|
|
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) |
|
|
|
