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import streamlit as st
from transformers import pipeline
import scipy.io.wavfile
import numpy as np
import io # Import the io module for BytesIO
# -----------------------------------------------------------
def load_css(file_path):
with open(file_path) as f:
css_to_load = f.read()
st.markdown(f"<style>{css_to_load}</style>", unsafe_allow_html=True)
# -----------------------------------------------------------
def load_model():
# Initializes and returns the pipeline for text-to-audio generation
return pipeline("text-to-audio", model="facebook/musicgen-small")
# -----------------------------------------------------------
def main():
try:
st.set_page_config(page_title="Text to Music", page_icon="🎵")
load_css("styles/styles.css")
st.title("**Turn Your Text Into Music**")
# Load the model
synthesizer = load_model()
user_text = st.text_area("What type of beat would you like to hear: ")
# Generate and play music based on user input
if user_text:
# Generate music with the synthesizer
music = synthesizer(user_text, forward_params={"do_sample": True, "max_new_tokens": 500})
# Check if the audio data needs scaling and convert to int16
if music["audio"].dtype != np.int16:
audio_data = np.int16(music["audio"] / np.max(np.abs(music["audio"])) * 32767)
else:
audio_data = music["audio"]
# Instead of writing to a temp file, write the audio data to a BytesIO object
audio_bytes_io = io.BytesIO()
scipy.io.wavfile.write(audio_bytes_io, rate=music["sampling_rate"], data=audio_data)
audio_bytes_io.seek(0) # Go to the beginning of the BytesIO object
# Use Streamlit's audio widget to display the audio player with the BytesIO object
st.audio(audio_bytes_io, format="audio/wav")
except Exception as e:
st.error(f"An error occurred: {e}")
# -----------------------------------------------------------
if __name__ == "__main__":
main()
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