29 lines
1.6 KiB
Markdown
29 lines
1.6 KiB
Markdown
## transcribe
|
|
Simple implementation of OpenAI's Whisper to transcribe audio files from your local folders.
|
|
|
|
### Instructions
|
|
#### Requirements
|
|
1. Whisper requires some additional libraries. The [setup](https://github.com/openai/whisper#setup) page states: "The codebase also depends on a few Python packages, most notably HuggingFace Transformers for their fast tokenizer implementation and ffmpeg-python for reading audio files."
|
|
Users might not need to specifically install Transfomers. However, a conda installation might be needed for ffmepg, which takes care of setting up PATH variables. Install with:
|
|
```
|
|
conda install -c conda-forge ffmpeg-python
|
|
```
|
|
3. The main functionality comes from openai-whisper. See their [page](https://github.com/openai/whisper) for details. As of 2023-03-22 you can install via:
|
|
```
|
|
pip install -U openai-whisper
|
|
```
|
|
#### Using the script
|
|
1. This package was made and tested in an Anaconda environment, if you're not familiar with python I would recommend this method.
|
|
See [here](https://docs.anaconda.com/anaconda/install/index.html) for instructions. You might need administrator rights.
|
|
2. This is a simple script with no installation. You can either clone the repository with
|
|
```
|
|
git clone https://github.com/soderstromkr/transcribe.git
|
|
```
|
|
and use the example.ipynb template to use the script.
|
|
OR download the .py file into your work folder. Then you can either import it to another script or notebook for use. I recommend jupyter notebook for new users.
|
|
|
|
### Example
|
|
See the [example](example.ipynb) implementation on jupyter notebook.
|
|
|
|
|