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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
# Add this function for downloading binary files
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="{file_label}">Download {file_label}</a>'
return href
@st.cache_resource()
def load_model():
model = MusicGen.get_pretrained('facebook/musicgen-small')
return model
def generate_music_tensors(descriptions, duration: int):
model = load_model()
model.set_generation_params(
use_sampling=True,
top_k=250,
duration=duration
)
with st.spinner("Generating Music..."):
st.markdown("### Generating Music... 🎵🎶🎹")
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 = "/tmp/audio_output" # Use /tmp directory
if not os.path.exists(save_path):
os.makedirs(save_path)
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")
try:
torchaudio.save(audio_path, audio, sample_rate)
except Exception as e:
st.error(f"Error saving audio file: {e}")
return None
return save_path
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)
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.subheader("Generated Music")
# Generate audio
descriptions = [f"{text_area} {selected_genre} {bpm} BPM" for _ in range(5)] # Adjust the batch size (5 in this case)
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()
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