app.py

# https://huggingface.co/spaces/asigalov61/Melody2Song-Seq2Seq-Music-Transformer

import os
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 GenerateSong(input_melody_seed_number):
    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 = 2560
    PAD_IDX = 514
    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('Melody2Song_Seq2Seq_Music_Transformer_Trained_Model_28482_steps_0.719_loss_0.7865_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)
    print('Input melody seed number:', input_melody_seed_number)
    print('-' * 70)

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

    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(
            "![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.Melody2Song-Seq2Seq-Music-Transformer&style=flat)\n\n")
        
        input_melody_seed_number = gr.Slider(0, 200000, value=0, step=1, label="Select seed melody number")

        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(GenerateSong, [input_melody_seed_number],
                                  [output_midi_title, output_midi_summary, output_midi, output_audio, output_plot])

        app.queue().launch()