{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-openai-whisper","slug":"pypi-openai-whisper","name":"openai-whisper","type":"repo","url":"https://pypi.org/project/openai-whisper/","page_url":"https://unfragile.ai/pypi-openai-whisper","categories":["voice-audio"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-openai-whisper__cap_0","uri":"capability://data.processing.analysis.multilingual.speech.to.text.transcription.with.automatic.language.detection","name":"multilingual speech-to-text transcription with automatic language detection","description":"Transcribes audio in 99+ languages using a single unified encoder-decoder transformer model trained on 680,000 hours of multilingual audio from the web. The model automatically detects the spoken language without requiring explicit language specification, using a shared embedding space learned across diverse linguistic data. Inference runs locally without API calls, enabling offline transcription at scale.","intents":["transcribe audio files in non-English languages without language-specific models","build speech recognition systems that work across global audiences without language configuration","process multilingual audio datasets and automatically identify spoken languages","deploy speech-to-text on edge devices or air-gapped systems without cloud dependencies"],"best_for":["developers building international voice applications","teams processing multilingual audio corpora","organizations with privacy/compliance requirements preventing cloud transcription","researchers studying speech recognition across language families"],"limitations":["Model size ranges 39MB to 3GB depending on variant (tiny to large), requiring 2-8GB RAM for inference","Accuracy degrades on heavily accented speech, background noise, or low-quality audio compared to fine-tuned language-specific models","No real-time streaming transcription — requires complete audio file before processing begins","Inference latency on CPU is 10-30x slower than commercial cloud APIs; GPU acceleration recommended for production"],"requires":["Python 3.7+","PyTorch 1.9.0+ or TensorFlow 2.0+","FFmpeg for audio preprocessing","4GB+ RAM minimum (8GB+ recommended for large models)","GPU with CUDA 11.0+ for production-grade throughput"],"input_types":["audio files (MP3, WAV, M4A, FLAC, OGG, OPUS)","video files with audio tracks (MP4, MKV, WebM)","raw audio bytes via file path or file-like objects"],"output_types":["plain text transcription","JSON with timestamps and confidence scores","VTT/SRT subtitle files with temporal alignment","structured segments with language metadata"],"categories":["data-processing-analysis","speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-openai-whisper__cap_1","uri":"capability://data.processing.analysis.timestamp.aligned.segment.level.transcription.with.confidence.scoring","name":"timestamp-aligned segment-level transcription with confidence scoring","description":"Breaks audio into temporal segments and returns transcription for each segment with precise start/end timestamps and per-token confidence scores. Uses the model's internal attention mechanisms to align decoded tokens to audio frames, enabling fine-grained temporal grounding without separate alignment models. Supports both word-level and sentence-level segmentation strategies.","intents":["generate subtitle files with accurate timing for video synchronization","identify low-confidence regions in transcriptions for manual review or re-processing","build interactive transcription UIs with seek-to-timestamp functionality","extract speaker turn boundaries and dialogue structure from audio"],"best_for":["video production and subtitle generation workflows","quality assurance teams validating transcription accuracy","developers building interactive media players with transcript sync","accessibility teams creating captions for video content"],"limitations":["Timestamp accuracy is ±100-500ms depending on audio quality and model variant; not suitable for frame-accurate video editing","Confidence scores are model-calibrated estimates, not true probability distributions; may not reflect actual error likelihood","Segment boundaries may not align with natural speech pauses in noisy audio or rapid speech","No speaker diarization — cannot distinguish between multiple speakers in the same segment"],"requires":["Python 3.7+","PyTorch or TensorFlow backend","Audio file with clear temporal structure (silence between segments improves accuracy)"],"input_types":["audio files with variable sample rates (8kHz to 48kHz supported)","video files with embedded audio tracks"],"output_types":["JSON with segment objects containing text, start_time, end_time, confidence","VTT subtitle format with timing cues","SRT subtitle format","raw token-level alignments for advanced processing"],"categories":["data-processing-analysis","speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-openai-whisper__cap_10","uri":"capability://data.processing.analysis.structured.output.extraction.with.json.schema.validation","name":"structured output extraction with json schema validation","description":"Transcription results can be returned as structured JSON with metadata (language, duration, segments with timestamps), enabling downstream processing without text parsing. Supports validation against JSON schemas to ensure output conforms to expected structure, useful for API contracts and data pipelines.","intents":["integrate transcription results into structured data pipelines without text parsing","validate transcription output against expected schema in automated workflows","export transcriptions with metadata for downstream processing (NLP, analytics)","build APIs that return transcriptions as structured JSON with guaranteed schema"],"best_for":["backend developers building transcription APIs","data engineers integrating transcription into ETL pipelines","teams using transcriptions as input to NLP models or analytics","developers building quality assurance workflows with schema validation"],"limitations":["JSON output adds ~5-10% overhead compared to plain text due to serialization","Schema validation is optional; no enforcement of output structure by default","Nested segment structures can become large for long audio (100+ segments); requires pagination for UI display","No support for custom JSON schemas; output structure is fixed by Whisper API"],"requires":["Python 3.7+","PyTorch or TensorFlow backend","JSON schema library for validation (jsonschema package, optional)"],"input_types":["audio file","output format specification: 'json'"],"output_types":["JSON object with keys: text, language, duration, segments (array of {id, seek, start, end, text, tokens, temperature, avg_logprob, compression_ratio, no_speech_prob})"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-openai-whisper__cap_2","uri":"capability://data.processing.analysis.model.variant.selection.with.accuracy.latency.tradeoffs","name":"model variant selection with accuracy-latency tradeoffs","description":"Provides five pre-trained model sizes (tiny, base, small, medium, large) ranging from 39MB to 3GB, enabling developers to choose optimal accuracy-speed-memory tradeoffs for their deployment constraints. Each variant uses identical architecture but different parameter counts; models are automatically downloaded and cached on first use. Supports quantization and distillation for further optimization.","intents":["deploy speech recognition on resource-constrained devices (mobile, edge, IoT)","balance transcription quality against latency requirements in real-time applications","optimize inference cost by selecting smallest model meeting accuracy thresholds","benchmark model performance across different hardware configurations"],"best_for":["mobile and embedded systems developers","teams optimizing inference cost in high-volume transcription services","researchers comparing model scaling laws in speech recognition","developers prototyping with tiny models before scaling to production"],"limitations":["Tiny model (39MB) has ~50% word error rate on English; only suitable for simple command recognition or non-critical applications","No automatic model selection based on hardware — developers must manually choose variant","Model caching directory can grow to 10GB+ if all variants are downloaded; requires manual cleanup","Quantized variants not officially provided; third-party quantization may degrade accuracy by 5-15%"],"requires":["Python 3.7+","PyTorch 1.9.0+ or TensorFlow 2.0+","Disk space: 39MB (tiny) to 3GB (large) per model variant","Internet connection for initial model download (cached locally thereafter)"],"input_types":["model size specification as string: 'tiny', 'base', 'small', 'medium', 'large'"],"output_types":["loaded model object ready for inference","model metadata (parameter count, size, expected latency)"],"categories":["data-processing-analysis","speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-openai-whisper__cap_3","uri":"capability://data.processing.analysis.audio.preprocessing.and.format.normalization","name":"audio preprocessing and format normalization","description":"Automatically handles audio format conversion, resampling, and normalization using FFmpeg as a backend. Accepts diverse input formats (MP3, WAV, M4A, FLAC, OGG, OPUS, video files) and converts to 16kHz mono PCM internally, matching the model's training data distribution. Handles variable sample rates, bit depths, and channel configurations transparently without user intervention.","intents":["process audio from heterogeneous sources (user uploads, streaming APIs, local files) without format-specific handling","normalize audio quality before transcription to improve model accuracy","extract audio from video files for transcription without separate tools","batch-process large audio collections with mixed formats and sample rates"],"best_for":["web applications accepting user-uploaded audio in arbitrary formats","data pipelines processing diverse audio sources","teams without audio engineering expertise","batch processing workflows handling legacy audio archives"],"limitations":["FFmpeg dependency adds ~500MB to deployment footprint; not available in minimal containers","Resampling introduces minor artifacts (typically inaudible but may affect edge cases)","No audio enhancement (noise reduction, echo cancellation) — preprocessing is format conversion only","Mono conversion loses spatial information from stereo/surround audio; may degrade accuracy for music or spatial audio"],"requires":["Python 3.7+","FFmpeg 4.0+ installed and in system PATH","PyTorch or TensorFlow backend"],"input_types":["audio files: MP3, WAV, FLAC, OGG, OPUS, M4A","video files: MP4, MKV, WebM, AVI (audio extracted automatically)","file paths (string) or file-like objects (BytesIO)"],"output_types":["normalized 16kHz mono PCM audio ready for model inference","audio duration metadata"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-openai-whisper__cap_4","uri":"capability://data.processing.analysis.batch.transcription.with.memory.efficient.streaming","name":"batch transcription with memory-efficient streaming","description":"Processes multiple audio files or long audio streams without loading entire files into memory simultaneously. Uses a sliding-window approach where audio is read in chunks, processed through the model, and results are yielded incrementally. Enables transcription of multi-hour audio files on systems with limited RAM by processing 30-second windows sequentially.","intents":["transcribe long-form audio (podcasts, lectures, meetings) on memory-constrained systems","process large audio collections without batching complexity or temporary storage","build streaming transcription pipelines that emit results as audio is processed","monitor transcription progress in real-time for long files"],"best_for":["teams processing podcast/audiobook archives on modest hardware","streaming applications requiring incremental output","data centers optimizing memory usage for high-volume transcription","developers building progress-tracking UIs for long transcriptions"],"limitations":["Chunk boundaries may split words or sentences, requiring post-processing to reconstruct coherent segments","No cross-chunk context — model cannot leverage information from previous chunks to improve current chunk accuracy","Streaming mode adds ~5-10% latency overhead vs. single-pass processing due to chunk management","Memory savings plateau at ~500MB for large models; further reduction requires model quantization"],"requires":["Python 3.7+","PyTorch or TensorFlow backend","Audio file with clear temporal structure (silence between chunks improves quality)"],"input_types":["audio file paths (processed in 30-second chunks)","file-like objects supporting seek/read operations","audio streams (if wrapped in seekable interface)"],"output_types":["generator yielding segment dictionaries with text and timestamps","accumulated full transcription after all chunks processed"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-openai-whisper__cap_5","uri":"capability://code.generation.editing.task.specific.model.fine.tuning.and.transfer.learning","name":"task-specific model fine-tuning and transfer learning","description":"Supports fine-tuning pre-trained models on custom audio datasets to improve accuracy for domain-specific speech (medical terminology, accented speech, noisy environments). Uses PyTorch's standard training loop with cross-entropy loss; developers can freeze encoder layers and train only the decoder for faster convergence, or train end-to-end for maximum adaptation. Includes utilities for dataset preparation and validation.","intents":["improve transcription accuracy for specialized domains (medical, legal, technical) with limited labeled data","adapt models to specific accents or speaker populations underrepresented in training data","reduce hallucinations and improve accuracy in noisy environments (call centers, factories)","build custom models for proprietary or sensitive audio without relying on general-purpose models"],"best_for":["organizations with domain-specific audio and labeling budgets","teams targeting underrepresented languages or accents","researchers studying transfer learning in speech recognition","companies with proprietary audio data requiring custom models"],"limitations":["Requires 100+ hours of labeled audio for meaningful improvement; smaller datasets risk overfitting","Fine-tuning adds 2-7 days of training time on single GPU depending on dataset size and model variant","No built-in active learning or curriculum learning — requires manual dataset curation","Fine-tuned models are not portable across PyTorch versions; requires matching PyTorch version for inference"],"requires":["Python 3.7+","PyTorch 1.9.0+","GPU with 8GB+ VRAM (16GB+ recommended for large models)","100+ hours of labeled audio in WebVTT or JSON format","Training infrastructure (single GPU minimum, multi-GPU for faster convergence)"],"input_types":["audio files in supported formats","transcription labels in WebVTT, JSON, or CSV format","validation dataset (10-20% of training data)"],"output_types":["fine-tuned model checkpoint (PyTorch .pt file)","training metrics (loss curves, validation WER)","inference-ready model compatible with standard Whisper API"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-openai-whisper__cap_6","uri":"capability://automation.workflow.command.line.interface.for.standalone.transcription","name":"command-line interface for standalone transcription","description":"Provides a CLI tool (`whisper` command) enabling transcription without writing Python code. Accepts audio file paths, outputs transcriptions to stdout or files, and supports flags for model selection, language specification, output format, and GPU acceleration. Useful for shell scripts, batch processing, and non-developers.","intents":["transcribe audio files from command line without Python knowledge","integrate transcription into shell scripts and CI/CD pipelines","batch-process audio collections using standard Unix tools (find, xargs, parallel)","quickly test Whisper on audio files without writing code"],"best_for":["DevOps engineers integrating transcription into automation workflows","non-technical users transcribing audio files","shell script developers building audio processing pipelines","teams using Whisper in Docker containers or serverless functions"],"limitations":["CLI has limited customization compared to Python API; advanced use cases require Python code","No streaming output — entire transcription buffered before writing to stdout","Error handling is basic; failures don't provide detailed debugging information","No progress reporting for long files; users cannot monitor transcription status"],"requires":["Python 3.7+ with openai-whisper package installed","FFmpeg 4.0+ in system PATH","Audio file in supported format"],"input_types":["audio file paths (single or multiple via shell globbing)","video file paths"],"output_types":["stdout (plain text transcription)","text files (.txt)","JSON files with metadata","VTT/SRT subtitle files","TSV format with timestamps"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-openai-whisper__cap_7","uri":"capability://planning.reasoning.language.specific.decoding.with.prompt.engineering","name":"language-specific decoding with prompt engineering","description":"Allows specifying the language explicitly or providing a text prompt to guide decoding toward specific vocabulary and phrasing. The model uses the prompt as a conditioning signal during beam search, biasing token selection toward words and phrases appearing in the prompt. Useful for improving accuracy on domain-specific terminology or correcting common hallucinations.","intents":["improve accuracy for domain-specific vocabulary (medical terms, product names, technical jargon)","correct systematic hallucinations by providing expected phrases in prompts","transcribe code or technical content with specialized terminology","guide model toward specific language variant (formal vs. colloquial)"],"best_for":["domain experts transcribing specialized content","teams with known vocabulary lists or expected phrases","applications requiring consistent terminology across transcriptions","developers debugging model hallucinations on specific content"],"limitations":["Prompt engineering is heuristic-based; no guarantee of improved accuracy without experimentation","Overly specific prompts may bias model away from correct transcriptions if prompt contains errors","Prompt length is limited (~200 tokens); cannot encode entire specialized vocabularies","Effectiveness varies by language and domain; minimal impact on some content types"],"requires":["Python 3.7+","PyTorch or TensorFlow backend","Domain knowledge to craft effective prompts"],"input_types":["audio file","optional language code (e.g., 'en', 'es', 'fr')","optional prompt text (string up to ~200 tokens)"],"output_types":["transcription text biased toward prompt vocabulary","confidence scores reflecting model uncertainty"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-openai-whisper__cap_8","uri":"capability://data.processing.analysis.multilingual.audio.classification.and.language.identification","name":"multilingual audio classification and language identification","description":"Automatically detects the spoken language in audio without explicit specification, using the model's multilingual encoder to classify audio into 99+ language categories. The language detection is performed as a preliminary step before decoding, with confidence scores indicating detection certainty. Supports explicit language specification to override automatic detection if needed.","intents":["automatically route audio to language-specific processing pipelines","identify language composition in multilingual audio collections","detect language switches or code-switching in audio","validate language metadata in audio datasets"],"best_for":["multilingual platforms processing user-uploaded audio","data teams validating language labels in audio corpora","applications serving global audiences with automatic language routing","researchers studying language identification in speech"],"limitations":["Language detection accuracy is ~95% for clear speech but degrades to 70-80% on heavily accented or noisy audio","Cannot distinguish between closely related languages (e.g., Spanish vs. Portuguese) with high confidence","No support for code-switching detection; treats mixed-language audio as single language","Detection latency adds ~500ms-1s to transcription pipeline (requires separate forward pass)"],"requires":["Python 3.7+","PyTorch or TensorFlow backend","Audio file with sufficient duration (minimum ~3 seconds for reliable detection)"],"input_types":["audio file in supported format","audio duration (used to determine detection confidence)"],"output_types":["language code (ISO 639-1 format, e.g., 'en', 'es', 'fr')","confidence score (0-1) indicating detection certainty","list of top-N language candidates with scores"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-openai-whisper__cap_9","uri":"capability://automation.workflow.inference.optimization.with.gpu.acceleration.and.mixed.precision","name":"inference optimization with gpu acceleration and mixed precision","description":"Supports GPU acceleration via PyTorch's CUDA backend and mixed-precision inference (float16) to reduce memory usage and latency. Automatically detects available GPU and uses it if present; developers can explicitly specify device placement. Mixed precision reduces model size by 50% with minimal accuracy loss, enabling larger models on memory-constrained GPUs.","intents":["accelerate transcription latency on GPU-equipped systems (10-50x speedup vs. CPU)","fit large models on GPUs with limited VRAM using mixed precision","optimize inference cost in high-volume transcription services","deploy models on cloud GPUs (AWS, GCP, Azure) with automatic device detection"],"best_for":["production transcription services requiring sub-second latency","teams deploying on cloud GPU instances","developers optimizing inference cost per transcription","researchers benchmarking model performance across hardware"],"limitations":["GPU acceleration requires CUDA 11.0+ and compatible NVIDIA GPU; no support for AMD or Intel GPUs","Mixed precision (float16) may introduce minor accuracy degradation (typically <1% WER increase)","GPU memory overhead is 2-3x model size due to activation caching; large models still require 16GB+ VRAM","Device placement is manual; no automatic multi-GPU distribution or load balancing"],"requires":["Python 3.7+","PyTorch 1.9.0+ with CUDA support","NVIDIA GPU with CUDA Compute Capability 3.5+ (Maxwell or newer)","CUDA 11.0+ and cuDNN 8.0+ installed","8GB+ VRAM for base/small models, 16GB+ for medium/large models"],"input_types":["device specification: 'cuda', 'cuda:0', 'cpu'","dtype specification: 'float32', 'float16'"],"output_types":["transcription with same format as CPU inference","inference timing metrics (latency, throughput)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":22,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","PyTorch 1.9.0+ or TensorFlow 2.0+","FFmpeg for audio preprocessing","4GB+ RAM minimum (8GB+ recommended for large models)","GPU with CUDA 11.0+ for production-grade throughput","PyTorch or TensorFlow backend","Audio file with clear temporal structure (silence between segments improves accuracy)","JSON schema library for validation (jsonschema package, optional)","Disk space: 39MB (tiny) to 3GB (large) per model variant","Internet connection for initial model download (cached locally thereafter)"],"failure_modes":["Model size ranges 39MB to 3GB depending on variant (tiny to large), requiring 2-8GB RAM for inference","Accuracy degrades on heavily accented speech, background noise, or low-quality audio compared to fine-tuned language-specific models","No real-time streaming transcription — requires complete audio file before processing begins","Inference latency on CPU is 10-30x slower than commercial cloud APIs; GPU acceleration recommended for production","Timestamp accuracy is ±100-500ms depending on audio quality and model variant; not suitable for frame-accurate video editing","Confidence scores are model-calibrated estimates, not true probability distributions; may not reflect actual error likelihood","Segment boundaries may not align with natural speech pauses in noisy audio or rapid speech","No speaker diarization — cannot distinguish between multiple speakers in the same segment","JSON output adds ~5-10% overhead compared to plain text due to serialization","Schema validation is optional; no enforcement of output structure by default","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.3,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"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":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:20.420Z","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=pypi-openai-whisper","compare_url":"https://unfragile.ai/compare?artifact=pypi-openai-whisper"}},"signature":"xlrGZdPfPPfVpn11X6qY40FTrvPG6Aa4EnNMAvbp5KlKDuj+/x43xPqtQuZ3IoFxKcKGZ6jCIuDUsKNwR9eIDQ==","signedAt":"2026-06-20T16:18:27.242Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-openai-whisper","artifact":"https://unfragile.ai/pypi-openai-whisper","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-openai-whisper","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"}}