{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-openai--whisper-large-v3-turbo","slug":"openai--whisper-large-v3-turbo","name":"whisper-large-v3-turbo","type":"model","url":"https://huggingface.co/openai/whisper-large-v3-turbo","page_url":"https://unfragile.ai/openai--whisper-large-v3-turbo","categories":["voice-audio"],"tags":["transformers","safetensors","whisper","automatic-speech-recognition","audio","en","zh","de","es","ru","ko","fr","ja","pt","tr","pl","ca","nl","ar","sv"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-openai--whisper-large-v3-turbo__cap_0","uri":"capability://data.processing.analysis.multilingual.speech.to.text.transcription.with.99.language.support","name":"multilingual speech-to-text transcription with 99-language support","description":"Converts audio waveforms to text across 99 languages using a transformer-based encoder-decoder architecture trained on 680K hours of multilingual audio data. The model uses mel-spectrogram feature extraction from raw audio, processes variable-length sequences through a 24-layer encoder, and generates text tokens via an autoregressive decoder with cross-attention. Supports both streaming and batch inference modes with automatic language detection when language is not specified.","intents":["transcribe podcast episodes or meeting recordings in multiple languages automatically","build multilingual voice assistant backends that understand global user input","extract text from video content across different language regions without manual translation","create accessibility features that caption audio in non-English languages"],"best_for":["teams building multilingual voice applications (chatbots, transcription services)","content creators needing automated captioning across language markets","researchers working on speech processing in low-resource languages"],"limitations":["Turbo variant trades accuracy for speed — ~3-5% lower WER (word error rate) vs full v3 model on some languages","No speaker diarization — cannot distinguish between multiple speakers in same audio","Requires 8GB+ VRAM for full model inference; quantized versions needed for edge deployment","Performance degrades on heavily accented speech, background noise, or technical jargon outside training distribution","No real-time streaming support in base implementation — requires external streaming wrapper"],"requires":["Python 3.8+","PyTorch 1.9+ or TensorFlow 2.5+","librosa or similar audio loading library","GPU with 8GB+ VRAM (CPU inference ~10-15x slower)","Audio files in WAV, MP3, FLAC, or OGG format"],"input_types":["raw audio waveforms (16kHz or higher)","audio file paths (MP3, WAV, FLAC, OGG, M4A)","byte streams from microphone or network sources"],"output_types":["plain text transcription","JSON with timestamps and confidence scores","VTT/SRT subtitle format with timing"],"categories":["data-processing-analysis","speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-large-v3-turbo__cap_1","uri":"capability://data.processing.analysis.automatic.language.detection.from.audio.content","name":"automatic language detection from audio content","description":"Identifies the spoken language in audio without explicit specification by analyzing mel-spectrogram features through the encoder's initial layers, which learn language-specific acoustic patterns. The model's multilingual token vocabulary includes language tokens that are predicted during decoding, allowing the system to infer language from phonetic and prosodic characteristics. Detection happens as a byproduct of transcription without separate inference passes.","intents":["automatically route audio to correct language model in multilingual call centers","detect language switches mid-conversation for code-switching analysis","pre-filter audio content by language for downstream processing pipelines"],"best_for":["multilingual SaaS platforms needing automatic language routing","research teams studying code-switching and multilingual speech patterns"],"limitations":["Language detection confidence not explicitly exposed in base API — requires custom wrapper to extract from token probabilities","Struggles with heavily accented speech or rare language variants not well-represented in training data","Cannot reliably detect language from <2 seconds of audio — needs minimum context window","Confuses closely-related languages (e.g., Norwegian/Swedish, Mandarin/Cantonese) with ~5-10% error rate"],"requires":["Python 3.8+","PyTorch or TensorFlow","Audio input with minimum 2 seconds duration for reliable detection"],"input_types":["raw audio waveforms","audio file paths"],"output_types":["ISO 639-1 language code (e.g., 'en', 'zh', 'es')","confidence score (0-1) derived from token probabilities"],"categories":["data-processing-analysis","speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-large-v3-turbo__cap_2","uri":"capability://data.processing.analysis.variable.length.audio.sequence.processing.with.automatic.padding.truncation","name":"variable-length audio sequence processing with automatic padding/truncation","description":"Handles audio inputs of arbitrary duration (from seconds to hours) by converting to mel-spectrograms with fixed 80-dimensional frequency bins, then applying dynamic padding to 3000 time-steps (~30 seconds) or chunking longer sequences. The encoder processes padded sequences through 24 transformer layers with positional embeddings, while the decoder generates tokens autoregressively with a maximum output length of 448 tokens. Attention masks automatically handle padded regions to prevent information leakage.","intents":["process variable-length audio files without manual preprocessing or splitting","handle real-world audio with silence, pauses, and irregular timing","batch process mixed-duration audio files in single inference call"],"best_for":["production systems handling diverse audio sources (podcasts, calls, meetings)","batch processing pipelines with heterogeneous audio lengths"],"limitations":["Fixed 30-second context window means long pauses or silence in middle of speech may be truncated incorrectly","Audio longer than 30 seconds requires manual chunking with potential loss of context at chunk boundaries","Padding adds computational overhead for short audio (<5 seconds) — ~20% wasted computation","No cross-chunk context — each 30-second segment transcribed independently, missing long-range dependencies"],"requires":["Audio resampled to 16kHz (automatic in most libraries)","Librosa or similar for mel-spectrogram computation"],"input_types":["raw waveforms of any duration","audio files (auto-loaded and resampled)"],"output_types":["text transcription (up to 448 tokens per 30-second chunk)","timestamps aligned to original audio"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-large-v3-turbo__cap_3","uri":"capability://data.processing.analysis.robust.speech.recognition.under.acoustic.noise.and.degradation","name":"robust speech recognition under acoustic noise and degradation","description":"Achieves noise robustness through training on 680K hours of diverse real-world audio including background noise, music, speech overlap, and poor recording conditions. The mel-spectrogram frontend acts as a lossy compression that emphasizes speech-relevant frequencies while attenuating noise. The encoder's deep transformer layers learn to suppress noise patterns through multi-head attention, which can focus on speech-dominant frequency bands. No explicit noise reduction preprocessing is required.","intents":["transcribe call center recordings with background noise and multiple speakers","process podcast audio with music, sound effects, and variable recording quality","extract speech from video with environmental noise (traffic, crowd, machinery)"],"best_for":["production systems handling real-world audio (not studio-quality)","applications where preprocessing infrastructure is unavailable"],"limitations":["Performance degrades significantly in SNR <5dB (very noisy conditions) — WER increases 15-30%","Music or singing in background causes hallucinations — model may transcribe non-existent speech","Heavy accent combined with noise creates compounding errors — not independently robust","Trained primarily on English-language noise patterns — robustness varies by language"],"requires":["Audio in any realistic condition (no preprocessing required)"],"input_types":["noisy audio waveforms","audio files from real-world sources"],"output_types":["text transcription (with potential hallucinations in very noisy conditions)"],"categories":["data-processing-analysis","speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-large-v3-turbo__cap_4","uri":"capability://data.processing.analysis.efficient.inference.through.knowledge.distillation.and.model.compression","name":"efficient inference through knowledge distillation and model compression","description":"The Turbo variant achieves 2-3x faster inference than full Whisper v3 through knowledge distillation, where a smaller student model learns to mimic the full model's output distributions. The architecture uses the same transformer encoder-decoder design but with reduced layer depth and hidden dimensions, maintaining the 99-language capability through shared multilingual embeddings. Inference is further optimized through operator fusion and quantization-friendly design that enables INT8 quantization without accuracy loss.","intents":["deploy speech recognition on resource-constrained devices (mobile, edge)","reduce latency in real-time transcription pipelines from 10s to 3-5s per minute of audio","lower inference costs in high-volume transcription services by 50-70%"],"best_for":["teams deploying to edge devices or mobile platforms","high-volume transcription services optimizing for cost and latency","real-time applications with strict latency budgets"],"limitations":["3-5% accuracy loss (WER increase) vs full v3 model on some languages","Quantization to INT8 adds additional 1-2% accuracy loss — requires careful tuning","Knowledge distillation optimizes for average case — may underperform on out-of-distribution audio","Smaller model capacity limits ability to handle very long or complex utterances"],"requires":["PyTorch 1.9+ or TensorFlow 2.5+","4GB+ VRAM for full precision, 2GB+ for quantized version","Optional: ONNX Runtime or TensorRT for further optimization"],"input_types":["audio waveforms","audio files"],"output_types":["text transcription","JSON with timing information"],"categories":["data-processing-analysis","model-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-large-v3-turbo__cap_5","uri":"capability://data.processing.analysis.timestamp.aligned.transcription.with.segment.level.timing.information","name":"timestamp-aligned transcription with segment-level timing information","description":"Generates transcription output with precise timing information by tracking the decoder's attention alignment to the encoder's mel-spectrogram time-steps. Each generated token is associated with a start and end timestamp (in seconds) corresponding to the audio segment it represents. The alignment is computed through attention weights without requiring separate forced-alignment models, enabling end-to-end timing extraction in a single inference pass.","intents":["generate subtitle files (SRT/VTT) with accurate timing for video synchronization","create searchable transcripts where users can click to jump to specific moments in audio","analyze speech patterns by correlating text content with precise timing information"],"best_for":["video production and captioning workflows","podcast platforms with interactive transcripts","accessibility applications requiring precise timing"],"limitations":["Timing accuracy degrades for very short words (<100ms) — may be off by ±50-100ms","Silence and pauses are not explicitly marked — timing reflects only speech segments","Alignment quality depends on attention mechanism — can be unreliable for overlapping speech or music","Timestamps are relative to 30-second chunks — requires manual adjustment for long audio split into chunks"],"requires":["PyTorch or TensorFlow with attention weight extraction capability","Custom wrapper to convert attention weights to timestamps"],"input_types":["audio waveforms","audio files"],"output_types":["JSON with text and timestamps (start_time, end_time in seconds)","SRT/VTT subtitle format","WebVTT with cue timing"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-large-v3-turbo__cap_6","uri":"capability://data.processing.analysis.batch.inference.with.dynamic.batching.and.padding.optimization","name":"batch inference with dynamic batching and padding optimization","description":"Processes multiple audio files simultaneously through batched tensor operations, with dynamic padding that groups audio of similar lengths to minimize wasted computation. The encoder processes all batch items in parallel through 24 transformer layers, while the decoder generates tokens autoregressively with cross-attention to the batch-encoded representations. Attention masks ensure each batch item only attends to its own padded sequence, preventing cross-contamination.","intents":["transcribe hundreds of audio files in parallel for batch processing jobs","maximize GPU utilization by processing multiple files in single forward pass","reduce per-file inference latency through amortized overhead"],"best_for":["batch transcription services processing large audio archives","data processing pipelines with variable-length inputs"],"limitations":["Batch size limited by GPU memory — typically 4-16 items for 8GB VRAM","Dynamic padding overhead increases with batch heterogeneity — worst case is 30% wasted computation","Autoregressive decoding cannot be parallelized across batch — each token generated sequentially","Memory usage scales linearly with batch size — no gradient checkpointing in inference mode"],"requires":["GPU with sufficient VRAM for batch size (8GB minimum for batch_size=4)","PyTorch or TensorFlow with batching support"],"input_types":["list of audio file paths","list of audio waveforms"],"output_types":["list of transcriptions","JSON with per-file results and metadata"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai--whisper-large-v3-turbo__headline","uri":"capability://voice.audio.automatic.speech.recognition.model","name":"automatic speech recognition model","description":"Whisper-large-v3-turbo is an advanced automatic speech recognition model that provides high accuracy in transcribing audio into text across multiple languages, making it ideal for developers seeking robust audio processing solutions.","intents":["best automatic speech recognition model","automatic speech recognition for multilingual transcription","top ASR tools for developers","best models for audio transcription","automatic speech recognition solutions for applications"],"best_for":["multilingual audio transcription","real-time speech recognition"],"limitations":["requires audio input","performance may vary by language"],"requires":["audio files"],"input_types":["audio"],"output_types":["text"],"categories":["voice-audio"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","PyTorch 1.9+ or TensorFlow 2.5+","librosa or similar audio loading library","GPU with 8GB+ VRAM (CPU inference ~10-15x slower)","Audio files in WAV, MP3, FLAC, or OGG format","PyTorch or TensorFlow","Audio input with minimum 2 seconds duration for reliable detection","Audio resampled to 16kHz (automatic in most libraries)","Librosa or similar for mel-spectrogram computation","Audio in any realistic condition (no preprocessing required)"],"failure_modes":["Turbo variant trades accuracy for speed — ~3-5% lower WER (word error rate) vs full v3 model on some languages","No speaker diarization — cannot distinguish between multiple speakers in same audio","Requires 8GB+ VRAM for full model inference; 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