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import subprocess
import sys
#update the system
subprocess.check_call(["apt-get", "update"])
subprocess.check_call(["apt-get", "install", "timidity", "-y"])
subprocess.check_call([sys.executable,"-m","pip","install", 'torch'])
# subprocess.check_call([sys.executable,"-m","pip","install",'causal-conv1d'])
subprocess.check_call([sys.executable, "-m", "pip", "install", 'numpy', 'miditok','transformers','gradio'])
# !pip install pretty_midi midi2audio
# !pip install miditok
# !apt-get install fluidsynth
# !apt install timidity -y
# !pip install causal-conv1d>=1.1.0
# !pip install mamba-ssm
# !pip install gradio
# !export LC_ALL="en_US.UTF-8"
# !export LD_LIBRARY_PATH="/usr/lib64-nvidia"
# !export LIBRARY_PATH="/usr/local/cuda/lib64/stubs"
# subprocess.check_call(['export', 'LC_ALL="en_US.UTF-8"'])
# subprocess.check_call(['export', 'LD_LIBRARY_PATH="/usr/lib64-nvidia"'])
# subprocess.check_call(['export', 'LIBRARY_PATH="/usr/local/cuda/lib64/stubs"'])
import os
from transformers import MambaConfig, MambaForCausalLM
import gradio as gr
import torch
import numpy as np
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model = MambaForCausalLM.from_pretrained("krystv/MIDI_Mamba-159M")
mc = MambaConfig()
mc.d_model = 768
mc.n_layer = 42
mc.vocab_size = 1536
from miditok import MIDILike,REMI,TokenizerConfig
from pathlib import Path
import torch
if os.path.isfile("tokenizer_1536mix_BPE.json") == False:
subprocess.check_call(['wget', 'https://huggingface.co/krystv/MIDI_Mamba-159M/resolve/main/tokenizer_1536mix_BPE.json'])
tokenizer = REMI(params='tokenizer_1536mix_BPE.json')
if torch.cuda.is_available():
model= model.to(device)
twitter_follow_link = "https://twitter.com/iamhemantindia"
instagram_follow_link = "https://instagram.com/iamhemantindia"
custom_html = f"""
<div style='text-align: center;'>
<a href="{twitter_follow_link}" target="_blank" style="margin-right: 5px;">
<img src="https://img.icons8.com/fluent/24/000000/twitter.png" alt="Follow on Twitter"/>
</a>
<a href="{instagram_follow_link}" target="_blank">
<img src="https://img.icons8.com/fluent/24/000000/instagram-new.png" alt="Follow on Instagram"/>
</a>
</div>
"""
def generate(number,top_k_selector,top_p_selector, temperature_selector):
input_ids = torch.tensor([[1,]])
if torch.cuda.is_available():
input_ids = input_ids.to(device)
out = model.generate(
input_ids=input_ids,
do_sample=True,
max_length=int(number),
temperature=temperature_selector,
top_p=top_p_selector,
top_k=top_k_selector,
eos_token_id=2,)
m = tokenizer.decode(np.array(out[0].cpu()))
m.dump_midi('output.mid')
# !timidity output.mid -Ow -o - | ffmpeg -y -f wav -i - output.mp3
timidity_cmd = ['timidity', 'output.mid', '-Ow', '-o', 'output.wav']
subprocess.check_call(timidity_cmd)
# Then convert the WAV to MP3 using ffmpeg
ffmpeg_cmd = ['ffmpeg', '-y', '-f', 'wav', '-i', 'output.wav', 'output.mp3']
subprocess.check_call(ffmpeg_cmd)
return "output.mp3"
# text_box = gr.Textbox(label="Enter Text")
def generate_and_save(number,top_k_selector,top_p_selector, temperature_selector,generate_button,custom_html_wid):
output_audio = generate(number,top_k_selector,top_p_selector, temperature_selector)
return gr.Audio(output_audio,autoplay=True),gr.File(label="Download MIDI",value="output.mid"),generate_button
with gr.Blocks() as b1:
gr.Markdown("<h1 style='text-align: center;'>MIDI Mamba-159M <h1/> ")
gr.Markdown("<h3 style='text-align: center;'>MIDI Mamba is a Mamba based model trained on MIDI data collected from open internet to train music model. Current accuracy is performance is decreased due to model conversion make sure to checkout the <a href='https://colab.research.google.com/github/HemantKArya/MIDI_Mamba/blob/main/MIDI_Mamba.ipynb'>colab notebook<a/> <br> by Hemant Kumar<h3/>")
with gr.Row():
with gr.Column():
number_selector = gr.Number(label="Select Length of output",value=512)
top_p_selector = gr.Slider(label="Select Top P", minimum=0, maximum=1.0, step=0.05, value=0.9)
temperature_selector = gr.Slider(label="Select Temperature", minimum=0, maximum=1.0, step=0.1, value=0.9)
top_k_selector = gr.Slider(label="Select Top K", minimum=1, maximum=1536, step=1, value=30)
generate_button = gr.Button(value="Generate",variant="primary")
custom_html_wid = gr.HTML(custom_html)
with gr.Column():
output_box = gr.Audio("output.mp3",autoplay=True,)
download_midi_button = gr.File(label="Download MIDI")
generate_button.click(generate_and_save,inputs=[number_selector,top_k_selector,top_p_selector, temperature_selector,generate_button,custom_html_wid],outputs=[output_box,download_midi_button,generate_button])
b1.launch(share=True) |