app.py

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 copy
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 GenerateGroove():
    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 = 4096 # Models seq len
    PAD_IDX = 1664 # 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 = 1024, depth = 24, 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('Groove_Music_Transformer_Medium_Trained_Model_23268_steps_0.7459_loss_0.797_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)
    
    print('Loading Google Magenta Groove processed MIDIs...')
    all_scores = TMIDIX.Tegridy_Any_Pickle_File_Reader('Google_Magenta_Groove_8675_Select_Processed_MIDIs')

    print('Done!')
    print('=' * 70)
    
    print('=' * 70)
    
    drums_score_idx = random.randint(0, len(all_scores))
    drums_score_fn = all_scores[drums_score_idx][0]
    drums_score = all_scores[drums_score_idx][1][:160]
    
    print('Drums score index', drums_score_idx)
    print('Drums score name', drums_score_fn)
    print('Drums score length', len(drums_score))
    print('=' * 70)

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

    print('=' * 70)
    
    print('Sample input events', drums_score[:5])
    print('=' * 70)
    print('Prepping drums track...')

    num_prime_chords = 7
    
    outy = []
    
    for d in drums_score[:num_prime_chords]:

        outy.extend(d)

    print('Generating...')

    max_notes_per_chord=8
    num_samples=4
    num_memory_tokens = 4096
    temperature=1.0
    
    for i in range(num_prime_chords, len(drums_score)):
    
        outy.extend(drums_score[i])
    
        if i == num_prime_chords:
          outy.append(256+12)

        input_seq = outy[-num_memory_tokens:]
        
        seq = copy.deepcopy(input_seq)
        
        batch_value = 256
        
        nc = 0
        
        while batch_value > 255 and nc < max_notes_per_chord:
        
          x = torch.tensor([seq] * num_samples, dtype=torch.long, device='cuda')
        
          with ctx:
            out = model.generate(x,
                                1,
                                temperature=temperature,
                                return_prime=False,
                                verbose=False)
        
          out1 = [o[0] for o in out.tolist() if o[0] > 255]
        
          if not out1:
            out1 = [-1]
        
          batch_value = random.choice(out1)
        
          if batch_value > 255:
            seq.append(batch_value)
        
          if batch_value > 383:
            nc += 1
    
        out = seq[len(input_seq):]
    
        outy.extend(out)

    print('=' * 70)
    print('Done!')
    print('=' * 70)
    
    #===============================================================================
    print('Rendering results...')
    
    print('=' * 70)
    print('Sample INTs', outy[:12])
    print('=' * 70)

    if len(outy) != 0:
    
        song = outy
        song_f = []
    
        time = 0
        dur = 32
        vel = 90
        dvels = [100, 120]
        pitch = 60
        channel = 0
        
        patches = [0, 10, 19, 24, 35, 40, 52, 56, 65, 9, 73, 0, 0, 0, 0, 0]
         
        for ss in song:
        
            if 0 <= ss < 128:
        
                time += ss * 32
        
            if 128 <= ss < 256:
        
                song_f.append(['note', time, 32, 9, ss-128, dvels[(ss-128) % 2], 128])
        
            if 256 < ss < 384:
        
                dur =  (ss-256) * 32
        
            if 384 < ss < 1664:
        
                chan = (ss-384) // 128
        
                if chan == 11:
                  channel = 9
                else:
                  if chan > 8:
                    channel = chan + 1
                  else:
                    channel = chan
        
                if channel == 9:
                  patch = 128
                else:
                  patch = channel * 8
        
                pitch = (ss-384) % 128

                vel = max(50, pitch)
        
                song_f.append(['note', time, dur, channel, pitch, vel, patch])


    fn1 = drums_score_fn
    
    detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,
                                                              output_signature = 'Groove Music 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'>Groove Music Transformer</h1>")
        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Generate music for Google Magenta Groove MIDI dataset drums tracks</h1>")
        gr.Markdown(
            "![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.Groove-Music-Transformer&style=flat)\n\n"
            "Generate music for Google Magenta Groove MIDI dataset drums tracks\n\n"
            "Based upon [Google Magenta Groove MIDI Dataset](https://magenta.tensorflow.org/datasets/groove)\n\n"
        )
         
        run_btn = gr.Button("generate groove", 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(GenerateGroove,
                                  outputs=[output_midi_title, output_midi_summary, output_midi, output_audio, output_plot])

        app.queue().launch()