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import os.path
import time as reqtime
import datetime
from pytz import timezone
import torch
import spaces
import gradio as gr
from x_transformer_1_23_2 import *
import random
import tqdm
from midi_to_colab_audio import midi_to_colab_audio
import TMIDIX
import matplotlib.pyplot as plt
in_space = os.getenv("SYSTEM") == "spaces"
# =================================================================================================
@spaces.GPU
def GenerateAccompaniment(input_midi, input_num_tokens, input_conditioning_type, input_strip_notes):
print('=' * 70)
print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
start_time = reqtime.time()
print('Loading model...')
SEQ_LEN = 8192 # Models seq len
PAD_IDX = 707 # Models pad index
DEVICE = 'cuda' # 'cuda'
# instantiate the model
model = TransformerWrapper(
num_tokens = PAD_IDX+1,
max_seq_len = SEQ_LEN,
attn_layers = Decoder(dim = 2048, depth = 4, heads = 16, attn_flash = True)
)
model = AutoregressiveWrapper(model, ignore_index = PAD_IDX)
model.to(DEVICE)
print('=' * 70)
print('Loading model checkpoint...')
model.load_state_dict(
torch.load('Chords_Progressions_Transformer_Small_2048_Trained_Model_12947_steps_0.9316_loss_0.7386_acc.pth',
map_location=DEVICE))
print('=' * 70)
model.eval()
if DEVICE == 'cpu':
dtype = torch.bfloat16
else:
dtype = torch.float16
ctx = torch.amp.autocast(device_type=DEVICE, dtype=dtype)
print('Done!')
print('=' * 70)
fn = os.path.basename(input_midi.name)
fn1 = fn.split('.')[0]
input_num_tokens = max(4, min(128, input_num_tokens))
print('-' * 70)
print('Input file name:', fn)
print('Req num toks:', input_num_tokens)
print('Conditioning type:', input_conditioning_type)
print('Strip notes:', input_strip_notes)
print('-' * 70)
#===============================================================================
raw_score = TMIDIX.midi2single_track_ms_score(input_midi.name)
#===============================================================================
# Enhanced score notes
escore_notes = TMIDIX.advanced_score_processor(raw_score, return_enhanced_score_notes=True)[0]
no_drums_escore_notes = [e for e in escore_notes if e[6] < 80]
if len(no_drums_escore_notes) > 0:
#=======================================================
# PRE-PROCESSING
#===============================================================================
# Augmented enhanced score notes
no_drums_escore_notes = TMIDIX.augment_enhanced_score_notes(no_drums_escore_notes)
cscore = TMIDIX.chordify_score([1000, no_drums_escore_notes])
clean_cscore = []
for c in cscore:
pitches = []
cho = []
for cc in c:
if cc[4] not in pitches:
cho.append(cc)
pitches.append(cc[4])
clean_cscore.append(cho)
#=======================================================
# FINAL PROCESSING
melody_chords = []
chords = []
times = [0]
durs = []
#=======================================================
# MAIN PROCESSING CYCLE
#=======================================================
pe = clean_cscore[0][0]
first_chord = True
for c in clean_cscore:
# Chords
c.sort(key=lambda x: x[4], reverse=True)
tones_chord = sorted(set([cc[4] % 12 for cc in c]))
try:
chord_token = TMIDIX.ALL_CHORDS_SORTED.index(tones_chord)
except:
checked_tones_chord = TMIDIX.check_and_fix_tones_chord(tones_chord)
chord_token = TMIDIX.ALL_CHORDS_SORTED.index(checked_tones_chord)
melody_chords.extend([chord_token+384])
if input_strip_notes:
if len(tones_chord) > 1:
chords.extend([chord_token+384])
else:
chords.extend([chord_token+384])
if first_chord:
melody_chords.extend([0])
first_chord = False
for e in c:
#=======================================================
# Timings...
time = e[1]-pe[1]
dur = e[2]
if time != 0 and time % 2 != 0:
time += 1
if dur % 2 != 0:
dur += 1
delta_time = int(max(0, min(255, time)) / 2)
# Durations
dur = int(max(0, min(255, dur)) / 2)
# Pitches
ptc = max(1, min(127, e[4]))
#=======================================================
# FINAL NOTE SEQ
# Writing final note asynchronously
if delta_time != 0:
melody_chords.extend([delta_time, dur+128, ptc+256])
if input_strip_notes:
if len(c) > 1:
times.append(delta_time)
durs.append(dur+128)
else:
times.append(delta_time)
durs.append(dur+128)
else:
melody_chords.extend([dur+128, ptc+256])
pe = e
#==================================================================
print('=' * 70)
print('Sample output events', melody_chords[:5])
print('=' * 70)
print('Generating...')
output = []
max_chords_limit = 8
temperature=0.9
num_memory_tokens=4096
output = []
idx = 0
for c in chords[:input_num_tokens]:
output.append(c)
if input_conditioning_type == 'Chords-Times' or input_conditioning_type == 'Chords-Times-Durations':
output.append(times[idx])
if input_conditioning_type == 'Chords-Times-Durations':
output.append(durs[idx])
x = torch.tensor([output] * 1, dtype=torch.long, device='cuda')
o = 0
ncount = 0
while o < 384 and ncount < max_chords_limit:
with ctx:
out = model.generate(x[-num_memory_tokens:],
1,
temperature=temperature,
return_prime=False,
verbose=False)
o = out.tolist()[0][0]
if 256 <= o < 384:
ncount += 1
if o < 384:
x = torch.cat((x, out), 1)
outy = x.tolist()[0][len(output):]
output.extend(outy)
idx += 1
if idx == len(chords[:input_num_tokens])-1:
break
print('=' * 70)
print('Done!')
print('=' * 70)
#===============================================================================
print('Rendering results...')
print('=' * 70)
print('Sample INTs', output[:12])
print('=' * 70)
out1 = output
if len(out1) != 0:
song = out1
song_f = []
time = 0
dur = 0
vel = 90
pitch = 0
channel = 0
patches = [0] * 16
channel = 0
for ss in song:
if 0 <= ss < 128:
time += ss * 32
if 128 <= ss < 256:
dur = (ss-128) * 32
if 256 <= ss < 384:
pitch = (ss-256)
vel = max(40, pitch)
song_f.append(['note', time, dur, channel, pitch, vel, 0])
fn1 = "Chords-Progressions-Transformer-Composition"
detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,
output_signature = 'Chords Progressions 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:', '')
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("<h1 style='text-align: center; margin-bottom: 1rem'>Melody2Song Seq2Seq Music Transformer</h1>")
gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Generate unique songs from melodies with se2seq music transformer</h1>")
gr.Markdown(
"\n\n")
input_midi = gr.File(label="Input MIDI", file_types=[".midi", ".mid", ".kar"])
input_num_tokens = gr.Slider(4, 128, value=32, step=1, label="Number of composition chords to generate progression for")
input_conditioning_type = gr.Radio(["Chords", "Chords-Times", "Chords-Times-Durations"], label="Conditioning type")
input_strip_notes = gr.Checkbox(label="Strip notes from the composition")
run_btn = gr.Button("generate", 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(GenerateAccompaniment, [input_midi, input_num_tokens, input_conditioning_type, input_strip_notes],
[output_midi_title, output_midi_summary, output_midi, output_audio, output_plot])
app.queue().launch() |