Ultimate-Drums-Transformer / app_onnx_gpu.py
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Rename app.py to app_onnx_gpu.py
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import argparse
import glob
import os.path
import torch
import torch.nn.functional as F
import gradio as gr
import onnxruntime as rt
import tqdm
from midi_synthesizer import synthesis
import TMIDIX
import matplotlib.pyplot as plt
in_space = os.getenv("SYSTEM") == "spaces"
#=================================================================================================
@torch.no_grad()
def GenerateMIDI(idrums, iinstr, progress=gr.Progress()):
if idrums:
drums = 3074
else:
drums = 3073
instruments_list = ["Piano", "Guitar", "Bass", "Violin", "Cello", "Harp", "Trumpet", "Sax", "Flute", 'Drums', "Choir", "Organ"]
first_note_instrument_number = instruments_list.index(iinstr)
start_tokens = [3087, drums, 3075+first_note_instrument_number]
seq_len = 512
max_seq_len = 2048
temperature = 0.9
verbose=False
return_prime=False
out = torch.FloatTensor([start_tokens])
st = len(start_tokens)
if verbose:
print("Generating sequence of max length:", seq_len)
progress(0, desc="Starting...")
step = 0
for i in progress.tqdm(range(seq_len)):
try:
x = out[:, -max_seq_len:]
torch_in = x.tolist()[0]
logits = torch.FloatTensor(session.run(None, {'input': [torch_in]})[0])[:, -1]
probs = F.softmax(logits / temperature, dim=-1)
sample = torch.multinomial(probs, 1)
out = torch.cat((out, sample), dim=-1)
if step % 16 == 0:
print(step, '/', seq_len)
step += 1
if step >= seq_len:
break
except Exception as e:
print('Error', e)
break
if return_prime:
melody_chords_f = out[:, :]
else:
melody_chords_f = out[:, st:]
melody_chords_f = melody_chords_f.tolist()[0]
print('=' * 70)
print('Sample INTs', melody_chords_f[:12])
print('=' * 70)
if len(melody_chords_f) != 0:
song = melody_chords_f
song_f = []
time = 0
dur = 0
vel = 0
pitch = 0
channel = 0
for ss in song:
ss1 = int(ss)
if ss1 > 0 and ss1 < 256:
time += ss1 * 8
if ss1 >= 256 and ss1 < 1280:
dur = ((ss1-256) // 8) * 32
vel = (((ss1-256) % 8)+1) * 15
if ss1 >= 1280 and ss1 < 2816:
channel = (ss1-1280) // 128
pitch = (ss1-1280) % 128
song_f.append(['note', int(time), int(dur), int(channel), int(pitch), int(vel) ])
output_signature = 'Allegro Music Transformer'
output_file_name = 'Allegro-Music-Transformer-Music-Composition'
track_name='Project Los Angeles'
list_of_MIDI_patches=[0, 24, 32, 40, 42, 46, 56, 71, 73, 0, 53, 19, 0, 0, 0, 0]
number_of_ticks_per_quarter=500
text_encoding='ISO-8859-1'
output_header = [number_of_ticks_per_quarter,
[['track_name', 0, bytes(output_signature, text_encoding)]]]
patch_list = [['patch_change', 0, 0, list_of_MIDI_patches[0]],
['patch_change', 0, 1, list_of_MIDI_patches[1]],
['patch_change', 0, 2, list_of_MIDI_patches[2]],
['patch_change', 0, 3, list_of_MIDI_patches[3]],
['patch_change', 0, 4, list_of_MIDI_patches[4]],
['patch_change', 0, 5, list_of_MIDI_patches[5]],
['patch_change', 0, 6, list_of_MIDI_patches[6]],
['patch_change', 0, 7, list_of_MIDI_patches[7]],
['patch_change', 0, 8, list_of_MIDI_patches[8]],
['patch_change', 0, 9, list_of_MIDI_patches[9]],
['patch_change', 0, 10, list_of_MIDI_patches[10]],
['patch_change', 0, 11, list_of_MIDI_patches[11]],
['patch_change', 0, 12, list_of_MIDI_patches[12]],
['patch_change', 0, 13, list_of_MIDI_patches[13]],
['patch_change', 0, 14, list_of_MIDI_patches[14]],
['patch_change', 0, 15, list_of_MIDI_patches[15]],
['track_name', 0, bytes(track_name, text_encoding)]]
output = output_header + [patch_list + song_f]
midi_data = TMIDIX.score2midi(output, text_encoding)
with open(f"Allegro-Music-Transformer-Music-Composition.mid", 'wb') as f:
f.write(midi_data)
output1 = []
itrack = 1
opus = TMIDIX.score2opus(output)
while itrack < len(opus):
for event in opus[itrack]:
if (event[0] == 'note_on') or (event[0] == 'note_off'):
output1.append(event)
itrack += 1
audio = synthesis([500, output1], 'SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2')
x = []
y =[]
c = []
colors = ['red', 'yellow', 'green', 'cyan', 'blue', 'pink', 'orange', 'purple', 'gray', 'white', 'gold', 'silver']
for s in song_f:
x.append(s[1] / 1000)
y.append(s[4])
c.append(colors[s[3]])
plt.figure(figsize=(14,5))
ax=plt.axes(title='Allegro Music Transformer')
ax.set_facecolor('black')
plt.scatter(x,y, c=c)
plt.xlabel("Time")
plt.ylabel("Pitch")
yield [500, output1], plt, "Allegro-Music-Transformer-Music-Composition.mid", (44100, audio)
#=================================================================================================
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
parser.add_argument("--port", type=int, default=7860, help="gradio server port")
opt = parser.parse_args()
print('Loading model...')
session = rt.InferenceSession('Allegro_Music_Transformer_Small_Trained_Model_56000_steps_0.9399_loss_0.7374_acc.onnx', providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
print('Done!')
app = gr.Blocks()
with app:
gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Allegro Music Transformer</h1>")
gr.Markdown("![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.Allegro-Music-Transformer&style=flat)\n\n"
"Full-attention multi-instrumental music transformer featuring asymmetrical encoding with octo-velocity, and chords counters tokens, optimized for speed and performance\n\n"
"Check out [Allegro Music Transformer](https://github.com/asigalov61/Allegro-Music-Transformer) on GitHub!\n\n"
"[Open In Colab]"
"(https://colab.research.google.com/github/asigalov61/Allegro-Music-Transformer/blob/main/Allegro_Music_Transformer_Composer.ipynb)"
" for faster execution and endless generation"
)
input_drums = gr.Checkbox(label="Drums Controls", value = True, info="Drums present or not")
input_instrument = gr.Radio(["Piano", "Guitar", "Bass", "Violin", "Cello", "Harp", "Trumpet", "Sax", "Flute", "Choir", "Organ"], value="Guitar", label="Lead Instrument Controls", info="Desired lead instrument")
run_btn = gr.Button("generate", variant="primary")
output_midi_seq = gr.Variable()
output_audio = gr.Audio(label="output audio", format="mp3", elem_id="midi_audio")
output_plot = gr.Plot(label="output plot")
output_midi = gr.File(label="output midi", file_types=[".mid"])
run_event = run_btn.click(GenerateMIDI, [input_drums, input_instrument], [output_midi_seq, output_plot, output_midi, output_audio])
app.queue(concurrency_count=1).launch(server_port=opt.port, share=opt.share, inbrowser=True)