# Project_whisperTrans01_Code

## Content

Transcriber code

## Setup

Before running this script, make sure to install the necessary python packages using the following command:

```bash
pip install -r requirements.txt
```

## Running the script

To generate transcripts create a new job with your specifications (see jobs section below).
<br/>
If you need to download the transcriber model (not saved in `data/models`yet) set your huggingface token:

On Linux/Mac:

```bash
export HF_TOKEN="your-token-here"
```

On Windows (Command prompt):

```cmd
set HF_TOKEN="your-token-here"
```

Then run the following command to generate your transcript:

```bash
main.py --job {name}
```

where name is the name of your job.

## Jobs

Jobs specify tasks for transcript creation. A job contains the following information.

- `name`: string: The name of the job
- `source`: List[string]: The sources of the videos (paths, YouTube links, TibAV links)
- `modelpath`: Optional[string]: The path to the model if already saved locally (will be downloaded temporarily if not)
- `language`: Optional[string]: The language of the video (If not specified employs auto-detection)

## to-do

- seperate data production form code
- remember to accept the conditions on diarization if you use huggingface
- do a config json
- do option to have models already stored and point to them (is this necessary?)
    - also for single language models
- check warnings
- downloader for tib-av
- more optional setting

## Structure

```
project_root/
├── src/
│   ├── file_manager.py   # Contains FileManager class
│   ├── transcriber.py    # Contains Transcriber class
│   ├── transcript_generator.py    # Contains TranscriptGenerator class
│   ├── data_handling.py  # Contains download_audio and convert_to_wav methods
│   └── main.py    # Main script that runs the process
├── tests/    
│   ├── test_file_manager.py
│   ├── test_transcriber.py
│   └── test_transcript_generator.py
├── data/
│   ├── audio/   # downloaded mp4 and output wav files
│   ├── models/   # models used for transcripts (are ignored by git and not necessarily)
│   └── transcripts/  # JSON and text transcript files
└── README.md
```

- **src/**: This directory will contain all your source code. Inside the src folder, I added:
    - **main.py**: This will be the main entry point for the program. It will use the `TranscriptGenerator` class which
      orchestrates the use of `FileManager` and `Transcriber`.
    - **file_manager.py**: Contains the `FileManager` class implementation.
    - **transcriber.py**: Contains the `Transcriber` class implementation.
    - **transcript_generator.py**: Contains the `TranscriptGenerator` class implementation.

- **tests/**: This directory contains all your test scripts corresponding to your source files.

- **data/**: This directory contains all the data needed for your code. For simplicity, I categorize the files into
  separate directories for audio, model and transcript files.