#=========================================================================
# https://huggingface.co./spaces/asigalov61/Score-2-Performance-Transformer
#=========================================================================
import os
import time as reqtime
import datetime
from pytz import timezone
import copy
from itertools import groupby
import tqdm
import spaces
import gradio as gr
import torch
from x_transformer_1_23_2 import *
import random
import TMIDIX
from midi_to_colab_audio import midi_to_colab_audio
from huggingface_hub import hf_hub_download
# =================================================================================================
print('Loading model...')
SEQ_LEN = 1802
PAD_IDX = 771
DEVICE = 'cuda' # 'cpu'
# instantiate the model
model = TransformerWrapper(
num_tokens = PAD_IDX+1,
max_seq_len = SEQ_LEN,
attn_layers = Decoder(dim = 1024,
depth = 8,
heads = 8,
rotary_pos_emb=True,
attn_flash = True
)
)
model = AutoregressiveWrapper(model, ignore_index = PAD_IDX)
print('=' * 70)
print('Loading model checkpoint...')
model_checkpoint = hf_hub_download(repo_id='asigalov61/Score-2-Performance-Transformer',
filename='Score_2_Performance_Transformer_Final_Small_Trained_Model_4496_steps_1.5185_loss_0.5589_acc.pth'
)
model.load_state_dict(torch.load(model_checkpoint, map_location='cpu', weights_only=True))
model = torch.compile(model, mode='max-autotune')
dtype = torch.bfloat16
ctx = torch.amp.autocast(device_type=DEVICE, dtype=dtype)
print('=' * 70)
print('Done!')
print('=' * 70)
# =================================================================================================
def load_midi(midi_file):
print('Loading MIDI...')
raw_score = TMIDIX.midi2single_track_ms_score(midi_file)
escore_notes = TMIDIX.advanced_score_processor(raw_score, return_enhanced_score_notes=True)
if escore_notes[0]:
escore_notes = TMIDIX.augment_enhanced_score_notes(escore_notes[0], timings_divider=16)
pe = escore_notes[0]
melody_chords = []
seen = []
for e in escore_notes:
if e[3] != 9:
#=======================================================
dtime = max(0, min(255, e[1]-pe[1]))
if dtime != 0:
seen = []
# Durations
dur = max(1, min(255, e[2]))
# Pitches
ptc = max(1, min(127, e[4]))
vel = max(1, min(127, e[5]))
if ptc not in seen:
melody_chords.append([dtime, dur, ptc, vel])
seen.append(ptc)
pe = e
print('=' * 70)
print('Number of notes in a composition:', len(melody_chords))
print('=' * 70)
src_melody_chords_f = []
for i in range(0, len(melody_chords), 150):
chunk = melody_chords[i:i+300]
src = []
for mm in chunk:
src.append([mm[0], mm[2]+256, mm[1]+384, mm[3]+640])
clen = len(src)
if clen < 300:
chunk_mult = (300 // clen) + 1
src += src * chunk_mult
src_melody_chords_f.append([clen, src[:300]])
print('Done!')
print('=' * 70)
print('Number of composition chunks:', len(src_melody_chords_f))
print('=' * 70)
return src_melody_chords_f
# =================================================================================================
@spaces.GPU
def Convert_Score_to_Performance(input_midi,
input_midi_type,
input_conv_type,
input_number_prime_notes,
input_number_conv_notes,
input_model_dur_top_k,
input_model_dur_temperature,
input_model_vel_temperature
):
#===============================================================================
print('=' * 70)
print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
start_time = reqtime.time()
print('=' * 70)
fn = os.path.basename(input_midi)
fn1 = fn.split('.')[0]
print('=' * 70)
print('Requested settings:')
print('=' * 70)
print('Input MIDI file name:', fn)
print('Input MIDI type:', input_midi_type)
print('Conversion type:', input_conv_type)
print('Number of prime notes:', input_number_prime_notes)
print('Number of notes to convert:', input_number_conv_notes)
print('Model durations sampling top value:', input_model_dur_top_k)
print('Model durations temperature:', input_model_dur_temperature)
print('Model velocities temperature:', input_model_vel_temperature)
print('=' * 70)
#==================================================================
src_melody_chords_f = load_midi(input_midi.name)
#==================================================================
print('Sample output events', src_melody_chords_f[0][1][:3])
print('=' * 70)
print('Generating...')
model.to(DEVICE)
model.eval()
#==================================================================
num_prime_notes = input_number_prime_notes # Priming improves the results but it is not necessary and you can set it to zero
dur_top_k = input_model_dur_top_k # Use k == 1 if src composition is score and k > 1 if src composition is performance
dur_temperature = input_model_dur_temperature # For best results, durations temperature should be more than 1.0 but less than velocities temperature
vel_temperature = input_model_vel_temperature # For best results, velocities temperature must be larger than 1.3 and larger than durations temperature
#==================================================================
if input_midi_type == 'Score':
dur_top_k = 1
dur_temperature = 1.1
vel_temperature = 1.5
elif input_midi_type == 'Performance':
dur_top_k = 100
dur_temperature = 1.5
vel_temperature = 1.9
else:
dur_top_k = input_model_dur_top_k # Use k == 1 if src composition is score and k > 1 if src composition is performance
dur_temperature = input_model_dur_temperature # For best results, durations temperature should be more than 1.0 but less than velocities temperature
vel_temperature = input_model_vel_temperature
final_song = []
for cc, (song_chunk_len, song_chunk) in enumerate(src_melody_chords_f):
print('=' * 70)
print('Rendering song chunk #', cc)
print('=' * 70)
#========================================================================
song = [768]
if cc == 0:
for m in song_chunk:
song.extend(m[:2])
song.append(769)
sidx = 0
eidx = 300
else:
for m in song_chunk[:150]:
psrc.extend(m[:2])
psrc.append(769)
song = copy.deepcopy(psrc + ptrg)
sidx = 150
eidx = 300
#========================================================================
for i in tqdm.tqdm(range(sidx, eidx)):
song.extend(song_chunk[i][:2])
if 'Durations' in input_conv_type:
if i < num_prime_notes and cc == 0:
song.append(song_chunk[i][2])
else:
# Durations
x = torch.LongTensor(song).cuda()
y = 0
while not 384 < y < 640:
with ctx:
out = model.generate(x,
1,
temperature=dur_temperature,
filter_logits_fn=top_k,
filter_kwargs={'k': dur_top_k},
return_prime=False,
verbose=False)
y = out.tolist()[0][0]
song.append(y)
else:
song.append(song_chunk[i][2])
#========================================================================
if 'Velocities' in input_conv_type:
if i < num_prime_notes and cc == 0:
song.append(song_chunk[i][3])
else:
# Velocities
x = torch.LongTensor(song).cuda()
y = 0
while not 640 < y < 768:
with ctx:
out = model.generate(x,
1,
temperature=vel_temperature,
return_prime=False,
verbose=False)
y = out.tolist()[0][0]
song.append(y)
else:
song.append(song_chunk[i][3])
#========================================================================
if cc == 0:
final_song.extend(song[602:][:(song_chunk_len * 4)])
else:
final_song.extend(song[602:][600:(song_chunk_len * 4)])
psrc = copy.deepcopy(song[1:301])
ptrg = copy.deepcopy(song[602:][:600])
#========================================================================
if len(final_song) >= input_number_conv_notes * 4:
break
#========================================================================
print('=' * 70)
print('Done!')
print('=' * 70)
#===============================================================================
print('Rendering results...')
print('=' * 70)
print('Sample INTs', final_song[:15])
print('=' * 70)
song_f = []
if len(final_song) != 0:
time = 0
dur = 0
vel = 90
pitch = 60
channel = 0
patch = 0
patches = [0] * 16
for ss in final_song:
if 0 <= ss < 256:
time += ss * 16
if 256 <= ss < 384:
pitch = ss-256
if 384 <= ss < 640:
dur = (ss-384) * 16
if 640 <= ss < 768:
vel = (ss-640)
song_f.append(['note', time, dur, channel, pitch, vel, patch])
fn1 = "Score-2-Performance-Transformer-Composition"
detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,
output_signature = 'Score 2 Performance Transformer',
output_file_name = fn1,
track_name='Project Los Angeles',
list_of_MIDI_patches=patches
)
new_fn = fn1+'.mid'
audio = midi_to_colab_audio(new_fn,
soundfont_path=soundfont,
sample_rate=16000,
volume_scale=10,
output_for_gradio=True
)
print('Done!')
print('=' * 70)
#========================================================
output_midi_title = str(fn1)
output_midi_summary = str(song_f[:3])
output_midi = str(new_fn)
output_audio = (16000, audio)
output_plot = TMIDIX.plot_ms_SONG(song_f, plot_title=output_midi, return_plt=True)
print('Output MIDI file name:', output_midi)
print('Output MIDI title:', output_midi_title)
print('Output MIDI summary:', output_midi_summary)
print('=' * 70)
#========================================================
print('-' * 70)
print('Req end time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
print('-' * 70)
print('Req execution time:', (reqtime.time() - start_time), 'sec')
return output_midi_title, output_midi_summary, output_midi, output_audio, output_plot
# =================================================================================================
if __name__ == "__main__":
PDT = timezone('US/Pacific')
print('=' * 70)
print('App start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
print('=' * 70)
soundfont = "SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2"
app = gr.Blocks()
with app:
gr.Markdown("Score 2 Performance Transformer
")
gr.Markdown("Convert any MIDI score to a nice performance
")
gr.Markdown("## Upload your MIDI or select a sample example MIDI below")
input_midi = gr.File(label="Input MIDI", file_types=[".midi", ".mid", ".kar"])
gr.Markdown("## Select MIDI type")
input_midi_type = gr.Radio(["Score", "Performance", "Custom"],
value="Score",
label="Input MIDI type",
info="Select 'Custom' option to enable model top_k and temperature settings below"
)
gr.Markdown("## Select conversion type")
input_conv_type = gr.Radio(["Durations and Velocities", "Durations", "Velocities"],
value="Durations and Velocities",
label="Conversion type"
)
gr.Markdown("## Conversion options")
input_number_prime_notes = gr.Slider(0, 512, value=0, step=8, label="Number of prime notes")
input_number_conv_notes = gr.Slider(8, 2048, value=512, step=8, label="Number of notes to convert")
gr.Markdown("## Custom MIDI type model options")
input_model_dur_top_k = gr.Slider(1, 100, value=1, step=1, label="Model sampling top k value for durations")
input_model_dur_temperature = gr.Slider(0.5, 1.5, value=1.1, step=0.05, label="Model temperature for durations")
input_model_vel_temperature = gr.Slider(0.5, 1.5, value=1.5, step=0.05, label="Model temperature for velocities")
run_btn = gr.Button("convert", variant="primary")
gr.Markdown("## Generation results")
output_midi_title = gr.Textbox(label="Output MIDI title")
output_midi_summary = gr.Textbox(label="Output MIDI summary")
output_audio = gr.Audio(label="Output MIDI audio", format="wav", elem_id="midi_audio")
output_plot = gr.Plot(label="Output MIDI score plot")
output_midi = gr.File(label="Output MIDI file", file_types=[".mid"])
run_event = run_btn.click(Convert_Score_to_Performance, [input_midi,
input_midi_type,
input_conv_type,
input_number_prime_notes,
input_number_conv_notes,
input_model_dur_top_k,
input_model_dur_temperature,
input_model_vel_temperature
],
[output_midi_title, output_midi_summary, output_midi, output_audio, output_plot])
gr.Examples(
[["asap_midi_score_21.mid", "Score", "Durations and Velocities", 8, 600, 1, 1.1, 1.5],
["asap_midi_score_45.mid", "Score", "Durations and Velocities", 8, 600, 1, 1.1, 1.5],
["asap_midi_score_69.mid", "Score", "Durations and Velocities", 8, 600, 1, 1.1, 1.5],
["asap_midi_score_118.mid", "Score", "Durations and Velocities", 8, 600, 1, 1.1, 1.5],
["asap_midi_score_167.mid", "Score", "Durations and Velocities", 8, 600, 1, 1.1, 1.5],
],
[input_midi,
input_midi_type,
input_conv_type,
input_number_prime_notes,
input_number_conv_notes,
input_model_dur_top_k,
input_model_dur_temperature,
input_model_vel_temperature
],
[output_midi_title, output_midi_summary, output_midi, output_audio, output_plot],
Convert_Score_to_Performance
)
app.queue().launch()