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import streamlit as st
import whisper
import os
from transformers import pipeline
from pydub import AudioSegment
def transcribe_audio(audiofile):
st.session_state['audio'] = audiofile
print(f"audio_file_session_state:{st.session_state['audio'] }")
#get size of audio file
audio_size = round(os.path.getsize(st.session_state['audio'])/(1024*1024),1)
print(f"audio file size:{audio_size}")
#determine audio duration
podcast = AudioSegment.from_mp3(st.session_state['audio'])
st.session_state['audio_segment'] = podcast
podcast_duration = podcast.duration_seconds
print(f"Audio Duration: {podcast_duration}")
st.info('Breaking podcast into 5 minute chunks.')
#break into 5 minute chunks
chunk_length_five_minutes = 5 * 60 * 1000
podcast_chunks = podcast[::chunk_length_five_minutes]
st.info('Transcribing...')
device = "cuda:0" if torch.cuda.is_available() else "cpu"
pipe = pipeline(
"automatic-speech-recognition",
model="openai/whisper-large-v2",
chunk_length_s=30,
device=device,
)
transcription = pipe(audiofile, batch_size=8)["text"]
st.session_state['transcription'] = transcription
print(f"transcription: {transcription}")
st.info('Done Transcription')
return transcription
st.markdown("# Podcast Q&A")
st.markdown(
"""
This helps understand information-dense podcast episodes by doing the following:
- Speech to Text transcription - using OpenSource Whisper Model
- Summarizes the episode
- Allows you to ask questions and returns direct quotes from the episode.
"""
)
if st.button("Process Audio File"):
transcribe_audio("marketplace-2023-06-14.mp3")
#audio_file = st.file_uploader("Upload audio copy of file", key="upload", type=['.mp3'])
# if audio_file:
# transcribe_audio(audio_file)
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