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import streamlit as st |
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import whisper |
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import os |
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import torch |
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from transformers import pipeline |
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from pydub import AudioSegment |
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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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st.info("Getting size of file") |
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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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podcast = AudioSegment.from_mp3(st.session_state['audio']) |
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st.session_state['audio_segment'] = podcast |
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podcast_duration = podcast.duration_seconds |
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print(f"Audio Duration: {podcast_duration}") |
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st.info("Transcribing") |
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whisper_model = whisper.load_model("small.en") |
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transcription = whisper_model.transcribe(audiofile) |
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st.session_state['transcription'] = transcription |
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print(f"ranscription: {transcription['text']}") |
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st.info('Done Transcription') |
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return transcription |
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def summarize_podcast(audiotranscription): |
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summarizer = pipeline("summarization", model="philschmid/flan-t5-base-samsum", device=0) |
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summarized_text = summarizer(audiotranscription) |
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return summarized_text |
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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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st.audio("marketplace-2023-06-14.mp3") |
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if st.button("Process Audio File"): |
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podcast_text = transcribe_audio("marketplace-2023-06-14.mp3") |
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st.expander("See Transcription"): |
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st.caption(podcast_text) |
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podcast_summary = summarize_podcast(podcast_text) |
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st.markdown( |
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""" |
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##Summary of Text |
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""" |
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) |
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st.text(podcast_summary) |
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