A powerful CLI tool and Python module for transcribing and translating audio/video files using OpenAI's Whisper and GPT models.
- Extract audio from various video and audio formats
- Transcribe audio using OpenAI's Whisper API
- Translate transcriptions to English or other languages
- Generate transcript and subtitle files (SRT)
- Support for bilingual subtitles
- Smart handling of large files by automatic chunking
- Interactive language selection with auto-detection support
- Modular design for use as a Python library
- Progress indicators and detailed logging
- Python 3.8 or higher
- OpenAI API key
Install SonicScribe directly from PyPI:
pip install sonicscribeSonicScribe requires an OpenAI API key to function. You can set the API key as an environment variable to avoid hardcoding it or using a .env file.
-
Set the API Key as an Environment Variable:
- On Windows
setx OPENAI_API_KEY "YourKeyGoesHere" /M
- On macOS/Linux:
export OPENAI_API_KEY=YourKeyGoesHere -
Verify the Environment Variable: Run the following command to ensure the API key is set correctly
- On Windows
echo %OPENAI_API_KEY%
- On macOS/Linux:
echo $OPENAI_API_KEY
The main script provides full functionality for processing audio/video files:
sonicscribe --input "path/to/your/video.mp4" --translate --output-dir "output/folder"--input: Path to input audio/video file (required)--translate: Enable translation to English (optional)--language: Specify the language of the input file (e.g.,en,fr,es). If not provided, auto-detection will be used.--output-dir: Directory to save output files (default:output/transcripts)--whisper-model: Model to use for transcription (default:whisper-1)--gpt-model: Model to use for translation (default:gpt-4o-mini)--chunk-size: Size of chunks in MB for large files (default: 20)--verboseor-v: Enable verbose logging--bilingual: Create bilingual subtitles with both original and translated text
When processing files, SonicScribe provides an interactive language selection feature:
- You can select a language from a predefined list using arrow keys.
- You can manually input a language code by typing
/. - If no language is selected, SonicScribe will auto-detect the language using GPT, with a warning about potential additional API costs.
If you already have an SRT file and just want to translate it:
translate_srt --input "path/to/your/subtitles.srt" --bilingual--input: Path to input SRT file (required)--output: Path to output SRT file (default:input_english.srt)--model: GPT model to use for translation (default:gpt-4o-mini)--bilingual: Create bilingual SRT with original and translated text--language: Specify the language of the input subtitles. If not provided, auto-detection will be used.
sonicscribe --input "lecture.mp4"This will:
- Extract audio from
lecture.mp4 - Transcribe the audio using Whisper API
- Save a transcript and SRT file to the default output directory
sonicscribe --input "foreign_movie.mp4" --translate --gpt-model "gpt-4o"This will:
- Extract audio from
foreign_movie.mp4 - Transcribe the audio using Whisper API
- Translate the transcription to English using GPT-4o
- Save all output files to the default directory
sonicscribe --input "interview.mp3" --translate --bilingualThis will:
- Extract audio from
interview.mp3 - Transcribe the audio using Whisper API
- Translate the transcription to English
- Create a bilingual SRT file with both original and translated text
translate-srt --input "movie.srt" --bilingual --model "gpt-4o-mini"This will:
- Read the existing SRT file
- Translate the subtitles to English using GPT-4o-mini
- Create a bilingual SRT with both original and translated text
SonicScribe generates several types of output files:
{filename}_transcribed.txt: Plain text transcript{filename}.srt: SRT subtitle file with timestamps{filename}_bilingual.srt: Optional bilingual SRT file (when using--bilingual){filename}_english.srt: Translated SRT file (when usingtranslate_srt.py)
SonicScribe automatically handles large audio files:
- Files smaller than 25MB are processed directly through the Whisper API.
- Larger files are split into chunks, processed separately, and then recombined.
- The
--chunk-sizeparameter controls the size of these chunks (default: 20MB).
If you encounter "API key not found" errors:
- Ensure your
.envfile exists in the project root directory. - Verify that your API key is correct and active.
- Try setting the API key directly in your environment.
If SonicScribe fails to process your file:
- Verify the file exists and is not corrupted.
- Check that the format is supported (mp4, mkv, mov, mp3, wav, etc.).
- Try converting the file to a more standard format like MP4 or WAV.
If you encounter memory errors with very large files:
- Try reducing the
--chunk-sizeparameter. - Ensure your system has sufficient free memory.
- Consider pre-splitting very large files manually.
- OpenAI API rate limits may affect processing speed.
- Transcription quality depends on audio clarity.
- Translation quality varies by language and content complexity.
- Processing very large files (multiple hours) can take significant time.
SonicScribe logs all operations to the logs directory. If you encounter issues, check the log files for detailed information. Use the --verbose flag for more detailed logging.
For issues, questions, or contributions, please create an issue on the GitHub repository.