whisper-live

A nearly-live implementation of OpenAI's Whisper.

This project is a real-time transcription application that uses the OpenAI Whisper model to convert speech input into text output. It can be used to transcribe both live audio input from microphone and pre-recorded audio files.

Unlike traditional speech recognition systems that rely on continuous audio streaming, we use voice activity detection (VAD) to detect the presence of speech and only send the audio data to whisper when speech is detected. This helps to reduce the amount of data sent to the API and improves the accuracy of the transcription output.

Installation

• Install PyAudio and ffmpeg

 bash setup.sh

• To install client requirements

 pip install -r requirements/client.txt

• To install server requirements

 pip install -r requirements/server.txt

Getting Started

• Run the server

 python server.py

• On the client side

  • To transcribe an audio file:

   python client.py --audio "audio.wav"
  

  • To transcribe from microphone:

   python client.py
  

• Connect to our whisper live server

 python client.py --host "transcription.kurg.org" --port 5901

Citations

@article{Whisper
  title = {Robust Speech Recognition via Large-Scale Weak Supervision},
  url = {https://arxiv.org/abs/2212.04356},
  author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
  publisher = {arXiv},  
  year = {2022},
}

@misc{Silero VAD,
  author = {Silero Team},
  title = {Silero VAD: pre-trained enterprise-grade Voice Activity Detector (VAD), Number Detector and Language Classifier},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/snakers4/silero-vad}},
  commit = {insert_some_commit_here},
  email = {hello@silero.ai}
}