Upload 2 files

main.py

@@ -0,0 +1,129 @@
+import streamlit as st
+import torch
+import torchaudio
+import os
+import numpy as np
+import base64
+from audiocraft.models import MusicGen
+
+# Before
+batch_size = 64
+
+# After
+batch_size = 32 
+torch.cuda.empty_cache()
+
+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(description, 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=description,
+            progress=True,
+            return_tokens=True
+        )
+
+    st.success("Music Generation Complete!")
+    return output
+
+def save_audio(samples: torch.Tensor):
+    print("Samples (inside function): ", samples)
+    sample_rate = 30000
+    save_path = "audio_output/"
+    sample= samples[0]
+    assert sample.dim() == 2 or sample.dim() == 3
+
+    sample = sample.detach().cpu()
+    if sample.dim() == 2:
+        sample = sample[None, ...]
+
+    for idx, audio in enumerate(sample):
+        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():
+    st.title("🎧 AI Composer Medium-Model 🎧")
+
+    st.subheader("Craft your perfect melody!")
+    bpm = st.number_input("Enter Speed in BPM", min_value=2)
+
+    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, 30, 10)
+    mood = st.selectbox("Select Mood (Optional)", ["Happy", "Sad", "Angry", "Relaxed", "Energetic"], None)
+    instrument = st.selectbox("Select Instrument (Optional)", ["Piano", "Guitar", "Flute", "Violin", "Drums"], None)
+    tempo = st.selectbox("Select Tempo (Optional)", ["Slow", "Moderate", "Fast"], None)
+    melody = st.text_input("Enter Melody or Chord Progression (Optional) e.g: C D:min G:7 C, Twinkle Twinkle Little Star", " ")
+
+    if st.button('Let\'s Generate 🎶'):
+        st.text('\n\n')
+        st.subheader("Generated Music")
+        
+        # Generate audio
+        description = text_area  # Initialize description with text_area
+        if selected_genre:
+            description += f" {selected_genre}"
+            st.empty()  # Hide the selected_genre selectbox after selecting one option
+        if bpm:
+            description += f" {bpm} BPM"
+        if mood:
+            description += f" {mood}"
+            st.empty()  # Hide the mood selectbox after selecting one option
+        if instrument:
+            description += f" {instrument}"
+            st.empty()  # Hide the instrument selectbox after selecting one option
+        if tempo:
+            description += f" {tempo}"
+            st.empty()  # Hide the tempo selectbox after selecting one option
+        if melody:
+            description += f" {melody}"
+
+        # Clear CUDA memory cache before generating music
+        torch.cuda.empty_cache()
+
+        music_tensors = generate_music_tensors(description, time_slider)
+
+        # Only play the full audio for index 0
+        # idx = 0
+        # music_tensor = music_tensors[idx]
+        # music_tensor = 1
+        save_audio(music_tensors)
+        audio_filepath = f'audio_output/audio_0.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'), unsafe_allow_html=True)
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,22 @@
+
+av
+einops
+flashy>=0.0.1
+hydra-core>=1.1
+hydra_colorlog
+julius
+num2words
+numpy
+sentencepiece
+spacy==3.5.2
+torch>=2.0.0
+torchaudio>=2.0.0
+huggingface_hub
+tqdm
+transformers>=4.31.0  # need Encodec there.
+xformers
+demucs
+librosa
+gradio
+torchmetrics
+encodec