Metadata-Version: 2.1
Name: whisper-live
Version: 0.3.0
Summary: A nearly-live implementation of OpenAI's Whisper.
Home-page: https://github.com/collabora/WhisperLive
Author: Collabora Ltd
Author-email: vineet.suryan@collabora.com
License: MIT
Description: # WhisperLive
        
        <h2 align="center">
          <a href="https://www.youtube.com/watch?v=0PHWCApIcCI"><img
        src="https://img.youtube.com/vi/0PHWCApIcCI/0.jpg" style="background-color:rgba(0,0,0,0);" height=300 alt="WhisperLive"></a>
          <br><br>A nearly-live implementation of OpenAI's Whisper.
        <br><br>
        </h2>
        
        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
         bash scripts/setup.sh
        ```
        
        - Install whisper-live from pip
        ```bash
         pip install whisper-live
        ```
        
        ### Setting up NVIDIA/TensorRT-LLM for TensorRT backend
        - Please follow [TensorRT_whisper readme](https://github.com/collabora/WhisperLive/blob/main/TensorRT_whisper.md) for setup of [NVIDIA/TensorRT-LLM](https://github.com/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](https://github.com/collabora/WhisperLive/blob/main/TensorRT_whisper.md)
        
        ### Running the Server
        - [Faster Whisper](https://github.com/SYSTRAN/faster-whisper) backend
        ```bash
        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](https://github.com/collabora/WhisperLive/blob/main/TensorRT_whisper.md) which works as expected. Make sure to build your TensorRT Engines before running the server with TensorRT backend.
        ```bash
        # 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:
        ```python
        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:
        ```python
        client("tests/jfk.wav")
        ```
        
        - To transcribe from microphone:
        ```python
        client()
        ```
        
        - To transcribe from a HLS stream:
        ```python
        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](https://github.com/collabora/WhisperLive?tab=readme-ov-file#running-the-server).
        - Transcribe audio directly from your browser using our Chrome or Firefox extensions. Refer to [Audio-Transcription-Chrome](https://github.com/collabora/whisper-live/tree/main/Audio-Transcription-Chrome#readme) and [Audio-Transcription-Firefox](https://github.com/collabora/whisper-live/tree/main/Audio-Transcription-Firefox#readme) for setup instructions.
        
        ## Whisper Live Server in Docker
        - GPU
          - Faster-Whisper
          ```bash
          docker run -it --gpus all -p 9090:9090 ghcr.io/collabora/whisperlive-gpu:latest
          ```
        
          - TensorRT. Follow [TensorRT_whisper readme](https://github.com/collabora/WhisperLive/blob/main/TensorRT_whisper.md) 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
        ```bash
        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.
        - [x] 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](mailto:vineet.suryan@collabora.com) and [marcus.edel@collabora.com](mailto:marcus.edel@collabora.com).
        
        ## Citations
        ```bibtex
        @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},
        }
        ```
        
        ```bibtex
        @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}
        }
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8
Description-Content-Type: text/markdown
