Insanely Fast Whisper (CLI)

Powered by 🤗 Transformers & Optimum and based on Vaibhavs10/insanely-fast-whisper.

TL;DR - 🎙️ Transcribe 300 minutes (5 hours) of audio in less than 10 minutes - with OpenAI's Whisper Large v2. Blazingly fast transcription is now a reality!⚡️

Features

✨ ASR Model: Choose from different 🤗 Hugging Face ASR models, including all sizes of openai/whisper and even use an English-only variant (for non-large models).

🚀 Performance: Customizable optimizations ASR processing with options for batch size, data type, and BetterTransformer, all from the comfort of your terminal! 😎

📝 Timestamps: Get an SRT output file with accurate timestamps, allowing you to create subtitles for your audio or video content.

Installation

• Clone git repository with git clone https://github.com/ochen1/insanely-fast-whisper-cli
• Switch to that folder with cd insanely-fast-whisper-cli/
• (optional) Create a new Python environment with python -m venv venv
• (optional) Activate environment with source venv/bin/activate
• Install packages from requirements with pip install -r requirements.txt
• Run program with python insanely-fast-whisper.py

Usage

insanely-fast-whisper --model openai/whisper-base --device cuda:0 --dtype float32 --batch-size 8 --better-transformer --chunk-length 30 your_audio_file.wav

• model: Specify the ASR model (default is "openai/whisper-base").
• device: Choose the computation device (default is "cuda:0").
• dtype: Set the data type for computation ("float32" or "float16").
• batch-size: Adjust the batch size for processing (default is 8).
• better-transformer: Use BetterTransformer for improved processing (flag).
• chunk-length: Define audio chunk length in seconds (default is 30).

Example

Transcribing an audio file with English-only Whisper model and returning timestamps:

insanely-fast-whisper --model openai/whisper-base.en your_audio_file.wav

Output

The tool will save an SRT transcription of your audio file in the current working directory.

License

This project is licensed under the MIT License.

Acknowledgments

• This tool is powered by Hugging Face's ASR models, primarily Whisper by OpenAI.
• Optimizations are developed by Vaibhavs10/insanely-fast-whisper.
• Developed by @ochen1.

📞 Contact

Have questions or feedback? Feel free to create an issue!

🌟 Star this repository if you find it helpful!

🚀 Happy transcribing with Insanely Fast Whisper! 🚀