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-# Whisper-WebUI
-A Gradio-based browser interface for [Whisper](https://github.com/openai/whisper). You can use it as an Easy Subtitle Generator!
-![screen](https://github.com/user-attachments/assets/caea3afd-a73c-40af-a347-8d57914b1d0f)
-## Notebook
-If you wish to try this on Colab, you can do it in [here](https://colab.research.google.com/github/jhj0517/Whisper-WebUI/blob/master/notebook/whisper-webui.ipynb)!
-# Feature
-- 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 subtitles from various sources, including :
-  - Files
-  - Youtube
-  - Microphone
-- Currently supported subtitle formats :
-  - SRT
-  - WebVTT
-  - txt ( only text file without timeline )
-- Speech to Text Translation
-  - From other languages to English. ( This is Whisper's end-to-end speech-to-text translation feature )
-- Text to Text Translation
-  - Translate subtitle files using Facebook NLLB models
-  - Translate subtitle files using DeepL API
-- Pre-processing audio input with [Silero VAD](https://github.com/snakers4/silero-vad).
-- Pre-processing audio input to separate BGM with [UVR](https://github.com/Anjok07/ultimatevocalremovergui).
-- 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
-### Pipeline Diagram
-![Transcription Pipeline](https://github.com/user-attachments/assets/1d8c63ac-72a4-4a0b-9db0-e03695dcf088)
-# Installation and Running
-- ## Running with Pinokio
-The app is able to run with [Pinokio](https://github.com/pinokiocomputer/pinokio).
-1. Install [Pinokio Software](https://program.pinokio.computer/#/?id=install).
-2. Open the software and search for Whisper-WebUI and install it.
-3. Start the Whisper-WebUI and connect to the `http://localhost:7860`.
-- ## Running with Docker
-1. Install and launch [Docker-Desktop](https://www.docker.com/products/docker-desktop/).
-2. Git clone the repository
-```sh
-git clone https://github.com/jhj0517/Whisper-WebUI.git
-```
-3. Build the image ( Image is about 7GB~ )
-```sh
-docker compose build
-```
-4. Run the container
-```sh
-docker compose up
-```
-5. Connect to the WebUI with your browser at `http://localhost:7860`
-If needed, update the [`docker-compose.yaml`](https://github.com/jhj0517/Whisper-WebUI/blob/master/docker-compose.yaml) to match your environment.
-- ## Run Locally
-### Prerequisite
-To run this WebUI, you need to have `git`, `3.10 <= python <= 3.12`, `FFmpeg`. <br>
-And if you're not using an Nvida GPU, or using a different `CUDA` version than 12.4,  edit the [`requirements.txt`](https://github.com/jhj0517/Whisper-WebUI/blob/master/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/) **`3.10 ~ 3.12` is recommended.**
-- 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. git clone this repository
-```shell
-git clone https://github.com/jhj0517/Whisper-WebUI.git
-```
-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)
-And you can also run the project with command line arguments if you like to, see [wiki](https://github.com/jhj0517/Whisper-WebUI/wiki/Command-Line-Arguments) for a guide to arguments.
-# VRAM Usages
-This project is integrated with [faster-whisper](https://github.com/guillaumekln/faster-whisper) by default for better VRAM usage and transcription speed.
-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          |
-If you want to use an implementation other than faster-whisper, use `--whisper_type` arg and the repository name.<br>
-Read [wiki](https://github.com/jhj0517/Whisper-WebUI/wiki/Command-Line-Arguments) for more info about CLI args.
-If you want to use a fine-tuned model, manually place the models in `models/Whisper/` corresponding to the implementation.
-Alternatively, if you enter the huggingface repo id (e.g, [deepdml/faster-whisper-large-v3-turbo-ct2](https://huggingface.co/deepdml/faster-whisper-large-v3-turbo-ct2)) in the "Model" dropdown, it will be automatically downloaded in the directory.
-![image](https://github.com/user-attachments/assets/76487a46-b0a5-4154-b735-ded73b2d83d4)
-# REST API
-If you're interested in deploying this app as a REST API, please check out [/backend](https://github.com/jhj0517/Whisper-WebUI/tree/master/backend).
-## TODO🗓
-- [x] Add DeepL API translation
-- [x] Add NLLB Model translation
-- [x] Integrate with faster-whisper
-- [x] Integrate with insanely-fast-whisper
-- [x] Integrate with whisperX ( Only speaker diarization part )
-- [x] Add background music separation pre-processing with [UVR](https://github.com/Anjok07/ultimatevocalremovergui)
-- [x] Add fast api script
-- [ ] Add CLI usages
-- [ ] Support real-time transcription for microphone
-### Translation 🌐
-Any PRs that translate the language into [translation.yaml](https://github.com/jhj0517/Whisper-WebUI/blob/master/configs/translation.yaml) would be greatly appreciated!

+---
+sdk: gradio
+---
+# Whisper-WebUI
+A Gradio-based browser interface for [Whisper](https://github.com/openai/whisper). You can use it as an Easy Subtitle Generator!
+![screen](https://github.com/user-attachments/assets/caea3afd-a73c-40af-a347-8d57914b1d0f)
+## Notebook
+If you wish to try this on Colab, you can do it in [here](https://colab.research.google.com/github/jhj0517/Whisper-WebUI/blob/master/notebook/whisper-webui.ipynb)!
+# Feature
+- 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 subtitles from various sources, including :
+  - Files
+  - Youtube
+  - Microphone
+- Currently supported subtitle formats :
+  - SRT
+  - WebVTT
+  - txt ( only text file without timeline )
+- Speech to Text Translation
+  - From other languages to English. ( This is Whisper's end-to-end speech-to-text translation feature )
+- Text to Text Translation
+  - Translate subtitle files using Facebook NLLB models
+  - Translate subtitle files using DeepL API
+- Pre-processing audio input with [Silero VAD](https://github.com/snakers4/silero-vad).
+- Pre-processing audio input to separate BGM with [UVR](https://github.com/Anjok07/ultimatevocalremovergui).
+- 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
+### Pipeline Diagram
+![Transcription Pipeline](https://github.com/user-attachments/assets/1d8c63ac-72a4-4a0b-9db0-e03695dcf088)
+# Installation and Running
+- ## Running with Pinokio
+The app is able to run with [Pinokio](https://github.com/pinokiocomputer/pinokio).
+1. Install [Pinokio Software](https://program.pinokio.computer/#/?id=install).
+2. Open the software and search for Whisper-WebUI and install it.
+3. Start the Whisper-WebUI and connect to the `http://localhost:7860`.
+- ## Running with Docker
+1. Install and launch [Docker-Desktop](https://www.docker.com/products/docker-desktop/).
+2. Git clone the repository
+```sh
+git clone https://github.com/jhj0517/Whisper-WebUI.git
+```
+3. Build the image ( Image is about 7GB~ )
+```sh
+docker compose build
+```
+4. Run the container
+```sh
+docker compose up
+```
+5. Connect to the WebUI with your browser at `http://localhost:7860`
+If needed, update the [`docker-compose.yaml`](https://github.com/jhj0517/Whisper-WebUI/blob/master/docker-compose.yaml) to match your environment.
+- ## Run Locally
+### Prerequisite
+To run this WebUI, you need to have `git`, `3.10 <= python <= 3.12`, `FFmpeg`. <br>
+And if you're not using an Nvida GPU, or using a different `CUDA` version than 12.4,  edit the [`requirements.txt`](https://github.com/jhj0517/Whisper-WebUI/blob/master/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/) **`3.10 ~ 3.12` is recommended.**
+- 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. git clone this repository
+```shell
+git clone https://github.com/jhj0517/Whisper-WebUI.git
+```
+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)
+And you can also run the project with command line arguments if you like to, see [wiki](https://github.com/jhj0517/Whisper-WebUI/wiki/Command-Line-Arguments) for a guide to arguments.
+# VRAM Usages
+This project is integrated with [faster-whisper](https://github.com/guillaumekln/faster-whisper) by default for better VRAM usage and transcription speed.
+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          |
+If you want to use an implementation other than faster-whisper, use `--whisper_type` arg and the repository name.<br>
+Read [wiki](https://github.com/jhj0517/Whisper-WebUI/wiki/Command-Line-Arguments) for more info about CLI args.
+If you want to use a fine-tuned model, manually place the models in `models/Whisper/` corresponding to the implementation.
+Alternatively, if you enter the huggingface repo id (e.g, [deepdml/faster-whisper-large-v3-turbo-ct2](https://huggingface.co/deepdml/faster-whisper-large-v3-turbo-ct2)) in the "Model" dropdown, it will be automatically downloaded in the directory.
+![image](https://github.com/user-attachments/assets/76487a46-b0a5-4154-b735-ded73b2d83d4)
+# REST API
+If you're interested in deploying this app as a REST API, please check out [/backend](https://github.com/jhj0517/Whisper-WebUI/tree/master/backend).
+## TODO🗓
+- [x] Add DeepL API translation
+- [x] Add NLLB Model translation
+- [x] Integrate with faster-whisper
+- [x] Integrate with insanely-fast-whisper
+- [x] Integrate with whisperX ( Only speaker diarization part )
+- [x] Add background music separation pre-processing with [UVR](https://github.com/Anjok07/ultimatevocalremovergui)
+- [x] Add fast api script
+- [ ] Add CLI usages
+- [ ] Support real-time transcription for microphone
+### Translation 🌐
+Any PRs that translate the language into [translation.yaml](https://github.com/jhj0517/Whisper-WebUI/blob/master/configs/translation.yaml) would be greatly appreciated!