whisper.cpp

Load the quantized whisper.cpp model on the device.

Capture audio input from the microphone.

Process the audio using whisper.cpp with Core ML acceleration.

Display the transcribed text in real-time.

Store the transcribed text locally.

Connect a microphone to the Raspberry Pi.

Run whisper.cpp with the appropriate model.

Record the audio and transcribe it in real-time.

Store the transcript locally.

Optionally, display the transcript on a connected screen.

Capture audio input using a microphone.

Transcribe the audio using whisper.cpp.

Parse the transcribed text to identify the command.

Execute the command locally.

Provide the output or response.

Load the audio file.

Segment the audio into smaller chunks.

Transcribe each segment using whisper.cpp.

Combine the transcribed segments.

Proofread and correct any errors.

Capture the audio stream.

Transcribe the audio stream using whisper.cpp.

Format the transcribed text as subtitles.

Overlay the subtitles onto the video stream.

Display the video with subtitles in real-time.