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import streamlit as st
from st_audiorec import st_audiorec
import matplotlib.pyplot as plt
import sounddevice as sd
import numpy as np
import pandas as pd
# import torch
# import torchaudio
import wave
import io
from scipy.io import wavfile
# MODEL LOADING and INITIALISATION
# model = torch.jit.load("snorenetv1_small.ptl")
# model.eval()
# 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.
''')
wav_audio_data = st_audiorec()
if wav_audio_data is not None:
data = np.frombuffer(wav_audio_data, dtype=np.int16)
st.write(len(data))
duration = len(data)//100000
num_of_samples = len(data)
sample_rate = num_of_samples // duration
# data = np.array(wav_audio_data, dtype=float)
max_abs_value = np.max(np.abs(data))
np_array = (data/max_abs_value) * 32767
scaled_data = np_array.astype(np.int16).tobytes()
with io.BytesIO() as fp, wave.open(fp, mode="wb") as waveobj:
waveobj.setnchannels(1)
waveobj.setframerate(sample_rate)
waveobj.setsampwidth(2)
waveobj.setcomptype("NONE", "NONE")
waveobj.writeframes(scaled_data)
wav_make = fp.getvalue()
with open("output.wav", 'wb') as wav_file:
wav_file.write(wav_make)
st.audio("output.wav" , format="audio/wav")
# st.audio(loaded_waveform, "audio/wav")
# PERCENTAGE OF SNORING PLOT
# waveform, sample_rate = torchaudio.load('test/0_10.wav')
# resampler = T.Resample(sample_rate, RESAMPLE_RATE, dtype=waveform.dtype)
# signal = resampler(waveform)
# signal = torch.mean(signal, dim=0, keepdim=True)
# ptl_model(signal)
# snore = 0
# other = 0
# for row in model:
# for element in row:
# if element > 0.5:
# snore += 1
# else:
# other += 1
# total = snore + other
# snore_percentage = (snore / total) * 100
# other_percentage = (other / total) * 100
# categories = ["Snore", "Other"]
# percentages = [snore_percentage, other_percentage]
# plt.figure(figsize=(8, 4))
# plt.barh(categories, percentages, color=['#ff0033', '#00ffee'])
# plt.xlabel('Percentage')
# plt.title('Percentage of "Snore" and "Other"')
# plt.xlim(0, 100)
# for i, percentage in enumerate(percentages):
# plt.text(percentage, i, f' {percentage:.2f}%', va='center')
# st.pyplot(plt)
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