import argparse import glob import os.path import gradio as gr import numpy as np import onnxruntime as rt import tqdm import json from huggingface_hub import hf_hub_download import MIDI from midi_synthesizer import synthesis from midi_tokenizer import MIDITokenizer in_space = os.getenv("SYSTEM") == "spaces" @torch.inference_mode() def generate(prompt=None, max_len=512, temp=1.0, top_p=0.98, top_k=20, disable_patch_change=False, disable_control_change=False, disable_channels=None, amp=True): 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 else: prompt = prompt[:, :max_token_seq] if prompt.shape[-1] < max_token_seq: prompt = np.pad(prompt, ((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) input_tensor = input_tensor.unsqueeze(0) cur_len = input_tensor.shape[1] bar = tqdm.tqdm(desc="generating", total=max_len - cur_len) with bar, torch.cuda.amp.autocast(enabled=amp): while cur_len < max_len: end = False hidden = model.forward(input_tensor)[0, -1].unsqueeze(0) next_token_seq = None event_name = "" for i in range(max_token_seq): mask = torch.zeros(tokenizer.vocab_size, dtype=torch.int64, device=model.device) 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[mask_ids] = 1 else: param_name = tokenizer.events[event_name][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[mask_ids] = 1 logits = model.forward_token(hidden, next_token_seq)[:, -1:] scores = torch.softmax(logits / temp, dim=-1) * mask sample = model.sample_top_p_k(scores, top_p, top_k) if i == 0: next_token_seq = sample eid = sample.item() if eid == tokenizer.eos_id: end = True break event_name = tokenizer.id_events[eid] else: next_token_seq = torch.cat([next_token_seq, sample], dim=1) if len(tokenizer.events[event_name]) == i: 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.reshape(-1).cpu().numpy() if end: break def run(tab, instruments, drum_kit, mid, midi_events, gen_events, temp, top_p, top_k, allow_cc, amp): mid_seq = [] max_len = int(gen_events) img_len = 1024 img = np.full((128 * 2, img_len, 3), 255, dtype=np.uint8) state = {"t1": 0, "t": 0, "cur_pos": 0} rand = np.random.RandomState(0) colors = {(i, j): rand.randint(0, 200, 3) for i in range(128) for j in range(16)} def draw_event(tokens): if tokens[0] in tokenizer.id_events: name = tokenizer.id_events[tokens[0]] if len(tokens) <= len(tokenizer.events[name]): return params = tokens[1:] params = [params[i] - tokenizer.parameter_ids[p][0] for i, p in enumerate(tokenizer.events[name])] if not all([0 <= params[i] < tokenizer.event_parameters[p] for i, p in enumerate(tokenizer.events[name])]): return event = [name] + params state["t1"] += event[1] t = state["t1"] * 16 + event[2] state["t"] = t if name == "note": tr, d, c, p = event[3:7] shift = t + d - (state["cur_pos"] + img_len) if shift > 0: img[:, :-shift] = img[:, shift:] img[:, -shift:] = 255 state["cur_pos"] += shift t = t - state["cur_pos"] img[p * 2:(p + 1) * 2, t: t + d] = colors[(tr, c)] def get_img(): t = state["t"] - state["cur_pos"] img_new = img.copy() img_new[:, t: t + 2] = 0 return PIL.Image.fromarray(np.flip(img_new, 0)) disable_patch_change = False disable_channels = None if tab == 0: i = 0 mid = [[tokenizer.bos_id] + [tokenizer.pad_id] * (tokenizer.max_token_seq - 1)] patches = {} 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, c, p])) mid_seq = mid mid = np.asarray(mid, dtype=np.int64) if len(instruments) > 0 or drum_kit != "None": disable_patch_change = True disable_channels = [i for i in range(16) if i not in patches] elif mid is not None: mid = tokenizer.tokenize(MIDI.midi2score(mid)) mid = np.asarray(mid, dtype=np.int64) mid = mid[:int(midi_events)] max_len += len(mid) for token_seq in mid: mid_seq.append(token_seq) draw_event(token_seq) generator = generate(mid, 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, amp=amp) for token_seq in generator: mid_seq.append(token_seq) draw_event(token_seq) yield mid_seq, get_img(), None, None mid = tokenizer.detokenize(mid_seq) with open(f"output.mid", 'wb') as f: f.write(MIDI.score2midi(mid)) audio = synthesis(MIDI.score2opus(mid), soundfont_path) yield mid_seq, get_img(), "output.mid", (44100, audio) def cancel_run(mid_seq): mid = tokenizer.detokenize(mid_seq) with open(f"output.mid", 'wb') as f: f.write(MIDI.score2midi(mid)) audio = synthesis(MIDI.score2opus(mid), soundfont_path) return "output.mid", (44100, audio) def load_model(path): ckpt = torch.load(path, map_location="cpu") state_dict = ckpt.get("state_dict", ckpt) model.load_state_dict(state_dict, strict=False) model.eval() return "success" def get_model_path(): model_paths = sorted(glob.glob("**/*.ckpt", recursive=True)) return model_path_input.update(choices=model_paths) 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()} if __name__ == "__main__": parser = argparse.ArgumentParser() 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") soundfont_path = hf_hub_download(repo_id="skytnt/midi-model", filename="soundfont.sf2") opt = parser.parse_args() tokenizer = MIDITokenizer() model = MIDIModel(tokenizer).to(device=opt.device) app = gr.Blocks() with app: with gr.Accordion(label="Model option", open=False): load_model_path_btn = gr.Button("Get Models") model_path_input = gr.Dropdown(label="model") load_model_path_btn.click(get_model_path, [], model_path_input) load_model_btn = gr.Button("Load") model_msg = gr.Textbox() load_model_btn.click( load_model, model_path_input, model_msg ) tab_select = gr.Variable(value=0) with gr.Tabs(): with gr.TabItem("instrument 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") example1 = gr.Examples([ [[], "None"], [["Acoustic Grand"], "None"], [["Acoustic Grand", "Violin", "Viola", "Cello", "Contrabass"], "Orchestra"], [["Flute", "Cello", "Bassoon", "Tuba"], "None"], [["Violin", "Viola", "Cello", "Contrabass", "Trumpet", "French Horn", "Brass Section", "Flute", "Piccolo", "Tuba", "Trombone", "Timpani"], "Orchestra"], [["Acoustic Guitar(nylon)", "Acoustic Guitar(steel)", "Electric Guitar(jazz)", "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) tab1.select(lambda: 0, None, tab_select, queue=False) tab2.select(lambda: 1, None, tab_select, queue=False) input_gen_events = gr.Slider(label="generate n midi events", minimum=1, maximum=4096, step=1, value=512) 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=0.98) input_top_k = gr.Slider(label="top k", minimum=1, maximum=20, step=1, value=12) input_allow_cc = gr.Checkbox(label="allow midi cc event", value=True) input_amp = gr.Checkbox(label="enable amp", value=True) example3 = gr.Examples([[1, 0.98, 12], [1.2, 0.95, 8]], [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.Variable() output_midi_img = gr.Image(label="output image") output_midi = gr.File(label="output midi", file_types=[".mid"]) output_audio = gr.Audio(label="output audio", format="mp3") run_event = run_btn.click(run, [tab_select, input_instruments, input_drum_kit, input_midi, input_midi_events, input_gen_events, input_temp, input_top_p, input_top_k, input_allow_cc, input_amp], [output_midi_seq, output_midi_img, output_midi, output_audio]) stop_btn.click(cancel_run, output_midi_seq, [output_midi, output_audio], cancels=run_event, queue=False) app.queue(1).launch(server_port=opt.port)