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| import streamlit as st | |
| import torch | |
| import os | |
| import base64 | |
| import torchaudio | |
| import numpy as np | |
| from audiocraft.models import MusicGen | |
| genres = ["Pop", "Rock", "Jazz", "Electronic", "Hip-Hop", "Classical", | |
| "Lofi", "Chillpop","Country","R&G", "Folk","EDM", "Disco", "House", "Techno",] | |
| def load_model(model_name): | |
| model = MusicGen.get_pretrained(model_name) | |
| return model | |
| def generate_music_tensors(description, duration: int, batch_size=1, models=None): | |
| outputs = {} | |
| for model_name, model in models.items(): | |
| model.set_generation_params( | |
| use_sampling=True, | |
| top_k=250, | |
| duration=duration | |
| ) | |
| with st.spinner(f"Generating Music with {model_name}..."): | |
| output = model.generate( | |
| descriptions=description, | |
| progress=True, | |
| return_tokens=True | |
| ) | |
| outputs[model_name] = output | |
| st.success("Music Generation Complete!") | |
| return outputs | |
| def save_audio(samples: torch.Tensor, filename): | |
| 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"{filename}_{idx}.wav") | |
| torchaudio.save(audio_path, audio, sample_rate) | |
| return audio_path | |
| 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 🎧") | |
| st.subheader("Generate Music") | |
| st.write("Craft your perfect melody! Fill in the blanks below to create your music masterpiece:") | |
| bpm = st.number_input("Enter Speed in BPM", min_value=60) | |
| text_area = st.text_area('Example: 80s rock song with guitar and drums') | |
| selected_genre = st.selectbox("Select Genre", genres) | |
| time_slider = st.slider("Select time duration (In Seconds)", 0, 30, 10) | |
| mood = st.selectbox("Select Mood", ["Happy", "Sad", "Angry", "Relaxed", "Energetic"]) | |
| instrument = st.selectbox("Select Instrument", ["Piano", "Guitar", "Flute", "Violin", "Drums"]) | |
| tempo = st.selectbox("Select Tempo", ["Slow", "Moderate", "Fast"]) | |
| melody = st.text_input("Enter Melody or Chord Progression", "e.g., C D:min G:7 C, Twinkle Twinkle Little Star") | |
| models = { | |
| 'Medium': load_model('facebook/musicgen-medium'), | |
| 'Large': load_model('facebook/musicgen-large'), | |
| # Add more models here as needed | |
| } | |
| if st.button('Let\'s Generate 🎶'): | |
| st.text('\n\n') | |
| st.subheader("Generated Music") | |
| description = f"{text_area} {selected_genre} {bpm} BPM {mood} {instrument} {tempo} {melody}" | |
| music_outputs = generate_music_tensors(description, time_slider, batch_size=2, models=models) | |
| for model_name, output in music_outputs.items(): | |
| idx = 0 # Assuming you want to access the first audio file for each model | |
| audio_filepath = save_audio(output, f'audio_{model_name}_{idx}') | |
| audio_file = open(audio_filepath, 'rb') | |
| audio_bytes = audio_file.read() | |
| st.audio(audio_bytes, format='audio/wav', label=f'{model_name} Model') | |
| st.markdown(get_binary_file_downloader_html(audio_filepath, f'Audio_{model_name}_{idx}'), unsafe_allow_html=True) | |
| if __name__ == "__main__": | |
| main() | |