app.py

import streamlit as st
from transformers import pipeline
from pydub import AudioSegment, silence
#import speech_recognition as sr

pipe = pipeline('sentiment-analysis')
text = st.text_area('Enter your notes')

if text:
    out = pipe(text)
    st.json(out)

st.markdown("<h1 style = text align:center;'> Group Therapy Notes </h1>",unsafe_allow_html = True)
st.markdown("---",unsafe_allow_html=True)
audio=st.file_uploader("Upload Your Audio File", type=['mp3','wav','m4a'])

if audio:

        pipe = pipeline(model="facebook/wav2vec2-base-960h")
# stride_length_s is a tuple of the left and right stride length.
# With only 1 number, both sides get the same stride, by default
# the stride_length on one side is 1/6th of the chunk_length_s
        output = pipe("very_long_file.mp3", chunk_length_s=10, stride_length_s=(4, 2))
        st.json(output)
#    audio_segment= AudioSegment.from_file(audio)
#   asr = pipeline('automatic-speech-recognition')
#    asr_out = asr (audio_segment)
#    st.json(asr_out)
    
         
#    chunks=silence.split_on_silence(audio_segment, min_silence_len=500, silence_thresh= audio_segment.dBFS-20,keep_silence=100)
#   for index, chunk in enumerate (chunks):
#        chunk.export(str(index)+".wav", format="wav")
#        print(chunk)