Showing 1-19 of 19 projects
WhisperX for fast ASR with word-level timestamps and diarization
A comprehensive speech recognition toolkit with state-of-the-art pretrained models for various speech tasks.
A PyTorch-based toolkit for speech processing, including ASR, speaker recognition, and speech enhancement.
An offline-capable speech processing library for embedded systems, supporting a wide range of languages and platforms.
A privacy-focused, open-source AI meeting assistant with faster transcription, speaker diarization, and summarization, built on Rust.
End-to-end speech processing toolkit for tasks like speech recognition, synthesis, translation, and more.
A neural network library for speaker diarization, including speech activity detection, speaker change detection, and speaker embedding.
Automatic Speech Recognition with Speaker Diarization using OpenAI Whisper
AI subtitle generator for video with DaVinci Resolve integration, speaker diarization, runs locally.
Standalone Windows executables for Whisper speech-to-text & diarization without Python setup.
A library for single- and multi-modal speaker verification, recognition, and diarization.
An open-source library for multilingual automatic speech recognition with word-level timestamps and confidence.
A Python package for building real-time, AI-powered audio applications like speaker diarization and voice activity detection.
A curated list of resources for speaker diarization, a speech processing task to identify who spoke when.
Frontier CoreML audio models for iOS and macOS apps with text-to-speech, speech-to-text, voice activity detection, and speaker diarization.
A library for the Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm, for supervised speaker diarization.
Synchronized Translation for Videos: Automatic dubbing and subtitling for video content.
A research and production-oriented toolkit for speaker verification, recognition, and diarization using AI and ML techniques.
A Python GUI tool for offline transcription of speech recordings, including speaker diarization, using state-of-the-art machine learning models.
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