.

app.py

@@ -43,7 +43,12 @@ def run():
             duration = split_audio(tmpdirname, uploaded_fp)
 
         placeholder.write("Removing noise...")
-        get_speakers(tmpdirname)
+        
+        use_auth_token = True
+        if st.secrets.get("ACCESS_TOKEN"):
+            use_auth_token = st.secrets["ACCESS_TOKEN"]
+
+        get_speakers(tmpdirname, use_auth_token)
         progress_bar.progress(50)
 
             # https://docs.streamlit.io/knowledge-base/using-streamlit/where-file-uploader-store-when-deleted

backend.py

@@ -81,7 +81,7 @@ def split_audio(tmpdirname, video, chunk_size=120):
     return audio.duration
 
 
-def get_speakers(tmpdirname):
+def get_speakers(tmpdirname, use_auth_token=True):
     files = find_audio_files(opj(tmpdirname, 'noisy_chunks'))
     dset = Audioset(files, with_path=True,
                     sample_rate=denoise_model.sample_rate, channels=denoise_model.chin, convert=True)
@@ -111,10 +111,7 @@ def get_speakers(tmpdirname):
         final_clip.write_audiofile(cleaned_path)
         print('identifying speakers...')
 
-        if st.secrets.get("ACCESS_TOKEN"):
-            pipeline = Pipeline.from_pretrained('pyannote/speaker-diarization', use_auth_token=st.secrets["ACCESS_TOKEN"])
-        else:
-            pipeline = Pipeline.from_pretrained('pyannote/speaker-diarization', use_auth_token=True)
+        pipeline = Pipeline.from_pretrained('pyannote/speaker-diarization', use_auth_token=use_auth_token)
     
         return str(pipeline({'uri': '', 'audio': cleaned_path})).split('\n'), cleaned_path
 

requirements.txt

@@ -2,5 +2,4 @@ pyannote.audio==2.1.1
 denoiser==0.1.5
 moviepy==1.0.3
 pydub==0.25.1
-git+https://github.com/openai/whisper.git 
-# omegaconf
+git+https://github.com/openai/whisper.git