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 tqdm

import pprint
import io

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 GenerateMusic():
    
    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 = 2048
    PAD_IDX = 780
    DEVICE = 'cuda' # 'cuda'

    # instantiate the model

    model = TransformerWrapper(
        num_tokens = PAD_IDX+1,
        max_seq_len = SEQ_LEN,
        attn_layers = Decoder(dim = 1024, depth = 32, heads = 16, attn_flash = True)
        )
    
    model = AutoregressiveWrapper(model, ignore_index = PAD_IDX, pad_value=PAD_IDX)

    model.to(DEVICE)
    print('=' * 70)

    print('Loading model checkpoint...')

    model.load_state_dict(
        torch.load('Descriptive_Music_Transformer_Trained_Model_20631_steps_0.3218_loss_0.8947_acc.pth',
                   map_location=DEVICE))
    print('=' * 70)

    model.eval()

    if DEVICE == 'cpu':
        dtype = torch.bfloat16
    else:
        dtype = torch.bfloat16

    ctx = torch.amp.autocast(device_type=DEVICE, dtype=dtype)

    print('Done!')
    print('=' * 70)

    input_num_tokens = 1024+512
    print('-' * 70)

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

    print('=' * 70)
    print('Loading helper functions...')

    def txt2tokens(txt):
        return [ord(char)+648 if 0 < ord(char) < 128 else 0+648 for char in txt.lower()]
    
    def tokens2txt(tokens):
        return [chr(tok-648) for tok in tokens if 0+648 < tok < 128+648 ]

    def pprint_to_string(obj, compact=True):
        output = io.StringIO()
        pprint.pprint(obj, stream=output, compact=compact)
        return output.getvalue()
    
    print('=' * 70)
    print('Generating...')
    
    #@title Standard Text-to-Music Generator
    
    #@markdown Generation settings
    
    number_of_tokens_to_generate = input_num_tokens
    number_of_batches_to_generate = 1 #@param {type:"slider", min:1, max:16, step:1}
    temperature = 0.9 # @param {type:"slider", min:0.1, max:1, step:0.05}

    print('=' * 70)
    print('Descriptive Music Transformer Model Generator')
    print('=' * 70)
    
    outy = [777]
    
    torch.cuda.empty_cache()
    
    inp = [outy] * number_of_batches_to_generate
    
    inp = torch.LongTensor(inp).cuda()
    
    with ctx:
      out = model.generate(inp,
                            number_of_tokens_to_generate,
                            temperature=temperature,
                            return_prime=True,
                            verbose=False)
    
    out0 = out.tolist()
    
    print('=' * 70)
    print('Done!')
    print('=' * 70)
    
    #===============================================================================
    print('Rendering results...')
    print('=' * 70)
 
    out1 = out0[0]
    
    print('Sample INTs', out1[:12])
    print('=' * 70)

    descr = ''.join(tokens2txt(out1)).split('. ')
    descr1 = descr[0].capitalize()
    descr2 = descr[1].capitalize()
    generated_song_description = str(pprint_to_string(descr1).replace(" '", "").replace("'", "")[1:-2] +'.\n\n' + pprint_to_string(descr2).replace("'", "").replace(" '", "")[1:-2])

    if len(out1) != 0:
    
      song = out1
      song_f = []
    
      time = 0
      dur = 0
      vel = 90
      pitch = 0
      pat = 0
      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:

              pat = (ss-256)

              channel = pat // 8

              if channel == 9:
                channel = 15
              if channel == 16:
                channel = 9
  
          if 384 < ss < 640:
  
              pitch = (ss-384) % 128

          if 640 <= ss < 648:
  
              vel = ((ss-640)+1) * 15
            
              song_f.append(['note', time, dur, channel, pitch, vel, pat])

    song_f, patches, overflow_patches = TMIDIX.patch_enhanced_score_notes(song_f)

    fn1 = "Descriptive-Music-Transformer-Composition"
    
    detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,
                                                              output_signature = 'Descriptive 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).replace('-', ' ')
    output_midi_summary = str(generated_song_description)
    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("<h1 style='text-align: center; margin-bottom: 1rem'>Descriptive Music Transformer</h1>")
        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>A music transformer that describes music it generates</h1>")
        gr.Markdown(
            "![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.Descriptive-Music-Transformer&style=flat)\n\n"
            'This is a demo for Annotated MIDI Dataset.\n\n'
            "Check out [Annotated MIDI Dataset](https://huggingface.co/datasets/asigalov61/Annotated-MIDI-Dataset) on Hugging Face!\n\n"
        )

        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="Generated music description")
        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(GenerateMusic, outputs=[output_midi_title, output_midi_summary, output_midi, output_audio, output_plot])

        app.queue().launch()