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import gradio as gr
from transformers import AutoModel
from miditok import MusicTokenizer
import torch
import numpy as np
import pretty_midi
from io import BytesIO
import os
import requests
# URL of the file to download
url = "https://raw.githubusercontent.com/urish/cinto/refs/heads/master/media/FluidR3%20GM.sf2"
# Local filename to save the file
filename = "FluidR3_GM.sf2"
if not os.path.exists(filename):
response = requests.get(url)
response.raise_for_status()
with open(filename, "wb") as file:
file.write(response.content)
def score_to_audio(score, sample_rate: int = 44100) -> tuple[int, np.ndarray]:
"""
Convert a symusic Score to integer PCM audio data.
Args:
score (Score): symusic Score object
sample_rate (int): desired sample rate in Hz, defaults to 44100
Returns:
tuple[int, np.ndarray]: Tuple of (sample_rate, audio_data as int16)
"""
# Get MIDI bytes and create MIDI object
midi_data = BytesIO(score.dumps_midi())
pm = pretty_midi.PrettyMIDI(midi_data)
# Synthesize to float array first
# float_audio = pm.synthesize(fs=sample_rate)
float_audio = pm.fluidsynth(
fs=sample_rate, sf2_path="./FluidR3_GM.sf2"
)
# Convert to 16-bit integer PCM
# Scale to full int16 range (-32768 to 32767)
int_audio = (float_audio * 32767).astype(np.int16)
int_audio = np.trim_zeros(int_audio, "fb")
return sample_rate, int_audio
device = "cuda" if torch.cuda.is_available() else "cpu"
device = "cpu"
tokenizer = MusicTokenizer.from_pretrained("shikhr/music_maker")
model = AutoModel.from_pretrained("shikhr/music_maker", trust_remote_code=True)
model.to(device)
def generate_music():
# Generate some music
out = model.generate(
torch.tensor([[1]]).to(device), max_new_tokens=200, temperature=1.0, top_k=100
)
# Save the generated MIDI
res = tokenizer(out[0].tolist())
res.dump_midi("output.mid")
nx = score_to_audio(res)
# print(nx)
return "Generated" , nx
demo = gr.Interface(generate_music, inputs=[], outputs=["text", "audio"], flagging_mode="never")
if __name__ == "__main__":
demo.launch()
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