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| import streamlit as st | |
| import time as t | |
| 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('automatic-speech-recognition',model="facebook/wav2vec2-base-960h") | |
| audio_segment= AudioSegment.from_file(audio) | |
| audio_segment.export("audio.wav", format="wav") | |
| output = pipe("audio.wav", chunk_length_s=10, stride_length_s=(4, 2)) | |
| st.json(output) | |
| # 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 | |
| # chunk.export(str(index)+".wav", format="wav") | |
| # audio_segment= AudioSegment.from_file(audio) | |
| # 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): | |
| # #output = pipe(audio_segment, chunk_length_s=10, stride_length_s=(4, 2)) | |
| # print (chunk) | |
| # st.json("wav") | |