the model's output vocabulary to a predefined set of words or phrases, enabling domain-specific transcription where only relevant terms are recognized. The implementation uses a constrained beam search decoder that masks invalid tokens at each decoding step, forcing the model to output only words from the allowed vocabulary. This is useful for transcribing specialized domains (medical, legal, technical) where out-of-vocabulary terms should be suppressed or replaced with domain-specific alternatives.","intents":["I need to transcribe medical or legal audio where only domain-specific terminology should be recognized","I want to prevent the model from hallucinating out-of-vocabulary terms in specialized domains","I need to ensure transcripts contain only approved terminology for compliance or consistency"],"best_for":["medical transcription services requiring domain-specific terminology","legal document transcription with controlled vocabulary","specialized technical documentation (aviation, engineering) with fixed terminology"],"limitations":["Constrained decoding reduces accuracy by 2-5% because the model is forced to choose from a limited vocabulary, even when out-of-vocabulary terms are more likely","Requires manual curation of vocabulary lists; incomplete or poorly chosen vocabularies degrade transcription quality significantly","Beam search with vocabulary constraints adds 10-30% latency overhead compared to unconstrained decoding","Cannot handle new or emerging terminology not in the predefined vocabulary; requires frequent vocabulary updates","Subword tokenization complicates vocabulary constraint implementation; must account for BPE token boundaries"],"requires":["Python 3.8+","transformers library 4.20+ with constrained beam search support","Predefined vocabulary list (text file or Python list)","Beam search decoder implementation (not available in all Whisper wrappers)"],"input_types":["audio-file","raw-audio-array","vocabulary-list (text file or Python list)"],"output_types":["constrained-transcript (text containing only vocabulary terms)","confidence-scores (with lower scores for forced vocabulary choices)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-large-v3__cap_7","uri":"capability://data.processing.analysis.confidence.scoring.and.uncertainty.quantification","name":"confidence-scoring-and-uncertainty-quantification","description":"Provides token-level and segment-level confidence scores derived from the model's softmax probability distribution over the vocabulary. The implementation extracts log-probabilities from the decoder's output distribution at each step, enabling developers to identify low-confidence regions in the transcript. Confidence scores can be aggregated to word or segment level, and used to flag uncertain transcriptions for human review or to trigger fallback mechanisms.","intents":["I need to identify which parts of a transcript are unreliable and require human review","I want to automatically flag low-confidence transcriptions for quality assurance","I need to measure transcription confidence to decide whether to accept or reject results"],"best_for":["quality assurance systems for transcription services","human-in-the-loop workflows where uncertain transcriptions are escalated for review","confidence-based filtering for downstream NLP tasks"],"limitations":["Confidence scores are not well-calibrated; high probability does not guarantee correctness, and low probability does not guarantee errors","Scores are biased toward common words and languages; rare words and low-resource languages have artificially low confidence even when correct","Token-level scores do not directly translate to word-level accuracy; aggregation methods (mean, min, max) are heuristic and not theoretically grounded","Confidence scores do not account for systematic errors (e.g., consistent mishearing of specific phonemes); a confident wrong answer is indistinguishable from a confident correct answer","Requires manual threshold tuning to determine what confidence level warrants human review; no universal threshold exists"],"requires":["Python 3.8+","transformers library 4.20+ with output_scores=True","Post-processing logic to aggregate token-level scores to word or segment level"],"input_types":["audio-file","raw-audio-array"],"output_types":["transcript-with-confidence (list of {text, confidence_score})","confidence-metadata (token-level, word-level, or segment-level scores)","uncertainty-flags (binary indicators for low-confidence regions)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-large-v3__cap_8","uri":"capability://data.processing.analysis.prompt.based.context.injection","name":"prompt-based-context-injection","description":"Accepts optional text prompts to guide transcription toward specific terminology or style, improving accuracy for domain-specific or specialized content. The implementation prepends context tokens to the decoder input, biasing the model toward generating text consistent with the prompt. For example, providing a prompt like 'This is a medical conversation about cardiology' or 'Transcribe the following technical specification' influences token selection during decoding without retraining the model.","intents":["I want to improve transcription accuracy by providing context about the audio content (domain, topic, speaker)","I need to guide the model toward specific terminology or phrasing for consistency","I want to transcribe specialized content (medical, legal, technical) with better accuracy without fine-tuning"],"best_for":["domain-specific transcription services where context is known in advance","specialized transcription workflows (medical, legal, technical) with consistent terminology","applications where user-provided context can improve accuracy"],"limitations":["Prompt effectiveness is highly variable and difficult to predict; poorly chosen prompts may degrade accuracy by 1-3%","Prompts must be carefully crafted; vague or misleading prompts confuse the model and reduce accuracy","No mechanism to enforce that the model follows the prompt; the model may ignore context if it conflicts with acoustic evidence","Prompt engineering is empirical and requires trial-and-error; no principled method exists to design optimal prompts","Prompts add minimal latency (~10-50ms) but require manual specification for each transcription task"],"requires":["Python 3.8+","transformers library 4.20+ with prompt support","Manually crafted text prompts describing the audio content or domain"],"input_types":["audio-file","raw-audio-array","text-prompt (optional context string)"],"output_types":["context-guided-transcript (text influenced by the provided prompt)","prompt-effectiveness-metadata (optional metrics on prompt impact)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-large-v3__cap_9","uri":"capability://data.processing.analysis.cross.lingual.transfer.and.zero.shot.translation","name":"cross-lingual-transfer-and-zero-shot-translation","description":"Transcribes audio in one language and optionally translates the output to another language using the model's multilingual encoder-decoder architecture. The model was trained on parallel multilingual data, enabling it to perform zero-shot translation (translating to languages not explicitly trained on) by leveraging shared semantic representations across languages. The implementation uses language tokens to specify the target language, enabling on-the-fly translation without separate translation models.","intents":["I need to transcribe audio in one language and provide translations in multiple target languages","I want to create multilingual transcripts from single-language audio without separate translation models","I need to support users who speak different languages from a single audio source"],"best_for":["international conference transcription with multilingual output","global customer support systems requiring multilingual transcripts","content distribution platforms needing transcripts in multiple languages"],"limitations":["Translation quality is lower than dedicated translation models (e.g., mBART, mT5); expect 2-5% BLEU score degradation","Zero-shot translation to low-resource languages is unreliable; translation quality degrades significantly for languages with limited training data","Translation introduces additional latency (~50-100% increase) because the model must generate both transcription and translation tokens","No mechanism to preserve formatting, speaker labels, or timestamps across translation; post-processing required","Hallucination risk increases with translation; the model may generate plausible-sounding but incorrect translations"],"requires":["Python 3.8+","transformers library 4.20+ with translation support","Target language code (ISO 639-1 or Whisper language token)","Audio input in source language"],"input_types":["audio-file (in source language)","raw-audio-array (in source language)","target-language-code (ISO 639-1 or Whisper token)"],"output_types":["transcription (in source language)","translation (in target language)","bilingual-transcript (source and target side-by-side)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-large-v3__headline","uri":"capability://voice.audio.automatic.speech.recognition.model","name":"automatic speech recognition model","description":"Whisper-large-v3 is an advanced automatic speech recognition model that accurately transcribes spoken language into text, supporting multiple languages and dialects.","intents":["best automatic speech recognition model","automatic speech recognition for multilingual transcription","top ASR model for audio processing","automatic speech recognition solutions for developers","high-performance ASR for real-time applications"],"best_for":["multilingual transcription","real-time audio processing"],"limitations":[],"requires":[],"input_types":["audio files","live audio streams"],"output_types":["text transcriptions"],"categories":["voice-audio"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 1.9+ OR JAX 0.3+ OR ONNX Runtime 1.10+","librosa or similar audio loading library for preprocessing","transformers library 4.20+","Audio input: WAV, MP3, FLAC, or raw PCM at 16kHz sample rate (model resamples automatically)","Audio input minimum 2-3 seconds at 16kHz sample rate","PyTorch 1.9+","GPU with 16GB+ VRAM (A100, V100, or RTX 3090+)","100-1000 hours of labeled audio data in target domain","Training infrastructure (distributed training for large datasets recommended)"],"failure_modes":["Inference latency ~5-15 seconds per minute of audio on CPU; GPU acceleration required for real-time use cases","No speaker diarization or speaker identification — outputs single continuous transcript without speaker labels","Trained primarily on English-dominant web audio; performance degrades on low-resource languages and highly specialized domains (medical, legal terminology)","Output is raw transcription without punctuation or capitalization; post-processing required for production-grade formatting","Memory footprint ~3GB for large-v3 variant; requires 8GB+ RAM for comfortable inference with batching","Language detection accuracy varies significantly by language; low-resource languages (e.g., Icelandic, Swahili) have lower confidence scores","Cannot reliably detect language switches within a single audio segment — treats entire clip as single language","Requires minimum ~2-3 seconds of audio for reliable detection; very short clips may misclassify","No confidence threshold mechanism built-in; developers must manually interpret probability scores to determine detection reliability","Requires 100-1000 hours of labeled audio data; 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