Create app.py

app.py

@@ -0,0 +1,109 @@
+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()