import argparse import glob import os.path import gradio as gr import tqdm import json import MIDI from midi_synthesizer import synthesis in_space = os.getenv("SYSTEM") == "spaces" def generate(model, 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): 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 = np.full((1, max_token_seq), tokenizer.pad_id, dtype=np.int64) 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 = prompt input_tensor = input_tensor[None, :, :] cur_len = input_tensor.shape[1] bar = tqdm.tqdm(desc="generating", total=max_len - cur_len, disable=in_space) with bar: while cur_len < max_len: end = False hidden = model[0].run(None, {'x': input_tensor})[0][:, -1] next_token_seq = np.empty((1, 0), dtype=np.int64) event_name = "" for i in range(max_token_seq): mask = np.zeros(tokenizer.vocab_size, dtype=np.int64) 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[1].run(None, {'x': next_token_seq, "hidden": hidden})[0][:, -1:] scores = softmax(logits / temp, -1) * mask sample = 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 = np.concatenate([next_token_seq, sample], axis=1) if len(tokenizer.events[event_name]) == i: break if next_token_seq.shape[1] < max_token_seq: next_token_seq = np.pad(next_token_seq, ((0, 0), (0, max_token_seq - next_token_seq.shape[-1])), mode="constant", constant_values=tokenizer.pad_id) next_token_seq = next_token_seq[None, :, :] input_tensor = np.concatenate([input_tensor, next_token_seq], axis=1) cur_len += 1 bar.update(1) yield next_token_seq.reshape(-1) if end: break def create_msg(name, data): return {"name": name, "data": data} def run(model_name, tab, instruments, drum_kit, mid, midi_events, gen_events, temp, top_p, top_k, allow_cc): mid_seq = [] gen_events = int(gen_events) max_len = gen_events 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: 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.tolist()) init_msgs = [create_msg("visualizer_clear", None)] for tokens in mid_seq: init_msgs.append(create_msg("visualizer_append", tokenizer.tokens2event(tokens))) yield mid_seq, None, None, init_msgs model = models[model_name] generator = generate(model, 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) for i, token_seq in enumerate(generator): token_seq = token_seq.tolist() mid_seq.append(token_seq) event = tokenizer.tokens2event(token_seq) yield mid_seq, None, None, [create_msg("visualizer_append", event), create_msg("progress", [i + 1, gen_events])] 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, "output.mid", (44100, audio), [create_msg("visualizer_end", None)] def cancel_run(mid_seq): if mid_seq is None: return 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) return "output.mid", (44100, audio), [create_msg("visualizer_end", None)] 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: 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 class JSMsgReceiver(gr.HTML): def __init__(self, **kwargs): super().__init__(elem_id="msg_receiver", visible=False, **kwargs) def postprocess(self, y): if y: y = f"
{json.dumps(y)}
" return super().postprocess(y) def get_block_name(self) -> str: return "html" 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("--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("--max-gen", type=int, default=1024, help="max") opt = parser.parse_args() soundfont_path = hf_hub_download(repo_id="skytnt/midi-model", filename="soundfont.sf2") models_info = {"generic pretrain model": ["skytnt/midi-model", ""], "j-pop finetune model": ["skytnt/midi-model-ft", "jpop/"], "touhou finetune model": ["skytnt/midi-model-ft", "touhou/"]} models = {} tokenizer = MIDITokenizer() providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] for name, (repo_id, path) in models_info.items(): model_base_path = hf_hub_download(repo_id=repo_id, filename=f"{path}onnx/model_base.onnx") model_token_path = hf_hub_download(repo_id=repo_id, filename=f"{path}onnx/model_token.onnx") model_base = rt.InferenceSession(model_base_path, providers=providers) model_token = rt.InferenceSession(model_token_path, providers=providers) models[name] = [model_base, model_token] load_javascript() app = gr.Blocks() with app: gr.Markdown("