From 0b839f3f019d6b71773c07fe2f1d671091f4420f Mon Sep 17 00:00:00 2001 From: Max Bain <36994049+m-bain@users.noreply.github.com> Date: Sun, 7 May 2023 20:36:08 +0100 Subject: [PATCH] Update README.md --- README.md | 7 ------- 1 file changed, 7 deletions(-) diff --git a/README.md b/README.md index ae3d5bd..a6d400c 100644 --- a/README.md +++ b/README.md @@ -52,13 +52,6 @@ This repository provides fast automatic speaker recognition (70x realtime with l **Speaker Diarization** is the process of partitioning an audio stream containing human speech into homogeneous segments according to the identity of each speaker. -- v3 pre-release [this branch](https://github.com/m-bain/whisperX/tree/v3) *70x speed-up open-sourced. Using batched whisper with faster-whisper backend*! -- v2 released, code cleanup, imports whisper library. VAD filtering is now turned on by default, as in the paper. -- Paper drop🎓👨‍🏫! Please see our [ArxiV preprint](https://arxiv.org/abs/2303.00747) for benchmarking and details of WhisperX. We also introduce more efficient batch inference resulting in large-v2 with *60-70x REAL TIME speed (not provided in this repo). -- VAD filtering: Voice Activity Detection (VAD) from [Pyannote.audio](https://huggingface.co/pyannote/voice-activity-detection) is used as a preprocessing step to remove reliance on whisper timestamps and only transcribe audio segments containing speech. add `--vad_filter True` flag, increases timestamp accuracy and robustness (requires more GPU mem due to 30s inputs in wav2vec2) -- Character level timestamps (see `*.char.ass` file output) -- Diarization (still in beta, add `--diarize`) -

New🚨

- v3 transcript segment-per-sentence: using nltk sent_tokenize for better subtitlting & better diarization