{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-pyannote-audio","slug":"pypi-pyannote-audio","name":"pyannote-audio","type":"repo","url":"https://pypi.org/project/pyannote-audio/","page_url":"https://unfragile.ai/pypi-pyannote-audio","categories":["voice-audio"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-pyannote-audio__cap_0","uri":"capability://data.processing.analysis.end.to.end.speaker.diarization.with.neural.segmentation","name":"end-to-end speaker diarization with neural segmentation","description":"Performs speaker diarization by combining neural segmentation models (trained on Pyannote's proprietary datasets) with speaker embedding extraction and clustering. The pipeline uses a two-stage approach: first, a temporal convolutional network (TCN) or transformer-based segmentation model identifies speaker boundaries and speech/non-speech regions frame-by-frame; second, speaker embeddings are extracted and clustered using agglomerative hierarchical clustering with dynamic threshold tuning. The system supports both batch processing and streaming inference modes.","intents":["I need to identify who spoke when in a multi-speaker audio file","I want to automatically segment a meeting recording by speaker turns","I need to extract speaker boundaries and cluster speakers without manual annotation","I want to process long-form audio (podcasts, interviews) to identify distinct speakers"],"best_for":["Speech processing researchers and practitioners","Teams building meeting transcription or call center analytics systems","Developers creating speaker-aware audio analysis pipelines","Organizations processing large-scale audio archives for speaker identification"],"limitations":["Requires 8+ GB RAM for processing long audio files; memory usage scales with audio duration","Clustering quality degrades with >10 speakers in a single file due to embedding space saturation","No built-in speaker identification (matching speakers across files); only within-file diarization","Inference latency is ~0.5-2x real-time depending on model size and hardware; GPU strongly recommended for production","Pretrained models optimized for English and European languages; performance drops significantly on low-resource languages"],"requires":["Python 3.8+","PyTorch 1.9+ (CPU or CUDA 11.0+)","librosa or torchaudio for audio I/O","scipy for clustering operations","Pretrained model weights (~100-500 MB depending on variant)"],"input_types":["WAV, MP3, FLAC, OGG audio files","Raw audio arrays (numpy, torch tensors)","Audio streams with sample rate specification (16 kHz recommended)"],"output_types":["RTTM format (Rich Transcription Time Marked) with speaker labels and timestamps","Speaker segments as Python objects with start/end times and speaker IDs","Speaker embedding vectors for downstream analysis"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pyannote-audio__cap_1","uri":"capability://data.processing.analysis.speaker.embedding.extraction.with.pretrained.neural.encoders","name":"speaker embedding extraction with pretrained neural encoders","description":"Extracts fixed-dimensional speaker embeddings (typically 192-512 dims) from audio segments using pretrained speaker verification models (e.g., ECAPA-TDNN, ResNet-based architectures). The embeddings capture speaker-specific acoustic characteristics and are designed to be speaker-discriminative while speaker-invariant to content. Embeddings can be extracted at segment or utterance level and are compatible with standard distance metrics (cosine, Euclidean) for downstream clustering or similarity matching.","intents":["I need speaker embeddings to cluster speakers in an audio file","I want to compare speaker similarity across different audio files","I need to extract speaker representations for use in a custom ML pipeline","I want to perform speaker verification or identification using embeddings"],"best_for":["ML engineers building speaker identification or verification systems","Researchers experimenting with speaker embedding spaces and clustering algorithms","Teams integrating speaker analysis into larger audio processing pipelines","Developers needing speaker-agnostic representations for downstream tasks"],"limitations":["Embeddings are model-specific; switching models requires re-extracting all embeddings","No built-in speaker normalization (e.g., i-vector centering); requires manual preprocessing for cross-dataset generalization","Embedding quality depends on segment duration; segments <1 second produce noisy representations","Models trained primarily on read speech and clean audio; performance degrades on noisy, accented, or heavily processed speech"],"requires":["Python 3.8+","PyTorch 1.9+","Pretrained speaker encoder model (bundled or from Hugging Face Hub)","Audio preprocessing pipeline (resampling to model's expected sample rate, typically 16 kHz)"],"input_types":["Audio segments (numpy arrays or torch tensors)","Segment duration metadata (start/end timestamps)","Raw waveforms or mel-spectrogram features"],"output_types":["Speaker embeddings as numpy arrays or torch tensors (shape: [batch_size, embedding_dim])","Embedding metadata (segment timestamps, speaker labels if available)"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pyannote-audio__cap_10","uri":"capability://image.visual.visualization.and.debugging.tools.for.diarization.results","name":"visualization and debugging tools for diarization results","description":"Provides utilities for visualizing diarization results, including speaker timeline plots, embedding space visualizations (t-SNE, UMAP), and spectrogram overlays with speaker labels. Includes debugging tools for analyzing segmentation errors, embedding quality, and clustering decisions. Supports interactive HTML visualizations and static plots for reports. Can overlay ground truth annotations for error analysis.","intents":["I want to visualize speaker timelines and segment boundaries","I need to debug why speakers are being incorrectly merged or split","I want to visualize speaker embeddings to understand clustering decisions","I need to compare predictions against ground truth annotations visually"],"best_for":["Researchers analyzing diarization errors and model behavior","Teams debugging diarization quality issues","Practitioners creating reports and presentations","Developers building interactive diarization tools"],"limitations":["Visualization of large files (>1 hour) is slow and memory-intensive","Interactive HTML visualizations require web browser; not suitable for headless environments","Embedding visualizations (t-SNE, UMAP) are computationally expensive for >10k embeddings","No built-in support for multi-file comparison; requires manual aggregation"],"requires":["Python 3.8+","matplotlib or plotly for visualization","scikit-learn for t-SNE/UMAP (optional)","Jupyter notebook or web browser for interactive visualizations"],"input_types":["Diarization results (speaker labels, timestamps)","Ground truth RTTM annotations (optional)","Speaker embeddings (for embedding space visualization)","Audio files (for spectrogram visualization)"],"output_types":["Static plots (PNG, PDF) of speaker timelines and spectrograms","Interactive HTML visualizations","Embedding space plots (t-SNE, UMAP)","Error analysis reports"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pyannote-audio__cap_11","uri":"capability://automation.workflow.batch.processing.and.pipeline.orchestration.for.large.audio.collections","name":"batch processing and pipeline orchestration for large audio collections","description":"Provides utilities for processing large collections of audio files in batches with automatic job scheduling, error handling, and result aggregation. Supports parallel processing across multiple CPU cores or GPUs, with configurable batch sizes and queue management. Includes checkpointing to resume interrupted jobs and logging for monitoring progress. Can be integrated with workflow orchestration tools (e.g., Airflow, Prefect) for production pipelines.","intents":["I need to process 1000s of audio files efficiently","I want to parallelize diarization across multiple cores/GPUs","I need to handle failures gracefully and resume interrupted jobs","I want to integrate diarization into a larger data processing pipeline"],"best_for":["Teams processing large-scale audio archives","Organizations building production diarization pipelines","Practitioners optimizing throughput for batch processing","Developers integrating diarization into data processing workflows"],"limitations":["Batch processing adds overhead; not suitable for single-file processing","Error handling is basic; complex failure scenarios require custom logic","Checkpointing requires careful state management; resuming jobs may produce duplicate results if not handled correctly","No built-in support for distributed processing across machines; requires external orchestration tools"],"requires":["Python 3.8+","Multiprocessing or concurrent.futures for parallelization","Disk space for intermediate results and checkpoints"],"input_types":["List of audio file paths","Batch configuration (batch size, number of workers)","Processing parameters (model, thresholds, etc.)"],"output_types":["Diarization results for all files (RTTM format or JSON)","Processing logs and error reports","Performance metrics (throughput, latency)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pyannote-audio__cap_2","uri":"capability://data.processing.analysis.temporal.speaker.segmentation.with.frame.level.classification","name":"temporal speaker segmentation with frame-level classification","description":"Performs frame-level speaker activity detection and speaker change detection using neural segmentation models (TCN or transformer-based) that process audio spectrograms and output per-frame probabilities for speech/non-speech and speaker boundaries. The model operates on fixed-size windows (typically 10-20ms frames) and uses temporal convolutions or attention mechanisms to capture context across frames. Outputs are post-processed (smoothing, peak detection) to produce clean segment boundaries.","intents":["I need to detect when speakers change in an audio file","I want to identify speech vs. non-speech regions frame-by-frame","I need to extract speaker turn boundaries for transcript alignment","I want to detect overlapping speech regions in multi-speaker audio"],"best_for":["Speech processing teams building real-time speaker detection systems","Researchers studying speaker change detection and speech activity detection","Developers integrating frame-level segmentation into audio analysis pipelines","Teams requiring fine-grained temporal boundaries for downstream processing"],"limitations":["Frame-level predictions require post-processing (smoothing, thresholding) to produce usable segments; raw outputs are noisy","Latency is ~10-50ms per frame depending on model size; real-time processing requires GPU acceleration","Overlapping speech detection is limited; model struggles with >2 simultaneous speakers","Threshold tuning is dataset-dependent; default thresholds may not generalize to new domains (e.g., noisy environments, accented speech)"],"requires":["Python 3.8+","PyTorch 1.9+","Pretrained segmentation model (bundled or custom)","Audio preprocessing (mel-spectrogram extraction, normalization)"],"input_types":["Mel-spectrograms (numpy arrays or torch tensors)","Raw audio waveforms (resampled to 16 kHz)","Spectrogram features with time and frequency dimensions"],"output_types":["Frame-level probability scores (shape: [num_frames, num_classes])","Segment boundaries with confidence scores","RTTM format with speech/non-speech and speaker change labels"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pyannote-audio__cap_3","uri":"capability://tool.use.integration.pretrained.model.management.and.loading.from.hugging.face.hub","name":"pretrained model management and loading from hugging face hub","description":"Provides a unified interface for discovering, downloading, and loading pretrained diarization and speaker embedding models from Hugging Face Model Hub. Models are versioned, cached locally, and can be instantiated with a single function call. The system handles model card parsing, dependency resolution, and automatic fallback to CPU if GPU is unavailable. Users can also upload custom models to Hugging Face Hub for sharing and reproducibility.","intents":["I want to use a pretrained diarization model without manually downloading weights","I need to switch between different model architectures (e.g., ECAPA-TDNN vs. ResNet) easily","I want to share my custom diarization model with the community","I need to ensure reproducibility by pinning specific model versions"],"best_for":["Practitioners wanting quick-start diarization without model training","Researchers sharing and comparing pretrained models","Teams managing multiple model versions for A/B testing","Developers integrating diarization into production systems with version control"],"limitations":["Requires internet connectivity for initial model download; subsequent runs use local cache","Model cache can grow large (100s of MB per model); no built-in cache management or cleanup utilities","Hugging Face Hub dependency introduces external service dependency; outages block model loading","Custom models require manual model card creation and Hub upload; no automated validation of model compatibility"],"requires":["Python 3.8+","huggingface-hub library (installed as dependency)","Internet connectivity for model downloads","Hugging Face account (optional, for uploading custom models)"],"input_types":["Model identifier strings (e.g., 'pyannote/speaker-diarization-3.0')","Local model paths or Hugging Face Hub URLs"],"output_types":["Instantiated PyTorch model objects ready for inference","Model metadata (architecture, training data, performance metrics)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pyannote-audio__cap_4","uri":"capability://data.processing.analysis.agglomerative.hierarchical.clustering.with.dynamic.threshold.tuning","name":"agglomerative hierarchical clustering with dynamic threshold tuning","description":"Clusters speaker embeddings using agglomerative hierarchical clustering (bottom-up merging) with dynamic threshold selection based on embedding statistics. The algorithm computes pairwise distances between embeddings (cosine or Euclidean), builds a dendrogram, and cuts at a threshold that maximizes cluster separation. Threshold tuning can be automatic (based on silhouette score, gap statistic) or manual. Supports custom linkage criteria (complete, average, ward) and distance metrics.","intents":["I need to cluster speaker embeddings into distinct speakers","I want to automatically determine the optimal number of speakers in an audio file","I need to tune clustering parameters for different audio domains (e.g., clean vs. noisy)","I want to merge or split clusters based on custom similarity thresholds"],"best_for":["Speech processing teams building speaker diarization systems","Researchers experimenting with clustering algorithms and threshold selection strategies","Practitioners tuning diarization quality for specific domains or languages","Developers integrating speaker clustering into larger audio analysis pipelines"],"limitations":["Computational complexity is O(n²) for distance matrix computation; scales poorly with >1000 speakers (rare but possible in large meetings)","Threshold selection is sensitive to embedding quality; poor embeddings lead to over/under-clustering","No built-in handling of speaker imbalance (e.g., one speaker dominates); may require custom weighting","Hierarchical clustering is deterministic but sensitive to initialization; no probabilistic uncertainty estimates"],"requires":["Python 3.8+","scipy for hierarchical clustering (scipy.cluster.hierarchy)","numpy for distance computation","Speaker embeddings as input (from embedding extraction capability)"],"input_types":["Speaker embeddings (numpy arrays, shape: [num_segments, embedding_dim])","Pairwise distance matrix (optional, for precomputed distances)","Threshold value or automatic threshold selection criterion"],"output_types":["Cluster assignments (numpy array of speaker IDs per segment)","Dendrogram structure (for visualization or custom post-processing)","Cluster statistics (size, centroid, silhouette scores)"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pyannote-audio__cap_5","uri":"capability://data.processing.analysis.streaming.online.diarization.with.incremental.speaker.updates","name":"streaming/online diarization with incremental speaker updates","description":"Performs speaker diarization on streaming audio by processing frames incrementally and updating speaker clusters in real-time. The system maintains a running set of speaker embeddings and updates cluster assignments as new frames arrive. Segmentation is performed frame-by-frame, and new speakers are detected by comparing incoming embeddings against existing speaker clusters using a dynamic threshold. Supports both online (single-pass) and semi-online (buffered) modes for latency/accuracy tradeoffs.","intents":["I need to perform diarization on live audio streams (e.g., real-time transcription)","I want to detect new speakers as they join a conversation","I need low-latency speaker identification for interactive applications","I want to update speaker assignments incrementally without reprocessing entire audio"],"best_for":["Teams building real-time meeting transcription or call center systems","Developers creating live speaker detection for broadcasting or podcasting","Researchers studying online learning and incremental clustering","Practitioners requiring sub-second latency for interactive applications"],"limitations":["Online diarization accuracy is lower than batch processing due to limited context; typically 5-15% higher diarization error rate","Requires careful tuning of buffer size and update frequency to balance latency and accuracy","No retroactive speaker merging; once speakers are separated, they cannot be merged without reprocessing","Memory usage grows with number of unique speakers; no built-in speaker pruning or forgetting mechanism","Streaming mode assumes continuous audio; gaps or silence can disrupt speaker tracking"],"requires":["Python 3.8+","PyTorch 1.9+","Audio streaming interface (e.g., pyaudio, sounddevice)","Pretrained segmentation and embedding models"],"input_types":["Audio frames (numpy arrays, typically 16 kHz, 10-20ms windows)","Streaming audio buffers or real-time audio device input"],"output_types":["Real-time speaker labels and timestamps","Incremental segment updates (new speaker detected, speaker changed, etc.)","Running speaker embedding database"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pyannote-audio__cap_6","uri":"capability://data.processing.analysis.audio.preprocessing.and.feature.extraction.mel.spectrograms.mfccs","name":"audio preprocessing and feature extraction (mel-spectrograms, mfccs)","description":"Provides utilities for converting raw audio waveforms into acoustic features (mel-spectrograms, MFCCs, chromagrams) required by neural models. Handles audio resampling, normalization, windowing, and feature computation using librosa or torchaudio backends. Supports both offline (batch) and online (streaming) feature extraction with configurable window sizes, hop lengths, and frequency ranges. Features are cached and can be reused across multiple model runs.","intents":["I need to convert raw audio to mel-spectrograms for model input","I want to resample audio to a specific sample rate (e.g., 16 kHz)","I need to normalize audio levels before processing","I want to extract acoustic features for custom analysis or visualization"],"best_for":["Audio processing engineers building preprocessing pipelines","Researchers experimenting with different acoustic features","Developers integrating audio feature extraction into larger systems","Teams optimizing audio processing for specific hardware or latency constraints"],"limitations":["Feature extraction adds ~10-50ms latency per audio chunk; significant for real-time applications","Mel-spectrogram computation is memory-intensive for long audio files; requires careful buffer management","Feature normalization is dataset-dependent; global normalization may not generalize across domains","No built-in augmentation (e.g., SpecAugment); requires manual implementation for training robustness"],"requires":["Python 3.8+","librosa or torchaudio for feature extraction","numpy for array operations","Audio files in supported formats (WAV, MP3, FLAC, OGG)"],"input_types":["Raw audio waveforms (numpy arrays or torch tensors)","Audio file paths (WAV, MP3, FLAC, OGG)","Audio streams or buffers"],"output_types":["Mel-spectrograms (shape: [num_frames, num_mels])","MFCCs (shape: [num_frames, num_coefficients])","Chromagrams or other acoustic features","Feature metadata (sample rate, frame duration, frequency range)"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pyannote-audio__cap_7","uri":"capability://data.processing.analysis.rttm.format.i.o.and.annotation.management","name":"rttm format i/o and annotation management","description":"Reads, writes, and manipulates speaker diarization annotations in RTTM (Rich Transcription Time Marked) format, a standard format for speaker diarization ground truth and predictions. Provides utilities for parsing RTTM files into Python objects, filtering/merging segments, computing metrics (DER, JER, purity, coverage), and exporting results back to RTTM. Supports validation of RTTM files and conversion between RTTM and other formats (JSON, CSV).","intents":["I need to load and parse RTTM annotation files","I want to evaluate diarization predictions against ground truth","I need to export diarization results in RTTM format for downstream tools","I want to merge or filter speaker segments programmatically"],"best_for":["Researchers evaluating diarization systems using standard metrics","Teams managing diarization annotations and ground truth data","Developers integrating diarization into larger annotation pipelines","Practitioners converting between diarization formats"],"limitations":["RTTM format is text-based and not optimized for large-scale annotation storage; parsing is slow for files with >10k segments","No built-in support for speaker metadata (e.g., speaker names, demographics); requires custom extensions","Metric computation (DER, JER) requires ground truth; no unsupervised quality estimation","RTTM format assumes non-overlapping speakers; overlapping speech is not well-represented"],"requires":["Python 3.8+","RTTM-formatted annotation files or ground truth data"],"input_types":["RTTM files (text format with speaker segments)","Python objects representing speaker segments","Diarization predictions (speaker labels + timestamps)"],"output_types":["Parsed RTTM data as Python objects (segments, speaker IDs, timestamps)","Evaluation metrics (DER, JER, purity, coverage)","RTTM-formatted output files","Converted formats (JSON, CSV)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pyannote-audio__cap_8","uri":"capability://automation.workflow.multi.gpu.and.distributed.inference.support","name":"multi-gpu and distributed inference support","description":"Enables distributed inference across multiple GPUs or machines using PyTorch's distributed data parallel (DDP) and model parallel patterns. The system automatically partitions audio files across GPUs, processes segments in parallel, and aggregates results. Supports both data parallelism (same model on multiple GPUs) and model parallelism (large models split across GPUs). Handles synchronization, gradient aggregation, and result merging transparently.","intents":["I need to process large audio files faster using multiple GPUs","I want to scale diarization to handle high-throughput scenarios (e.g., processing 1000s of files daily)","I need to run inference on a distributed cluster","I want to reduce per-GPU memory usage by splitting models across devices"],"best_for":["Teams processing large-scale audio archives with multiple GPUs","Organizations deploying diarization at scale (cloud, on-premise clusters)","Researchers benchmarking diarization on large datasets","Practitioners optimizing inference throughput and latency"],"limitations":["Distributed setup adds complexity; requires careful synchronization and error handling","Communication overhead between GPUs can exceed computation time for small audio files; best for files >30 seconds","Requires homogeneous hardware (same GPU type across all devices); heterogeneous setups require custom load balancing","Debugging distributed inference is difficult; errors may occur on specific ranks and be hard to reproduce"],"requires":["Python 3.8+","PyTorch 1.9+ with distributed training support","Multiple GPUs (NVIDIA, AMD, or Intel) or distributed cluster setup","NCCL (NVIDIA Collective Communications Library) for GPU communication"],"input_types":["Audio files or paths (distributed across workers)","Batch configurations (batch size, number of workers)"],"output_types":["Aggregated diarization results (speaker labels, timestamps)","Performance metrics (throughput, latency per file)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pyannote-audio__cap_9","uri":"capability://code.generation.editing.custom.model.training.and.fine.tuning.on.user.data","name":"custom model training and fine-tuning on user data","description":"Provides a training framework for fine-tuning pretrained diarization models on custom datasets or training models from scratch. Includes data loaders for RTTM-annotated audio, loss functions (e.g., focal loss for imbalanced data), optimization strategies (Adam, SGD with learning rate scheduling), and validation/evaluation loops. Supports mixed-precision training for memory efficiency and gradient accumulation for large batch sizes. Integrates with Weights & Biases for experiment tracking.","intents":["I need to fine-tune a pretrained model on my domain-specific audio data","I want to train a diarization model from scratch on a custom dataset","I need to optimize model performance for a specific language or acoustic environment","I want to track training experiments and compare model variants"],"best_for":["Researchers training custom diarization models","Teams fine-tuning models for domain-specific applications (e.g., medical, legal, noisy environments)","Practitioners optimizing model performance for low-resource languages","Organizations building proprietary diarization systems"],"limitations":["Training requires substantial labeled data (1000s of hours); limited data leads to overfitting","Hyperparameter tuning is manual and dataset-dependent; no automated hyperparameter search","Training is computationally expensive (days to weeks on single GPU); requires GPU access","No built-in data augmentation strategies; requires manual implementation for robustness","Convergence is sensitive to learning rate and batch size; careful tuning required"],"requires":["Python 3.8+","PyTorch 1.9+","RTTM-annotated audio dataset with speaker labels","GPU with sufficient VRAM (16+ GB recommended)","Weights & Biases account (optional, for experiment tracking)"],"input_types":["Audio files with corresponding RTTM annotations","Training/validation/test splits","Hyperparameter configurations (learning rate, batch size, epochs)"],"output_types":["Trained model weights (PyTorch .pt or .pth files)","Training logs and metrics (loss, validation DER, etc.)","Model checkpoints at regular intervals"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 1.9+ (CPU or CUDA 11.0+)","librosa or torchaudio for audio I/O","scipy for clustering operations","Pretrained model weights (~100-500 MB depending on variant)","PyTorch 1.9+","Pretrained speaker encoder model (bundled or from Hugging Face Hub)","Audio preprocessing pipeline (resampling to model's expected sample rate, typically 16 kHz)","matplotlib or plotly for visualization","scikit-learn for t-SNE/UMAP (optional)"],"failure_modes":["Requires 8+ GB RAM for processing long audio files; memory usage scales with audio duration","Clustering quality degrades with >10 speakers in a single file due to embedding space saturation","No built-in speaker identification (matching speakers across files); only within-file diarization","Inference latency is ~0.5-2x real-time depending on model size and hardware; GPU strongly recommended for production","Pretrained models optimized for English and European languages; performance drops significantly on low-resource languages","Embeddings are model-specific; switching models requires re-extracting all embeddings","No built-in speaker normalization (e.g., i-vector centering); requires manual preprocessing for cross-dataset generalization","Embedding quality depends on segment duration; segments <1 second produce noisy representations","Models trained primarily on read speech and clean audio; performance degrades on noisy, accented, or heavily processed speech","Visualization of large files (>1 hour) is slow and memory-intensive","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"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":"2026-05-03T15:20:21.281Z","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-pyannote-audio","compare_url":"https://unfragile.ai/compare?artifact=pypi-pyannote-audio"}},"signature":"rZK7iZMyLc57qMoPEqUs6fCr4pXoeVCewrtWm0JqeHXiRg091s29WuXnk3eFxdm2/CMM+RK4aatYiswKr3JSCg==","signedAt":"2026-06-22T13:56:35.369Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-pyannote-audio","artifact":"https://unfragile.ai/pypi-pyannote-audio","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-pyannote-audio","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"}}