Whisper_auto2lrc

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Whisper_auto2lrc is a tool that uses the whisper model and a Python program to convert all audio files in a folder (and its subfolders) into .lrc subtitle files. If an lrc subtitle file already exists, it will be automatically skipped.

What can this program do?

It uses the whisper speech-to-text model released by OpenAI. Users can specify the audio file folder to be processed, and the program will automatically recursively search for all audio files in the folder and transcribe them into lrc format subtitle files.

It is suitable for users who need to transcribe a large number of audio files into subtitles.

Screenshots

How to install

First, install Python, which has been tested on Python 3.11

Then, install the Python dependencies required by whisper openai/whisper:

pip install git+https://github.com/openai/whisper.git 

How to use

Using the source code program

Pull this repo and install the Python dependencies:

git clone https://github.com/bai0012/Whisper_auto2lrc

cd Whisper_auto2lrc

pip install -r requirements.txt 

Download FFmpeg and add the folder containing ffmpeg.exe to the system variables. For example, BtbN/FFmpeg-Builds.

With the terminal at the base of the project folder, run

python main.py

In the window, select the folder path, choose the model size to use, enter the language of the audio files to be processed, and click start.

Troubleshooting

Why is my Whisper only using the CPU?

Whisper can call GPUs that support CUDA. If you are sure that your GPU has been correctly installed and supports CUDA, try the following steps:

pip uninstall torch

pip cache purge

Then install the latest PyTorch using the command on the PyTorch

For example:

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126

You should be able to call the graphics card normally.

Why is ffmpeg giving an error when I use it?

You can try the following steps to reinstall ffmpeg:

pip uninstall ffmpeg

pip uninstall ffmpeg-python

pip install ffmpeg-python

Which model should I choose and what are the differences between each model?

Refer to openai/whisper

Size	Parameters	English-only model	Multilingual model	Required VRAM	Relative speed
tiny	39 M	tiny.en	tiny	~1 GB	~10x
base	74 M	base.en	base	~1 GB	~7x
small	244 M	small.en	small	~2 GB	~4x
medium	769 M	medium.en	medium	~5 GB	~2x
large	1550 M	N/A	large	~10 GB	1x
turbo	809 M	N/A	turbo	~6 GB	~8x

Choose the model based on the size of the VRAM on your GPU. Generally, the larger the model, the faster the speed and the lower the error rate.

(For example, if you have an RTX3060 12G, you can choose the large model, but if you have a GTX 1050ti 4G, you can only use the small model)

Faster Whisper uses the same models but requires less VRAM, so try it out for yourself.

(Faster Whisper can even run the large model on a 1050ti 4G, although it sometimes runs out of VRAM, so a 6G VRAM should be enough to run the large model)

What should I enter in the language input box?

Whisper supports speech-to-text in multiple languages, commonly used ones include:

Language	Code
Chinese	zh
English	en
Russian	ru
Japanese	ja
Korean	ko
German	de
Italian	it

For more languages, refer to: whisper/tokenizer.py

Star History

This program was completed under the guidance of GPT-4, with special thanks to chshkhr for their contributions.