pyannote/pyannote-audio

Features

• Speaker Diarization - Partitions audio recordings into segments and assigns each to a specific speaker identity to determine who spoke when.
• Diarization Evaluation Suites - Provides a comprehensive suite of metrics for computing diarization error rates and speaker boundary precision.
• Neural Network Implementations - Provides a PyTorch-based neural architecture for extracting audio features and classifying speaker identities.
• Diarization - Computes the overall diarization error rate by measuring false alarms, missed detections, and speaker confusion.
• Speaker Identification Frameworks - Provides a system for classifying speaker identities and measuring accuracy within supervised audio datasets.
• Identification Accuracy Metrics - Measures the precision and recall of supervised classification models to identify specific individuals.
• Identification Error Metrics - Determines the precision and recall of supervised speaker classification using identification error rates.
• Speech Activity Detection - Implements a pipeline for distinguishing human speech from background noise through binary classification.
• Voice Activity Detection - Distinguishes human speech from background noise and non-speech audio to isolate active speaking segments.
• Segmentation Evaluation - Assesses the accuracy of detected speaker boundaries to determine how precisely speech turns are divided.
• Classification Metrics - Evaluates model performance using error rates and precision-recall plots to analyze voice classification.
• Inference Strategies - Implements a sliding-window inference mechanism to process long audio files for local speaker predictions.
• Overlap Speech Handling - Provides specialized tools to detect and manage segments where multiple speakers talk simultaneously.
• Classification Error Analysis - Breaks down speaker identification mistakes by labeling segments as correct, confused, missed, or false alarms.
• Boundary Accuracy Metrics - Provides specialized metrics to evaluate the precision and purity of detected speaker boundaries in diarization tasks.
• Clustering Algorithms - Implements clustering-based algorithms to group similar audio embeddings into distinct speaker identities.
• Audio Feature Extraction - Employs learnable band-pass filters via SincNet for advanced audio feature extraction from raw waveforms.
• Audio Representation Learning - Toolkit for speaker diarization and audio representation analysis.
• Frameworks And Toolkits - Neural building blocks and end-to-end speaker diarization pipelines.
• Natural Language Processing - Neural building blocks for speaker diarization.
• Speech and Audio - Neural building blocks for speaker diarization and classification.
• Speech and Audio Processing - Neural building blocks for speaker diarization.
• Speech Processing - Neural building blocks for speaker diarization.
• Speaker diarization - Listed in the “Speaker diarization” section of the Ailia Models awesome list.

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