Find out who said what in the video.
148
stars
13
commits
Jupyter Notebook
primary language
Jan 22, 2026
updated
Visually grounded speaker transcription for long videos
Track who speaks, and align speech to faces
| WhisperX | WhisperVideo (Ours) |
|---|---|
Text-only transcript
|
Visually grounded panel |
WhisperVideo is a clean demo for long-form, multi-speaker videos. It links speech to on-screen speakers and keeps identities consistent. It is built for real conversations, not short clips.
We recommend using the existing environment:
/home/siyuan/miniconda3/envs/whisperv/bin/python -V
If you need to (re)install packages, install the core stack:
pip install torch torchvision torchaudio
pip install whisperx pyannote.audio scenedetect opencv-python python_speech_features pysrt
TalkNet checkpoint auto-download uses gdown (included in whisperv/requirement.txt):
pip install gdown
Create a .env file at repo root:
HF_TOKEN=your_huggingface_token
/home/siyuan/miniconda3/envs/whisperv/bin/python whisperv/inference_folder_sam3.py \
--videoFolder demos/your_video_folder \
--renderPanel \
--panelTheme twitter \
--panelCompose subtitles \
--subtitle
The main results are written under:
<videoFolder>/pyavi/video_with_panel.mp4
<videoFolder>/pywork/*.pckl
If you find our work helpful, please kindly consider citing our paper. Thank you!
@misc{whispervideo,
title={WhisperVideo},
url={https://github.com/showlab/whisperVideo},
author={Siyuan Hu*, Kevin Qinghong Lin*, Mike Zheng SHOU},
publisher = {Zenodo},
version = {v0.1.0},
month={January},
year={2026}
}
8 commits
5 commits
Jupyter Notebook
75.4%
Python
24.6%
Find out who said what in the video.
148
stars
13
commits
Jupyter Notebook
primary language
Jan 22, 2026
updated
Visually grounded speaker transcription for long videos
Track who speaks, and align speech to faces
| WhisperX | WhisperVideo (Ours) |
|---|---|
Text-only transcript
|
Visually grounded panel |
WhisperVideo is a clean demo for long-form, multi-speaker videos. It links speech to on-screen speakers and keeps identities consistent. It is built for real conversations, not short clips.
We recommend using the existing environment:
/home/siyuan/miniconda3/envs/whisperv/bin/python -V
If you need to (re)install packages, install the core stack:
pip install torch torchvision torchaudio
pip install whisperx pyannote.audio scenedetect opencv-python python_speech_features pysrt
TalkNet checkpoint auto-download uses gdown (included in whisperv/requirement.txt):
pip install gdown
Create a .env file at repo root:
HF_TOKEN=your_huggingface_token
/home/siyuan/miniconda3/envs/whisperv/bin/python whisperv/inference_folder_sam3.py \
--videoFolder demos/your_video_folder \
--renderPanel \
--panelTheme twitter \
--panelCompose subtitles \
--subtitle
The main results are written under:
<videoFolder>/pyavi/video_with_panel.mp4
<videoFolder>/pywork/*.pckl
If you find our work helpful, please kindly consider citing our paper. Thank you!
@misc{whispervideo,
title={WhisperVideo},
url={https://github.com/showlab/whisperVideo},
author={Siyuan Hu*, Kevin Qinghong Lin*, Mike Zheng SHOU},
publisher = {Zenodo},
version = {v0.1.0},
month={January},
year={2026}
}
8 commits
5 commits
Jupyter Notebook
75.4%
Python
24.6%