{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-openai--whisper-base","slug":"openai--whisper-base","name":"whisper-base","type":"model","url":"https://huggingface.co/openai/whisper-base","page_url":"https://unfragile.ai/openai--whisper-base","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-base__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 uses mel-spectrogram feature extraction on the audio input, processes it through a 12-layer transformer encoder, and generates text tokens via a 12-layer transformer decoder with cross-attention, enabling robust transcription without language-specific fine-tuning.","intents":["I need to transcribe audio files in multiple languages without building separate models per language","I want to convert speech to text with minimal preprocessing and automatic language detection","I need a production-ready ASR model that handles diverse audio conditions and accents across 99 languages"],"best_for":["developers building multilingual voice applications (chatbots, transcription services, accessibility tools)","teams deploying ASR in low-resource languages where language-specific models don't exist","researchers prototyping speech-to-text pipelines without extensive labeled training data"],"limitations":["Base model (74M parameters) trades accuracy for speed — WER ~4-5% on English test sets vs ~3% for larger variants; larger models (medium/large) require more compute","No speaker diarization or speaker identification — outputs single continuous transcript without speaker labels","Trained primarily on English-dominant web audio; performance degrades on heavily accented speech, background noise, or domain-specific terminology (medical, legal jargon)","No real-time streaming support in base implementation — requires full audio buffer before inference; latency ~5-10 seconds for 1-minute audio on CPU","Mel-spectrogram preprocessing assumes 16kHz sample rate; resampling required for other rates adds preprocessing overhead"],"requires":["Python 3.8+","PyTorch 1.9+ OR TensorFlow 2.8+ OR JAX (framework-specific weights available)","librosa or scipy for audio preprocessing (mel-spectrogram computation)","4GB+ RAM for base model inference (8GB+ recommended for batch processing)","Audio input: WAV, MP3, FLAC, or other formats supported by librosa (requires ffmpeg for MP3/FLAC)"],"input_types":["audio-waveform (numpy array, shape [sample_rate * duration])","audio-file-path (string path to WAV/MP3/FLAC)","raw-bytes (audio file bytes, auto-detected format)"],"output_types":["text-transcript (string)","token-logits (float array for confidence scoring)","language-code (ISO 639-1 code detected from audio)"],"categories":["data-processing-analysis","speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-base__cap_1","uri":"capability://data.processing.analysis.automatic.language.detection.from.audio","name":"automatic-language-detection-from-audio","description":"Identifies the spoken language in audio by processing mel-spectrograms through the transformer encoder and classifying the resulting embeddings against 99 language tokens without explicit language labels. The model learns language-specific acoustic patterns during training on multilingual web audio, enabling implicit language detection as a byproduct of the transcription task.","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 to the user's language without explicit language selection","I need to filter or categorize audio datasets by language without manual annotation"],"best_for":["multilingual voice application developers who need language routing without user input","data engineers processing large audio corpora for language-based categorization","teams building voice interfaces for international users with unknown language preferences"],"limitations":["Language detection accuracy depends on audio duration — requires minimum 3-5 seconds of speech for reliable detection; shorter clips may misclassify","Struggles with code-switching (mixing multiple languages in single utterance) — outputs single dominant language, not language boundaries","No confidence scores returned for language predictions — cannot distinguish between high-confidence and ambiguous detections","Performs poorly on heavily accented speech or non-native speakers; acoustic patterns may not match training distribution"],"requires":["Python 3.8+","PyTorch 1.9+ OR TensorFlow 2.8+ OR JAX","librosa for mel-spectrogram extraction","Audio input: 16kHz sample rate (resampling required for other rates)"],"input_types":["audio-waveform (numpy array)","audio-file-path (string)"],"output_types":["language-code (ISO 639-1 code string)","language-name (human-readable language name)"],"categories":["data-processing-analysis","speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-base__cap_2","uri":"capability://data.processing.analysis.robust.audio.preprocessing.and.normalization","name":"robust-audio-preprocessing-and-normalization","description":"Automatically handles diverse audio formats and sample rates by converting input audio to 16kHz mono waveforms and computing mel-spectrograms (80 mel-frequency bins, 400ms window, 160ms stride) as fixed-size feature representations. The preprocessing pipeline uses librosa's resampling and mel-scale filterbank computation, normalizing audio to a standard format that the transformer encoder expects, with automatic gain control via log-amplitude scaling.","intents":["I need to process audio from various sources (microphones, files, streams) without manual format conversion","I want to normalize audio quality variations (background noise, volume differences) before transcription","I need to handle real-world audio with different sample rates and channel counts transparently"],"best_for":["developers building production voice applications that accept user-uploaded audio","teams processing audio from heterogeneous sources (multiple microphones, platforms, codecs)","researchers prototyping ASR pipelines who want to abstract away audio engineering details"],"limitations":["Mel-spectrogram normalization assumes speech-like audio; music, environmental sounds, or heavily distorted audio may not normalize appropriately","No explicit noise reduction or speech enhancement — relies on model robustness; very noisy audio (SNR < 5dB) degrades transcription accuracy significantly","Resampling from high sample rates (48kHz, 44.1kHz) to 16kHz introduces aliasing artifacts; anti-aliasing filter quality depends on librosa implementation","Fixed mel-spectrogram window size (400ms) may not capture rapid acoustic changes in music or non-speech audio","No voice activity detection (VAD) — processes silence and non-speech segments, wasting compute"],"requires":["librosa 0.9+ for mel-spectrogram computation","scipy for resampling filters","NumPy for array operations","Audio input: any format supported by librosa (WAV, MP3, FLAC, OGG, etc. with ffmpeg backend)"],"input_types":["audio-file-path (string)","raw-audio-bytes (binary)","numpy-waveform (float32 array, any sample rate)"],"output_types":["mel-spectrogram (float32 array, shape [80, time_steps])","normalized-waveform (float32 array, 16kHz mono)"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-base__cap_3","uri":"capability://data.processing.analysis.batch.audio.transcription.with.variable.length.handling","name":"batch-audio-transcription-with-variable-length-handling","description":"Processes multiple audio files of different lengths in a single batch by padding shorter sequences to match the longest sequence in the batch, computing mel-spectrograms for all audios, and running the transformer encoder-decoder in parallel. The implementation uses attention masks to ignore padded positions, enabling efficient GPU utilization while handling variable-length inputs without truncation or resampling.","intents":["I need to transcribe hundreds of audio files efficiently without processing them one-by-one","I want to maximize GPU utilization when transcribing audio of different lengths","I need to reduce total inference time for large audio datasets by batching"],"best_for":["teams processing large audio corpora (podcasts, call recordings, meeting transcripts)","developers building batch transcription services with SLA requirements","researchers evaluating ASR performance on large test sets"],"limitations":["Batch size limited by GPU memory — base model requires ~1GB per 1-minute audio at batch size 8; larger batches require A100/H100 GPUs","Padding overhead increases with batch diversity — if batch contains 10-second and 60-second audios, shorter ones waste compute on padding; optimal batches have similar lengths","No streaming/online inference — must buffer entire audio before processing; unsuitable for real-time transcription","Attention masks add ~5-10% compute overhead compared to fixed-length processing","No automatic batch size tuning — developers must manually set batch size based on GPU memory"],"requires":["PyTorch 1.9+ with CUDA support (GPU recommended; CPU batching is very slow)","8GB+ GPU VRAM for batch size 8 (base model)","transformers library with batch processing support","Audio files: 16kHz mono WAV format (preprocessing handles conversion)"],"input_types":["list-of-audio-paths (list of strings)","list-of-waveforms (list of numpy arrays)","audio-dataset (HuggingFace Dataset or PyTorch DataLoader)"],"output_types":["batch-transcripts (list of strings)","batch-with-metadata (list of dicts with transcript, language, confidence)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-base__cap_4","uri":"capability://tool.use.integration.framework.agnostic.model.inference.across.pytorch.tensorflow.jax","name":"framework-agnostic-model-inference-across-pytorch-tensorflow-jax","description":"Provides unified model weights and inference APIs compatible with PyTorch, TensorFlow, and JAX through HuggingFace's transformers library abstraction layer. The model is distributed in SafeTensors format (a safe, fast serialization standard) with framework-specific weight loading, allowing developers to choose their preferred framework without retraining or format conversion.","intents":["I want to use Whisper in my TensorFlow/JAX project without converting PyTorch weights manually","I need to deploy the same model across multiple frameworks in different services","I want to avoid framework lock-in when building ASR infrastructure"],"best_for":["teams with heterogeneous ML stacks (some services use PyTorch, others TensorFlow)","developers evaluating frameworks and wanting to prototype in multiple backends","organizations migrating from one framework to another without retraining models"],"limitations":["Framework-specific optimizations may differ — TensorFlow inference may be 10-20% slower than PyTorch due to different graph compilation strategies","JAX version requires functional programming style; not all PyTorch-specific features (in-place operations) are available","SafeTensors loading adds ~500ms overhead on first load (weights are cached after); subsequent loads are fast","Framework-specific quantization and optimization tools vary — ONNX export requires additional conversion steps","No automatic framework selection — developers must explicitly specify framework when loading model"],"requires":["transformers 4.20+","PyTorch 1.9+ OR TensorFlow 2.8+ OR JAX 0.3+ (framework-specific)","safetensors library for weight loading","HuggingFace Hub access (for downloading weights)"],"input_types":["framework-agnostic-audio-input (numpy arrays, lists)"],"output_types":["framework-native-tensors (torch.Tensor, tf.Tensor, or jax.Array)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-base__cap_5","uri":"capability://data.processing.analysis.quantized.inference.for.edge.deployment","name":"quantized-inference-for-edge-deployment","description":"Supports inference on resource-constrained devices (mobile, edge) through quantization to 8-bit or 16-bit precision using PyTorch's quantization APIs or ONNX Runtime quantization. Quantized models reduce memory footprint from 300MB (float32) to ~75MB (int8) and accelerate inference by 2-4x on CPU, enabling deployment on devices with <1GB RAM.","intents":["I need to run Whisper on mobile devices or edge hardware with limited memory and compute","I want to reduce model size for on-device inference without cloud dependencies","I need faster inference on CPU-only devices for real-time transcription"],"best_for":["mobile developers building offline voice features (iOS, Android)","edge computing teams deploying ASR on IoT devices or embedded systems","privacy-focused applications requiring on-device processing without cloud transmission"],"limitations":["Quantization reduces accuracy by 1-3% WER on average; performance degradation varies by language and audio quality","Quantized models require specific inference runtimes (ONNX Runtime, TensorFlow Lite) — not all PyTorch quantization methods are portable","No dynamic quantization support for variable-length sequences — requires static shape definition at quantization time","Quantization tools (PyTorch quantization, ONNX quantizer) require careful calibration on representative audio data; poor calibration causes significant accuracy loss","Mobile deployment requires additional framework-specific tooling (CoreML for iOS, TFLite for Android) — not a single unified deployment"],"requires":["PyTorch 1.9+ with quantization support OR ONNX Runtime 1.10+","Calibration dataset (100-1000 audio samples representative of target domain)","Mobile framework: CoreML (iOS), TensorFlow Lite (Android), or ONNX Runtime Mobile","Device: minimum 512MB RAM, ARMv8 processor (for mobile)"],"input_types":["audio-waveform (numpy array, 16kHz mono)"],"output_types":["quantized-model-artifact (ONNX, TFLite, or CoreML format)","text-transcript (string from quantized inference)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","PyTorch 1.9+ OR TensorFlow 2.8+ OR JAX (framework-specific weights available)","librosa or scipy for audio preprocessing (mel-spectrogram computation)","4GB+ RAM for base model inference (8GB+ recommended for batch processing)","Audio input: WAV, MP3, FLAC, or other formats supported by librosa (requires ffmpeg for MP3/FLAC)","PyTorch 1.9+ OR TensorFlow 2.8+ OR JAX","librosa for mel-spectrogram extraction","Audio input: 16kHz sample rate (resampling required for other rates)","librosa 0.9+ for mel-spectrogram computation","scipy for resampling filters"],"failure_modes":["Base model (74M parameters) trades accuracy for speed — WER ~4-5% on English test sets vs ~3% for larger variants; 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