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import time as reqtime |
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import datetime |
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from pytz import timezone |
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import torch |
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import spaces |
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import gradio as gr |
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from x_transformer_1_23_2 import * |
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import random |
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import tqdm |
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from midi_to_colab_audio import midi_to_colab_audio |
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import TMIDIX |
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import matplotlib.pyplot as plt |
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in_space = os.getenv("SYSTEM") == "spaces" |
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@spaces.GPU |
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def GenerateSong(input_melody_seed_number): |
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print('=' * 70) |
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print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT))) |
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start_time = reqtime.time() |
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print('Loading model...') |
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SEQ_LEN = 2560 |
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PAD_IDX = 514 |
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DEVICE = 'cuda' |
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model = TransformerWrapper( |
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num_tokens = PAD_IDX+1, |
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max_seq_len = SEQ_LEN, |
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attn_layers = Decoder(dim = 1024, depth = 24, heads = 16, attn_flash = True) |
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) |
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model = AutoregressiveWrapper(model, ignore_index = PAD_IDX) |
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model.to(DEVICE) |
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print('=' * 70) |
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print('Loading model checkpoint...') |
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model.load_state_dict( |
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torch.load('Melody2Song_Seq2Seq_Music_Transformer_Trained_Model_28482_steps_0.719_loss_0.7865_acc.pth', |
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map_location=DEVICE)) |
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print('=' * 70) |
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model.eval() |
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if DEVICE == 'cpu': |
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dtype = torch.bfloat16 |
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else: |
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dtype = torch.bfloat16 |
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ctx = torch.amp.autocast(device_type=DEVICE, dtype=dtype) |
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print('Done!') |
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print('=' * 70) |
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print('Input melody seed number:', input_melody_seed_number) |
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print('-' * 70) |
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print('=' * 70) |
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print('Sample output events', melody_chords[:5]) |
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print('=' * 70) |
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print('Generating...') |
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output = [] |
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max_chords_limit = 8 |
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temperature=0.9 |
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num_memory_tokens=4096 |
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output = [] |
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idx = 0 |
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for c in chords[:input_num_tokens]: |
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output.append(c) |
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if input_conditioning_type == 'Chords-Times' or input_conditioning_type == 'Chords-Times-Durations': |
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output.append(times[idx]) |
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if input_conditioning_type == 'Chords-Times-Durations': |
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output.append(durs[idx]) |
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x = torch.tensor([output] * 1, dtype=torch.long, device='cuda') |
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o = 0 |
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ncount = 0 |
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while o < 384 and ncount < max_chords_limit: |
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with ctx: |
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out = model.generate(x[-num_memory_tokens:], |
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1, |
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temperature=temperature, |
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return_prime=False, |
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verbose=False) |
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o = out.tolist()[0][0] |
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if 256 <= o < 384: |
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ncount += 1 |
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if o < 384: |
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x = torch.cat((x, out), 1) |
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outy = x.tolist()[0][len(output):] |
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output.extend(outy) |
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idx += 1 |
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if idx == len(chords[:input_num_tokens])-1: |
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break |
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print('=' * 70) |
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print('Done!') |
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print('=' * 70) |
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print('Rendering results...') |
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print('=' * 70) |
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print('Sample INTs', output[:12]) |
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print('=' * 70) |
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out1 = output |
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if len(out1) != 0: |
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song = out1 |
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song_f = [] |
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time = 0 |
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dur = 0 |
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vel = 90 |
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pitch = 0 |
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channel = 0 |
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patches = [0] * 16 |
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channel = 0 |
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for ss in song: |
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if 0 <= ss < 128: |
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time += ss * 32 |
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if 128 <= ss < 256: |
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dur = (ss-128) * 32 |
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if 256 <= ss < 384: |
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pitch = (ss-256) |
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vel = max(40, pitch) |
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song_f.append(['note', time, dur, channel, pitch, vel, 0]) |
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fn1 = "Chords-Progressions-Transformer-Composition" |
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detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f, |
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output_signature = 'Chords Progressions Transformer', |
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output_file_name = fn1, |
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track_name='Project Los Angeles', |
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list_of_MIDI_patches=patches |
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) |
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new_fn = fn1+'.mid' |
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audio = midi_to_colab_audio(new_fn, |
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soundfont_path=soundfont, |
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sample_rate=16000, |
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volume_scale=10, |
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output_for_gradio=True |
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) |
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print('Done!') |
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print('=' * 70) |
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output_midi_title = str(fn1) |
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output_midi_summary = str(song_f[:3]) |
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output_midi = str(new_fn) |
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output_audio = (16000, audio) |
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output_plot = TMIDIX.plot_ms_SONG(song_f, plot_title=output_midi, return_plt=True) |
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print('Output MIDI file name:', output_midi) |
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print('Output MIDI title:', output_midi_title) |
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print('Output MIDI summary:', '') |
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print('=' * 70) |
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print('-' * 70) |
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print('Req end time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT))) |
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print('-' * 70) |
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print('Req execution time:', (reqtime.time() - start_time), 'sec') |
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return output_midi_title, output_midi_summary, output_midi, output_audio, output_plot |
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if __name__ == "__main__": |
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PDT = timezone('US/Pacific') |
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print('=' * 70) |
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print('App start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT))) |
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print('=' * 70) |
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soundfont = "SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2" |
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app = gr.Blocks() |
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with app: |
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gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Melody2Song Seq2Seq Music Transformer</h1>") |
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gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Generate unique songs from melodies with se2seq music transformer</h1>") |
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gr.Markdown( |
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"\n\n") |
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input_melody_seed_number = gr.Slider(0, 200000, value=0, step=1, label="Select seed melody number") |
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run_btn = gr.Button("generate", variant="primary") |
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gr.Markdown("## Generation results") |
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output_midi_title = gr.Textbox(label="Output MIDI title") |
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output_midi_summary = gr.Textbox(label="Output MIDI summary") |
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output_audio = gr.Audio(label="Output MIDI audio", format="wav", elem_id="midi_audio") |
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output_plot = gr.Plot(label="Output MIDI score plot") |
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output_midi = gr.File(label="Output MIDI file", file_types=[".mid"]) |
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run_event = run_btn.click(GenerateSong, [input_melody_seed_number], |
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[output_midi_title, output_midi_summary, output_midi, output_audio, output_plot]) |
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app.queue().launch() |
