This project uses OpenAI's GPT model and the Whisper ASR system to generate a summary from an audio file.
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Clone the repository
git clone https://github.com/albinati/audioSummaryGPT.git cd audioSummaryGPT
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Install the required Python libraries
pip install -r requirements.txt
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Set up your environment variables
Create a
.env
file in the root of your project and fill it with your own values:# .env file OPENAI_KEY=[your-key] INPUT_FOLDER=./content AUDIO_FILENAME=file.wav OUTPUT_FOLDER=./output OUTPUT_FILENAME=summary TRANSCRIPT_FILENAME=transcript CHUNK_FILENAME=chunk USER_PROMPT1=Faça um resumo do arquivo em bullet points USER_PROMPT2=Corrija possiveis erros na transcrição AI_ROLE=Você é um assistente expert em resumo de textos GPT_MODEL=gpt-3.5-turbo GPT_TEMP=0.7 #Default Temperature
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Transcribe the audio
Run the transcriber script to convert your audio into text:
python transcriber.py
This will create a
transcript.txt
file in the output folder. -
Generate the summary
Run the summarizer script to generate a summary of the transcript:
python summarizer.py
This will create a
summary.txt
andsummary_summarized.txt
files in the output folder with the summary.
The transcription and summary quality can be affected by the clarity of the audio and the specific GPT model you're using. The Whisper model is used for transcription, and GPT-3.5-Turbo or GPT-4 can be used for summary generation. The .env
file should contain all the necessary configuration.