makaveli10
96 lines
3.8 KiB
Markdown
96 lines
3.8 KiB
Markdown
# whisper-live
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A nearly-live implementation of OpenAI's Whisper.
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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.
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Unlike traditional speech recognition systems that rely on continuous audio streaming, we use [voice activity detection (VAD)](https://github.com/snakers4/silero-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.
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## Installation
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- Install PyAudio and ffmpeg
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```bash
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bash setup.sh
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```
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- To install client requirements
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```bash
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pip install -r requirements/client.txt
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```
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- To install server requirements
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```bash
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pip install -r requirements/server.txt
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```
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## Getting Started
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- Run the server
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```bash
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python server.py
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```
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- On the client side
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- To transcribe an audio file:
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```bash
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python client.py --audio "audio.wav" --host "localhost" --port "9090" --multilingual --language "hi" --task "transcribe"
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"translate"
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```
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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 also enables the multilingual feature, allowing transcription in multiple languages. The --language flag specifies the target language for transcription, in this case, Hindi ("hi"). The --task flag is set to "transcribe" to indicate that transcription is the desired task. Also, --task can be set to "translate" to translate source language to English.
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- To transcribe from microphone:
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```bash
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python client.py --host "localhost" --port "9090" --multilingual --language "en" --task "transcribe"
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```
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This command captures audio from the microphone and sends it to the server for transcription. It uses the same options as the previous command, enabling the multilingual feature and specifying the target language and task.
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## Transcribe audio from browser
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- Run the server
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```bash
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python server.py
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```
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This would start the websocket server on port ```9090```.
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### Chrome Extension
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- Refer to [Audio-Transcription-Chrome](https://github.com/collabora/whisper-live/tree/main/Audio-Transcription-Chrome#readme) to use Chrome extension.
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### Firefox Extension
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- Refer to [Audio-Transcription-Firefox](https://github.com/collabora/whisper-live/tree/main/Audio-Transcription-Firefox#readme) to use Mozilla Firefox extension.
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## Whisper Live Server in Docker
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- Build docker container
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```bash
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docker build . -t whisper-live
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```
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- Run docker container
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```bash
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docker run -it --gpus all -p 9090:9090 whisper-live:latest
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```
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## Future Work
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- [x] Update Documentation.
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- [x] Keep only a single server implementation i.e. websockets and get rid of the socket implementation in ```server.py```. Also, update ```client.py``` to websockets-client implemenation.
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- [ ] Add translation to other languages on top of transcription.
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## Citations
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```bibtex
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@article{Whisper
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title = {Robust Speech Recognition via Large-Scale Weak Supervision},
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url = {https://arxiv.org/abs/2212.04356},
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author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
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publisher = {arXiv},
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year = {2022},
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}
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```
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```bibtex
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@misc{Silero VAD,
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author = {Silero Team},
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title = {Silero VAD: pre-trained enterprise-grade Voice Activity Detector (VAD), Number Detector and Language Classifier},
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year = {2021},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\url{https://github.com/snakers4/silero-vad}},
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commit = {insert_some_commit_here},
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email = {hello@silero.ai}
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}
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