openai-whisper-setup.md

Open-AI Whisper Setup on Raspberry Pi Debian.

1) source env/bin/activate

first of all, when You connect to the RasPi, set up a virtual environment

2) pip install -U openai-whisper

3) install ffmpeg

In order for the setup to work, You will need a command-line tool ffmpeg. install it by running sudo apt update && sudo apt install ffmpeg.

Available sizes and models:

Size	Parameters	English-only model	Multilingual model	Required VRAM	Relative speed
tiny	39 M	tiny.en	tiny	~1 GB	~10x
base	74 M	base.en	base	~1 GB	~7x
small	244 M	small.en	small	~2 GB	~4x
medium	769 M	medium.en	medium	~5 GB	~2x
large	1550 M	N/A	large	~10 GB	~1x
turbo	809 M	N/A	turbo	~6 GB	~8x

In our instance, using the Raspberry Pi 4, the best model would be turbo, since we can go up to 7GB VRAM on the RasPi 4, which has 8GB of RAM.

A basic Python code example to test transcription and detecting speech:

import whisper

model = whisper.load_model("turbo")

# load audio and pad/trim it to fit 30 seconds
audio = whisper.load_audio("audio.mp3")
audio = whisper.pad_or_trim(audio)

# make log-Mel spectrogram and move to the same device as the model
mel = whisper.log_mel_spectrogram(audio).to(model.device)

# detect the spoken language
_, probs = model.detect_language(mel)
print(f"Detected language: {max(probs, key=probs.get)}")

# decode the audio
options = whisper.DecodingOptions()
result = whisper.decode(model, mel, options)

# print the recognized text
print(result.text)

Use whisper-help to get more familiar with the commands and feel free to then implement the model into your code.