app.py

#============================================================================================
# https://huggingface.co/spaces/projectlosangeles/Orpheus-MIDI-Loops-Generator
#============================================================================================

print('=' * 70)
print('Orpheus MIDI Loops Generator Gradio App')

print('=' * 70)
print('Loading core Orpheus MIDI Loops Generator modules...')

import os
import copy

import time as reqtime
import datetime
from pytz import timezone

print('=' * 70)
print('Loading main Orpheus MIDI Loops Generator modules...')

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

import torch

torch.set_float32_matmul_precision('high')
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_flash_sdp(True)

from huggingface_hub import hf_hub_download

import TMIDIX

from midi_to_colab_audio import midi_to_colab_audio

from x_transformer_2_3_1 import *

import random

import tqdm

print('=' * 70)
print('Loading aux Orpheus MIDI Loops Generator 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 = 'Orpheus_Music_Transformer_Loops_Fine_Tuned_Model_3441_steps_0.7715_loss_0.7992_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 = 1668
PAD_IDX = 18819

model = TransformerWrapper(num_tokens = PAD_IDX+1,
                           max_seq_len = SEQ_LEN,
                           attn_layers = Decoder(dim = 2048,
                                                 depth = 8,
                                                 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/Orpheus-Music-Transformer', 
                                   filename=MODEL_CHECKPOINT
                                  )

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

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

model.to(device_type)
model.eval()

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

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

print('=' * 70)
print('Loading Orpheus MIDI Loops dataset...')

orpheus_loops_dataset_file = hf_hub_download(repo_id='asigalov61/Orpheus-Music-Transformer', 
                                             filename='orpheus_data/230414_Select_Orpheus_MIDI_Loops_Dataset_CC_BY_NC_SA.pickle'
                                            )

loops_data = TMIDIX.Tegridy_Any_Pickle_File_Reader(orpheus_loops_dataset_file)

print('=' * 70)
print('Done!')
print('=' * 70)
print('Loaded', len(loops_data), 'loops')
print('=' * 70)

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

def tokens_to_score(tokens):

    song_f = []
    
    time = 0
    dur = 1
    vel = 90
    pitch = 60
    channel = 0
    patch = 0

    patches = [-1] * 16

    channels = [0] * 16
    channels[9] = 1

    for ss in tokens:

        if 0 <= ss < 256:

            time += ss * 16

        if 256 <= ss < 16768:

            patch = (ss-256) // 128

            if patch < 128:

                if patch not in patches:
                  if 0 in channels:
                      cha = channels.index(0)
                      channels[cha] = 1
                  else:
                      cha = 15

                  patches[cha] = patch
                  channel = patches.index(patch)
                else:
                  channel = patches.index(patch)

            if patch == 128:
                channel = 9

            pitch = (ss-256) % 128

        if 16768 <= ss < 18816:

            dur = ((ss-16768) // 8) * 16
            vel = (((ss-16768) % 8)+1) * 15

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

    return song_f

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

@spaces.GPU
def generate_loops(start_loop_seq,
                   num_prime_toks,
                   num_loops_to_generate,
                   model_temperature,
                   model_sampling_top_p
                  ):

    prime_seq = start_loop_seq[:num_prime_toks]

    x = torch.LongTensor([prime_seq] * num_loops_to_generate).cuda()
    
    with ctx:
        out = model.generate(x,
                             SEQ_LEN-x.shape[1],
                             temperature=model_temperature,
                             filter_logits_fn=top_p,
                             filter_kwargs={'thres': model_sampling_top_p},
                             return_prime=True,
                             eos_token=18818,
                             verbose=True)
    
    y = out.tolist()
    
    outputs = []
    
    for seq in y:
        try:
            eidx = seq.index(18818)+1
            if len(seq[:eidx]) - seq.index(18817) == 122:
                outputs.append(seq[:eidx-120])
    
        except:
            continue
    
    sidx = start_loop_seq.index(18817)+2
    song = start_loop_seq[:sidx]
    
    for o in outputs:
        song.extend(o[2:])

    #==============================================================================
    
    print('=' * 70)
    print('Done!')
    print('=' * 70)
    print('Song has', len(outputs)+1, 'loops')
    print('=' * 70)

    return song

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

def Generate_MIDI_Loops(num_loops_to_generate,
                        num_prime_toks,
                        model_temperature,
                        model_sampling_top_p
                       ):

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

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

    print('=' * 70)
    print('Requested settings:')
    print('=' * 70)
    print('Num loops to generate:', num_loops_to_generate)
    print('Num of prime toks:', num_prime_toks)
    print('Model temperature:', model_temperature)
    print('Model top k:', model_sampling_top_p)
   
    print('=' * 70)

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

    print('Generating...')

    #==============================================================================
    
    start_loop_idx = random.randint(0, len(loops_data))
    start_loop = loops_data[start_loop_idx]

    #==============================================================================
    
    print('=' * 70)
    print('Song:', start_loop[0])
    print('Artist:', start_loop[1])
    print('=' * 70)

    #==============================================================================
    
    song = generate_loops(start_loop[2],
                          num_prime_toks,
                          num_loops_to_generate,
                          model_temperature,
                          model_sampling_top_p
                         )

    print('=' * 70)
    print('Done!')
    print('=' * 70)
    
    #===============================================================================
    
    print('Rendering results...')

    #===============================================================================
        
    print('=' * 70)
    print('Sample INTs', song[:15])
    print('=' * 70)

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

    output_score = tokens_to_score(song)

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

    patched_score, patches, overflow_patches = TMIDIX.patch_enhanced_score_notes(output_score)

    fn1 = "Orpheus-MIDI-Loops-Generator-Composition"
    
    detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(patched_score,
                                                              output_signature = 'Orpheus MIDI Loops Generator',
                                                              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=SOUDFONT_PATH,
                                sample_rate=16000,
                                output_for_gradio=True
                                )

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

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

    output_title_artist = 'Song title: ' + start_loop[0] + '\n'
    output_title_artist += 'Artist: ' + start_loop[1]
    
    output_midi = str(new_fn)
    output_audio = (16000, audio)
    output_plot = TMIDIX.plot_ms_SONG(patched_score, 
                                      plot_title=output_midi, 
                                      return_plt=True
                                     )

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

    print(output_title_artist)
    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_title_artist, 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: left; margin-bottom: 1rem'>Orpheus MIDI Loops Generator</h1>")
    gr.Markdown("<h1 style='text-align: left; margin-bottom: 1rem'>Generate awesome MIDI loops!</h1>")
    gr.HTML("""            
            <p> 
                <a href="https://huggingface.co/spaces/projectlosangeles/Orpheus-MIDI-Loops-Generator?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("## Generation options")
    
    num_loops_to_generate = gr.Slider(2, 32, value=16, step=1, label="Number of loops to generate")
    num_prime_toks = gr.Slider(32, 256, value=128, step=1, label="Number of prime tokens")
    model_temperature = gr.Slider(0.1, 1, value=0.9, step=0.01, label="Model temperature")
    model_sampling_top_p = gr.Slider(0.1, 0.99, value=0.96, step=0.01, label="Model sampling top p value")
    
    generate_btn = gr.Button("Generate Loops", variant="primary")

    gr.Markdown("## Generation results")

    output_title_artist = gr.Textbox(label="MIDI loops title/artist", lines=2)
    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_MIDI_Loops, 
                       [num_loops_to_generate,
                        num_prime_toks,
                        model_temperature,
                        model_sampling_top_p
                       ], 
                       [output_title_artist,
                        output_audio,
                        output_plot,
                        output_midi                          
                       ]
                      )
    
#==================================================================================

demo.launch()

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