Update streamlit_app.py

streamlit_app.py

@@ -10,7 +10,6 @@ from scipy.io.wavfile import write
 st.title("Real-Time Speech-to-Text Transcriber")
 st.markdown("Upload an audio file to get the transcription with timestamps.")
 
-# ⏳ Cache model to reduce loading time
 @st.cache_resource
 def load_model():
     return whisper.load_model("small")
@@ -20,10 +19,8 @@ model = load_model()
 def transcribe_audio(file):
     audio, sr = librosa.load(file, sr=None)
 
-    # Noise reduction
     audio = nr.reduce_noise(y=audio, sr=sr)
 
-    # Trim to 30s for performance
     max_duration = 30
     audio = audio[:sr * max_duration]
 
@@ -31,7 +28,6 @@ def transcribe_audio(file):
         audio = librosa.resample(audio, orig_sr=sr, target_sr=16000)
         sr = 16000
 
-    # Save to temp WAV
     with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
         write(tmp.name, sr, (audio * 32767).astype(np.int16))
         result = model.transcribe(tmp.name, fp16=False)