GPT-4o API Voice Commands Python

Capturing Audio Input

Use a library like `speech_recognition` in Python to capture audio input from the user's microphone. For example:

python
import speech_recognition as sr

r = sr.Recognizer()
with sr.Microphone() as source:
    print("Listening...")
    audio = r.listen(source)

Transcribing Audio to Text

Send the captured audio to the GPT-4o API using `openai.Audio.transcribe()` to transcribe it to text. GPT-4o can process the audio directly without requiring a separate speech recognition model[1][4]:

python
transcription = openai.Audio.transcribe(
    model="whisper-1",
    file=audio,
)

The transcribed text will be available in `transcription.text`.

Generating a Response

Use `openai.Completion.create()` with the GPT-4o model to generate a response to the voice command. Pass the transcribed text as the prompt[1][2]:

python
response = openai.Completion.create(
    model="gpt-4o",
    prompt=f"User: {transcription.text}\nAssistant: ",
    max_tokens=100,
    n=1,
    stop=None,
    temperature=0.7,
)

The generated response will be in `response.choices.text`.

Responding with Text-to-Speech

Convert the text response to speech using a library like `pyttsx3` or the OpenAI TTS API to speak the response back to the user[2][3].

Handling Context

Optionally, the voice command could also trigger other actions like taking a screenshot, capturing from the webcam, or extracting clipboard text. These visual inputs can be sent to GPT-4o along with the voice command to provide a more contextual response[3].

By leveraging GPT-4o's direct audio processing capabilities, you can create Python applications that understand voice commands, process them in context, and respond back to the user in a conversational manner. This enables a more natural and intuitive user experience compared to traditional text-based interactions.