{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-openai--whisper-small","slug":"openai--whisper-small","name":"whisper-small","type":"model","url":"https://huggingface.co/openai/whisper-small","page_url":"https://unfragile.ai/openai--whisper-small","categories":["voice-audio"],"tags":["transformers","pytorch","tf","jax","safetensors","whisper","automatic-speech-recognition","audio","hf-asr-leaderboard","en","zh","de","es","ru","ko","fr","ja","pt","tr","pl"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-openai--whisper-small__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 from the web. The model processes variable-length audio by converting it to mel-spectrograms, encoding through a 12-layer transformer encoder, and decoding via a 12-layer transformer decoder with cross-attention, outputting tokenized text that can be detokenized to readable transcriptions. Handles diverse audio conditions (background noise, accents, technical jargon) through large-scale diverse training data rather than explicit noise reduction preprocessing.","intents":["I need to transcribe audio files in multiple languages without maintaining separate models per language","I want to build a speech-to-text pipeline that handles real-world noisy audio without preprocessing","I need to extract text from audio for downstream NLP tasks like summarization or translation","I want to support non-English languages in my voice application without significant accuracy degradation"],"best_for":["multilingual applications serving global audiences","developers building voice-enabled features without language-specific model management","teams prototyping speech-to-text without fine-tuning infrastructure","researchers benchmarking ASR performance across language families"],"limitations":["Small model variant (244M parameters) trades accuracy for speed — word error rate ~8-12% on clean English vs 4-6% for large variant, wider gap on noisy audio","No speaker diarization or speaker identification — outputs single continuous transcript regardless of speaker changes","Trained primarily on English-dominant web audio — performance degrades on low-resource languages and specialized domains (medical, legal terminology)","No real-time streaming support in base model — requires full audio loaded before inference, unsuitable for live transcription without external streaming wrapper","Mel-spectrogram preprocessing assumes 16kHz sample rate — requires resampling for other rates, may lose information above 8kHz","No punctuation or capitalization in raw output — requires post-processing or separate models for formatting"],"requires":["Python 3.8+","PyTorch 1.9+ or TensorFlow 2.24+ or JAX (framework-specific)","librosa or similar audio loading library for preprocessing","transformers library 4.20.0+","~1GB VRAM for inference (FP32), ~500MB for FP16 quantization","Audio files in WAV, MP3, FLAC, or other common formats (librosa-compatible)"],"input_types":["audio waveform (numpy array, shape [channels, samples])","audio file path (string, librosa-loadable format)","raw bytes (audio stream)","mel-spectrogram (pre-computed, shape [80, time_steps])"],"output_types":["text string (raw transcription)","token IDs (integer sequence)","structured dict with transcription and language detection","logits (raw model outputs for confidence scoring)"],"categories":["data-processing-analysis","audio-speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-small__cap_1","uri":"capability://data.processing.analysis.language.detection.from.audio","name":"language-detection-from-audio","description":"Automatically identifies the spoken language from audio input by leveraging language-specific tokens embedded in the decoder's vocabulary and learned during training on multilingual data. The model predicts a language token as the first output token after processing the audio through the encoder, enabling downstream decoding to use language-specific vocabulary and attention patterns. This detection happens implicitly during transcription without separate inference passes, making it a zero-cost auxiliary output.","intents":["I need to automatically detect which language is spoken in an audio file before routing to language-specific processing","I want to build a multilingual voice assistant that adapts its behavior based on detected input language","I need to filter or categorize audio files by language without manual labeling","I want to validate that audio matches expected language before transcription"],"best_for":["multilingual voice applications requiring dynamic language routing","data processing pipelines that need to categorize audio by language","teams building language-aware chatbots or voice assistants"],"limitations":["Language detection confidence is not explicitly exposed — only the predicted language token is returned, requiring external confidence estimation or multiple-pass inference","Performance degrades on code-switching (mixing multiple languages in single utterance) — model commits to single language token, losing mixed-language context","Short audio clips (<2 seconds) may produce unreliable language detection due to insufficient acoustic context","Trained on web audio distribution — may misidentify rare language variants or heavily accented speech as related major language"],"requires":["Python 3.8+","transformers library 4.20.0+","PyTorch/TensorFlow/JAX backend","Audio input (same formats as transcription capability)"],"input_types":["audio waveform (numpy array)","audio file path (string)","mel-spectrogram (pre-computed)"],"output_types":["language code string (e.g., 'en', 'zh', 'fr')","language token ID (integer)","language name (e.g., 'English', 'Chinese', 'French')"],"categories":["data-processing-analysis","audio-speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-small__cap_2","uri":"capability://data.processing.analysis.variable.length.audio.processing.with.padding","name":"variable-length-audio-processing-with-padding","description":"Handles audio files of arbitrary length by converting them to fixed-size mel-spectrogram representations with automatic padding/truncation, enabling batch processing of heterogeneous audio lengths. The model pads shorter spectrograms to a maximum sequence length (default 3000 frames ≈ 30 seconds) and truncates longer audio, with padding tokens masked during attention computation to prevent information leakage. This design allows efficient GPU batching without reshaping individual samples.","intents":["I need to process audio files of varying lengths in a single batch without reshaping or padding manually","I want to transcribe both short voice messages and long audio files with the same model","I need to optimize GPU memory usage when processing heterogeneous audio lengths"],"best_for":["batch processing pipelines handling diverse audio sources","production systems requiring efficient GPU utilization","applications with variable-length user-generated audio"],"limitations":["Audio longer than 30 seconds (3000 mel-spectrogram frames) is truncated — loses information beyond this window, requiring sliding-window or chunking strategies for long-form audio","Padding adds computational overhead for short audio — model processes full 3000-frame sequences even for 5-second clips","No explicit handling of audio discontinuities — if audio is chunked and processed separately, context is lost between chunks","Mel-spectrogram padding uses zero-padding which may introduce artifacts at boundaries if not properly masked"],"requires":["Audio preprocessing library (librosa, torchaudio, or equivalent)","Mel-spectrogram computation (80 frequency bins, 160 hop length standard)","Attention mask generation for padding tokens"],"input_types":["audio waveform of any length (numpy array)","audio file path (string)","pre-computed mel-spectrogram of any time dimension"],"output_types":["padded mel-spectrogram (shape [batch_size, 80, 3000])","attention mask (shape [batch_size, 3000])","truncated/padded audio representation"],"categories":["data-processing-analysis","audio-speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-small__cap_3","uri":"capability://tool.use.integration.cross.framework.model.inference","name":"cross-framework-model-inference","description":"Provides unified model weights compatible with PyTorch, TensorFlow, JAX, and ONNX runtimes through HuggingFace's transformers library abstraction layer, automatically handling framework-specific tensor operations and device placement. The model weights are stored in safetensors format (safer than pickle, faster loading) and can be loaded into any supported framework with identical numerical outputs, enabling framework-agnostic deployment and experimentation.","intents":["I want to use Whisper in my PyTorch project but my team uses TensorFlow — I need framework-agnostic model loading","I need to deploy Whisper to edge devices using ONNX Runtime for faster inference","I want to experiment with different frameworks without re-downloading or converting models"],"best_for":["teams with heterogeneous ML stacks (PyTorch + TensorFlow + JAX)","edge deployment scenarios requiring ONNX or lightweight runtimes","researchers comparing framework performance on same model"],"limitations":["Framework conversion adds ~5-10% numerical precision loss due to floating-point rounding across frameworks","ONNX export requires additional conversion step and may not support all dynamic shapes (variable audio length)","JAX version requires jax-transformers wrapper which lags behind PyTorch in feature updates","TensorFlow version slower than PyTorch due to less optimized attention implementations in TF ecosystem"],"requires":["transformers library 4.20.0+","Framework-specific backend: torch, tensorflow, jax, or onnxruntime","safetensors library for safe weight loading"],"input_types":["model identifier string ('openai/whisper-small')","local safetensors file path"],"output_types":["framework-specific model object (torch.nn.Module, tf.keras.Model, etc.)","ONNX model file (if exported)"],"categories":["tool-use-integration","audio-speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-small__cap_4","uri":"capability://data.processing.analysis.quantization.aware.inference.with.reduced.memory","name":"quantization-aware-inference-with-reduced-memory","description":"Supports inference in reduced-precision formats (FP16, INT8) through transformers library quantization backends, reducing model memory footprint from ~1GB (FP32) to ~500MB (FP16) or ~250MB (INT8) without retraining. The model uses post-training quantization where weights are converted to lower precision after training, with dynamic quantization of activations during inference, maintaining accuracy within 1-2% of full precision while enabling deployment on memory-constrained devices.","intents":["I need to run Whisper on a mobile device or edge hardware with limited VRAM","I want to reduce inference latency by using lower precision without retraining","I need to batch more audio samples on a single GPU by reducing per-sample memory"],"best_for":["edge deployment (mobile, embedded systems, IoT)","cost-optimized cloud inference (smaller instance types)","latency-sensitive applications requiring batch processing"],"limitations":["INT8 quantization introduces 2-5% accuracy degradation on noisy audio compared to FP32, wider gap on low-resource languages","Quantization requires framework-specific implementations — PyTorch quantization differs from TensorFlow quantization, not all backends support all precision levels","Dynamic quantization adds ~10-15% latency overhead compared to static quantization, but static requires calibration data","No quantization-aware training — post-training quantization may not be optimal for this architecture"],"requires":["PyTorch 1.9+ with quantization support, or TensorFlow 2.5+, or ONNX with quantization tools","Device with FP16 support (most modern GPUs) or INT8 support (varies by hardware)","transformers library with quantization backends"],"input_types":["model in FP32 format","quantization configuration (precision level, backend)"],"output_types":["quantized model (FP16 or INT8)","quantization statistics (scale factors, zero points)"],"categories":["data-processing-analysis","audio-speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-small__cap_5","uri":"capability://automation.workflow.batch.inference.with.dynamic.padding","name":"batch-inference-with-dynamic-padding","description":"Processes multiple audio samples in parallel by dynamically padding each sample to the longest sequence in the batch, then using attention masks to ignore padding tokens during computation. This approach reduces wasted computation compared to padding all samples to the global maximum (3000 frames), enabling efficient batching of heterogeneous audio lengths. The implementation uses transformers' DataCollator pattern to automatically handle padding and mask generation during batch construction.","intents":["I need to transcribe 100 audio files efficiently on a single GPU without processing them sequentially","I want to maximize GPU utilization when processing audio of varying lengths","I need to reduce inference time for bulk transcription tasks"],"best_for":["batch processing pipelines (transcription services, data annotation)","production systems processing high-volume audio","teams optimizing GPU utilization and inference cost"],"limitations":["Dynamic padding requires sorting or grouping by length for optimal efficiency — random batch composition may negate benefits","Attention mask computation adds ~5% overhead compared to fixed-size batching","Memory savings diminish with large batch sizes if one sample is much longer than others (worst-case: padding entire batch to longest sample)","Requires careful batch composition — batching very short and very long audio together wastes computation"],"requires":["transformers library with DataCollator support","PyTorch or TensorFlow with attention mask support","Batch composition logic (sorting by length or grouping)"],"input_types":["list of audio waveforms (variable lengths)","list of mel-spectrograms (variable time dimensions)","batch of audio file paths"],"output_types":["batch of transcriptions (list of strings)","batch of token sequences (list of token IDs)","structured batch output with metadata"],"categories":["automation-workflow","audio-speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-small__cap_6","uri":"capability://data.processing.analysis.token.level.confidence.scoring","name":"token-level-confidence-scoring","description":"Exposes raw model logits for each predicted token, enabling downstream confidence scoring by computing softmax probabilities over the vocabulary and extracting the probability of the predicted token. This allows builders to identify low-confidence predictions, implement confidence thresholding for quality control, or generate alternative hypotheses by sampling from the probability distribution. The logits are available through the model's output structure without additional inference passes.","intents":["I need to identify uncertain transcriptions and flag them for human review","I want to implement confidence-based filtering to improve transcription quality","I need to generate N-best hypotheses or alternative transcriptions for downstream ranking"],"best_for":["quality assurance pipelines requiring confidence filtering","human-in-the-loop systems that escalate low-confidence predictions","research on model uncertainty and calibration"],"limitations":["Logits are not calibrated — raw softmax probabilities don't reflect true error likelihood, requiring temperature scaling or calibration for reliable confidence estimates","Token-level confidence doesn't account for error propagation — early errors may inflate confidence of downstream tokens due to autoregressive decoding","No sentence-level or utterance-level confidence aggregation — requires custom logic to combine token confidences","Logits require full vocabulary softmax computation (~50K tokens) which adds ~20% latency compared to greedy decoding"],"requires":["transformers library with output_scores=True or return_dict=True","PyTorch or TensorFlow for logit processing","Custom confidence aggregation logic"],"input_types":["audio waveform or mel-spectrogram","model output with logits enabled"],"output_types":["token logits (shape [sequence_length, vocab_size])","token probabilities (shape [sequence_length, vocab_size])","confidence scores per token (shape [sequence_length])"],"categories":["data-processing-analysis","audio-speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-small__cap_7","uri":"capability://automation.workflow.streaming.audio.chunking.with.context.windows","name":"streaming-audio-chunking-with-context-windows","description":"Enables streaming transcription by implementing sliding-window inference where overlapping audio chunks are processed sequentially with context overlap to maintain coherence across chunk boundaries. While the base model requires full audio loading, this capability describes the pattern for adapting Whisper to streaming by chunking audio into 30-second windows with 5-10 second overlap, processing each chunk independently, and merging transcriptions with overlap-based deduplication. This is not a native streaming capability but a documented inference pattern for streaming adaptation.","intents":["I need to transcribe live audio streams or very long recordings without loading entire file into memory","I want to provide real-time transcription feedback while audio is still being recorded","I need to process multi-hour audio files that exceed the 30-second context window"],"best_for":["live transcription applications (meetings, podcasts, lectures)","long-form audio processing (interviews, audiobooks)","memory-constrained environments processing large files"],"limitations":["Chunking introduces boundary artifacts — transcription quality degrades at chunk boundaries due to lost context, typically 5-10% WER increase at boundaries","Overlap-based deduplication is heuristic and may produce duplicate or missing text at boundaries","No native streaming support — requires external audio buffering and chunk management logic","Latency increases due to per-chunk inference overhead — 30-second chunks with 5-second overlap require ~6 inference passes per minute of audio","Context loss at boundaries affects punctuation and capitalization — model cannot see across chunk boundaries to make informed decisions"],"requires":["Audio streaming library (pyaudio, sounddevice, or equivalent)","Chunk buffering and overlap management logic","Deduplication logic for merging overlapping transcriptions","transformers library for per-chunk inference"],"input_types":["audio stream (continuous bytes or samples)","audio file path (for chunked processing)","pre-computed mel-spectrogram chunks with overlap"],"output_types":["streaming transcription (incremental text updates)","chunk-level transcriptions (list of strings)","merged final transcription (deduplicated)"],"categories":["automation-workflow","audio-speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","PyTorch 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