|
--- |
|
sdk: gradio |
|
sdk_version: 5.16.0 |
|
--- |
|
# Whisper-WebUI |
|
A Gradio-based browser interface for [Whisper](https://github.com/openai/whisper) |
|
|
|
# Features |
|
- Select the Whisper implementation you want to use between: |
|
- [openai/whisper](https://github.com/openai/whisper) |
|
- [SYSTRAN/faster-whisper](https://github.com/SYSTRAN/faster-whisper) (used by default) |
|
- [Vaibhavs10/insanely-fast-whisper](https://github.com/Vaibhavs10/insanely-fast-whisper) |
|
- Generate transcriptions from various sources, including **files** & **microphone** |
|
- Currently supported output formats: **csv**, **srt** & **txt** |
|
- Speech to Text Translation: |
|
- From other languages to English (This is Whisper's end-to-end speech-to-text translation feature) |
|
- Translate transcription files using Facebook NLLB models |
|
- Pre-processing audio input with [Silero VAD](https://github.com/snakers4/silero-vad) |
|
- Post-processing with speaker diarization using the [pyannote](https://huggingface.co/pyannote/speaker-diarization-3.1) model: |
|
- To download the pyannote model, you need to have a Huggingface token and manually accept their terms in the pages below: |
|
1. https://huggingface.co/pyannote/speaker-diarization-3.1 |
|
2. https://huggingface.co/pyannote/segmentation-3.0 |
|
|
|
# Installation and Running |
|
|
|
- ## Run Locally |
|
|
|
### Prerequisite |
|
To run this WebUI, you need to have `git`, `python` version 3.8 ~ 3.10 & `FFmpeg`.<BR>If you're not using an Nvida GPU, or using a different `CUDA` version than 12.4, edit the file `requirements.txt` to match your environment. |
|
|
|
Please follow the links below to install the necessary software: |
|
- git : [https://git-scm.com/downloads](https://git-scm.com/downloads) |
|
- python : [https://www.python.org/downloads/](https://www.python.org/downloads/) |
|
- FFmpeg : [https://ffmpeg.org/download.html](https://ffmpeg.org/download.html) |
|
- CUDA : [https://developer.nvidia.com/cuda-downloads](https://developer.nvidia.com/cuda-downloads) |
|
|
|
After installing `FFmpeg`, make sure to **add** the `FFmpeg/bin` folder to your system `PATH` |
|
|
|
### Installation using the script files |
|
|
|
1. Download the the repository and extract its contents |
|
2. Run `install.bat` or `install.sh` to install dependencies (It will create a `venv` directory and install dependencies there) |
|
3. Start WebUI with `start-webui.bat` or `start-webui.sh` (It will run `python app.py` after activating the venv) |
|
|
|
- ## Running with Docker |
|
|
|
1. Install and launch [Docker-Desktop](https://www.docker.com/products/docker-desktop/) |
|
|
|
2. Get the repository |
|
|
|
3. If needed, update the `docker-compose.yaml` to match your environment |
|
|
|
4. Docker commands: |
|
|
|
Build the image ( Image is about ~7GB) |
|
```sh |
|
docker compose build |
|
``` |
|
|
|
Run the container |
|
```sh |
|
docker compose up |
|
``` |
|
|
|
5. Connect to the WebUI with your browser at `http://localhost:7860` |
|
|
|
# VRAM Usages |
|
- This project is integrated with [faster-whisper](https://github.com/guillaumekln/faster-whisper) by default for better VRAM usage and transcription speed.<BR>According to faster-whisper, the efficiency of the optimized whisper model is as follows: |
|
| Implementation | Precision | Beam size | Time | Max. GPU memory | Max. CPU memory | |
|
|-------------------|-----------|-----------|-------|-----------------|-----------------| |
|
| openai/whisper | fp16 | 5 | 4m30s | 11325MB | 9439MB | |
|
| faster-whisper | fp16 | 5 | 54s | 4755MB | 3244MB | |
|
|
|
- Whisper's original VRAM usage table for available models: |
|
| Size | Parameters | English-only model | Multilingual model | Required VRAM | Relative speed | |
|
|:------:|:----------:|:------------------:|:------------------:|:-------------:|:--------------:| |
|
| tiny | 39 M | `tiny.en` | `tiny` | ~1 GB | ~32x | |
|
| base | 74 M | `base.en` | `base` | ~1 GB | ~16x | |
|
| small | 244 M | `small.en` | `small` | ~2 GB | ~6x | |
|
| medium | 769 M | `medium.en` | `medium` | ~5 GB | ~2x | |
|
| large | 1550 M | N/A | `large` | ~10 GB | 1x | |
|
|
|
Note: `.en` models are for English only, and you can use the `Translate to English` option from the other models |
