Merge branch 'main' into cuda-11.8

README.md

@@ -54,6 +54,7 @@ This repository provides fast automatic speech recognition (70x realtime with la
 
 <h2 align="left", id="highlights">New🚨</h2>
 
+- _WhisperX_ accepted at INTERSPEECH 2023 
 - v3 transcript segment-per-sentence: using nltk sent_tokenize for better subtitlting & better diarization
 - v3 released, 70x speed-up open-sourced. Using batched whisper with [faster-whisper](https://github.com/guillaumekln/faster-whisper) backend!
 - v2 released, code cleanup, imports whisper library VAD filtering is now turned on by default, as in the paper.
@@ -74,7 +75,7 @@ GPU execution requires the NVIDIA libraries cuBLAS 11.x and cuDNN 8.x to be inst
 
 ### 2. Install PyTorch2.0, e.g. for Linux and Windows CUDA11.7:
 
-`conda install pytorch==2.0.0 torchvision==0.15.0 torchaudio==2.0.0 pytorch-cuda=11.8 -c pytorch -c nvidia`
+`conda install pytorch==2.0.0 torchaudio==2.0.0 pytorch-cuda=11.8 -c pytorch -c nvidia`
 
 See other methods [here.](https://pytorch.org/get-started/previous-versions/#v200)
 
@@ -184,6 +185,11 @@ print(diarize_segments)
 print(result["segments"]) # segments are now assigned speaker IDs
 ```
 
+## Demos 🚀
+
+[![Replicate](https://replicate.com/daanelson/whisperx/badge)](https://replicate.com/daanelson/whisperx) 
+
+If you don't have access to your own GPUs, use the link above to try out WhisperX. 
 
 <h2 align="left" id="whisper-mod">Technical Details 👷‍♂️</h2>
 
@@ -276,7 +282,7 @@ If you use this in your research, please cite the paper:
 @article{bain2022whisperx,
   title={WhisperX: Time-Accurate Speech Transcription of Long-Form Audio},
   author={Bain, Max and Huh, Jaesung and Han, Tengda and Zisserman, Andrew},
-  journal={arXiv preprint, arXiv:2303.00747},
+  journal={INTERSPEECH 2023},
   year={2023}
 }
 ```