import spaces
import random
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
OUTPUT_BATCH_SIZE = 4
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)
with bar:
while cur_len < max_len:
end = [False] * batch_size
hidden = model.forward(input_tensor)[:, -1]
next_token_seq = None
event_names = [""] * batch_size
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)
logits = model.forward_token(hidden, next_token_seq)[:, -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):
if "large" in model_name:
return gen_events // 10 + 15
else:
return gen_events // 20 + 15
@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()
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 > 0.2:
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:
return None, None, []
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])
audio = synthesizer.synthesis(MIDI.score2opus(mid))
with open(f"outputs/output{i + 1}.mid", 'wb') as f:
f.write(MIDI.score2midi(mid))
outputs += [(44100, audio), 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 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:
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
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 (tv2o-medium) by skytnt": ["skytnt/midi-model-ft", "jpop-tv2o-medium/", "tv2o-medium"],
"touhou finetune model (tv2o-medium) by skytnt": ["skytnt/midi-model-ft", "touhou-tv2o-medium/", "tv2o-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) 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)
models[name] = model
load_javascript()
app = gr.Blocks()
with app:
gr.Markdown("Midi Composer
")
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)"
" 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=0.98)
input_top_k = gr.Slider(label="top k", minimum=1, maximum=128, step=1, value=30)
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])
batch_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"])
batch_outputs += [output_audio, output_midi]
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)
run_event.then(fn=finish_run,
inputs=[input_model, output_midi_seq],
outputs=batch_outputs + [js_msg],
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)