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import gradio as gr
import json, urllib
from model import GPT, GPTConfig
from utils import sample
import torch
import pickle
device = torch.device('cpu')
# Create the model
vocab_size=147
block_size=128
mconf = GPTConfig(vocab_size, block_size,
n_layer=6, n_head=8, n_embd=256)
model = GPT(mconf)
# Load checkpoint
model.load_state_dict(torch.load('another_epoch_1.75total.ckpt', map_location=device))
# Vocab
stoi = pickle.load(open('stoi.pkl', 'rb'))
itos = pickle.load(open('itos.pkl', 'rb'))
# Post-process generation
# Completion
def completion_to_song(c):
lines = c.split('\n')
kept_lines = []
notes = False
for l in lines:
# Record if we've hit music
if '|' in l:
notes = True
# Stop if we then go back to the start of another song
if 'T' in l and notes:
break
if 'T' in l and notes:
break
# Stop on an empty line
if len(l.strip()) < 2 and notes:
break
# Otherwise keep the line
kept_lines.append(l)
return '\n'.join(kept_lines)
# Generate function
def generate_song(randomize, title, nu, ks, key):
# Start sequence
context = b"""T:"""
if not randomize:
context += bytes(title+'\n', 'utf-8')
context += bytes('M:'+ks+'\n', 'utf-8')
context += bytes('K:'+key+'\n', 'utf-8')
context += bytes('L:'+nu+'\n', 'utf-8')
# Model inputs
x = torch.tensor([stoi[s] for s in context], dtype=torch.long)[None,...].to(device)
# Completion
y = sample(model, x, 400, temperature=1.0, sample=True, top_k=10)[0]
completion = ''.join([chr(itos[int(i)]) for i in y])
# Return the first song
song = completion_to_song(completion)
html_song = song.replace('\n', '<br>')
url_song = urllib.parse.quote(song, safe='~@#$&()*!+=:;,?/\'')
html_text = '<p><a href="https://editor.drawthedots.com?t='+url_song+'" target="_blank"><b>EDIT LINK - click to open abcjs editor (allows download and playback)</b></a></p>'+"<p>"+html_song+'</p>'
return html_text
# Gradio demo
demo = gr.Blocks()
with demo:
gr.Markdown("Quick demo for [WhistleGen v2](https://wandb.ai/johnowhitaker/whistlegen_v2/reports/WhistleGen-v2--VmlldzoyMTAwNjAz) which lets you generate folk music using a transformer model. I can't get the javascript needed for rendering and playback working with gradio, so this shows the raw ABC notation from the model and a link to view it properly in an external editor.")
with gr.Row():
title = gr.Text(label='Title', value='The March of AI')
with gr.Column():
nu = gr.Text(label='Note unit', value='1/8')
with gr.Row():
key_signature = gr.Dropdown(['3/4', '4/4', '6/8', 'Random'], value='4/4', label='Time Signature')
with gr.Column():
key = gr.Text(label='Key', value='D')
with gr.Row():
randomize = gr.Checkbox(label='Randomize (ignores settings above)', value=True)
with gr.Row():
out = gr.HTML(label="Output", value='Output should appear here (takes ~30s)')
btn = gr.Button("Run")
btn.click(fn=generate_song, inputs=[randomize, title, nu, key_signature, key], outputs=out)
with gr.Row():
gr.Markdown("")
gr.Markdown("This is currently using an early model. See the [report](https://wandb.ai/johnowhitaker/whistlegen_v2/reports/WhistleGen-v2--VmlldzoyMTAwNjAz) for training info and updates.")
demo.launch(enable_queue=True) |