Turned off logging and file now stored in temp dir.

app.py

@@ -5,7 +5,7 @@ import subprocess
 from simple_diarizer.diarizer import Diarizer
 import streamlit as st
 import base64
-
+import tempfile
 
 
 
@@ -17,7 +17,7 @@ def create_download_link(val, filename, label):
 def segment(nu_speakers):
 
     diar = Diarizer(embed_model='ecapa',cluster_method='sc')
-    segments = diar.diarize('mono.wav', num_speakers=nu_speakers)
+    segments = diar.diarize(temp_file, num_speakers=nu_speakers)
 
     sdf = pd.DataFrame(segments)
 
@@ -31,14 +31,14 @@ def segment(nu_speakers):
 def audio_to_df(uploaded):
     monotize(uploaded)
     model = whisper.load_model(model_size)
-    result = model.transcribe('mono.wav',verbose=True,
+    result = model.transcribe(temp_file,
                           without_timestamps=False,
                            task = task)
     tdf = pd.DataFrame(result['segments'])
     return tdf
 
 def monotize(uploaded):
-    cmd = f"ffmpeg -y -i {uploaded} -acodec pcm_s16le -ar 16000 -ac 1 mono.wav"
+    cmd = f"ffmpeg -y -i {uploaded} -acodec pcm_s16le -ar 16000 -ac 1 {temp_file}"
     subprocess.Popen(cmd, shell=True).wait()
 
 def add_preface(row):
@@ -50,7 +50,7 @@ def transcribe(uploaded, nu_speakers):
     with st.spinner(text="Converting file..."):
         monotize('temp_audio')
 
-    audio_file = open('mono.wav', 'rb')
+    audio_file = open(temp_file, 'rb')
     audio_bytes = audio_file.read()
     st.audio('mono.wav', format='audio/wav')
 
@@ -124,6 +124,10 @@ if submit:
     else:
         task = 'transcribe'
 
+    #temporary file to store audio_file
+    tmp_dir = tempfile.TemporaryDirectory()
+    temp_file = tmp_dir.name + '/mono.wav'
+
     bytes_data = uploaded.getvalue()
     with open('temp_audio', 'wb') as outfile:
         outfile.write(bytes_data)
@@ -136,3 +140,4 @@ if submit:
 
     download_url = create_download_link(csv, 'transcript.csv', 'Download transcript as CSV (with time codes)')
     st.markdown(download_url, unsafe_allow_html=True)
+    tmp_dir.cleanup()