File size: 1,572 Bytes
23c3153 71950a8 bf299de 71950a8 02edf9e 71950a8 a14ac5a a34af3d aa22372 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 |
---
title: Whisper Webui
emoji: ⚡
colorFrom: pink
colorTo: purple
sdk: gradio
sdk_version: 3.3.1
app_file: app.py
pinned: false
license: apache-2.0
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
# Running Locally
To run this program locally, first install Python 3.9+ and Git. Then install Pytorch 10.1+ and all the other dependencies:
```
pip install -r requirements.txt
```
Finally, run the full version (no audio length restrictions) of the app:
```
python app-full.py
```
# Docker
To run it in Docker, first install Docker and optionally the NVIDIA Container Toolkit in order to use the GPU. Then
check out this repository and build an image:
```
sudo docker build -t whisper-webui:1 .
```
You can then start the WebUI with GPU support like so:
```
sudo docker run -d --gpus=all -p 7860:7860 whisper-webui:1
```
Leave out "--gpus=all" if you don't have access to a GPU with enough memory, and are fine with running it on the CPU only:
```
sudo docker run -d -p 7860:7860 whisper-webui:1
```
## Caching
Note that the models themselves are currently not included in the Docker images, and will be downloaded on the demand.
To avoid this, bind the directory /root/.cache/whisper to some directory on the host (for instance /home/administrator/.cache/whisper), where you can (optionally)
prepopulate the directory with the different Whisper models.
```
sudo docker run -d --gpus=all -p 7860:7860 --mount type=bind,source=/home/administrator/.cache/whisper,target=/root/.cache/whisper whisper-webui:1
``` |