Initial commit

app.py

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+import streamlit as st
+from transformers import pipeline
+import scipy
+
+# Load the Bark model
+@st.cache_resource  # Caches the model to avoid reloading
+def load_model():
+    return pipeline("text-to-speech", model="suno/bark")
+
+synthesiser = load_model()
+
+# Streamlit app layout
+st.title("AI Song Generator")
+st.markdown("Generate AI songs from text using Suno's Bark model! 🎵 Add special cues like `♪ lyrics ♪` or `[laughs]` for customization.")
+
+# Input field for song lyrics
+lyrics = st.text_area("Enter your song lyrics or text (e.g., ♪ Twinkle, twinkle, little star... ♪):", height=150)
+
+# Button to trigger song generation
+if st.button("Generate Song"):
+    if lyrics.strip():
+        st.info("Generating the song... This may take a few moments.")
+        try:
+            # Generate audio
+            result = synthesiser(lyrics, forward_params={"do_sample": True})
+            
+            # Save audio to a file
+            audio_path = "generated_song.wav"
+            scipy.io.wavfile.write(audio_path, rate=result["sampling_rate"], data=result["audio"])
+            
+            # Play audio
+            st.audio(audio_path, format="audio/wav")
+            st.success("Song generation complete!")
+        except Exception as e:
+            st.error(f"An error occurred: {e}")
+    else:
+        st.warning("Please provide song lyrics to generate audio.")
+
+# Optional cleanup (use if audio files are temporary)
+import os
+if os.path.exists("generated_song.wav"):
+    os.remove("generated_song.wav")

requirements.txt

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+torch
+transformers
+scipy
+streamlit