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import streamlit as st
from st_audiorec import st_audiorec
# Session state
if 'text' not in st.session_state:
st.session_state['text'] = 'Listening...'
st.session_state['run'] = False
# Audio parameters
st.sidebar.header('Audio Parameters')
FRAMES_PER_BUFFER = int(st.sidebar.text_input('Frames per buffer', 3200))
FORMAT = 'audio/wav'
CHANNELS = 1
RATE = int(st.sidebar.text_input('Rate', 16000))
# Open an audio stream
monitoring = False
audio_data = []
def start_monitoring():
global monitoring
st.session_state['run'] = True
monitoring = True
def stop_monitoring():
global monitoring
st.session_state['run'] = False
monitoring = False
st.title('🎙️ Real-Time Snore Detection App')
with st.expander('About this App'):
st.markdown('''
This streamlit app from Hypermind Labs Helps users detect
how much they are snoring during their sleep.
''')
col1, col2, col3 = st.columns(2)
col1.button('Start', on_click=start_monitoring)
col3.button('Stop', on_click=stop_monitoring)
record_button = col3.button('Monitor')
while st.session_state.run:
wav_audio_data = st_audiorec()
if wav_audio_data is not None:
st.audio(wav_audio_data, format=FORMAT, sample_rate=16000)
# audio_chunk = stream.read(FRAMES_PER_BUFFER)
# audio_data.append(np.frombuffer(audio_chunk, dtype=np.int16))
# if monitoring and len(audio_chunk) >= RATE // FRAMES_PER_BUFFER:
# audio_data_chunk = np.concatenate(audio_chunk[:RATE//FRAMES_PER_BUFFER])
# print("PROCESSING BY MODEL GOES HERE")
# # if model is not None:
# # input_tensor = torch.tensor(audio_data_chunk)
# # with torch.no_grad():
# # output = model(input_tensor)
# audio_chunk = audio_chunk[1:]
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