Spaces:
Running
on
Zero
Running
on
Zero
File size: 15,072 Bytes
1ea42dc ed10990 1b97a00 9868b75 1ea42dc ed10990 1ea42dc 6481a43 ed10990 1ea42dc ed10990 a3e7293 9868b75 ed10990 1ea42dc 573d12d ed10990 1ea42dc ed10990 1ea42dc ed10990 1ea42dc a3e7293 ed10990 1ea42dc 573d12d ed10990 1ea42dc ed10990 1ea42dc 573d12d ed10990 1ea42dc ed10990 1ea42dc ed10990 1ea42dc ed10990 1ea42dc 6481a43 1b97a00 1ea42dc 5825808 6481a43 5825808 6481a43 1ea42dc 743aa2c 1ea42dc 1752002 1ea42dc ed10990 1ea42dc 6481a43 5825808 573d12d 1ea42dc ed10990 6481a43 5825808 1ea42dc f60d1e9 5825808 1ea42dc 5825808 1ea42dc 1b97a00 1ea42dc 5825808 6481a43 1b97a00 98bc719 1ea42dc a52dad5 1ea42dc 98bc719 573d12d 7963005 743aa2c 573d12d 1ea42dc ed10990 573d12d 98bc719 573d12d 1ea42dc 6481a43 1ea42dc 22dc587 9438ab2 942f170 ed10990 5825808 1b97a00 f88cef7 743aa2c 1ea42dc ed10990 1ea42dc ed10990 fe820fd ed10990 fe820fd ed10990 1ea42dc ed10990 1ea42dc 22dc587 a52dad5 d660a99 976ec92 d660a99 1ea42dc a52dad5 743aa2c 6481a43 6373391 6481a43 573d12d ed10990 5825808 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 |
import argparse
import glob
import os.path
import uuid
import gradio as gr
import numpy as np
import onnxruntime as rt
import tqdm
import json
from huggingface_hub import hf_hub_download
import MIDI
from midi_synthesizer import synthesis
from midi_tokenizer import MIDITokenizer
in_space = os.getenv("SYSTEM") == "spaces"
def softmax(x, axis):
x_max = np.amax(x, axis=axis, keepdims=True)
exp_x_shifted = np.exp(x - x_max)
return exp_x_shifted / np.sum(exp_x_shifted, axis=axis, keepdims=True)
def sample_top_p_k(probs, p, k):
probs_idx = np.argsort(-probs, axis=-1)
probs_sort = np.take_along_axis(probs, probs_idx, -1)
probs_sum = np.cumsum(probs_sort, axis=-1)
mask = probs_sum - probs_sort > p
probs_sort[mask] = 0.0
mask = np.zeros(probs_sort.shape[-1])
mask[:k] = 1
probs_sort = probs_sort * mask
probs_sort /= np.sum(probs_sort, axis=-1, keepdims=True)
shape = probs_sort.shape
probs_sort_flat = probs_sort.reshape(-1, shape[-1])
probs_idx_flat = probs_idx.reshape(-1, shape[-1])
next_token = np.stack([np.random.choice(idxs, p=pvals) for pvals, idxs in zip(probs_sort_flat, probs_idx_flat)])
next_token = next_token.reshape(*shape[:-1])
return next_token
def generate(model, 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):
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 = np.full((1, max_token_seq), tokenizer.pad_id, dtype=np.int64)
input_tensor[0, 0] = tokenizer.bos_id # bos
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 = prompt
input_tensor = input_tensor[None, :, :]
cur_len = input_tensor.shape[1]
bar = tqdm.tqdm(desc="generating", total=max_len - cur_len, disable=in_space)
with bar:
while cur_len < max_len:
end = False
hidden = model[0].run(None, {'x': input_tensor})[0][:, -1]
next_token_seq = np.empty((1, 0), dtype=np.int64)
event_name = ""
for i in range(max_token_seq):
mask = np.zeros(tokenizer.vocab_size, dtype=np.int64)
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[1].run(None, {'x': next_token_seq, "hidden": hidden})[0][:, -1:]
scores = softmax(logits / temp, -1) * mask
sample = sample_top_p_k(scores, top_p, top_k)
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 = np.concatenate([next_token_seq, sample], axis=1)
if len(tokenizer.events[event_name]) == i:
break
if next_token_seq.shape[1] < max_token_seq:
next_token_seq = np.pad(next_token_seq, ((0, 0), (0, max_token_seq - next_token_seq.shape[-1])),
mode="constant", constant_values=tokenizer.pad_id)
next_token_seq = next_token_seq[None, :, :]
input_tensor = np.concatenate([input_tensor, next_token_seq], axis=1)
cur_len += 1
bar.update(1)
yield next_token_seq.reshape(-1)
if end:
break
def create_msg(name, data):
return {"name": name, "data": data, "uuid": uuid.uuid4().hex}
def send_msgs(msgs, msgs_history):
msgs_history.append(msgs)
if len(msgs_history) > 50:
msgs_history.pop(0)
return json.dumps(msgs_history)
def run(model_name, tab, instruments, drum_kit, mid, midi_events, gen_events, temp, top_p, top_k, allow_cc):
msgs_history = []
mid_seq = []
gen_events = int(gen_events)
max_len = gen_events
disable_patch_change = False
disable_channels = None
if tab == 0:
i = 0
mid = [[tokenizer.bos_id] + [tokenizer.pad_id] * (tokenizer.max_token_seq - 1)]
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, 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 mid is not None:
mid = tokenizer.tokenize(MIDI.midi2score(mid))
mid = np.asarray(mid, dtype=np.int64)
mid = mid[:int(midi_events)]
max_len += len(mid)
for token_seq in mid:
mid_seq.append(token_seq.tolist())
init_msgs = [create_msg("visualizer_clear", None)]
for tokens in mid_seq:
init_msgs.append(create_msg("visualizer_append", tokenizer.tokens2event(tokens)))
yield mid_seq, None, None, send_msgs(init_msgs, msgs_history), msgs_history
model = models[model_name]
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)
for i, token_seq in enumerate(generator):
token_seq = token_seq.tolist()
mid_seq.append(token_seq)
event = tokenizer.tokens2event(token_seq)
yield mid_seq, None, None, send_msgs([create_msg("visualizer_append", event), create_msg("progress", [i + 1, gen_events])], msgs_history), msgs_history
mid = tokenizer.detokenize(mid_seq)
with open(f"output.mid", 'wb') as f:
f.write(MIDI.score2midi(mid))
audio = synthesis(MIDI.score2opus(mid), soundfont_path)
yield mid_seq, "output.mid", (44100, audio), send_msgs([create_msg("visualizer_end", None)], msgs_history), msgs_history
def cancel_run(mid_seq, msgs_history):
if mid_seq is None:
return None, None, []
mid = tokenizer.detokenize(mid_seq)
with open(f"output.mid", 'wb') as f:
f.write(MIDI.score2midi(mid))
audio = synthesis(MIDI.score2opus(mid), soundfont_path)
return "output.mid", (44100, audio), send_msgs([create_msg("visualizer_end", None)], msgs_history)
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):
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()}
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("--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")
models_info = {"generic pretrain model": ["skytnt/midi-model", ""],
"j-pop finetune model": ["skytnt/midi-model-ft", "jpop/"],
"touhou finetune model": ["skytnt/midi-model-ft", "touhou/"],
}
models = {}
tokenizer = MIDITokenizer()
providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
for name, (repo_id, path) in models_info.items():
model_base_path = hf_hub_download_retry(repo_id=repo_id, filename=f"{path}onnx/model_base.onnx")
model_token_path = hf_hub_download_retry(repo_id=repo_id, filename=f"{path}onnx/model_token.onnx")
model_base = rt.InferenceSession(model_base_path, providers=providers)
model_token = rt.InferenceSession(model_token_path, providers=providers)
models[name] = [model_base, model_token]
load_javascript()
app = gr.Blocks()
with app:
gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Midi Composer</h1>")
gr.Markdown("![Visitors](https://api.visitorbadge.io/api/visitors?path=skytnt.midi-composer&style=flat)\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"
)
js_msg_history_state = gr.State(value=[])
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("instrument 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")
example1 = gr.Examples([
[[], "None"],
[["Acoustic Grand"], "None"],
[["Acoustic Grand", "Violin", "Viola", "Cello", "Contrabass"], "Orchestra"],
[["Flute", "Cello", "Bassoon", "Tuba"], "None"],
[["Violin", "Viola", "Cello", "Contrabass", "Trumpet", "French Horn", "Brass Section",
"Flute", "Piccolo", "Tuba", "Trombone", "Timpani"], "Orchestra"],
[["Acoustic Guitar(nylon)", "Acoustic Guitar(steel)", "Electric Guitar(jazz)",
"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)
example2 = gr.Examples([[file, 128] for file in glob.glob("example/*.mid")],
[input_midi, input_midi_events])
tab1.select(lambda: 0, None, tab_select, queue=False)
tab2.select(lambda: 1, None, tab_select, queue=False)
input_gen_events = gr.Slider(label="generate 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=20, step=1, value=12)
input_allow_cc = gr.Checkbox(label="allow midi cc event", value=True)
example3 = gr.Examples([[1, 0.98, 12], [1.2, 0.95, 8]], [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_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, input_instruments, input_drum_kit, input_midi,
input_midi_events, input_gen_events, input_temp, input_top_p, input_top_k,
input_allow_cc],
[output_midi_seq, output_midi, output_audio, js_msg, js_msg_history_state],
concurrency_limit=3)
stop_btn.click(cancel_run, [output_midi_seq, js_msg_history_state], [output_midi, output_audio, js_msg], cancels=run_event, queue=False)
app.launch(server_port=opt.port, share=opt.share, inbrowser=True)
|