import spaces import random import argparse import glob import json import os import time from concurrent.futures import ThreadPoolExecutor import gradio as gr import numpy as np import torch import torch.nn.functional as F import tqdm from huggingface_hub import hf_hub_download import MIDI from midi_model import MIDIModel, MIDIModelConfig from midi_synthesizer import MidiSynthesizer MAX_SEED = np.iinfo(np.int32).max in_space = os.getenv("SYSTEM") == "spaces" @torch.inference_mode() def generate(model: MIDIModel, prompt=None, batch_size=1, max_len=512, temp=1.0, top_p=0.98, top_k=20, disable_patch_change=False, disable_control_change=False, disable_channels=None, generator=None): tokenizer = model.tokenizer if disable_channels is not None: disable_channels = [tokenizer.parameter_ids["channel"][c] for c in disable_channels] else: disable_channels = [] max_token_seq = tokenizer.max_token_seq if prompt is None: input_tensor = torch.full((1, max_token_seq), tokenizer.pad_id, dtype=torch.long, device=model.device) input_tensor[0, 0] = tokenizer.bos_id # bos input_tensor = input_tensor.unsqueeze(0) input_tensor = torch.cat([input_tensor] * batch_size, dim=0) else: if len(prompt.shape) == 2: prompt = prompt[None, :] prompt = np.repeat(prompt, repeats=batch_size, axis=0) elif prompt.shape[0] == 1: prompt = np.repeat(prompt, repeats=batch_size, axis=0) elif len(prompt.shape) != 3 or prompt.shape[0] != batch_size: raise ValueError(f"invalid shape for prompt, {prompt.shape}") prompt = prompt[..., :max_token_seq] if prompt.shape[-1] < max_token_seq: prompt = np.pad(prompt, ((0, 0), (0, 0), (0, max_token_seq - prompt.shape[-1])), mode="constant", constant_values=tokenizer.pad_id) input_tensor = torch.from_numpy(prompt).to(dtype=torch.long, device=model.device) cur_len = input_tensor.shape[1] bar = tqdm.tqdm(desc="generating", total=max_len - cur_len) with bar: while cur_len < max_len: end = [False] * batch_size hidden = model.forward(input_tensor)[:, -1] next_token_seq = None event_names = [""] * batch_size for i in range(max_token_seq): mask = torch.zeros((batch_size, tokenizer.vocab_size), dtype=torch.int64, device=model.device) for b in range(batch_size): if end[b]: mask[b, tokenizer.pad_id] = 1 continue if i == 0: mask_ids = list(tokenizer.event_ids.values()) + [tokenizer.eos_id] if disable_patch_change: mask_ids.remove(tokenizer.event_ids["patch_change"]) if disable_control_change: mask_ids.remove(tokenizer.event_ids["control_change"]) mask[b, mask_ids] = 1 else: param_names = tokenizer.events[event_names[b]] if i > len(param_names): mask[b, tokenizer.pad_id] = 1 continue param_name = param_names[i - 1] mask_ids = tokenizer.parameter_ids[param_name] if param_name == "channel": mask_ids = [i for i in mask_ids if i not in disable_channels] mask[b, mask_ids] = 1 mask = mask.unsqueeze(1) logits = model.forward_token(hidden, next_token_seq)[:, -1:] scores = torch.softmax(logits / temp, dim=-1) * mask samples = model.sample_top_p_k(scores, top_p, top_k, generator=generator) if i == 0: next_token_seq = samples for b in range(batch_size): if end[b]: continue eid = samples[b].item() if eid == tokenizer.eos_id: end[b] = True else: event_names[b] = tokenizer.id_events[eid] else: next_token_seq = torch.cat([next_token_seq, samples], dim=1) if all([len(tokenizer.events[event_names[b]]) == i for b in range(batch_size) if not end[b]]): break if next_token_seq.shape[1] < max_token_seq: next_token_seq = F.pad(next_token_seq, (0, max_token_seq - next_token_seq.shape[1]), "constant", value=tokenizer.pad_id) next_token_seq = next_token_seq.unsqueeze(1) input_tensor = torch.cat([input_tensor, next_token_seq], dim=1) cur_len += 1 bar.update(1) yield next_token_seq[:, 0].cpu().numpy() if all(end): break def create_msg(name, data): return {"name": name, "data": data} def send_msgs(msgs): return json.dumps(msgs) def calc_time(x): return 5.849e-5*x**2 + 0.04781*x + 0.1168 def get_duration(model_name, tab, mid_seq, continuation_state, continuation_select, instruments, drum_kit, bpm, time_sig, key_sig, mid, midi_events, reduce_cc_st, remap_track_channel, add_default_instr, remove_empty_channels, seed, seed_rand, gen_events, temp, top_p, top_k, allow_cc): if tab == 0: start_events = 1 elif tab == 1 and mid is not None: start_events = midi_events elif tab == 2 and mid_seq is not None: start_events = len(mid_seq[0]) else: start_events = 1 t = calc_time(start_events + gen_events) - calc_time(start_events) + 5 if "large" in model_name: t *= 2 return t @spaces.GPU(duration=get_duration) def run(model_name, tab, mid_seq, continuation_state, continuation_select, instruments, drum_kit, bpm, time_sig, key_sig, mid, midi_events, reduce_cc_st, remap_track_channel, add_default_instr, remove_empty_channels, seed, seed_rand, gen_events, temp, top_p, top_k, allow_cc): model = models[model_name] model.to(device=opt.device) tokenizer = model.tokenizer bpm = int(bpm) if time_sig == "auto": time_sig = None time_sig_nn = 4 time_sig_dd = 2 else: time_sig_nn, time_sig_dd = time_sig.split('/') time_sig_nn = int(time_sig_nn) time_sig_dd = {2: 1, 4: 2, 8: 3}[int(time_sig_dd)] if key_sig == 0: key_sig = None key_sig_sf = 0 key_sig_mi = 0 else: key_sig = (key_sig - 1) key_sig_sf = key_sig // 2 - 7 key_sig_mi = key_sig % 2 gen_events = int(gen_events) max_len = gen_events if seed_rand: seed = random.randint(0, MAX_SEED) generator = torch.Generator(opt.device).manual_seed(seed) disable_patch_change = False disable_channels = None if tab == 0: i = 0 mid = [[tokenizer.bos_id] + [tokenizer.pad_id] * (tokenizer.max_token_seq - 1)] if tokenizer.version == "v2": if time_sig is not None: mid.append(tokenizer.event2tokens(["time_signature", 0, 0, 0, time_sig_nn - 1, time_sig_dd - 1])) if key_sig is not None: mid.append(tokenizer.event2tokens(["key_signature", 0, 0, 0, key_sig_sf + 7, key_sig_mi])) if bpm != 0: mid.append(tokenizer.event2tokens(["set_tempo", 0, 0, 0, bpm])) patches = {} if instruments is None: instruments = [] for instr in instruments: patches[i] = patch2number[instr] i = (i + 1) if i != 8 else 10 if drum_kit != "None": patches[9] = drum_kits2number[drum_kit] for i, (c, p) in enumerate(patches.items()): mid.append(tokenizer.event2tokens(["patch_change", 0, 0, i + 1, c, p])) mid = np.asarray([mid] * OUTPUT_BATCH_SIZE, dtype=np.int64) mid_seq = mid.tolist() if len(instruments) > 0: disable_patch_change = True disable_channels = [i for i in range(16) if i not in patches] elif tab == 1 and mid is not None: eps = 4 if reduce_cc_st else 0 mid = tokenizer.tokenize(MIDI.midi2score(mid), cc_eps=eps, tempo_eps=eps, remap_track_channel=remap_track_channel, add_default_instr=add_default_instr, remove_empty_channels=remove_empty_channels) mid = mid[:int(midi_events)] mid = np.asarray([mid] * OUTPUT_BATCH_SIZE, dtype=np.int64) mid_seq = mid.tolist() elif tab == 2 and mid_seq is not None: mid = np.asarray(mid_seq, dtype=np.int64) if continuation_select > 0: continuation_state.append(mid_seq) mid = np.repeat(mid[continuation_select - 1:continuation_select], repeats=OUTPUT_BATCH_SIZE, axis=0) mid_seq = mid.tolist() else: continuation_state.append(mid.shape[1]) else: continuation_state = [0] mid = [[tokenizer.bos_id] + [tokenizer.pad_id] * (tokenizer.max_token_seq - 1)] mid = np.asarray([mid] * OUTPUT_BATCH_SIZE, dtype=np.int64) mid_seq = mid.tolist() if mid is not None: max_len += mid.shape[1] init_msgs = [create_msg("progress", [0, gen_events])] if not (tab == 2 and continuation_select == 0): for i in range(OUTPUT_BATCH_SIZE): events = [tokenizer.tokens2event(tokens) for tokens in mid_seq[i]] init_msgs += [create_msg("visualizer_clear", [i, tokenizer.version]), create_msg("visualizer_append", [i, events])] yield mid_seq, continuation_state, seed, send_msgs(init_msgs) midi_generator = generate(model, mid, batch_size=OUTPUT_BATCH_SIZE, max_len=max_len, temp=temp, top_p=top_p, top_k=top_k, disable_patch_change=disable_patch_change, disable_control_change=not allow_cc, disable_channels=disable_channels, generator=generator) events = [list() for i in range(OUTPUT_BATCH_SIZE)] t = time.time() for i, token_seqs in enumerate(midi_generator): token_seqs = token_seqs.tolist() for j in range(OUTPUT_BATCH_SIZE): token_seq = token_seqs[j] mid_seq[j].append(token_seq) events[j].append(tokenizer.tokens2event(token_seq)) if time.time() - t > 0.2: msgs = [create_msg("progress", [i + 1, gen_events])] for j in range(OUTPUT_BATCH_SIZE): msgs += [create_msg("visualizer_append", [j, events[j]])] events[j] = list() yield mid_seq, continuation_state, seed, send_msgs(msgs) t = time.time() yield mid_seq, continuation_state, seed, send_msgs([]) def finish_run(model_name, mid_seq): if mid_seq is None: outputs = [None] * OUTPUT_BATCH_SIZE return *outputs, [] tokenizer = models[model_name].tokenizer outputs = [] end_msgs = [create_msg("progress", [0, 0])] if not os.path.exists("outputs"): os.mkdir("outputs") for i in range(OUTPUT_BATCH_SIZE): events = [tokenizer.tokens2event(tokens) for tokens in mid_seq[i]] mid = tokenizer.detokenize(mid_seq[i]) with open(f"outputs/output{i + 1}.mid", 'wb') as f: f.write(MIDI.score2midi(mid)) outputs.append(f"outputs/output{i + 1}.mid") end_msgs += [create_msg("visualizer_clear", [i, tokenizer.version]), create_msg("visualizer_append", [i, events]), create_msg("visualizer_end", i)] return *outputs, send_msgs(end_msgs) def synthesis_task(mid): return synthesizer.synthesis(MIDI.score2opus(mid)) def render_audio(model_name, mid_seq, should_render_audio): if (not should_render_audio) or mid_seq is None: outputs = [None] * OUTPUT_BATCH_SIZE return tuple(outputs) tokenizer = models[model_name].tokenizer outputs = [] if not os.path.exists("outputs"): os.mkdir("outputs") audio_futures = [] for i in range(OUTPUT_BATCH_SIZE): mid = tokenizer.detokenize(mid_seq[i]) audio_future = thread_pool.submit(synthesis_task, mid) audio_futures.append(audio_future) for future in audio_futures: outputs.append((44100, future.result())) return tuple(outputs) def undo_continuation(model_name, mid_seq, continuation_state): if mid_seq is None or len(continuation_state) < 2: return mid_seq, continuation_state, send_msgs([]) tokenizer = models[model_name].tokenizer if isinstance(continuation_state[-1], list): mid_seq = continuation_state[-1] else: mid_seq = [ms[:continuation_state[-1]] for ms in mid_seq] continuation_state = continuation_state[:-1] end_msgs = [create_msg("progress", [0, 0])] for i in range(OUTPUT_BATCH_SIZE): events = [tokenizer.tokens2event(tokens) for tokens in mid_seq[i]] end_msgs += [create_msg("visualizer_clear", [i, tokenizer.version]), create_msg("visualizer_append", [i, events]), create_msg("visualizer_end", i)] return mid_seq, continuation_state, send_msgs(end_msgs) def load_javascript(dir="javascript"): scripts_list = glob.glob(f"{dir}/*.js") javascript = "" for path in scripts_list: with open(path, "r", encoding="utf8") as jsfile: js_content = jsfile.read() js_content = js_content.replace("const MIDI_OUTPUT_BATCH_SIZE=4;", f"const MIDI_OUTPUT_BATCH_SIZE={OUTPUT_BATCH_SIZE};") javascript += f"\n" template_response_ori = gr.routes.templates.TemplateResponse def template_response(*args, **kwargs): res = template_response_ori(*args, **kwargs) res.body = res.body.replace( b'', f'{javascript}'.encode("utf8")) res.init_headers() return res gr.routes.templates.TemplateResponse = template_response def hf_hub_download_retry(repo_id, filename): print(f"downloading {repo_id} {filename}") retry = 0 err = None while retry < 30: try: return hf_hub_download(repo_id=repo_id, filename=filename) except Exception as e: err = e retry += 1 if err: raise err number2drum_kits = {-1: "None", 0: "Standard", 8: "Room", 16: "Power", 24: "Electric", 25: "TR-808", 32: "Jazz", 40: "Blush", 48: "Orchestra"} patch2number = {v: k for k, v in MIDI.Number2patch.items()} drum_kits2number = {v: k for k, v in number2drum_kits.items()} key_signatures = ['C♭', 'A♭m', 'G♭', 'E♭m', 'D♭', 'B♭m', 'A♭', 'Fm', 'E♭', 'Cm', 'B♭', 'Gm', 'F', 'Dm', 'C', 'Am', 'G', 'Em', 'D', 'Bm', 'A', 'F♯m', 'E', 'C♯m', 'B', 'G♯m', 'F♯', 'D♯m', 'C♯', 'A♯m'] if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--share", action="store_true", default=False, help="share gradio app") parser.add_argument("--port", type=int, default=7860, help="gradio server port") parser.add_argument("--device", type=str, default="cuda", help="device to run model") parser.add_argument("--batch", type=int, default=8, help="batch size") parser.add_argument("--max-gen", type=int, default=1024, help="max") opt = parser.parse_args() OUTPUT_BATCH_SIZE = opt.batch soundfont_path = hf_hub_download_retry(repo_id="skytnt/midi-model", filename="soundfont.sf2") thread_pool = ThreadPoolExecutor(max_workers=OUTPUT_BATCH_SIZE) synthesizer = MidiSynthesizer(soundfont_path) models_info = { "generic pretrain model (tv2o-medium) by skytnt": ["skytnt/midi-model-tv2o-medium", "", "tv2o-medium"], "generic pretrain model (tv2o-large) by asigalov61": ["asigalov61/Music-Llama", "", "tv2o-large"], "generic pretrain model (tv2o-medium) by asigalov61": ["asigalov61/Music-Llama-Medium", "", "tv2o-medium"], "generic pretrain model (tv1-medium) by skytnt": ["skytnt/midi-model", "", "tv1-medium"], "j-pop finetune model (tv2o-medium) by skytnt": ["skytnt/midi-model-ft", "jpop-tv2o-medium/", "tv2o-medium"], "touhou finetune model (tv2o-medium) by skytnt": ["skytnt/midi-model-ft", "touhou-tv2o-medium/", "tv2o-medium"], } models = {} if opt.device == "cuda": torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True torch.backends.cuda.enable_mem_efficient_sdp(True) torch.backends.cuda.enable_flash_sdp(True) for name, (repo_id, path, config) in models_info.items(): model_path = hf_hub_download_retry(repo_id=repo_id, filename=f"{path}model.ckpt") model = MIDIModel(config=MIDIModelConfig.from_name(config)) ckpt = torch.load(model_path, map_location="cpu", weights_only=True) state_dict = ckpt.get("state_dict", ckpt) model.load_state_dict(state_dict, strict=False) model.to(device="cpu", dtype=torch.float32) models[name] = model load_javascript() app = gr.Blocks() with app: gr.Markdown("

Midi Composer

") gr.Markdown("![Visitors](https://api.visitorbadge.io/api/visitors?path=skytnt.midi-composer&style=flat)\n\n" "Midi event transformer for symbolic music generation\n\n" "Demo for [SkyTNT/midi-model](https://github.com/SkyTNT/midi-model)\n\n" "[Open In Colab]" "(https://colab.research.google.com/github/SkyTNT/midi-model/blob/main/demo.ipynb)" " or [download windows app](https://github.com/SkyTNT/midi-model/releases)" " for unlimited generation\n\n" "**Update v1.3**: MIDITokenizerV2 and new MidiVisualizer" ) js_msg = gr.Textbox(elem_id="msg_receiver", visible=False) js_msg.change(None, [js_msg], [], js=""" (msg_json) =>{ let msgs = JSON.parse(msg_json); executeCallbacks(msgReceiveCallbacks, msgs); return []; } """) input_model = gr.Dropdown(label="select model", choices=list(models.keys()), type="value", value=list(models.keys())[0]) tab_select = gr.State(value=0) with gr.Tabs(): with gr.TabItem("custom prompt") as tab1: input_instruments = gr.Dropdown(label="🪗instruments (auto if empty)", choices=list(patch2number.keys()), multiselect=True, max_choices=15, type="value") input_drum_kit = gr.Dropdown(label="🥁drum kit", choices=list(drum_kits2number.keys()), type="value", value="None") input_bpm = gr.Slider(label="BPM (beats per minute, auto if 0)", minimum=0, maximum=255, step=1, value=0) input_time_sig = gr.Radio(label="time signature (only for tv2 models)", value="auto", choices=["auto", "4/4", "2/4", "3/4", "6/4", "7/4", "2/2", "3/2", "4/2", "3/8", "5/8", "6/8", "7/8", "9/8", "12/8"] ) input_key_sig = gr.Radio(label="key signature (only for tv2 models)", value="auto", choices=["auto"] + key_signatures, type="index" ) example1 = gr.Examples([ [[], "None"], [["Acoustic Grand"], "None"], [['Acoustic Grand', 'SynthStrings 2', 'SynthStrings 1', 'Pizzicato Strings', 'Pad 2 (warm)', 'Tremolo Strings', 'String Ensemble 1'], "Orchestra"], [['Trumpet', 'Oboe', 'Trombone', 'String Ensemble 1', 'Clarinet', 'French Horn', 'Pad 4 (choir)', 'Bassoon', 'Flute'], "None"], [['Flute', 'French Horn', 'Clarinet', 'String Ensemble 2', 'English Horn', 'Bassoon', 'Oboe', 'Pizzicato Strings'], "Orchestra"], [['Electric Piano 2', 'Lead 5 (charang)', 'Electric Bass(pick)', 'Lead 2 (sawtooth)', 'Pad 1 (new age)', 'Orchestra Hit', 'Cello', 'Electric Guitar(clean)'], "Standard"], [["Electric Guitar(clean)", "Electric Guitar(muted)", "Overdriven Guitar", "Distortion Guitar", "Electric Bass(finger)"], "Standard"] ], [input_instruments, input_drum_kit]) with gr.TabItem("midi prompt") as tab2: input_midi = gr.File(label="input midi", file_types=[".midi", ".mid"], type="binary") input_midi_events = gr.Slider(label="use first n midi events as prompt", minimum=1, maximum=512, step=1, value=128) input_reduce_cc_st = gr.Checkbox(label="reduce control_change and set_tempo events", value=True) input_remap_track_channel = gr.Checkbox( label="remap tracks and channels so each track has only one channel and in order", value=True) input_add_default_instr = gr.Checkbox( label="add a default instrument to channels that don't have an instrument", value=True) input_remove_empty_channels = gr.Checkbox(label="remove channels without notes", value=False) example2 = gr.Examples([[file, 128] for file in glob.glob("example/*.mid")], [input_midi, input_midi_events]) with gr.TabItem("last output prompt") as tab3: gr.Markdown("Continue generating on the last output.") input_continuation_select = gr.Radio(label="select output to continue generating", value="all", choices=["all"] + [f"output{i + 1}" for i in range(OUTPUT_BATCH_SIZE)], type="index" ) undo_btn = gr.Button("undo the last continuation") tab1.select(lambda: 0, None, tab_select, queue=False) tab2.select(lambda: 1, None, tab_select, queue=False) tab3.select(lambda: 2, None, tab_select, queue=False) input_seed = gr.Slider(label="seed", minimum=0, maximum=2 ** 31 - 1, step=1, value=0) input_seed_rand = gr.Checkbox(label="random seed", value=True) input_gen_events = gr.Slider(label="generate max n midi events", minimum=1, maximum=opt.max_gen, step=1, value=opt.max_gen // 2) with gr.Accordion("options", open=False): input_temp = gr.Slider(label="temperature", minimum=0.1, maximum=1.2, step=0.01, value=1) input_top_p = gr.Slider(label="top p", minimum=0.1, maximum=1, step=0.01, value=0.94) input_top_k = gr.Slider(label="top k", minimum=1, maximum=128, step=1, value=20) input_allow_cc = gr.Checkbox(label="allow midi cc event", value=True) input_render_audio = gr.Checkbox(label="render audio after generation", value=True) example3 = gr.Examples([[1, 0.94, 128], [1, 0.98, 20], [1, 0.98, 12]], [input_temp, input_top_p, input_top_k]) run_btn = gr.Button("generate", variant="primary") # stop_btn = gr.Button("stop and output") output_midi_seq = gr.State() output_continuation_state = gr.State([0]) midi_outputs = [] audio_outputs = [] with gr.Tabs(elem_id="output_tabs"): for i in range(OUTPUT_BATCH_SIZE): with gr.TabItem(f"output {i + 1}") as tab1: output_midi_visualizer = gr.HTML(elem_id=f"midi_visualizer_container_{i}") output_audio = gr.Audio(label="output audio", format="mp3", elem_id=f"midi_audio_{i}") output_midi = gr.File(label="output midi", file_types=[".mid"]) midi_outputs.append(output_midi) audio_outputs.append(output_audio) run_event = run_btn.click(run, [input_model, tab_select, output_midi_seq, output_continuation_state, input_continuation_select, input_instruments, input_drum_kit, input_bpm, input_time_sig, input_key_sig, input_midi, input_midi_events, input_reduce_cc_st, input_remap_track_channel, input_add_default_instr, input_remove_empty_channels, input_seed, input_seed_rand, input_gen_events, input_temp, input_top_p, input_top_k, input_allow_cc], [output_midi_seq, output_continuation_state, input_seed, js_msg], concurrency_limit=10, queue=True) finish_run_event = run_event.then(fn=finish_run, inputs=[input_model, output_midi_seq], outputs=midi_outputs + [js_msg], queue=False) finish_run_event.then(fn=render_audio, inputs=[input_model, output_midi_seq, input_render_audio], outputs=audio_outputs, queue=False) # stop_btn.click(None, [], [], cancels=run_event, # queue=False) undo_btn.click(undo_continuation, [input_model, output_midi_seq, output_continuation_state], [output_midi_seq, output_continuation_state, js_msg], queue=False) app.queue().launch(server_port=opt.port, share=opt.share, inbrowser=True) thread_pool.shutdown()