Update app.py

app.py

@@ -16,6 +16,7 @@ st.title("AudioSR: Versatile Audio Super-Resolution")
 st.write("""
 Upload your low-resolution audio files, and AudioSR will enhance them to high fidelity!
 Supports all types of audio (music, speech, sound effects, etc.) with arbitrary sampling rates.
+Only the first 10 seconds of the audio will be processed.
 """)
 
 # Upload audio file
@@ -49,16 +50,23 @@ if uploaded_file and st.button("Enhance Audio"):
     # Create temp directory for saving files
     with tempfile.TemporaryDirectory() as tmp_dir:
         input_path = os.path.join(tmp_dir, "input.wav")
+        truncated_path = os.path.join(tmp_dir, "truncated.wav")
         output_path = os.path.join(tmp_dir, "output.wav")
 
         # Save uploaded file locally
         with open(input_path, "wb") as f:
             f.write(uploaded_file.read())
 
-        # Plot input spectrogram
-        st.write("Input Audio Spectrogram:")
-        input_spectrogram = plot_spectrogram(input_path, title="Input Audio Spectrogram")
-        st.pyplot(input_spectrogram)
+        # Load and truncate the first 10 seconds
+        y, sr = librosa.load(input_path, sr=None)
+        max_samples = sr * 10  # First 10 seconds
+        y_truncated = y[:max_samples]
+        librosa.output.write_wav(truncated_path, y_truncated, sr)
+
+        # Plot truncated spectrogram
+        st.write("Truncated Input Audio Spectrogram (First 10 seconds):")
+        truncated_spectrogram = plot_spectrogram(truncated_path, title="Truncated Input Audio Spectrogram")
+        st.pyplot(truncated_spectrogram)
 
         # Build and load the model
         audiosr = build_model(model_name=model_name, device=device)
@@ -66,7 +74,7 @@ if uploaded_file and st.button("Enhance Audio"):
         # Perform super-resolution
         waveform = super_resolution(
             audiosr,
-            input_path,
+            truncated_path,
             seed=random_seed,
             guidance_scale=guidance_scale,
             ddim_steps=ddim_steps,
@@ -74,7 +82,7 @@ if uploaded_file and st.button("Enhance Audio"):
         )
 
         # Save enhanced audio
-        save_wave(waveform, inputpath=input_path, savepath=tmp_dir, name="output", samplerate=48000)
+        save_wave(waveform, inputpath=truncated_path, savepath=tmp_dir, name="output", samplerate=48000)
 
         # Plot output spectrogram
         st.write("Enhanced Audio Spectrogram:")
@@ -82,8 +90,8 @@ if uploaded_file and st.button("Enhance Audio"):
         st.pyplot(output_spectrogram)
 
         # Display audio players and download link
-        st.audio(input_path, format="audio/wav")
-        st.write("Original Audio:")
+        st.audio(truncated_path, format="audio/wav")
+        st.write("Truncated Original Audio (First 10 seconds):")
         
         st.audio(output_path, format="audio/wav")
         st.write("Enhanced Audio:")