A Fundamental End-to-End Speech Recognition Toolkit and Open Source SOTA Pretrained Models, Supporting Speech Recognition, Voice Activity Detection, Text Post-processing etc.
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Updated
May 23, 2024 - Python
A Fundamental End-to-End Speech Recognition Toolkit and Open Source SOTA Pretrained Models, Supporting Speech Recognition, Voice Activity Detection, Text Post-processing etc.
A Repository for Single- and Multi-modal Speaker Verification, Speaker Recognition and Speaker Diarization
Research and Production Oriented Speaker Verification, Recognition and Diarization Toolkit
Speaker Diarization, Recognition and Language Identification. Scripts to generate GT using our WebApp and Praat software
End-to-End Speech Processing Toolkit
Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
A PyTorch-based Speech Toolkit
Automatic Speech Recognition with Speaker Diarization based on OpenAI Whisper
speechlib is a library that can do speaker diarization, transcription and speaker recognition on an audio file to create transcripts with actual speaker names
Speaker diarization service
turnkey self-hosted offline transcription and diarization service with llm summary
Simplified diarization pipeline using some pretrained models - audio file to diarized segments in a few lines of code
On-device speaker diarization powered by deep learning
Multilingual Automatic Speech Recognition with word-level timestamps and confidence
Full-stack Transcription-UI: Features OpenAI Whisper and NVIDIA NeMo, with Docker for easy deployment.
Aims to create a comprehensive voice toolkit for training, testing, and deploying speaker verification systems.
Subtitle generation w/ Speaker Diarization using Whisper and pyannote.audio
WhisperX Slack bot for transcribing audio files
Pyannote/speaker-diarization-3.1 is an open-source toolkit written in Python for speaker diarization, which is the task of determining "who spoke when" in an audio recording. It is based on the PyTorch machine learning framework and provides a set of trainable end-to-end neural building blocks that can be combined and jointly optimized.
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