Comments (4)
Tried this too and the accuracy seems to have improved significantly. Thanks for sharing
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It fixed the issue for you, but what does it do?
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I read it on this website. it explains what setting dynamic energy to true and using adjust for ambient does.
according to this person, Using both together gives better results. so I tried it and it worked.
I kinda don't understand why, Since dynamic energy adjusts the value constantly, while adjust for ambient does it once. so It Should be fine with just using dynamic energy in theory.
https://www.codesofinterest.com/2017/04/energy-threshold-calibration-in-speech-recognition.html
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It does seem to make it better ill try to do some testing to see how it reacts when you move from environment to environment and if it needs re calibration
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Related Issues (20)
- How to terminate program HOT 1
- [Feature request][Bug] Improvement needed on the listen() method. Issues related to setup_mic() and listen_loop() method. HOT 3
- pip install whisper_mic not working HOT 4
- Feature request: Provide code sample for Web UI Mic recording HOT 1
- no transcript output HOT 2
- cannot import the function to another project HOT 2
- Takes considerable time to actually setup the mic and start transcribing HOT 1
- [Fix] Keyboard interrupt for listen_loop HOT 1
- mic.py does not exist in file or directory HOT 1
- Error while using the save_file to save the transcribed data HOT 1
- add large-v3 HOT 1
- Issues with Python Setup HOT 1
- ALSA lib error, invalid card HOT 4
- Proccess hanging in infinite loop when input audio is not loud enough HOT 2
- Feature Request: Use isolated-env to make the app bind to the GPU automatically on windows
- ModuleNotFoundError: No module named 'distutils' HOT 1
- Many incomplete segments, what is it even returning? predicted_text referenced before assignment. HOT 5
- Can't use mic in linux - ALSA errors HOT 1
- Sonoma 14.4.1 - Python 3.12 - Running whisper_mic returns errors HOT 1
- Feature requests HOT 1
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