WhisperLive

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.

Installation

• Install PyAudio and ffmpeg

 bash scripts/setup.sh

• Install whisper-live from pip

 pip install whisper-live

Setting up NVIDIA/TensorRT-LLM for TensorRT backend

• Please follow TensorRT_whisper readme for setup of NVIDIA/TensorRT-LLM and for building Whisper-TensorRT engine.

Getting Started

The server supports two backends faster_whisper and tensorrt. If running tensorrt backend follow TensorRT_whisper readme

Running the Server

• Faster Whisper backend

python3 run_server.py --port 9090 \
                      --backend faster_whisper
  
# running with custom model
python3 run_server.py --port 9090 \
                      --backend faster_whisper
                      -fw "/path/to/custom/faster/whisper/model"

• TensorRT backend. Currently, we recommend to only use the docker setup for TensorRT. Follow TensorRT_whisper readme which works as expected. Make sure to build your TensorRT Engines before running the server with TensorRT backend.

# Run English only model
python3 run_server.py -p 9090 \
                      -b tensorrt \
                      -trt /home/TensorRT-LLM/examples/whisper/whisper_small_en

# Run Multilingual model
python3 run_server.py -p 9090 \
                      -b tensorrt \
                      -trt /home/TensorRT-LLM/examples/whisper/whisper_small \
                      -m

Running the Client

• Initializing the client:

from whisper_live.client import TranscriptionClient
client = TranscriptionClient(
  "localhost",
  9090,
  lang="en",
  translate=False,
  model="small",
  use_vad=False,
)

It connects to the server running on localhost at port 9090. Using a multilingual model, language for the transcription will be automatically detected. You can also use the language option to specify the target language for the transcription, in this case, English ("en"). The translate option should be set to True if we want to translate from the source language to English and False if we want to transcribe in the source language.

• Trancribe an audio file:

client("tests/jfk.wav")

• To transcribe from microphone:

client()

• To transcribe from a HLS stream:

client(hls_url="http://as-hls-ww-live.akamaized.net/pool_904/live/ww/bbc_1xtra/bbc_1xtra.isml/bbc_1xtra-audio%3d96000.norewind.m3u8") 

Browser Extensions

• Run the server with your desired backend as shown here.
• Transcribe audio directly from your browser using our Chrome or Firefox extensions. Refer to Audio-Transcription-Chrome and Audio-Transcription-Firefox for setup instructions.

Whisper Live Server in Docker

• GPU

  • Faster-Whisper

  docker run -it --gpus all -p 9090:9090 ghcr.io/collabora/whisperlive-gpu:latest

  • TensorRT. Follow TensorRT_whisper readme in order to setup docker and use TensorRT backend. We provide a pre-built docker image which has TensorRT-LLM built and ready to use.

• CPU

docker run -it -p 9090:9090 ghcr.io/collabora/whisperlive-cpu:latest

Note: By default we use "small" model size. To build docker image for a different model size, change the size in server.py and then build the docker image.

Future Work

• Add translation to other languages on top of transcription.
• TensorRT backend for Whisper.

Contact

We are available to help you with both Open Source and proprietary AI projects. You can reach us via the Collabora website or vineet.suryan@collabora.com and marcus.edel@collabora.com.

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}},
  email = {hello@silero.ai}
}