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| 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 | |
| from transformers import DynamicCache | |
| 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" | |
| 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) | |
| cache1 = DynamicCache() | |
| with bar: | |
| while cur_len < max_len: | |
| end = [False] * batch_size | |
| hidden = model.forward(input_tensor[:, -1:], cache=cache1)[:, -1] | |
| next_token_seq = None | |
| event_names = [""] * batch_size | |
| cache2 = DynamicCache() | |
| 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) | |
| x = next_token_seq | |
| if i != 0: | |
| hidden = None | |
| x = x[:, -1:] | |
| logits = model.forward_token(hidden, x, cache=cache2)[:, -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 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): | |
| t = gen_events // 23 | |
| if "large" in model_name: | |
| t = gen_events // 14 | |
| return t + 5 | |
| 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() + 1 | |
| 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 > 0.5: | |
| 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())) | |
| if OUTPUT_BATCH_SIZE == 1: | |
| return outputs[0] | |
| 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<!-- {path} --><script>{js_content}</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("--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", { | |
| "jpop": "skytnt/midi-model-tv2om-jpop-lora", | |
| "touhou": "skytnt/midi-model-tv2om-touhou-lora" | |
| } | |
| ], | |
| "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", {} | |
| ] | |
| } | |
| 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, loras) 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).eval() | |
| models[name] = model | |
| for lora_name, lora_repo in loras.items(): | |
| model = MIDIModel(config=MIDIModelConfig.from_name(config)) | |
| model.load_state_dict(state_dict, strict=False) | |
| print(f"loading lora {lora_repo} for {name}") | |
| model = model.load_merge_lora(lora_repo) | |
| model.to(device="cpu", dtype=torch.float32).eval() | |
| models[f"{name} with {lora_name} lora"] = 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 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=0.95) | |
| 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() | |