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In this tutorial, you’ll learn how to transcribe a short audio clip using OpenAI’s Whisper API. We’ve prepared a trimmed MP3 of the first five minutes of Steve Jobs’ Stanford commencement speech for this demo.

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Prerequisites

  • Python 3.7+
  • An active OpenAI API key
  • openai Python package (pip install openai)
  • IPython for in-notebook audio playback (pip install ipython)

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1. Play Audio Locally

Before sending the file to Whisper, verify playback in an IPython environment: 
import IPython

file_name = "data/jobs.mp3"
IPython.display.Audio(file_name)

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2. Transcribe with Whisper

Whisper currently offers the whisper-1 model for speech-to-text. Set your API key in the environment, then transcribe: 
import openai
import os

# Load your OpenAI API key from environment
openai.api_key = os.getenv("OPENAI_API_KEY")

file_name = "data/jobs.mp3"
with open(file_name, "rb") as audio_file:
    transcript = openai.Audio.transcribe("whisper-1", audio_file)

print(transcript.text)
Make sure OPENAI_API_KEY is correctly set. On macOS/Linux:
export OPENAI_API_KEY="your_api_key_here"

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3. Next Steps: NLP Pipelines

Once you have the raw transcript, you can feed it into large language models like GPT-3.5 Turbo or GPT-4 to:
  • Summarize the speech
  • Generate Q&A bots
  • Classify or analyze sentiment
  • Extract key topics
Use CaseModelExample Link
SummarizationGPT-3.5 TurboAPI Reference
Question & AnswerGPT-4API Reference
Sentiment AnalysisGPT-3.5 TurboCustom prompt engineering

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4. Run Whisper Locally

If you prefer not to use the API, you can run Whisper on your machine via the open-source repository:
The image shows a code editor with Python code for transcribing audio using OpenAI's API, along with a large block of transcribed text.

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References

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