| import spaces |
| import argparse |
| import glob |
| import json |
| import os |
| import time |
|
|
| 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, 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 |
| 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: |
| 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, generator=generator) |
| 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 create_msg(name, data): |
| return {"name": name, "data": data} |
|
|
|
|
| def send_msgs(msgs): |
| return json.dumps(msgs) |
|
|
| def get_duration(model_name, tab, mid_seq, continuation_state, 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 "large" in model_name: |
| return gen_events // 10 |
| else: |
| return gen_events // 20 |
|
|
|
|
| @spaces.GPU(duration=get_duration) |
| def run(model_name, tab, mid_seq, continuation_state, 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, dtype=torch.bfloat16 if opt.device == "cuda" else torch.float32).eval() |
| 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 |
| from random import randint |
| if seed_rand: |
| seed = np.random.randint(0, MAX_SEED) |
| seed = randint(0, MAX_SEED) |
| print(f"Random Seed: {seed}") |
| print(f"Numpy Randint: {np.random.randint(0, MAX_SEED)}, Python Randint: {randint(0, MAX_SEED)}") |
| generator = torch.Generator(opt.device).manual_seed(seed) |
| print(f"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_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 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 = np.asarray(mid, dtype=np.int64) |
| mid = mid[:int(midi_events)] |
| mid_seq = [] |
| for token_seq in mid: |
| mid_seq.append(token_seq.tolist()) |
| elif tab == 2 and mid_seq is not None: |
| continuation_state.append(len(mid_seq)) |
| mid = np.asarray(mid_seq, dtype=np.int64) |
| else: |
| continuation_state = [0] |
| mid_seq = [] |
| mid = None |
|
|
| if mid is not None: |
| max_len += len(mid) |
|
|
| events = [tokenizer.tokens2event(tokens) for tokens in mid_seq] |
| init_msgs = [create_msg("progress", [0, gen_events])] |
| if tab != 2: |
| init_msgs += [create_msg("visualizer_clear", tokenizer.version), |
| create_msg("visualizer_append", events)] |
| yield mid_seq, continuation_state, None, None, seed, send_msgs(init_msgs) |
| ctx = torch.amp.autocast(device_type=opt.device, dtype=torch.bfloat16, enabled=opt.device != "cpu") |
| with ctx: |
| midi_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, generator=generator) |
| events = [] |
| t = time.time() + 1 |
| for i, token_seq in enumerate(midi_generator): |
| token_seq = token_seq.tolist() |
| mid_seq.append(token_seq) |
| events.append(tokenizer.tokens2event(token_seq)) |
| ct = time.time() |
| if ct - t > 0.5: |
| yield (mid_seq, continuation_state, None, None, seed, |
| send_msgs([create_msg("visualizer_append", events), |
| create_msg("progress", [i + 1, gen_events])])) |
| t = ct |
| events = [] |
|
|
| events = [tokenizer.tokens2event(tokens) for tokens in mid_seq] |
| mid = tokenizer.detokenize(mid_seq) |
| audio = synthesizer.synthesis(MIDI.score2opus(mid)) |
| with open(f"output.mid", 'wb') as f: |
| f.write(MIDI.score2midi(mid)) |
| end_msgs = [create_msg("visualizer_clear", tokenizer.version), |
| create_msg("visualizer_append", events), |
| create_msg("visualizer_end", None), |
| create_msg("progress", [0, 0])] |
| yield mid_seq, continuation_state, "output.mid", (44100, audio), seed, send_msgs(end_msgs) |
|
|
|
|
| def cancel_run(model_name, mid_seq): |
| if mid_seq is None: |
| return None, None, [] |
| tokenizer = models[model_name].tokenizer |
| events = [tokenizer.tokens2event(tokens) for tokens in mid_seq] |
| mid = tokenizer.detokenize(mid_seq) |
| audio = synthesizer.synthesis(MIDI.score2opus(mid)) |
| with open(f"output.mid", 'wb') as f: |
| f.write(MIDI.score2midi(mid)) |
| end_msgs = [create_msg("visualizer_clear", tokenizer.version), |
| create_msg("visualizer_append", events), |
| create_msg("visualizer_end", None), |
| create_msg("progress", [0, 0])] |
| return "output.mid", (44100, audio), send_msgs(end_msgs) |
|
|
|
|
| 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([]) |
| mid_seq = mid_seq[:continuation_state[-1]] |
| continuation_state = continuation_state[:-1] |
| tokenizer = models[model_name].tokenizer |
| events = [tokenizer.tokens2event(tokens) for tokens in mid_seq] |
| end_msgs = [create_msg("visualizer_clear", tokenizer.version), |
| create_msg("visualizer_append", events), |
| create_msg("visualizer_end", None), |
| create_msg("progress", [0, 0])] |
| 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: |
| javascript += f"\n<!-- {path} --><script>{jsfile.read()}</script>" |
| template_response_ori = gr.routes.templates.TemplateResponse |
|
|
| def template_response(*args, **kwargs): |
| res = template_response_ori(*args, **kwargs) |
| res.body = res.body.replace( |
| b'</head>', f'{javascript}</head>'.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("--max-gen", type=int, default=1024, help="max") |
| opt = parser.parse_args() |
| soundfont_path = hf_hub_download_retry(repo_id="skytnt/midi-model", filename="soundfont.sf2") |
| 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 (tv1-medium) by skytnt": ["skytnt/midi-model-ft", "jpop/", "tv1-medium"], |
| "touhou finetune model (tv1-medium) by skytnt": ["skytnt/midi-model-ft", "touhou/", "tv1-medium"], |
| } |
| models = {} |
| if opt.device == "cuda": |
| pass |
| |
| |
| 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) |
| models[name] = model |
|
|
| load_javascript() |
| app = gr.Blocks() |
| with app: |
| gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Midi Composer</h1>") |
| gr.Markdown("\n\n" |
| "Midi event transformer for 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)" |
| " for faster running and longer generation\n\n" |
| "**Update v1.2**: Optimise the tokenizer and dataset" |
| ) |
| 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. Just click the generate button") |
| 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=0.98) |
| input_top_k = gr.Slider(label="top k", minimum=1, maximum=128, step=1, value=12) |
| input_allow_cc = gr.Checkbox(label="allow midi cc event", value=True) |
| example3 = gr.Examples([[1, 0.95, 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]) |
| output_midi_visualizer = gr.HTML(elem_id="midi_visualizer_container") |
| output_audio = gr.Audio(label="output audio", format="mp3", elem_id="midi_audio") |
| output_midi = gr.File(label="output midi", file_types=[".mid"]) |
| run_event = run_btn.click(run, [input_model, tab_select, output_midi_seq, output_continuation_state, |
| 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, |
| output_midi, output_audio, input_seed, js_msg], |
| concurrency_limit=10) |
| stop_btn.click(cancel_run, [input_model, output_midi_seq], |
| [output_midi, output_audio, js_msg], |
| 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) |
|
|
