app.py

#==================================================================================
# https://huggingface.co/spaces/asigalov61/Guided-Accompaniment-Transformer
#==================================================================================

print('=' * 70)
print('Guided Accompaniment Transformer Gradio App')

print('=' * 70)
print('Loading core Guided Accompaniment Transformer modules...')

import os
import copy

import time as reqtime
import datetime
from pytz import timezone

print('=' * 70)
print('Loading main Guided Accompaniment Transformer modules...')

os.environ['USE_FLASH_ATTENTION'] = '1'

import torch

torch.set_float32_matmul_precision('medium')
torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul
torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn
torch.backends.cuda.enable_mem_efficient_sdp(True)
torch.backends.cuda.enable_math_sdp(True)
torch.backends.cuda.enable_flash_sdp(True)
torch.backends.cuda.enable_cudnn_sdp(True)

from huggingface_hub import hf_hub_download

import TMIDIX

from midi_to_colab_audio import midi_to_colab_audio

from x_transformer_1_23_2 import *

import random

import tqdm

print('=' * 70)
print('Loading aux Guided Accompaniment Transformer modules...')

import matplotlib.pyplot as plt

import gradio as gr
import spaces

print('=' * 70)
print('PyTorch version:', torch.__version__)
print('=' * 70)
print('Done!')
print('Enjoy! :)')
print('=' * 70)

#==================================================================================

MODEL_CHECKPOINT = 'Guided_Accompaniment_Transformer_Trained_Model_36457_steps_0.5384_loss_0.8417_acc.pth'

SOUDFONT_PATH = 'SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2'

#==================================================================================

print('=' * 70)
print('Instantiating model...')

device_type = 'cuda'
dtype = 'bfloat16'

ptdtype = {'bfloat16': torch.bfloat16, 'float16': torch.float16}[dtype]
ctx = torch.amp.autocast(device_type=device_type, dtype=ptdtype)

SEQ_LEN = 4096
PAD_IDX = 1794

model = TransformerWrapper(
        num_tokens = PAD_IDX+1,
        max_seq_len = SEQ_LEN,
        attn_layers = Decoder(dim = 2048,
                              depth = 4,
                              heads = 32,
                              rotary_pos_emb = True,
                              attn_flash = True
                              )
)

model = AutoregressiveWrapper(model, ignore_index=PAD_IDX, pad_value=PAD_IDX)

print('=' * 70)
print('Loading model checkpoint...')      

model_checkpoint = hf_hub_download(repo_id='asigalov61/Guided-Accompaniment-Transformer', filename=MODEL_CHECKPOINT)

model.load_state_dict(torch.load(model_checkpoint, map_location='cpu', weights_only=True))

model = torch.compile(model, mode='max-autotune')

print('=' * 70)
print('Done!')
print('=' * 70)
print('Model will use', dtype, 'precision...')
print('=' * 70)

#==================================================================================

def load_midi(input_midi, melody_patch=-1, use_nth_note=1):

    raw_score = TMIDIX.midi2single_track_ms_score(input_midi)

    escore_notes = TMIDIX.advanced_score_processor(raw_score, return_enhanced_score_notes=True)[0]
    escore_notes = TMIDIX.augment_enhanced_score_notes(escore_notes, timings_divider=32)
    
    sp_escore_notes = TMIDIX.solo_piano_escore_notes(escore_notes, keep_drums=False)

    if melody_patch == -1:
        zscore = TMIDIX.recalculate_score_timings(sp_escore_notes)

    else:
        mel_score = [e for e in sp_escore_notes if e[6] == melody_patch]

        if mel_score:
            zscore = TMIDIX.recalculate_score_timings(mel_score)

        else:
            zscore = TMIDIX.recalculate_score_timings(sp_escore_notes)
    
    cscore = TMIDIX.chordify_score([1000, zscore])[::use_nth_note]
    
    score = []
    
    score_list = []
    
    pc = cscore[0]
    
    for c in cscore:
        score.append(max(0, min(127, c[0][1]-pc[0][1])))
    
        scl = [[max(0, min(127, c[0][1]-pc[0][1]))]]
    
        n = c[0]
        
        score.extend([max(1, min(127, n[2]))+128, max(1, min(127, n[4]))+256])
        scl.append([max(1, min(127, n[2]))+128, max(1, min(127, n[4]))+256])
    
        score_list.append(scl)
    
        pc = c
    
    score_list.append(scl)

    return score, score_list

#==================================================================================

@spaces.GPU
def Generate_Accompaniment(input_midi, 
                           generation_type,
                           melody_patch,
                           use_nth_note,
                           model_temperature
                          ):

    #===============================================================================

    def generate_full_seq(input_seq, temperature=0.9, verbose=True):
    
        seq_abs_run_time = sum([t for t in input_seq if t < 128])
    
        cur_time = 0
    
        full_seq = copy.deepcopy(input_seq)
    
        toks_counter = 0
    
        while cur_time <= seq_abs_run_time:
    
            if verbose:
                if toks_counter % 128 == 0:
                    print('Generated', toks_counter, 'tokens')
    
            x = torch.LongTensor(full_seq).cuda()
    
            with ctx:
                out = model.generate(x,
                                     1,
                                     temperature=temperature,
                                     return_prime=False,
                                     verbose=False)
            
            y = out.tolist()[0][0]
    
            if y < 128:
                cur_time += y
    
            full_seq.append(y)
    
            toks_counter += 1
    
        return full_seq

    #===============================================================================

    def generate_block_seq(input_seq, trg_dtime, temperature=0.9):
    
        inp_seq = copy.deepcopy(input_seq)
    
        block_seq = []
    
        cur_time = 0
    
        while cur_time < trg_dtime:
    
            x = torch.LongTensor(inp_seq).cuda()
            
            with ctx:
                out = model.generate(x,
                                     1,
                                     temperature=temperature,
                                     return_prime=False,
                                     verbose=False)
            
            y = out.tolist()[0][0]
    
            if y < 128:
                cur_time += y
    
            inp_seq.append(y)
            block_seq.append(y)
    
        if cur_time != trg_dtime:
            return []
    
        else:
            return block_seq

    #===============================================================================
    
    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('Generation type:', generation_type)
    print('Source melody patch:', melody_patch)
    print('Use nth melody note:', use_nth_note)
    print('Model temperature:', model_temperature)
   
    print('=' * 70)

    #==================================================================

    score, score_list = load_midi(input_midi.name, melody_patch, use_nth_note)

    print('Sample score events', score[:12])

    #==================================================================
    
    print('=' * 70)
    print('Generating...')

    model.to(device_type)
    model.eval()

    #==================================================================

    start_score_seq = [1792] + score + [1793]

    #==================================================================

    if generation_type == 'Guided':

        input_seq = []

        input_seq.extend(start_score_seq)
        input_seq.extend(score_list[0][0])
        
        block_seq_lens = []
        
        idx = 0
        
        max_retries = 3
        mrt = 0
        
        while idx < len(score_list)-1:
        
            if idx % 10 == 0:
                print('Generating', idx, 'block')
        
            input_seq.extend(score_list[idx][1])
        
            block_seq = []
        
            for _ in range(max_retries):
        
                block_seq = generate_block_seq(input_seq, score_list[idx+1][0][0])
        
                if block_seq:
                    break
        
            if block_seq:
                input_seq.extend(block_seq)
                block_seq_lens.append(len(block_seq))
                idx += 1
                mrt = 0
        
            else:
        
                if block_seq_lens:
                    input_seq = input_seq[:-(block_seq_lens[-1]+2)]
                    block_seq_lens.pop()
                    idx -= 1
                    mrt += 1
        
                else:
                    break
        
                if mrt == max_retries:
                    break

    else:
        input_seq = generate_full_seq(start_score_seq, temperature=model_temperature)

    final_song = input_seq[len(start_score_seq):]
   
    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 = 0
        channel = 0
        patch = 0
    
        channels_map = [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 9, 12, 13, 14, 15]
        patches_map = [40, 0, 10, 19, 24, 35, 40, 52, 56, 9, 65, 73, 0, 0, 0, 0]
        velocities_map = [125, 80, 100, 80, 90, 100, 100, 80, 110, 110, 110, 110, 80, 80, 80, 80]
    
        for m in final_song:
    
            if 0 <= m < 128:
                time += m * 32
    
            elif 128 < m < 256:
                dur = (m-128) * 32
    
            elif 256 < m < 1792:
                cha = (m-256) // 128
                pitch = (m-256) % 128
    
                channel = channels_map[cha]
                patch = patches_map[channel]
                vel = velocities_map[channel]
    
                song_f.append(['note', time, dur, channel, pitch, vel, patch])

    fn1 = "Guided-Accompaniment-Transformer-Composition"
    
    detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,
                                                              output_signature = 'Guided Accompaniment Transformer',
                                                              output_file_name = fn1,
                                                              track_name='Project Los Angeles',
                                                              list_of_MIDI_patches=patches_map
                                                              )
    
    new_fn = fn1+'.mid'
            
    
    audio = midi_to_colab_audio(new_fn, 
                        soundfont_path=SOUDFONT_PATH,
                        sample_rate=16000,
                        volume_scale=10,
                        output_for_gradio=True
                        )
    
    print('Done!')
    print('=' * 70)

    #========================================================

    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('=' * 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_audio, output_plot, output_midi
    
#==================================================================================

PDT = timezone('US/Pacific')

print('=' * 70)
print('App start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
print('=' * 70)

#==================================================================================

with gr.Blocks() as demo:

    #==================================================================================

    gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Guided Accompaniment Transformer</h1>")
    gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Guided melody accompaniment generation with transformers</h1>")
    gr.HTML("""            
            <p> 
                <a href="https://huggingface.co/spaces/asigalov61/Guided-Accompaniment-Transformer?duplicate=true">
                    <img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-md.svg" alt="Duplicate in Hugging Face">
                </a>
            </p>
            
            for faster execution and endless generation!
            """)
    
    #==================================================================================
    
    gr.Markdown("## Upload source melody MIDI or select an example MIDI below")
    
    input_midi = gr.File(label="Input MIDI", file_types=[".midi", ".mid", ".kar"])
    
    gr.Markdown("## Generation options")
    
    generation_type = gr.Radio(["Guided", "Freestyle"], value="Guided", label="Generation type")
    melody_patch = gr.Slider(-1, 127, value=-1, step=1, label="Source melody MIDI patch")
    use_nth_note = gr.Slider(1, 8, value=1, step=1, label="Use each nth melody note")
    model_temperature = gr.Slider(0.1, 1, value=0.9, step=0.01, label="Model temperature")
    
    generate_btn = gr.Button("Generate", variant="primary")

    gr.Markdown("## Generation results")
    
    output_audio = gr.Audio(label="MIDI audio", format="wav", elem_id="midi_audio")
    output_plot = gr.Plot(label="MIDI score plot")
    output_midi = gr.File(label="MIDI file", file_types=[".mid"])

    generate_btn.click(Generate_Accompaniment, 
                       [input_midi, 
                        generation_type,
                        melody_patch,
                        use_nth_note,
                        model_temperature
                       ], 
                       [output_audio,
                        output_plot,
                        output_midi                          
                       ]
                      )

    gr.Examples(
                [["USSR-National-Anthem-Seed-Melody.mid", "Guided", -1, 1, 0.9],
                 ["Hotel-California-Seed-Melody.mid", "Guided", -1, 1, 0.9],
                 ["Sparks-Fly-Seed-Melody.mid", "Guided", -1, 1, 0.9]
                ],
                [input_midi, 
                 generation_type,
                 melody_patch,
                 use_nth_note,
                 model_temperature
                ],
                [output_audio,
                 output_plot,
                 output_midi
                ],
                Generate_Accompaniment
    )
        
#==================================================================================

demo.launch()

#==================================================================================