How to Create a Transcription Tool Using OpenAI API
Are you looking to streamline your transcription process? Harness the power of OpenAI’s Whisper and GPT-4 models to develop an automated transcription tool that can convert audio into text, generate summaries, and extract key points with remarkable efficiency.
Step-by-Step Guide to Creating an Automated Transcription Tool
1. Setup and Installation
Create a new Python environment and install the necessary libraries:
python -m venv env
source env/bin/activate
pip install openai
pip install python-docx
2. Transcribing Audio with Whisper
First, you need to transcribe the audio into text using OpenAI’s Whisper model. Here’s a basic example of how to do this:
import openai
# Set your API key
openai.api_key = 'your_openai_api_key'
def transcribe_audio(file_path):
audio_file = open(file_path, 'rb')
transcript = openai.Audio.transcribe("whisper-1", audio_file)
return transcript['text']
# Example usage
transcribed_text = transcribe_audio('path_to_audio_file.mp3')
print(transcribed_text)
3. Generating Summaries with GPT-4
Once you have the transcribed text, you can use GPT-4 to generate a summary of the meeting. Here’s how to do it:
def summarize_text(text):
response = openai.Completion.create(
engine="text-davinci-002",
prompt=f"Summarize the following meeting transcript:\n\n{text}\n\nSummary:",
max_tokens=150
)
summary = response.choices[0].text.strip()
return summary
# Example usage
summary = summarize_text(transcribed_text)
print("Summary:", summary)
4. Extracting Action Items and Sentiment Analysis
You can also extract action items and perform sentiment analysis on the transcribed text. Here are examples of how to do this:
Extracting Action Items:
def extract_action_items(text):
response = openai.Completion.create(
engine="text-davinci-002",
prompt=f"Extract the action items from the following meeting transcript:\n\n{text}\n\nAction Items:",
max_tokens=150
)
action_items = response.choices[0].text.strip()
return action_items
# Example usage
action_items = extract_action_items(transcribed_text)
print("Action Items:", action_items)
Sentiment Analysis:
def analyze_sentiment(text):
response = openai.Completion.create(
engine="text-davinci-002",
prompt=f"Perform a sentiment analysis on the following meeting transcript:\n\n{text}\n\nSentiment Analysis:",
max_tokens=100
)
sentiment = response.choices[0].text.strip()
return sentiment
# Example usage
sentiment = analyze_sentiment(transcribed_text)
print("Sentiment Analysis:", sentiment)
Benefits of Automated Transcription
? Efficiency: Reduces transcription time by up to 90%.
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? Accuracy: High precision with advanced AI models.
? Productivity: Focus on analysis and decision-making rather than manual transcription.
For Those Who Want a Ready-to-Use Solution
If you prefer a ready-made solution, check out the AI Transcribe Audio and Video app available on the App Store. This app leverages the latest AI technology to transcribe audio and video files efficiently.
Key Features of the AI Transcribe Audio and Video App:
? On-Device Transcription: Ensures 100% privacy by keeping all files on your device.
? Multi-Language Support: Accurate transcription for various accents and languages.
? User-Friendly Interface: Start transcribing within minutes, no email required.
? Cost-Effective: Transcribe at just $0.02 per minute, making it affordable for all.
Use Cases:
? Market Research: Turn audio into quantitative text for deeper insights.
? Podcast Transcription: Effortlessly transcribe episodes, making content searchable.
? Consulting: Fast, accurate transcription for due diligence and research.
? Academic Research: Simplify the transcription of lectures and interviews.
Embrace the future of transcription with AI. Boost your productivity, reduce costs, and ensure accuracy with OpenAI’s API or the AI Transcribe Audio and Video app.
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