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app.py

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+import streamlit as st
+import torch
+import os
+import librosa
+import librosa.display
+import matplotlib.pyplot as plt
+from audiosr import build_model, super_resolution, save_wave
+import tempfile
+import numpy as np
+
+# Set MPS device if available (for Mac M-Series GPUs)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Title and Description
+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.
+""")
+
+# Upload audio file
+uploaded_file = st.file_uploader("Upload an audio file (WAV format)", type=["wav"])
+
+# Model Parameters
+st.sidebar.title("Model Parameters")
+model_name = st.sidebar.selectbox("Select Model", ["basic", "speech"], index=0)
+ddim_steps = st.sidebar.slider("DDIM Steps", min_value=10, max_value=100, value=50)
+guidance_scale = st.sidebar.slider("Guidance Scale", min_value=1.0, max_value=10.0, value=3.5)
+random_seed = st.sidebar.number_input("Random Seed", min_value=0, value=42, step=1)
+latent_t_per_second = 12.8
+
+# Helper function to plot spectrogram
+def plot_spectrogram(audio_path, title):
+    y, sr = librosa.load(audio_path, sr=None)
+    S = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128, fmax=sr // 2)
+    S_dB = librosa.power_to_db(S, ref=np.max)
+    
+    plt.figure(figsize=(10, 4))
+    librosa.display.specshow(S_dB, sr=sr, x_axis='time', y_axis='mel', fmax=sr // 2, cmap='viridis')
+    plt.colorbar(format='%+2.0f dB')
+    plt.title(title)
+    plt.tight_layout()
+    return plt
+
+# Process Button
+if uploaded_file and st.button("Enhance Audio"):
+    st.write("Processing audio...")
+
+    # Create temp directory for saving files
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        input_path = os.path.join(tmp_dir, "input.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)
+
+        # Build and load the model
+        audiosr = build_model(model_name=model_name, device=device)
+
+        # Perform super-resolution
+        waveform = super_resolution(
+            audiosr,
+            input_path,
+            seed=random_seed,
+            guidance_scale=guidance_scale,
+            ddim_steps=ddim_steps,
+            latent_t_per_second=latent_t_per_second,
+        )
+
+        # Save enhanced audio
+        save_wave(waveform, inputpath=input_path, savepath=tmp_dir, name="output", samplerate=48000)
+
+        # Plot output spectrogram
+        st.write("Enhanced Audio Spectrogram:")
+        output_spectrogram = plot_spectrogram(output_path, title="Enhanced Audio Spectrogram")
+        st.pyplot(output_spectrogram)
+
+        # Display audio players and download link
+        st.audio(input_path, format="audio/wav")
+        st.write("Original Audio:")
+        
+        st.audio(output_path, format="audio/wav")
+        st.write("Enhanced Audio:")
+        st.download_button("Download Enhanced Audio", data=open(output_path, "rb").read(), file_name="enhanced_audio.wav")
+
+# Footer
+st.write("Built with [Streamlit](https://streamlit.io) and [AudioSR](https://audioldm.github.io/audiosr)")

requirements.txt

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+torch
+matplotlib
+git+https://github.com/haoheliu/versatile_audio_super_resolution.git
+streamlit