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import streamlit as st
import os
import subprocess
import tempfile
import matlab.engine
from st_audiorec import st_audiorec
import os.path
import numpy as np
import sounddevice as sd
from scipy.io.wavfile import write
from scipy.io import wavfile
def record(duration):
fs = 16000
seconds = duration
myrecording = sd.rec(int(seconds * fs), samplerate=fs, channels=1)
sd.wait()
write('clean_waveform.wav', fs, myrecording)
def main():
st.title("Upload WAV File, make the file noisy, run enhancement and transcribe")
uploaded_wav_file = st.file_uploader("Upload a WAV file", type=["wav"])
uploaded_noise_file = st.file_uploader("Upload a noise file", type = ["wav"])
snr = st.text_input("Enter SNR", "")
temp_dir = tempfile.mkdtemp()
if st.button("Record"):
record(5)
if st.button("Add Noise"):
wav_file_path = os.path.join(temp_dir, uploaded_wav_file.name)
with open(wav_file_path, "wb") as f1:
f1.write(uploaded_wav_file.getvalue())
noise_file_path = os.path.join(temp_dir, uploaded_noise_file.name)
with open(noise_file_path, "wb") as f2:
f2.write(uploaded_noise_file.getvalue())
#run_matlab_script(snr)
samplerate, signal = wavfile.read(wav_file_path)
samplerate, noise = wavfile.read(noise_file_path)
mix_audio(signal, noise, snr)
if st.button("Enhance"):
run_batch_script()
if st.button("Transcribe_zeroshot"):
transcribe_zeroshot()
if st.button("Transcribe_trained"):
transcribe_trained()
def run_matlab_script(snr):
read_fd, write_fd = os.pipe()
matlab_executable = 'matlab'
# Path to your MATLAB script
matlab_script = 'mixFiles.m'
# Data to send to MATLAB (replace with your actual data)
data_to_send = snr
# Run the MATLAB script
process = subprocess.Popen([matlab_executable, '-nodesktop', '-nosplash', '-r', f'run("{matlab_script}");exit;'], stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
# Send data to MATLAB
process.stdin.write(data_to_send)
process.stdin.close()
def run_batch_script():
command = r"OmniClear_cloud_demo noisy_waveform.wav enhanced_waveform.wav"
subprocess.run(command, shell=True)
def transcribe_zeroshot():
command = r"streamlit run loadlocal_zeroshot.py"
subprocess.run(command)
def transcribe_trained():
command = r"streamlit run loadlocal_trained.py"
subprocess.run(command)
def mix_audio(signal, noise, snr):
noise = noise[np.arange(len(signal)) % len(noise)]
noise = noise.astype(np.float32)
signal = signal.astype(np.float32)
signal_energy = np.mean(signal**2)
noise_energy = np.mean(noise**2)
snr = float(snr)
g = np.sqrt(10.0 ** (-snr/10) * signal_energy / noise_energy)
a = np.sqrt(1 / (1 + g**2))
b = np.sqrt(g**2 / (1 + g**2))
# mix the signals
rate = 16000
noisy_signal = a * signal + b * noise
scaled = np.int16(noisy_signal / np.max(np.abs(noisy_signal)) * 32767)
# Write the array to a WAV file
#print(scaled.shape)
write('noisy_waveform.wav', rate, scaled)
if __name__ == "__main__":
main() |