Update README.md

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`)
-
 <h2 align="left", id="highlights">New🚨</h2>
 
 - v3 transcript segment-per-sentence: using nltk sent_tokenize for better subtitlting & better diarization