app.py

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
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"

@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)
 cache1 = DynamicCache()
 past_len = 0
 with bar:
 while cur_len < max_len:
 end = [False] * batch_size
 hidden = model.forward(input_tensor[:, past_len:], 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)
 past_len = cur_len
 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

@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() + 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']






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 create_arpeggio_events(chord, pattern, duration=480):
 events = []
 notes = {
 'C': [60, 64, 67],
 'D': [62, 66, 69],
 'Am': [57, 60, 64],
 'G': [55, 59, 62]
 }
 
 for step in pattern:
 note = notes[chord][step]
 events.extend([
 ['note_on', 0, 0, 0, 0, note, 80],
 ['note_off', duration, 0, 0, 0, note, 0]
 ])
 
 return events

def add_arpeggio_sequence(tokenizer, mid_seq, sequence, pattern):
 events = []
 for chord in sequence:
 events.extend(create_arpeggio_events(chord, pattern))
 
 tokens = [tokenizer.event2tokens(event) for event in events]
 mid_seq[0].extend(tokens)
 return mid_seq



 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", {
 "jpop": "skytnt/midi-model-tv2om-jpop-lora",
 "touhou": "skytnt/midi-model-tv2om-touhou-lora"
 }
 ],
 "generic pretrain model (tv2o-large) by asigalov61": [
 "asigalov61/Music-Llama", {}
 ],
 "generic pretrain model (tv2o-medium) by asigalov61": [
 "asigalov61/Music-Llama-Medium", {}
 ],
 "generic pretrain model (tv1-medium) by skytnt": [
 "skytnt/midi-model", {}
 ]
 }
 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, loras) in models_info.items():
 model = MIDIModel.from_pretrained(repo_id)
 model.to(device="cpu", dtype=torch.float32)
 models[name] = model
 for lora_name, lora_repo in loras.items():
 model = MIDIModel.from_pretrained(repo_id)
 print(f"loading lora {lora_repo} for {name}")
 model = model.load_merge_lora(lora_repo)
 model.to(device="cpu", dtype=torch.float32)
 models[f"{name} with {lora_name} lora"] = model

 load_javascript()
 app = gr.Blocks(theme=gr.themes.Soft())
 with app:
 gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Midi Composer with Arpeggios</h1>")
 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")
 
 with gr.Row():
 arpeggio_intro = gr.Button("🎵 Intro Arpeggio", variant="primary")
 arpeggio_verse = gr.Button("🎸 Verse Arpeggio", variant="primary")
 arpeggio_chorus = gr.Button("🎹 Chorus Arpeggio", variant="primary")
 arpeggio_outro = gr.Button("🎷 Outro Arpeggio", variant="primary")


 
 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")



 
 def add_intro_arpeggio(model_name, mid_seq):
 tokenizer = models[model_name].tokenizer
 sequence = ['C', 'D', 'Am', 'G']
 pattern = [0, 1, 2, 1] # Root, Third, Fifth, Third
 return add_arpeggio_sequence(tokenizer, mid_seq, sequence, pattern)

 def add_verse_arpeggio(model_name, mid_seq):
 tokenizer = models[model_name].tokenizer
 sequence = ['D', 'C', 'Am', 'G']
 pattern = [0, 2, 1, 2] # Root, Fifth, Third, Fifth
 return add_arpeggio_sequence(tokenizer, mid_seq, sequence, pattern)

 def add_chorus_arpeggio(model_name, mid_seq):
 tokenizer = models[model_name].tokenizer
 sequence = ['G', 'D', 'Am', 'C']
 pattern = [0, 1, 2, 1, 0, 2] # Root, Third, Fifth, Third, Root, Fifth
 return add_arpeggio_sequence(tokenizer, mid_seq, sequence, pattern)

 def add_outro_arpeggio(model_name, mid_seq):
 tokenizer = models[model_name].tokenizer
 sequence = ['Am', 'G', 'D', 'C']
 pattern = [2, 1, 0, 1] # Fifth, Third, Root, Third
 return add_arpeggio_sequence(tokenizer, mid_seq, sequence, pattern)

 arpeggio_intro.click(add_intro_arpeggio, [input_model, output_midi_seq], output_midi_seq)
 arpeggio_verse.click(add_verse_arpeggio, [input_model, output_midi_seq], output_midi_seq)
 arpeggio_chorus.click(add_chorus_arpeggio, [input_model, output_midi_seq], output_midi_seq)
 arpeggio_outro.click(add_outro_arpeggio, [input_model, output_midi_seq], output_midi_seq)



 
 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, ssr_mode=False)
 thread_pool.shutdown()