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 whisper model and improves the accuracy of the transcription output.

• Install PyAudio and ffmpeg

 bash scripts/setup.sh

• Install whisper-live from pip

 pip install whisper-live

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

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

• 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

• To transcribe an audio file:

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

client("tests/jfk.wav")

This command transcribes the specified audio file (audio.wav) using the Whisper model. It connects to the server running on localhost at port 9090. It can also enable the multilingual feature, allowing transcription in multiple languages. The language option specifies the target language for 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.

• To transcribe from microphone:

from whisper_live.client import TranscriptionClient
client = TranscriptionClient(
  "localhost",
  9090,
  is_multilingual=True,
  lang="hi",
  translate=True,
  model="small"
)
client()

This command captures audio from the microphone and sends it to the server for transcription. It uses the multilingual option with hi as the selected language, enabling the multilingual feature and specifying the target language and task. We use whisper small by default but can be changed to any other option based on the requirements and the hardware running the server.

• To transcribe from a HLS stream:

from whisper_live.client import TranscriptionClient
client = TranscriptionClient(host, port, is_multilingual=True, lang="en", translate=False) 
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") 

This command streams audio into the server from a HLS stream. It uses the same options as the previous command, enabling the multilingual feature and specifying the target language and task.

• Run the server with your desired backend as shown here

• Refer to Audio-Transcription-Chrome to use Chrome extension.

• Refer to Audio-Transcription-Firefox to use Mozilla Firefox extension.

• GPU

  • Faster-Whisper

  docker build . -t whisper-live -f docker/Dockerfile.gpu
  docker run -it --gpus all -p 9090:9090 whisper-live: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 build . -t whisper-live -f docker/Dockerfile.cpu
docker run -it -p 9090:9090 whisper-live: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.

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

We are available to help you with both Open Source and proprietary AI projects. You can reach us via the Collabora website or [email protected] and [email protected].

@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}
}