{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-speechbrain","slug":"pypi-speechbrain","name":"speechbrain","type":"repo","url":"https://pypi.org/project/speechbrain/","page_url":"https://unfragile.ai/pypi-speechbrain","categories":["voice-audio"],"tags":["speech","audio","pytorch","deep-learning"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-speechbrain__cap_0","uri":"capability://code.generation.editing.speaker.independent.automatic.speech.recognition.asr.with.pretrained.models","name":"speaker-independent automatic speech recognition (asr) with pretrained models","description":"Provides end-to-end neural ASR pipelines using PyTorch with pretrained checkpoints for multiple languages and acoustic conditions. Implements CTC (Connectionist Temporal Classification) and attention-based sequence-to-sequence architectures that map raw audio spectrograms to text tokens, with built-in support for language model rescoring and beam search decoding. Models are loaded via a unified checkpoint system that handles feature extraction, acoustic modeling, and text decoding in a single inference pass.","intents":["I need to transcribe speech to text in production without training a model from scratch","I want to evaluate different ASR architectures (CTC vs attention) on my audio data","I need ASR that works across multiple languages with a single API"],"best_for":["ML engineers building speech applications who want modular, research-grade ASR","teams needing multilingual transcription without cloud API dependencies","researchers comparing acoustic modeling approaches"],"limitations":["Inference latency depends on audio length and model size; real-time factor (RTF) typically 0.1-0.5 on GPU but can exceed 1.0 on CPU","Pretrained models optimized for clean speech; performance degrades significantly on noisy audio without domain adaptation","No streaming/online decoding by default; requires full audio before inference","Limited to languages with available pretrained checkpoints (primarily English, French, Italian, Spanish, German)"],"requires":["Python 3.7+","PyTorch 1.9+","torchaudio for audio I/O","GPU recommended for real-time performance (CUDA 11.0+ or compatible)"],"input_types":["raw audio waveforms (numpy arrays, torch tensors)","audio file paths (WAV, MP3, FLAC)","audio URLs"],"output_types":["transcribed text strings","token-level confidence scores","character-level alignments"],"categories":["code-generation-editing","speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-speechbrain__cap_1","uri":"capability://data.processing.analysis.speaker.embedding.extraction.with.speaker.verification","name":"speaker embedding extraction with speaker verification","description":"Extracts fixed-dimensional speaker embeddings (typically 192-512 dims) from variable-length audio using neural speaker encoders trained on large-scale speaker datasets. Implements x-vector and ECAPA-TDNN architectures that learn speaker-discriminative features through metric learning (e.g., AAM-Softmax, Prototypical Networks). Embeddings can be compared via cosine similarity for speaker verification (1:1 matching) or used as features for speaker clustering and identification tasks.","intents":["I need to verify if two audio samples are from the same speaker","I want to extract speaker characteristics for clustering or identification","I need speaker embeddings as features for downstream speaker-dependent tasks"],"best_for":["voice authentication and biometric systems","speaker diarization pipelines that need speaker clustering","multi-speaker ASR systems requiring speaker adaptation"],"limitations":["Embeddings are speaker-specific but not speaker-interpretable; no explicit age/gender/accent information","Performance degrades with short utterances (<2 seconds); requires minimum 3-5 seconds for reliable verification","Domain mismatch between training data and target audio significantly impacts accuracy; cross-domain generalization requires fine-tuning","No built-in threshold calibration; developers must determine decision thresholds empirically for their use case"],"requires":["Python 3.7+","PyTorch 1.9+","torchaudio for audio I/O","GPU recommended (inference ~50-200ms per utterance on GPU vs 500ms+ on CPU)"],"input_types":["raw audio waveforms (numpy arrays, torch tensors)","audio file paths (WAV, MP3, FLAC)","variable-length utterances (1-30+ seconds)"],"output_types":["fixed-dimensional embeddings (numpy arrays, torch tensors)","cosine similarity scores (0-1 range)","verification decisions (match/non-match)"],"categories":["data-processing-analysis","speaker-verification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-speechbrain__cap_10","uri":"capability://automation.workflow.training.pipeline.with.distributed.data.parallelism.and.mixed.precision","name":"training pipeline with distributed data parallelism and mixed precision","description":"Provides end-to-end training infrastructure for speech models with support for distributed training across multiple GPUs/TPUs, automatic mixed precision (AMP) for memory efficiency, and gradient accumulation for large batch sizes. Implements PyTorch DistributedDataParallel (DDP) for multi-GPU training with automatic synchronization, combined with gradient scaling for stable training. Includes logging, checkpointing, and early stopping for efficient model development.","intents":["I need to train a speech model on large datasets efficiently using multiple GPUs","I want to reduce memory usage and training time using mixed precision training","I need reproducible training with checkpointing and early stopping"],"best_for":["researchers and engineers training large speech models on substantial datasets","teams with multi-GPU infrastructure seeking efficient training","rapid experimentation and hyperparameter tuning workflows"],"limitations":["Distributed training introduces synchronization overhead; speedup is sublinear with number of GPUs (typically 0.8-0.9x per GPU)","Mixed precision training can introduce numerical instability; requires careful gradient scaling and loss scaling tuning","Reproducibility across different hardware (different GPU models, CUDA versions) is not guaranteed","Requires significant GPU memory (16GB+ per GPU); not suitable for training on consumer-grade GPUs"],"requires":["Python 3.7+","PyTorch 1.9+","CUDA 11.0+ and cuDNN for GPU training","multiple GPUs (2+) for distributed training","torchaudio for audio I/O"],"input_types":["training data (audio files and labels)","validation data (audio files and labels)","model configuration (YAML or JSON)","hyperparameter settings"],"output_types":["trained model checkpoints","training logs and metrics","validation performance curves","final model weights"],"categories":["automation-workflow","training-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-speechbrain__cap_11","uri":"capability://automation.workflow.recipe.based.reproducible.experiments.with.configuration.management","name":"recipe-based reproducible experiments with configuration management","description":"Provides recipe-based experiment templates that bundle model architecture, training hyperparameters, data preprocessing, and evaluation metrics in a single configuration file (YAML/JSON). Recipes are self-contained and reproducible, enabling one-command training and evaluation with automatic logging of all hyperparameters and results. Supports recipe composition and inheritance for systematic experimentation and ablation studies.","intents":["I need to run reproducible speech experiments with full hyperparameter tracking","I want to compare different model architectures and training strategies systematically","I need to share experiment configurations with collaborators for reproducibility"],"best_for":["research teams conducting systematic experiments and ablation studies","practitioners seeking reproducible baselines for speech tasks","collaborative projects requiring experiment sharing and version control"],"limitations":["Recipe-based approach adds abstraction layer; debugging failing experiments requires understanding recipe structure","Limited to predefined recipe templates; custom architectures require writing new recipes","Recipe versioning and compatibility management can be complex; old recipes may break with library updates","No automatic hyperparameter search; grid search or Bayesian optimization requires manual recipe generation"],"requires":["Python 3.7+","PyTorch 1.9+","torchaudio for audio I/O","YAML or JSON configuration files"],"input_types":["recipe configuration files (YAML/JSON)","training data (audio files and labels)","validation data (audio files and labels)"],"output_types":["trained model checkpoints","experiment logs and metrics","configuration snapshots","evaluation results"],"categories":["automation-workflow","experiment-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-speechbrain__cap_12","uri":"capability://data.processing.analysis.evaluation.metrics.and.benchmarking.for.speech.tasks","name":"evaluation metrics and benchmarking for speech tasks","description":"Provides standard evaluation metrics for speech tasks including WER (Word Error Rate) for ASR, speaker verification EER (Equal Error Rate) and minDCF, diarization DER (Diarization Error Rate), and emotion recognition accuracy/F1-score. Implements efficient metric computation with support for batch processing and distributed evaluation across multiple GPUs. Includes benchmark datasets and baseline comparisons for standardized evaluation.","intents":["I need to evaluate ASR model performance using standard metrics (WER, CER)","I want to benchmark speaker verification models with industry-standard metrics (EER, minDCF)","I need to compare my models against published baselines on standard datasets"],"best_for":["researchers publishing speech models and comparing against baselines","practitioners evaluating model performance for production deployment","teams conducting systematic model comparisons and ablation studies"],"limitations":["Metric computation requires reference labels; evaluation not possible without ground truth annotations","Some metrics (e.g., WER) are sensitive to tokenization and normalization; different implementations may produce slightly different results","Benchmark datasets have licensing restrictions; not all datasets can be freely distributed","Metrics may not correlate with user-perceived quality; high WER doesn't always mean poor user experience"],"requires":["Python 3.7+","PyTorch 1.9+ (optional, for GPU-accelerated metric computation)","reference labels in standard formats (e.g., CTM for ASR, RTTM for diarization)"],"input_types":["model predictions (text for ASR, labels for classification)","reference labels (ground truth annotations)","audio files (optional, for some metrics)"],"output_types":["metric scores (WER, CER, EER, minDCF, DER, etc.)","per-sample metrics (for error analysis)","confidence intervals (for statistical significance)"],"categories":["data-processing-analysis","evaluation-metrics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-speechbrain__cap_2","uri":"capability://data.processing.analysis.speech.enhancement.and.noise.suppression.via.neural.beamforming","name":"speech enhancement and noise suppression via neural beamforming","description":"Reduces background noise and enhances speech quality using neural beamforming techniques that leverage multi-channel audio (if available) or single-channel neural enhancement. Implements learnable beamformers (e.g., MVDR-like networks) that estimate speech and noise subspaces from spectrograms, combined with masking-based enhancement (ideal ratio mask, phase-aware mask) to suppress noise while preserving speech intelligibility. Can operate on raw waveforms or spectrograms with configurable feature representations (MFCC, Fbank, raw spectrograms).","intents":["I need to clean noisy audio before ASR to improve transcription accuracy","I want to enhance speech quality for voice communication applications","I need to separate speech from background noise in multi-channel recordings"],"best_for":["ASR preprocessing pipelines operating on real-world noisy audio","voice communication systems (VoIP, conferencing) requiring real-time enhancement","audio forensics and speech analysis on degraded recordings"],"limitations":["Neural enhancement introduces artifacts and can distort speech characteristics; not suitable for forensic analysis requiring pristine audio","Multi-channel beamforming requires known microphone array geometry; performance degrades with unknown or irregular arrays","Real-time processing requires GPU; CPU inference adds 100-500ms latency depending on audio length","Training data domain mismatch (e.g., trained on office noise, applied to street noise) significantly impacts performance"],"requires":["Python 3.7+","PyTorch 1.9+","torchaudio for audio I/O","librosa or scipy for spectrogram computation","GPU recommended for real-time processing"],"input_types":["raw audio waveforms (mono or multi-channel)","spectrograms (magnitude or complex-valued)","audio file paths"],"output_types":["enhanced audio waveforms (same shape as input)","spectral masks (0-1 range)","estimated speech and noise spectrograms"],"categories":["data-processing-analysis","audio-enhancement"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-speechbrain__cap_3","uri":"capability://planning.reasoning.speaker.diarization.with.clustering.and.segmentation","name":"speaker diarization with clustering and segmentation","description":"Segments audio into speaker turns and clusters segments by speaker identity using a pipeline of speaker change detection, speaker embedding extraction, and hierarchical clustering. Implements end-to-end diarization via neural segmentation (predicting speaker change points) combined with speaker embedding-based clustering (e.g., spectral clustering, agglomerative clustering with cosine distance). Outputs speaker labels with timestamps, enabling downstream analysis of who spoke when.","intents":["I need to identify speaker boundaries and cluster speakers in multi-speaker conversations","I want to generate speaker-attributed transcripts from meeting recordings","I need to analyze speaker participation patterns in group discussions"],"best_for":["meeting transcription and analysis systems","podcast and broadcast audio processing","conversation analysis and research applications"],"limitations":["Clustering quality depends on speaker embedding quality; performance degrades with <3 speakers or very short speaker turns (<5 seconds)","No speaker identity linking across sessions; each audio file is processed independently","Overlapping speech handling is limited; simultaneous speakers are often assigned to a single cluster","Requires manual threshold tuning for clustering distance; no automatic optimal threshold selection"],"requires":["Python 3.7+","PyTorch 1.9+","torchaudio for audio I/O","scipy for clustering algorithms","GPU recommended for real-time processing on long audio"],"input_types":["raw audio waveforms (mono or multi-channel)","audio file paths (WAV, MP3, FLAC)","variable-length audio (seconds to hours)"],"output_types":["speaker labels with timestamps (RTTM format)","speaker change points (frame-level or time-based)","speaker embeddings for each segment","clustering dendrogram (optional)"],"categories":["planning-reasoning","speaker-diarization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-speechbrain__cap_4","uri":"capability://data.processing.analysis.voice.activity.detection.vad.with.frame.level.classification","name":"voice activity detection (vad) with frame-level classification","description":"Detects speech presence in audio by classifying short frames (typically 20-40ms) as speech or non-speech using neural networks trained on large-scale labeled datasets. Implements CNN or RNN-based classifiers that operate on spectrograms (MFCC, Fbank) or raw waveforms, outputting frame-level probabilities that can be aggregated into segment-level decisions via smoothing or post-processing. Enables efficient audio processing by skipping non-speech regions.","intents":["I need to trim silence and non-speech from audio before ASR","I want to detect speech activity in real-time streams for triggering downstream processing","I need to segment audio into speech and non-speech regions for analysis"],"best_for":["ASR preprocessing pipelines to reduce computational cost","real-time speech detection for voice-activated applications","audio segmentation and annotation tasks"],"limitations":["Frame-level classification introduces latency; real-time detection requires buffering 20-40ms of audio","Performance degrades on music, singing, or speech-like non-speech sounds (e.g., coughing, laughter)","No speaker-specific adaptation; same model used for all speakers regardless of voice characteristics","Threshold tuning required to balance false positives (non-speech detected as speech) vs false negatives (speech missed)"],"requires":["Python 3.7+","PyTorch 1.9+","torchaudio for audio I/O","librosa or scipy for spectrogram computation"],"input_types":["raw audio waveforms (mono)","spectrograms (magnitude)","audio file paths"],"output_types":["frame-level speech/non-speech probabilities (0-1 range)","segment-level VAD decisions (binary)","speech activity timestamps"],"categories":["data-processing-analysis","voice-activity-detection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-speechbrain__cap_5","uri":"capability://data.processing.analysis.emotion.recognition.from.speech.with.multi.class.classification","name":"emotion recognition from speech with multi-class classification","description":"Classifies emotional states (e.g., happy, sad, angry, neutral) from speech audio using neural classifiers that extract emotion-relevant features from spectrograms or embeddings. Implements CNN or RNN architectures trained on emotion-labeled speech datasets (e.g., IEMOCAP, RAVDESS), learning prosodic and spectral patterns associated with different emotions. Outputs class probabilities for each emotion category, enabling both hard classification and confidence-based ranking.","intents":["I need to detect emotional state from customer service calls for quality monitoring","I want to analyze emotional patterns in speech for mental health or research applications","I need to classify speech into emotion categories for interactive voice systems"],"best_for":["customer experience monitoring and sentiment analysis","mental health and psychological research applications","interactive voice systems requiring emotional awareness"],"limitations":["Emotion recognition is inherently subjective; inter-annotator agreement on emotion labels is typically 60-70%, limiting model accuracy ceiling","Performance degrades significantly across datasets; models trained on one emotion corpus often perform poorly on another due to acoustic differences","Language and cultural differences affect emotion expression; English-trained models may not generalize to other languages","No speaker normalization; individual voice characteristics can bias emotion predictions"],"requires":["Python 3.7+","PyTorch 1.9+","torchaudio for audio I/O","librosa or scipy for spectrogram computation"],"input_types":["raw audio waveforms (mono)","spectrograms (magnitude or mel-scale)","audio file paths","pre-extracted speaker embeddings"],"output_types":["emotion class probabilities (softmax distribution)","predicted emotion label (argmax)","confidence scores per emotion"],"categories":["data-processing-analysis","emotion-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-speechbrain__cap_6","uri":"capability://data.processing.analysis.speech.separation.and.source.extraction.from.multi.speaker.audio","name":"speech separation and source extraction from multi-speaker audio","description":"Separates individual speaker sources from mixed multi-speaker audio using neural source separation models that learn to decompose spectrograms into speaker-specific components. Implements Conv-TasNet, Conformer, or attention-based architectures that estimate speaker-specific masks or directly generate speaker waveforms. Can operate in supervised mode (known number of speakers) or unsupervised mode (unknown speaker count) with optional speaker embedding conditioning for speaker-specific extraction.","intents":["I need to extract individual speaker audio from multi-speaker conversations for separate transcription","I want to isolate a target speaker from background speakers in noisy environments","I need to separate speech from music or other audio sources in mixed recordings"],"best_for":["multi-speaker meeting transcription and analysis","podcast and broadcast audio processing with speaker isolation","audio forensics and speech enhancement in challenging acoustic conditions"],"limitations":["Separation quality degrades with >3 speakers or highly overlapping speech; speaker separation is fundamentally ill-posed with >2 speakers","Requires knowledge of speaker count in advance for supervised separation; unsupervised methods are less accurate","Inference is computationally expensive; real-time processing requires high-end GPU (RTF 0.5-2.0 on V100)","Separated audio may contain artifacts or distortions; not suitable for forensic analysis or high-fidelity audio applications"],"requires":["Python 3.7+","PyTorch 1.9+","torchaudio for audio I/O","GPU strongly recommended (inference infeasible on CPU for real-time processing)","CUDA 11.0+ for optimal performance"],"input_types":["raw audio waveforms (mono or multi-channel)","spectrograms (magnitude or complex-valued)","audio file paths","speaker embeddings (optional, for speaker-specific extraction)"],"output_types":["separated speaker waveforms (one per speaker)","speaker-specific spectrograms","separation masks (0-1 range per speaker)","estimated speaker count (for unsupervised methods)"],"categories":["data-processing-analysis","source-separation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-speechbrain__cap_7","uri":"capability://data.processing.analysis.language.identification.from.speech.with.multi.language.classification","name":"language identification from speech with multi-language classification","description":"Classifies the language spoken in audio using neural classifiers trained on multilingual speech datasets. Implements CNN or RNN architectures that learn language-specific acoustic patterns from spectrograms, outputting probabilities for each supported language. Enables automatic language detection for multilingual ASR pipelines or language-specific processing workflows.","intents":["I need to automatically detect language in audio before routing to language-specific ASR","I want to analyze language distribution in multilingual conversations","I need to filter or segment audio by language for downstream processing"],"best_for":["multilingual ASR pipelines requiring automatic language routing","content moderation and language-based filtering systems","multilingual conversation analysis and research"],"limitations":["Performance degrades on code-switching (mixing multiple languages in single utterance); typically handles only dominant language","Requires minimum audio duration (3-5 seconds) for reliable detection; very short utterances are often misclassified","Accent and speaker characteristics can bias language predictions; non-native speakers may be misclassified","Limited to languages present in training data; typically 50-100 languages supported, excluding low-resource languages"],"requires":["Python 3.7+","PyTorch 1.9+","torchaudio for audio I/O","librosa or scipy for spectrogram computation"],"input_types":["raw audio waveforms (mono)","spectrograms (magnitude or mel-scale)","audio file paths","variable-length utterances (1-30+ seconds)"],"output_types":["language class probabilities (softmax distribution)","predicted language label (argmax)","confidence scores per language"],"categories":["data-processing-analysis","language-identification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-speechbrain__cap_8","uri":"capability://data.processing.analysis.audio.feature.extraction.with.configurable.representations","name":"audio feature extraction with configurable representations","description":"Extracts diverse audio representations (MFCC, Fbank, spectrogram, mel-spectrogram, raw waveform) from audio files using PyTorch-based feature computation. Implements efficient batch processing of variable-length audio with configurable frame sizes, hop lengths, and frequency bins. Features are normalized and can be augmented (time-stretching, pitch-shifting, SpecAugment) for data augmentation in training pipelines.","intents":["I need to preprocess audio into standard feature representations for model training","I want to extract multiple feature types for comparison or ensemble methods","I need efficient batch feature extraction for large-scale audio datasets"],"best_for":["audio preprocessing pipelines for machine learning","feature engineering and exploratory audio analysis","large-scale audio dataset processing and caching"],"limitations":["Feature extraction adds computational overhead; batch processing required for efficiency on large datasets","Normalization strategies (mean/variance, min/max) must be consistent across training and inference; mismatched normalization degrades model performance","Data augmentation (SpecAugment, time-stretching) can introduce artifacts; augmentation parameters require tuning for specific tasks","No automatic feature selection; developers must choose appropriate features for their task"],"requires":["Python 3.7+","PyTorch 1.9+","torchaudio for audio I/O","librosa or scipy for advanced feature computation (optional)"],"input_types":["raw audio waveforms (numpy arrays, torch tensors)","audio file paths (WAV, MP3, FLAC)","variable-length audio"],"output_types":["MFCC features (time × coefficients)","Fbank features (time × frequency bins)","spectrograms (time × frequency)","mel-spectrograms (time × mel bins)","augmented features (same shape as input)"],"categories":["data-processing-analysis","feature-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-speechbrain__cap_9","uri":"capability://automation.workflow.pretrained.model.checkpoint.management.and.fine.tuning","name":"pretrained model checkpoint management and fine-tuning","description":"Provides a unified checkpoint system for loading, saving, and fine-tuning pretrained speech models with automatic handling of model architecture, weights, and hyperparameters. Implements checkpoint serialization that bundles model definition, weights, and training metadata, enabling reproducible model loading and transfer learning. Supports fine-tuning workflows with configurable learning rates, layer freezing, and gradient accumulation for efficient adaptation to new tasks or domains.","intents":["I need to load a pretrained model and fine-tune it on my custom speech dataset","I want to save and version control my trained models with full reproducibility","I need to adapt a pretrained model to a new language or acoustic domain"],"best_for":["transfer learning and domain adaptation for speech tasks","rapid prototyping of speech applications using pretrained models","research and experimentation with model architectures"],"limitations":["Fine-tuning requires labeled data; performance depends on dataset size and quality (typically need 1-10 hours of labeled audio)","Checkpoint files are large (100MB-1GB+); storage and download bandwidth can be limiting for large-scale deployment","No automatic hyperparameter tuning; developers must manually tune learning rates, batch sizes, and regularization for their task","Checkpoint compatibility across versions may break; older checkpoints may not load with newer library versions"],"requires":["Python 3.7+","PyTorch 1.9+","torchaudio for audio I/O","sufficient disk space for checkpoint storage (100MB-1GB per model)"],"input_types":["pretrained checkpoint files (PyTorch .pt or .pth format)","model configuration files (YAML or JSON)","training data (audio files and labels)"],"output_types":["fine-tuned model checkpoints","training logs and metrics","model configuration files"],"categories":["automation-workflow","model-management"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+","PyTorch 1.9+","torchaudio for audio I/O","GPU recommended for real-time performance (CUDA 11.0+ or compatible)","GPU recommended (inference ~50-200ms per utterance on GPU vs 500ms+ on CPU)","CUDA 11.0+ and cuDNN for GPU training","multiple GPUs (2+) for distributed training","YAML or JSON configuration files","PyTorch 1.9+ (optional, for GPU-accelerated metric computation)","reference labels in standard formats (e.g., CTM for ASR, RTTM for diarization)"],"failure_modes":["Inference latency depends on audio length and model size; real-time factor (RTF) typically 0.1-0.5 on GPU but can exceed 1.0 on CPU","Pretrained models optimized for clean speech; performance degrades significantly on noisy audio without domain adaptation","No streaming/online decoding by default; requires full audio before inference","Limited to languages with available pretrained checkpoints (primarily English, French, Italian, Spanish, German)","Embeddings are speaker-specific but not speaker-interpretable; no explicit age/gender/accent information","Performance degrades with short utterances (<2 seconds); requires minimum 3-5 seconds for reliable verification","Domain mismatch between training data and target audio significantly impacts accuracy; cross-domain generalization requires fine-tuning","No built-in threshold calibration; developers must determine decision thresholds empirically for their use case","Distributed training introduces synchronization overhead; speedup is sublinear with number of GPUs (typically 0.8-0.9x per GPU)","Mixed precision training can introduce numerical instability; requires careful gradient scaling and loss scaling tuning","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.42,"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-speechbrain","compare_url":"https://unfragile.ai/compare?artifact=pypi-speechbrain"}},"signature":"XQdkH52QoXXnyhIp7uqIAiLNlFgNecfsyvfBiqMCKTuOi9PIlAS9LwlC5LtTQ+AOTWjpkFIt4gKRcuHK2NZNAw==","signedAt":"2026-06-22T07:15:25.873Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-speechbrain","artifact":"https://unfragile.ai/pypi-speechbrain","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-speechbrain","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"}}