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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","Country","R&G", "Folk","Heavy Metal", | |
| "EDM", "Soil", "Funk","Reggae", "Disco", "Punk Rock", "House", | |
| "Techno","Indie Rock", "Grunge", "Ambient","Gospel", "Latin Music","Grime" ,"Trap", "Psychedelic Rock" ] | |
| def load_model(): | |
| model = MusicGen.get_pretrained('facebook/musicgen-medium') | |
| return model | |
| def generate_music_tensors(descriptions, duration: int): | |
| model = load_model() | |
| # model = load_model().to('cpu') | |
| model.set_generation_params( | |
| use_sampling=True, | |
| top_k=250, | |
| duration=duration | |
| ) | |
| with st.spinner("Generating Music with Medium-Model..."): | |
| 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 = "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) | |
| # time_slider = st.slider("Select time duration (In Minutes)", 0,300,10, step=1) | |
| 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.text('\n\n') | |
| st.subheader("Generated Music") | |
| # Generate audio | |
| # descriptions = [f"{text_area} {selected_genre} {bpm} BPM" for _ in range(5)] | |
| descriptions = [f"{text_area} {selected_genre} {bpm} BPM" for _ in range(1)] # Change the batch size to 1 | |
| 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'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() | |