Speech-to-Text Transcription

This project uses OpenAI's Whisper model to transcribe audio files from a directory and save the results as text files in another directory.

Requirements

• Python 3.x
• whisper library (install via pip install openai-whisper)
• ffmpeg (required by Whisper, install via your package manager)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/speech-to-text.git
   cd speech-to-text

2. Install dependencies:

   pip install -r requirements.txt

3. (Optional) Create and activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # On macOS/Linux
   venv\Scripts\activate     # On Windows

Usage

Using the Script Directly

1. Default directories and language:

   python3 src/transcribe_audio.py

   This will transcribe all .ogg and .wav files from the voice_input directory and save the results in the text_output directory. The default language is Russian (ru).

2. Custom directories and language:

   python3 src/transcribe_audio.py --input_dir my_input_folder --output_dir my_output_folder --language en

   This will transcribe files from my_input_folder and save the results in my_output_folder. The language is set to English (en).

Using Makefile

1. Default directories and language:

   make transcribe

   This will transcribe all .ogg and .wav files from the voice_input directory and save the results in the text_output directory. The default language is Russian (ru).

2. Custom directories and language:

   make transcribe INPUT_DIR=my_input_folder OUTPUT_DIR=my_output_folder LANGUAGE=ru

   This will transcribe files from my_input_folder and save the results in my_output_folder. The language is set to English (ru).

Notes

• Ensure that the voice_input directory exists and contains valid audio files.
• The text_output directory will be created automatically if it doesn't exist.
• Supported languages include ru (Russian), en (English), and others. Refer to the Whisper documentation for a full list.