Spaces:
Running
Running
| from transformers import MusicgenForConditionalGeneration | |
| from transformers import AutoProcessor | |
| import scipy | |
| import streamlit as st | |
| # set seed | |
| import torch | |
| torch.manual_seed(2912) | |
| st.set_page_config( | |
| page_title="Plant Orchestra with GenAI", | |
| page_icon="🎵" | |
| ) | |
| # initialise model | |
| def initialise_model(): | |
| try: | |
| processor = AutoProcessor.from_pretrained("facebook/musicgen-small") | |
| model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small") | |
| return processor, model | |
| except Exception as e: | |
| st.error(f"Error initializing the model: {str(e)}") | |
| return None, None | |
| processor, model = initialise_model() | |
| # Generate audio with given prompt | |
| def generate_audio(processor, model, prompt): | |
| if processor is not None and model is not None: | |
| try: | |
| inputs = processor( | |
| text=[prompt], | |
| padding=True, | |
| return_tensors="pt", | |
| ) | |
| audio_values = model.generate(**inputs.to("cpu"), do_sample=True, guidance_scale=3, max_new_tokens=256) | |
| return audio_values | |
| except Exception as e: | |
| st.error(f"Error generating audio: {str(e)}") | |
| return None | |
| # save audio file with scipy | |
| def save_file(model, audio_values, filename): | |
| sampling_rate = model.config.audio_encoder.sampling_rate | |
| scipy.io.wavfile.write(filename, rate=sampling_rate, data=audio_values[0, 0].cpu().numpy()) | |
| st.title("Plant Orchestra 🌻") | |
| st.markdown("Generate music based on your own plant orchestra.") | |
| prompt = st.text_input(label='Prompt:', value='Sunflower, roses') | |
| if st.button("Generate Music"): | |
| if processor is not None and model is not None: | |
| with st.spinner("Generating audio..."): | |
| results = generate_audio(processor, model, prompt) | |
| if results is not None: | |
| sampling_rate = model.config.audio_encoder.sampling_rate | |
| st.write("Listen to the generated music:") | |
| st.audio(sample_rate=sampling_rate, data=results[0, 0].cpu().numpy(), format="audio/wav") | |
| # Sidebar: How-to-use and Samples | |
| st.sidebar.header("How to Use:") | |
| st.sidebar.write("1. Enter a plant and condition (optional) in the text input. E.g. moss, 30C") | |
| st.sidebar.write("2. Click the 'Generate Music' button to create music based on the provided input.") | |
| st.sidebar.write("3. You can listen to the generated music and download it.") | |
| st.sidebar.write() | |
| st.sidebar.header('Samples 🎵') | |
| st.sidebar.write('Holland moss') | |
| st.sidebar.audio('sound/holland_moss.wav') | |
| st.sidebar.write('Nerve plant') | |
| st.sidebar.audio('sound/nerve_plant.wav') | |
| st.sidebar.write('Artillery plant') | |
| st.sidebar.audio('sound/artillery_plant.wav') | |
| st.sidebar.write('Malayan moss') | |
| st.sidebar.audio('sound/malayan_moss.wav') | |
| st.sidebar.write('Pilea') | |
| st.sidebar.audio('sound/pilea.wav') | |
| st.sidebar.write('Hydrocotyle tripartita') | |
| st.sidebar.audio('sound/hydrocotyle_tripartita.wav') | |
| st.sidebar.write('Oak leaf fig') | |
| st.sidebar.audio('sound/oak_leaf_fig.wav') | |
| # Footer | |
| st.write() | |
| st.write() | |
| st.write() | |
| st.markdown("---") | |
| st.markdown("Created with ❤️ by HS2912 W4 Group 2") | |