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from audiocraft.models import MusicGen | ||
import streamlit as st | ||
import torch | ||
import torchaudio | ||
import os | ||
import numpy as np | ||
import base64 | ||
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@st.cache_resource | ||
def load_model(): | ||
model = MusicGen.get_pretrained('facebook/musicgen-small') | ||
return model | ||
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def generate_music_tensors(description, duration: int): | ||
print("Description: ", description) | ||
print("Duration: ", duration) | ||
model = load_model() | ||
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model.set_generation_params( | ||
use_sampling=True, | ||
top_k=250, | ||
duration=duration | ||
) | ||
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output = model.generate( | ||
descriptions=[description], | ||
progress=True, | ||
return_tokens=True | ||
) | ||
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return output[0] | ||
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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. | ||
""" | ||
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print("Samples (inside function): ", samples) | ||
sample_rate = 32000 | ||
save_path = "audio_output/" | ||
assert samples.dim() == 2 or samples.dim() == 3 | ||
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samples = samples.detach().cpu() | ||
if samples.dim() == 2: | ||
samples = samples[None, ...] | ||
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for idx, audio in enumerate(samples): | ||
audio_path = os.path.join(save_path, f"audio_{idx}.wav") | ||
torchaudio.save(audio_path, audio, sample_rate) | ||
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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 | ||
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st.set_page_config( | ||
page_icon= "musical_note", | ||
page_title= "Music Gen" | ||
) | ||
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def main(): | ||
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st.title("Text to Music Generator🎵") | ||
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with st.expander("See explanation"): | ||
st.write("Music Generator app built using Meta's Audiocraft library. We are using Music Gen Small model.") | ||
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text_area = st.text_area("Enter your description.......") | ||
time_slider = st.slider("Select time duration (In Seconds)", 0, 20, 10) | ||
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if text_area and time_slider: | ||
st.json({ | ||
'Your Description': text_area, | ||
'Selected Time Duration (in Seconds)': time_slider | ||
}) | ||
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st.subheader("Generated Music") | ||
music_tensors = generate_music_tensors(text_area, time_slider) | ||
print("Musci Tensors: ", music_tensors) | ||
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) | ||
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if __name__ == "__main__": | ||
main() | ||
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