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import streamlit as st |
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import whisper |
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import os |
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from transformers import pipeline |
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def transcribe_audio(audiofile): |
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st.session_state['audio'] = audiofile |
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print(f"audio_file_session_state:{st.session_state['audio'] }") |
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audio_size = round(os.path.getsize(st.session_state['audio'])/(1024*1024),1) |
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print(f"audio file size:{audio_size}") |
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return audio_size |
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st.markdown("# Podcast Q&A") |
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st.markdown( |
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""" |
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This helps understand information-dense podcast episodes by doing the following: |
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- Speech to Text transcription - using OpenSource Whisper Model |
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- Summarizes the episode |
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- Allows you to ask questions and returns direct quotes from the episode. |
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""" |
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) |
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if st.button("Process Audio File"): |
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transcribe_audio("marketplace-2023-06-14.mp3") |
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