Update app.py

app.py

@@ -53,18 +53,18 @@ else:
     reset = True
 
 if audio_file is not None:
-    # Save the uploaded audio file to a temporary file
+ 
     with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
         tmp_file.write(audio_file.getvalue())
-        # tmp_file.write(audio_file.read())
+  
         tmp_file_name = tmp_file.name
 
-    # audio_file.seek(0)   # Seek to the beginning of the file
+
     tmp_file.close()
-    # print(audio_file)
+  
     plt.figure(figsize = (14,5))
     data, sample_rate = librosa.load(tmp_file_name,sr=16000)
-    # Plot the waveform
+ 
     plt.figure(figsize=(10, 4))
     librosa.display.waveshow(data, sr=16000)
     plt.title("Waveform")
@@ -72,7 +72,7 @@ if audio_file is not None:
     plt.ylabel("Amplitude")
     plt.tight_layout()
 
-    # Display the plot in Streamlit
+
     st.audio(data, format="audio/wav", sample_rate=sample_rate)
     st.caption("Raw Audio Waveform")
     st.pyplot(plt)
@@ -90,12 +90,11 @@ if audio_file is not None:
 
         sampling_rate = 16000
         wav = read_audio(audio_file, sampling_rate=sampling_rate) #type(wav) = <class 'torch.Tensor'>
-        # print(wav)
+
         speech_timestamps = get_speech_timestamps(wav, model, sampling_rate=sampling_rate)
-        # pprint(speech_timestamps)
 
         plt.figure(figsize = (14,5))
-        # data,sample_rate = librosa.load(local_audio_file_path, sr=sampling_rate)
+    
         librosa.display.waveshow(np.array(wav), sr = sampling_rate)
         if len(speech_timestamps) != 0:
             plt.title("Detected Speech Segments")