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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 | |
| 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 | |
| ) | |
| output = model.generate( | |
| descriptions=[description], | |
| progress=True, | |
| return_tokens=True | |
| ) | |
| return output[0] | |
| def save_audio(samples: torch.Tensor): | |
| """Renders an audio player for the given audio samples and saves them to a local directory. | |
| Args: | |
| samples (torch.Tensor): a Tensor of decoded audio samples | |
| with shapes [B, C, T] or [C, T] | |
| sample_rate (int): sample rate audio should be displayed with. | |
| save_path (str): path to the directory where audio should be saved. | |
| """ | |
| print("Samples (inside function): ", samples) | |
| 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("""⚙️ Parameters ⚙️""",divider="rainbow") | |
| st.text("") | |
| st.subheader("1. Enter your music description.......") | |
| text_area = st.text_area('Ex : 80s rock song with guitar and drums') | |
| st.text('') | |
| st.subheader("2. Select time duration (In Seconds)") | |
| time_slider = st.slider("Select time duration (In Seconds)", 0, 20, 10) | |
| st.title("""🎵 Text to Music Generator 🎵""") | |
| st.text('') | |
| left_co,right_co = st.columns(2) | |
| left_co.write("""Music Generation using Meta AI, through a prompt""") | |
| left_co.write(("""PS : First generation may take some time as it loads the full model and requirements""")) | |
| #container1 = st.container() | |
| #container1.write("""Music coupled with Image Generation using a prompt""") | |
| #container1.write("""PS : First generation may take some time as it loads the full model and requirements""") | |
| if st.sidebar.button('Generate !'): | |
| gif_url = "https://media.giphy.com/media/26Fffy7jqQW8gVg8o/giphy.gif" | |
| with right_co: | |
| with st.spinner("Generating"): | |
| st.image(gif_url,width=250) | |
| with left_co: | |
| st.text('') | |
| st.text('') | |
| st.text('') | |
| st.text('') | |
| st.text('') | |
| st.text('') | |
| st.subheader("Generated Music") | |
| music_tensors = generate_music_tensors(text_area, time_slider) | |
| save_music_file = save_audio(music_tensors) | |
| audio_filepath = 'audio_output/audio_0.wav' | |
| audio_file = open(audio_filepath, 'rb') | |
| audio_bytes = audio_file.read() | |
| st.audio(audio_bytes) | |
| st.markdown(get_binary_file_downloader_html(audio_filepath, 'Audio'), unsafe_allow_html=True) | |
| if __name__ == "__main__": | |
| main() | |