Update app.py

app.py

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+import streamlit as st
+import torch
+import torchaudio
+from audiocraft.models import MusicGen
+import os
+import numpy as np
+import base64
+
+genres = ["Pop", "Rock", "Jazz", "Electronic", "Hip-Hop", "Classical", "Lofi", "Chillpop"]
+
+@st.cache_resource()
+def load_model():
+    model = MusicGen.get_pretrained('facebook/musicgen-small')
+    return model
+
+def generate_music_tensors(descriptions, duration: int):
+    model = load_model()
+
+    model.set_generation_params(
+        use_sampling=True,
+        top_k=250,
+        duration=duration
+    )
+
+    with st.spinner("Generating Music..."):
+        output = model.generate(
+            descriptions=descriptions,
+            progress=True,
+            return_tokens=True
+        )
+
+    st.success("Music Generation Complete!")
+    return output
+
+
+def save_audio(samples: torch.Tensor):
+    sample_rate = 30000
+    save_path = "/content/drive/MyDrive/Colab Notebooks/audio_output"
+    assert samples.dim() == 2 or samples.dim() == 3
+
+    samples = samples.detach().cpu()
+    if samples.dim() == 2:
+        samples = samples[None, ...]
+
+    for idx, audio in enumerate(samples):
+        audio_path = os.path.join(save_path, f"audio_{idx}.wav")
+        torchaudio.save(audio_path, audio, sample_rate)
+
+def get_binary_file_downloader_html(bin_file, file_label='File'):
+    with open(bin_file, 'rb') as f:
+        data = f.read()
+    bin_str = base64.b64encode(data).decode()
+    href = f'<a href="data:application/octet-stream;base64,{bin_str}" download="{os.path.basename(bin_file)}">Download {file_label}</a>'
+    return href
+
+st.set_page_config(
+    page_icon= "musical_note",
+    page_title= "Music Gen"
+)
+
+def main():
+    with st.sidebar:
+        st.header("""⚙️Generate Music ⚙️""",divider="rainbow")
+        st.text("")
+        st.subheader("1. Enter your music description.......")
+        bpm = st.number_input("Enter Speed in BPM", min_value=60)
+
+        text_area = st.text_area('Ex : 80s rock song with guitar and drums')
+        st.text('')
+        # Dropdown for genres
+        selected_genre = st.selectbox("Select Genre", genres)
+        
+        st.subheader("2. Select time duration (In Seconds)")
+        time_slider = st.slider("Select time duration (In Seconds)", 0, 60, 10)
+
+    st.title("""🎵 Song Lab AI 🎵""")
+    st.text('')
+    left_co,right_co = st.columns(2)
+    left_co.write("""Music Generation through a prompt""")
+    left_co.write(("""PS : First generation may take some time ......."""))
+    
+    if st.sidebar.button('Generate !'):
+        with left_co:
+            st.text('')
+            st.text('')
+            st.text('')
+            st.text('')
+            st.text('')
+            st.text('')
+            st.subheader("Generated Music")
+
+            # Generate audio
+            descriptions = [f"{text_area} {selected_genre} {bpm} BPM" for _ in range(5)]  # Adjust the batch size (5 in this case)
+            music_tensors = generate_music_tensors(descriptions, time_slider)
+
+             # Only play the full audio for index 0
+            idx = 0
+            music_tensor = music_tensors[idx]
+            save_music_file = save_audio(music_tensor)
+            audio_filepath = f'/content/drive/MyDrive/Colab Notebooks/audio_output/audio_{idx}.wav'
+            audio_file = open(audio_filepath, 'rb')
+            audio_bytes = audio_file.read()
+
+            # Play the full audio
+            st.audio(audio_bytes, format='audio/wav')
+            st.markdown(get_binary_file_downloader_html(audio_filepath, f'Audio_{idx}'), unsafe_allow_html=True)
+
+
+if __name__ == "__main__":
+    main()
+