{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-pyannote--speaker-diarization-community-1","slug":"pyannote--speaker-diarization-community-1","name":"speaker-diarization-community-1","type":"model","url":"https://huggingface.co/pyannote/speaker-diarization-community-1","page_url":"https://unfragile.ai/pyannote--speaker-diarization-community-1","categories":["voice-audio"],"tags":["pyannote-audio","pyannote","pyannote-audio-pipeline","audio","voice","speech","speaker","speaker-diarization","speaker-change-detection","voice-activity-detection","overlapped-speech-detection","automatic-speech-recognition","arxiv:2104.03603","arxiv:2111.14448","arxiv:2012.01477","arxiv:2110.07058","license:cc-by-4.0","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-pyannote--speaker-diarization-community-1__cap_0","uri":"capability://data.processing.analysis.speaker.diarization.with.overlapped.speech.detection","name":"speaker-diarization-with-overlapped-speech-detection","description":"Performs end-to-end speaker diarization by segmenting audio into speaker-homogeneous regions and assigning speaker labels, with explicit handling of overlapped speech regions where multiple speakers talk simultaneously. Uses a neural pipeline combining voice activity detection, speaker embedding extraction via ResNet-based encoders, and agglomerative clustering with dynamic thresholding to handle variable speaker counts and overlapping segments.","intents":["Identify who spoke when in a multi-speaker audio recording without prior speaker enrollment","Detect and label regions where multiple speakers overlap in conversation","Segment a podcast, meeting, or interview into speaker-specific timelines for downstream processing","Build speaker-aware transcription pipelines that attribute speech segments to individual speakers"],"best_for":["Speech processing teams building meeting transcription systems","Researchers prototyping speaker-aware ASR pipelines","Developers creating podcast/interview analysis tools without speaker pre-registration"],"limitations":["Requires minimum ~5-10 seconds of speech per speaker for reliable clustering; performs poorly on very short utterances","Overlapped speech detection accuracy degrades with >3 simultaneous speakers or heavy background noise (SNR <10dB)","No speaker identity persistence across files — each audio file is processed independently; requires external tracking for cross-file speaker linking","Inference latency ~0.5-2x realtime on CPU depending on audio duration and hardware; GPU recommended for production","Trained primarily on English and European languages; performance on other languages not documented"],"requires":["Python 3.8+","PyTorch 1.9+ (CPU or CUDA 11.0+)","pyannote.audio library (pip install pyannote.audio)","HuggingFace transformers 4.0+","Audio file in WAV, MP3, or OGG format with sample rate 16kHz or higher"],"input_types":["audio file (WAV, MP3, OGG, FLAC)","raw audio array (numpy array, shape [samples] or [channels, samples])","audio stream via file path or bytes"],"output_types":["speaker diarization timeline (JSON/dict with speaker labels and timestamps)","overlapped speech segments (list of time ranges with speaker IDs)","speaker embeddings (vector representations for each detected speaker)"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-pyannote--speaker-diarization-community-1__cap_1","uri":"capability://data.processing.analysis.voice.activity.detection.with.speech.pause.handling","name":"voice-activity-detection-with-speech-pause-handling","description":"Detects speech presence/absence in audio using a neural binary classifier trained on variable-length audio frames, outputting frame-level probabilities that are post-processed with temporal smoothing and pause-duration thresholding to produce robust speech/non-speech segment boundaries. Architecture uses a ResNet-based encoder on mel-spectrogram features with attention mechanisms to handle variable audio lengths and distinguish speech from music/noise.","intents":["Remove silence and background noise from audio before speaker diarization or ASR","Identify speech activity regions for downstream processing without manual annotation","Detect and preserve natural pauses within speaker turns while removing inter-speaker silence","Pre-filter audio for efficiency in multi-stage speech processing pipelines"],"best_for":["Audio preprocessing teams in speech recognition pipelines","Developers building voice activity detection as a preprocessing step","Researchers needing robust VAD without training custom models"],"limitations":["Pause-duration threshold is fixed; cannot dynamically adapt to speaker-specific speech patterns (e.g., slow speakers with long pauses)","Performance degrades on music-heavy content or speech with singing; may misclassify singing as non-speech","Requires audio sample rate ≥16kHz; lower rates require resampling which may introduce artifacts","No speaker-specific VAD tuning; treats all speakers equally regardless of voice characteristics"],"requires":["Python 3.8+","PyTorch 1.9+","pyannote.audio library","Audio input with clear speech/silence distinction (SNR >5dB recommended)"],"input_types":["audio file (WAV, MP3, OGG)","numpy array (mono or multi-channel)"],"output_types":["speech activity timeline (list of [start_time, end_time] tuples in seconds)","frame-level probabilities (numpy array of shape [num_frames])"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-pyannote--speaker-diarization-community-1__cap_2","uri":"capability://data.processing.analysis.speaker.embedding.extraction.with.metric.learning","name":"speaker-embedding-extraction-with-metric-learning","description":"Extracts fixed-dimensional speaker embeddings (typically 192-512 dims) from variable-length speech segments using a ResNet-based encoder trained with metric learning objectives (e.g., AAM-Softmax, CosFace). Embeddings capture speaker identity in a learned metric space where same-speaker utterances cluster tightly and different-speaker utterances separate, enabling downstream clustering and speaker comparison without explicit speaker labels.","intents":["Generate speaker identity vectors for clustering in diarization pipelines","Compare speakers across different audio files or segments (speaker verification)","Build speaker-aware embeddings for downstream ML tasks (speaker classification, re-identification)","Enable few-shot speaker identification with minimal enrollment data"],"best_for":["Speech processing engineers building speaker clustering systems","Researchers working on speaker verification or identification tasks","Developers needing speaker embeddings as features for downstream models"],"limitations":["Embeddings are not directly interpretable; require metric space operations (cosine similarity, clustering) for use","Performance degrades on very short segments (<1 second); requires minimum 2-3 seconds for stable embeddings","No cross-lingual speaker embedding support; embeddings trained on specific language may not generalize","Embedding space is not normalized across model versions; cannot mix embeddings from different model checkpoints"],"requires":["Python 3.8+","PyTorch 1.9+","pyannote.audio library","Audio segments with clear speech (SNR >10dB recommended)"],"input_types":["audio file or segment (WAV, MP3, OGG)","numpy array (mono audio, shape [samples])","time-bounded segment (file path + [start_time, end_time])"],"output_types":["speaker embedding (numpy array, shape [embedding_dim], typically [192] or [512])","batch embeddings (numpy array, shape [num_segments, embedding_dim])"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-pyannote--speaker-diarization-community-1__cap_3","uri":"capability://automation.workflow.end.to.end.diarization.pipeline.orchestration","name":"end-to-end-diarization-pipeline-orchestration","description":"Orchestrates a multi-stage neural pipeline combining VAD, speaker embedding extraction, and agglomerative clustering into a single inference workflow with configurable component swapping and parameter tuning. Pipeline manages intermediate representations (mel-spectrograms, embeddings, similarity matrices) and applies post-processing (segment merging, label smoothing) to produce final speaker diarization output. Implemented as a modular PyTorch pipeline with lazy loading and batching support.","intents":["Run complete speaker diarization on audio files with minimal configuration","Customize pipeline components (e.g., swap VAD or embedding models) without rewriting orchestration logic","Tune clustering parameters (threshold, min-cluster-size) for domain-specific audio characteristics","Integrate diarization as a preprocessing step in larger speech processing workflows"],"best_for":["Teams building production speech processing systems requiring modular pipelines","Researchers experimenting with different diarization component combinations","Developers integrating diarization into existing audio processing workflows"],"limitations":["Pipeline is sequential; no parallelization across stages, limiting throughput on multi-GPU systems","Clustering threshold is global; cannot adapt per-speaker or per-segment, leading to suboptimal results on heterogeneous speaker populations","No built-in speaker identity persistence across files; requires external state management for cross-file linking","Memory usage scales linearly with audio duration; very long files (>1 hour) may require chunking"],"requires":["Python 3.8+","PyTorch 1.9+ with CUDA 11.0+ (GPU recommended for production)","pyannote.audio library with all sub-components installed","Sufficient GPU memory (4GB+ for typical files, 8GB+ for >30min audio)"],"input_types":["audio file path (WAV, MP3, OGG, FLAC)","audio bytes or numpy array","configuration dict specifying VAD/embedding/clustering parameters"],"output_types":["diarization timeline (JSON/dict with speaker labels and timestamps)","intermediate representations (embeddings, VAD probabilities, similarity matrix) for debugging"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-pyannote--speaker-diarization-community-1__cap_4","uri":"capability://data.processing.analysis.agglomerative.clustering.with.dynamic.threshold","name":"agglomerative-clustering-with-dynamic-threshold","description":"Performs hierarchical agglomerative clustering on speaker embeddings to group segments into speaker clusters, using cosine similarity as the distance metric and a dynamic threshold that adapts based on the distribution of pairwise similarities. Threshold selection uses a heuristic (e.g., elbow method, silhouette-based) to automatically determine the optimal number of speakers without requiring manual specification. Produces a dendrogram that can be cut at different levels to trade off speaker granularity.","intents":["Automatically determine the number of speakers in an audio file without prior knowledge","Cluster speaker segments into speaker-homogeneous groups based on embedding similarity","Adjust speaker granularity (merge/split clusters) via threshold tuning for domain-specific needs","Generate hierarchical speaker relationships for visualization or downstream analysis"],"best_for":["Speech processing teams needing automatic speaker count detection","Researchers experimenting with clustering hyperparameters","Developers building speaker diarization systems with variable speaker populations"],"limitations":["Dynamic threshold heuristics are not always optimal; may over-cluster (too many speakers) or under-cluster (too few speakers) on edge cases","Clustering is greedy and order-dependent; different orderings of input segments may produce different results","No handling of speaker identity drift (e.g., speaker changing voice due to emotion or fatigue); treats all segments of same speaker as identical","Computational complexity is O(n²) in number of segments; slow on very long audio with many segments (>1000)"],"requires":["Python 3.8+","scipy library (for hierarchical clustering)","numpy for embedding operations","Speaker embeddings as input (from embedding extraction stage)"],"input_types":["speaker embeddings (numpy array, shape [num_segments, embedding_dim])","segment timestamps (list of [start_time, end_time] tuples)","optional: clustering parameters (threshold, linkage method)"],"output_types":["speaker cluster assignments (numpy array of shape [num_segments], values are cluster IDs)","dendrogram (scipy dendrogram object for visualization)","similarity matrix (numpy array of pairwise cosine similarities)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-pyannote--speaker-diarization-community-1__cap_5","uri":"capability://data.processing.analysis.mel.spectrogram.feature.extraction.with.augmentation","name":"mel-spectrogram-feature-extraction-with-augmentation","description":"Converts raw audio waveforms into mel-spectrogram representations (typically 80-128 mel-frequency bins, 10-25ms frame length) as input features for neural models. Includes augmentation techniques (SpecAugment, time-stretching, pitch-shifting) applied during training to improve model robustness to acoustic variability. Features are normalized per-utterance using mean-variance normalization to handle different recording conditions and microphone characteristics.","intents":["Convert raw audio into neural network-compatible feature representations","Augment training data with realistic acoustic variations without collecting new recordings","Normalize features across different recording conditions and microphones","Prepare audio for downstream neural models (VAD, embedding extraction)"],"best_for":["Audio processing engineers building feature pipelines","Researchers training custom speech models","Developers needing robust audio features for neural inference"],"limitations":["Mel-spectrogram is lossy; discards phase information, limiting reconstruction quality","Feature extraction is computationally expensive; requires ~0.1-0.5x realtime on CPU for typical audio","Augmentation parameters (stretch factor, pitch shift range) are fixed; cannot adapt to specific audio characteristics","Normalization is per-utterance; may not generalize well to very short segments (<0.5 seconds)"],"requires":["Python 3.8+","librosa library (for mel-spectrogram computation)","numpy for feature manipulation","Audio sample rate ≥16kHz (resampling required for lower rates)"],"input_types":["raw audio waveform (numpy array, shape [samples])","audio file path (WAV, MP3, OGG)"],"output_types":["mel-spectrogram (numpy array, shape [num_frames, num_mel_bins])","normalized mel-spectrogram (zero-mean, unit-variance)"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-pyannote--speaker-diarization-community-1__cap_6","uri":"capability://data.processing.analysis.multi.speaker.overlap.detection.and.labeling","name":"multi-speaker-overlap-detection-and-labeling","description":"Explicitly detects and labels regions where multiple speakers overlap in time using a multi-task learning approach that jointly predicts speaker embeddings and overlap probability per frame. Overlapped regions are labeled separately from single-speaker regions, enabling downstream systems to handle them differently (e.g., separate ASR models for overlapped speech). Uses frame-level classification with temporal smoothing to produce robust overlap boundaries.","intents":["Identify regions where multiple speakers talk simultaneously for special handling","Separate overlapped speech processing from single-speaker processing in ASR pipelines","Quantify overlap prevalence in conversations for analysis (e.g., interruption patterns)","Enable overlap-aware speaker diarization that doesn't force hard speaker assignments"],"best_for":["Speech processing teams building robust ASR systems for conversational audio","Researchers analyzing conversation dynamics (interruptions, turn-taking)","Developers needing explicit overlap detection for downstream processing"],"limitations":["Overlap detection accuracy degrades with >3 simultaneous speakers; designed for 2-speaker overlap","Requires sufficient training data on overlapped speech; performance on rare overlap patterns is poor","Temporal smoothing may blur overlap boundaries; cannot precisely locate overlap start/end times","No speaker-specific overlap detection; treats all speakers equally regardless of voice characteristics"],"requires":["Python 3.8+","PyTorch 1.9+","pyannote.audio library","Audio with clear speaker separation (SNR >10dB recommended for reliable overlap detection)"],"input_types":["audio file or segment (WAV, MP3, OGG)","mel-spectrogram features (numpy array, shape [num_frames, num_mel_bins])"],"output_types":["overlap timeline (list of [start_time, end_time] tuples for overlapped regions)","frame-level overlap probability (numpy array, shape [num_frames])","speaker IDs for overlapped regions (list of speaker pairs)"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-pyannote--speaker-diarization-community-1__cap_7","uri":"capability://data.processing.analysis.speaker.count.estimation.via.similarity.analysis","name":"speaker-count-estimation-via-similarity-analysis","description":"Estimates the number of distinct speakers in an audio file by analyzing the distribution of pairwise cosine similarities between speaker embeddings. Uses statistical methods (e.g., gap statistic, silhouette analysis) to identify the optimal number of clusters without requiring manual specification. Produces a confidence score for the estimated speaker count to indicate reliability.","intents":["Automatically determine the number of speakers without prior knowledge","Validate diarization results by comparing estimated vs. clustered speaker counts","Provide confidence scores for speaker count estimates to flag uncertain cases","Enable adaptive downstream processing based on speaker count (e.g., different ASR models)"],"best_for":["Speech processing teams needing automatic speaker count detection","Researchers analyzing multi-speaker audio without ground truth labels","Developers building adaptive speech processing pipelines"],"limitations":["Estimation accuracy degrades with >5 speakers; designed for 1-5 speaker scenarios","Requires sufficient speech from each speaker; fails on files where some speakers have <5 seconds of speech","Statistical methods (gap statistic, silhouette) are heuristic; may produce incorrect estimates on edge cases","No confidence calibration; confidence scores may not reflect true estimation accuracy"],"requires":["Python 3.8+","scipy library (for statistical analysis)","numpy for similarity computation","Speaker embeddings as input"],"input_types":["speaker embeddings (numpy array, shape [num_segments, embedding_dim])","optional: similarity matrix (pre-computed cosine similarities)"],"output_types":["estimated speaker count (integer)","confidence score (float, 0-1 indicating reliability)","similarity distribution statistics (dict with mean, std, quantiles)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-pyannote--speaker-diarization-community-1__cap_8","uri":"capability://automation.workflow.batch.processing.with.memory.efficient.streaming","name":"batch-processing-with-memory-efficient-streaming","description":"Processes multiple audio files or long audio files in batches using streaming inference to minimize memory footprint. Divides long audio into overlapping chunks, processes each chunk independently, and merges results with overlap handling to produce seamless diarization across chunk boundaries. Supports parallel processing across multiple files with configurable batch size and GPU memory management.","intents":["Process large audio files (>1 hour) that don't fit in GPU memory","Batch-process multiple audio files efficiently with parallelization","Minimize memory usage for deployment on resource-constrained devices","Enable production-scale diarization with consistent throughput"],"best_for":["Production teams processing large-scale audio datasets","Developers deploying diarization on edge devices or resource-constrained servers","Researchers processing long-form audio (podcasts, lectures, meetings)"],"limitations":["Chunk overlap handling may introduce artifacts at boundaries; speaker labels may be inconsistent across chunks","Batch processing adds latency due to queueing; not suitable for real-time applications","Memory efficiency comes at cost of slower inference; streaming is slower than single-pass processing","Parallel processing requires careful synchronization; may introduce non-determinism in results"],"requires":["Python 3.8+","PyTorch 1.9+","pyannote.audio library","Configurable GPU memory (minimum 2GB, recommended 4GB+)"],"input_types":["list of audio file paths","audio file path (for single file streaming)","configuration dict with batch size, chunk size, overlap parameters"],"output_types":["diarization timeline per file (JSON/dict)","batch processing results (list of diarization outputs)","processing statistics (throughput, memory usage)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-pyannote--speaker-diarization-community-1__cap_9","uri":"capability://memory.knowledge.speaker.linking.across.files.with.enrollment","name":"speaker-linking-across-files-with-enrollment","description":"Links speaker identities across multiple audio files by maintaining a speaker enrollment database of embeddings and comparing new speakers against enrolled speakers using similarity thresholding. Supports incremental enrollment (adding new speakers) and re-identification (matching speakers across files). Uses a similarity threshold to determine if a new speaker matches an enrolled speaker, with configurable sensitivity.","intents":["Track the same speaker across multiple audio files or sessions","Build a speaker database for re-identification in future audio","Link speakers in multi-file conversations (e.g., multi-day meetings)","Enable speaker-aware cross-file analysis and statistics"],"best_for":["Teams managing multi-session speaker tracking (e.g., customer service calls, interviews)","Researchers analyzing speaker consistency across multiple recordings","Developers building speaker identification systems with enrollment"],"limitations":["Similarity threshold is global; cannot adapt per-speaker or per-file, leading to false positives/negatives","No speaker identity drift handling; assumes speaker embeddings are stable across time","Requires external state management (database) for enrollment storage; no built-in persistence","Enrollment quality depends on amount of speech; short enrollments (<5 seconds) produce unreliable matches"],"requires":["Python 3.8+","pyannote.audio library","External database or file storage for enrollment embeddings","Sufficient speech per speaker for reliable enrollment (5-10 seconds recommended)"],"input_types":["speaker embeddings (numpy array, shape [num_segments, embedding_dim])","enrollment database (dict mapping speaker IDs to embedding lists)","similarity threshold (float, typically 0.5-0.7)"],"output_types":["speaker identity assignments (dict mapping segment IDs to speaker IDs)","similarity scores (dict mapping segment IDs to similarity values)","new speaker detections (list of segments not matching any enrolled speaker)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":53,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 1.9+ (CPU or CUDA 11.0+)","pyannote.audio library (pip install pyannote.audio)","HuggingFace transformers 4.0+","Audio file in WAV, MP3, or OGG format with sample rate 16kHz or higher","PyTorch 1.9+","pyannote.audio library","Audio input with clear speech/silence distinction (SNR >5dB recommended)","Audio segments with clear speech (SNR >10dB recommended)","PyTorch 1.9+ with CUDA 11.0+ (GPU recommended for production)"],"failure_modes":["Requires minimum ~5-10 seconds of speech per speaker for reliable clustering; performs poorly on very short utterances","Overlapped speech detection accuracy degrades with >3 simultaneous speakers or heavy background noise (SNR <10dB)","No speaker identity persistence across files — each audio file is processed independently; requires external tracking for cross-file speaker linking","Inference latency ~0.5-2x realtime on CPU depending on audio duration and hardware; GPU recommended for production","Trained primarily on English and European languages; performance on other languages not documented","Pause-duration threshold is fixed; cannot dynamically adapt to speaker-specific speech patterns (e.g., slow speakers with long pauses)","Performance degrades on music-heavy content or speech with singing; may misclassify singing as non-speech","Requires audio sample rate ≥16kHz; lower rates require resampling which may introduce artifacts","No speaker-specific VAD tuning; treats all speakers equally regardless of voice characteristics","Embeddings are not directly interpretable; require metric space operations (cosine similarity, clustering) for use","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8024955191872988,"quality":0.45,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"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":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:22:52.900Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":2765322,"model_likes":343}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pyannote--speaker-diarization-community-1","compare_url":"https://unfragile.ai/compare?artifact=pyannote--speaker-diarization-community-1"}},"signature":"Vn59ijtbtOnuRYP3veqcKxeP/aAdDsbx2k+W6/KmxXm+wrlCNhk4KA0zG5ZhVg94pJw6QsP0Zei/nK4R2dp5Ag==","signedAt":"2026-06-22T01:53:35.224Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pyannote--speaker-diarization-community-1","artifact":"https://unfragile.ai/pyannote--speaker-diarization-community-1","verify":"https://unfragile.ai/api/v1/verify?slug=pyannote--speaker-diarization-community-1","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"}}