Update app.py

app.py

@@ -69,55 +69,55 @@ def main():
         #1st attempt
         #audio_path = " audio_file.name"
 
-        audio, sr = torchaudio.load(audio_file)
-        st.audio(audio_file, format="audio/mpeg")
-        audio= audio.unsqueeze(0)
-
-        st.markdown("SR")
-        st.markdown(sr)
-        st.markdown("after unsqueeze wav or mp3")
-        st.markdown(audio)
+        # audio, sr = torchaudio.load(audio_file)
+        # st.audio(audio_file, format="audio/mpeg")
+        # audio= audio.unsqueeze(0)
+
+        # st.markdown("SR")
+        # st.markdown(sr)
+        # st.markdown("after unsqueeze wav or mp3")
+        # st.markdown(audio)
         
         #2nd attempt
         # Save file to local storage
-        # tmp_input_audio_file = os.path.join("/tmp/", audio_file.name)
-        # file_extension = os.path.splitext(tmp_input_audio_file)[1].lower()
-        # st.markdown(file_extension)
-        # if file_extension in [".wav", ".flac"]:
-        #     with open("test.wav", "wb") as f:
-        #         f.write(audio_file.getbuffer())
+        tmp_input_audio_file = os.path.join("/tmp/", audio_file.name)
+        file_extension = os.path.splitext(tmp_input_audio_file)[1].lower()
+        st.markdown(file_extension)
+        if file_extension in [".wav", ".flac"]:
+            with open("test.wav", "wb") as f:
+                f.write(audio_file.getbuffer())
 
-        #     st.audio("test.wav", format="audio/wav")
+            st.audio("test.wav", format="audio/wav")
 
-        # elif file_extension == ".mp3":
-        #     with open("test.mp3", "wb") as f:
-        #         f.write(audio_file.getbuffer())
+        elif file_extension == ".mp3":
+            with open("test.mp3", "wb") as f:
+                f.write(audio_file.getbuffer())
 
-        #     st.audio("test.mp3", format="audio/mpeg")
+            st.audio("test.mp3", format="audio/mpeg")
 
-        # Load the WAV file using torchaudio
-        # if file_extension in [".wav", ".flac"]: 
-        #     wav, sample_rate = torchaudio.load("test.wav")
-        #     st.markdown("Before unsquueze wav")
-        #     st.markdown(wav)
+        #Load the WAV file using torchaudio
+        if file_extension in [".wav", ".flac"]: 
+            wav, sample_rate = torchaudio.load("test.wav")
+            st.markdown("Before unsquueze wav")
+            st.markdown(wav)
 
-        #     #Unsqueeze for line 176
-        #     wav= wav.unsqueeze(0)
+            #Unsqueeze for line 176
+            wav= wav.unsqueeze(0)
                 
-        # elif file_extension == ".mp3":
-        #     wav3, sample_rate = librosa.load("test.mp3")
-        #     st.markdown(wav3)
-        #     #RuntimeError: Could not infer dtype of numpy.float32
-        #     #wav = torch.tensor(wav3).float() / 32768.0
+        elif file_extension == ".mp3":
+            wav3, sample_rate = librosa.load("test.mp3")
+            st.markdown(wav3)
+            #RuntimeError: Could not infer dtype of numpy.float32
+            #wav = torch.tensor(wav3).float() / 32768.0
             
-        #     #RuntimeError: Numpy is not available
-        #     wav = torch.from_numpy(wav3) #/32768.0
-        #     wav = wav.unsqueeze(0).unsqueeze(0)
-        #     st.markdown("Before unsqueeze mp3")
-        #     st.markdown(wav)
+            #RuntimeError: Numpy is not available
+            wav = torch.from_numpy(wav3) #/32768.0
+            wav = wav.unsqueeze(0).unsqueeze(0)
+            st.markdown("Before unsqueeze mp3")
+            st.markdown(wav)
         
-        # #Unsqueeze for line 176
-        # wav= wav.unsqueeze(0)
+        #Unsqueeze for line 176
+        wav= wav.unsqueeze(0)
 
         # #2nd way
         # # Convert the tensor to a byte-like object in WAV format