The Best 27 Speaker Analysis Tools in 2025

This is a powerset-encoded speaker diarization model capable of processing 10-second audio clips to identify multiple speakers and their overlapping speech.

An audio processing model for speaker segmentation that can automatically detect and segment different speakers in audio.

An audio processing model for voice activity detection, overlap detection, and speaker diarization

Speaker diarization model based on pyannote.audio 2.1.1, used for automatic detection of speaker changes and overlap speech in audio

Speaker diarization pipeline trained on pyannote.audio 3.0.0, supporting automatic voice activity detection, speaker change detection and overlapping speech detection

Improved speaker diarization model based on pyannote3.0, achieving a 16.5% relative reduction in WDER across multiple test sets

A pre-trained model for detecting overlapped speech in audio, capable of identifying time segments where two or more speakers are active simultaneously.

This is a TDNN model pre-trained using SpeechBrain for extracting speaker embedding vectors, primarily applied to speaker verification and recognition tasks.

SpeechT5 is a voice conversion model fine-tuned on the CMU ARCTIC dataset, supporting the conversion of one voice to another while preserving content but altering timbre characteristics.

Speaker diarization model based on pyannote.audio 2.0, used for automatically detecting and segmenting different speakers in audio

A speaker identification model fine-tuned on the VoxCeleb1 dataset based on the Wav2Vec2-base pre-trained model, designed for voice classification tasks

Pyannote audio speaker segmentation pipeline for automatically detecting and segmenting different speakers in audio

This is a speaker verification model based on the Wav2Vec2 architecture, specifically designed for the speaker verification task in the SUPERB benchmark.

A synthetic voice detection model based on deep learning, which achieves efficient and accurate synthetic voice detection by fine-tuning the pre-trained model.

Hubert-based speaker recognition model optimized for the SUPERB benchmark tasks

This is an end-to-end speaker diarization model that supports voice activity detection, overlap speech detection, and resegmentation tasks.

Speaker recognition model based on Hubert-Large architecture, trained on the VoxCeleb1 dataset for speech classification tasks

The speaker diarization pipeline of Pyannote.audio, used to automatically detect speaker changes in audio and segment speech segments.

A speaker diarization model based on pyannote.audio 2.0, designed for automatic detection and segmentation of different speakers in audio.

A speaker embedding model based on ResNet293 architecture, optimized with large margin fine-tuning, supporting tasks such as speaker recognition, similarity calculation, and speech segmentation

A deepfake audio detection model fine-tuned based on facebook/wav2vec2-base, used to identify synthetic or tampered speech content

Speaker diarization model based on pyannote.audio 2.0 for automatic detection of speaker changes and speech activity in audio

A speaker embedding model based on the ResNet34 architecture, fine-tuned with large margin, trained on the VoxCeleb2 dataset, supporting tasks such as speaker recognition and similarity calculation.

Speaker identification model based on the Wav2Vec2-Large architecture, trained on the VoxCeleb1 dataset for classifying speech by speaker identity

A speaker diarization model modified based on pyannote/speaker-diarization-3.0, using speechbrain/spkrec-ecapa-voxceleb for speaker embedding, with better performance in certain tests

This is a speaker diarization model fine-tuned from the pyannote/segmentation-3.0 base model, specifically optimized for Japanese telephone conversation scenarios.

This is a speaker segmentation model based on powerset multi-class cross-entropy loss, capable of processing 10-second mono audio and outputting speaker segmentation results.