midi-composer / app.py
skytnt's picture
Update app.py
a3e7293
raw
history blame
13.5 kB
import argparse
import glob
import os
import os.path
from sys import exit
import shutil
import gradio as gr
import numpy as np
import onnxruntime as rt
import PIL
import PIL.ImageColor
import tqdm
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(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_base.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_token.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 run(tab, instruments, drum_kit, mid, midi_events, gen_events, temp, top_p, top_k, allow_cc):
mid_seq = []
max_len = int(gen_events)
img_len = 1024
img = np.full((128 * 2, img_len, 3), 255, dtype=np.uint8)
state = {"t1": 0, "t": 0, "cur_pos": 0}
colors = ['navy', 'blue', 'deepskyblue', 'teal', 'green', 'lightgreen', 'lime', 'orange',
'brown', 'grey', 'red', 'pink', 'aqua', 'orchid', 'bisque', 'coral']
colors = [PIL.ImageColor.getrgb(color) for color in colors]
def draw_event(tokens):
if tokens[0] in tokenizer.id_events:
name = tokenizer.id_events[tokens[0]]
if len(tokens) <= len(tokenizer.events[name]):
return
params = tokens[1:]
params = [params[i] - tokenizer.parameter_ids[p][0] for i, p in enumerate(tokenizer.events[name])]
if not all([0 <= params[i] < tokenizer.event_parameters[p] for i, p in enumerate(tokenizer.events[name])]):
return
event = [name] + params
state["t1"] += event[1]
t = state["t1"] * 16 + event[2]
state["t"] = t
if name == "note":
tr, d, c, p = event[3:7]
shift = t + d - (state["cur_pos"] + img_len)
if shift > 0:
img[:, :-shift] = img[:, shift:]
img[:, -shift:] = 255
state["cur_pos"] += shift
t = t - state["cur_pos"]
img[p * 2:(p + 1) * 2, t: t + d] = colors[c]
def get_img():
t = state["t"] - state["cur_pos"]
img_new = img.copy()
img_new[:, t: t + 2] = 0
return PIL.Image.fromarray(np.flip(img_new, 0))
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 = {}
for instr in instruments:
patches[i] = patch2number[instr]
i = (i + 1) if i != 9 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)
draw_event(token_seq)
generator = generate(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 token_seq in generator:
mid_seq.append(token_seq)
draw_event(token_seq)
yield mid_seq, get_img(), 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)
yield mid_seq, get_img(), "output.mid", (44100, audio)
def cancel_run(mid_seq):
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)
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=512, help="max")
opt = parser.parse_args()
soundfont_path = hf_hub_download(repo_id="skytnt/midi-model", filename="soundfont.sf2")
model_base_path = hf_hub_download(repo_id="skytnt/midi-model", filename="onnx/model_base.onnx")
model_token_path = hf_hub_download(repo_id="skytnt/midi-model", filename="onnx/model_token.onnx")
tokenizer = MIDITokenizer()
providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
model_base = rt.InferenceSession(model_base_path, providers=providers)
model_token = rt.InferenceSession(model_token_path, providers=providers)
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"
)
tab_select = gr.Variable(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", "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"],
[["Acoustic Grand", "String Ensemble 1", "Trombone", "Tuba", "Muted Trumpet", "French Horn", "Oboe",
"English Horn", "Bassoon", "Clarinet"], "Orchestra"]
], [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)
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=50, step=1, value=20)
input_allow_cc = gr.Checkbox(label="allow midi cc event", value=True)
run_btn = gr.Button("generate", variant="primary")
stop_btn = gr.Button("stop")
output_midi_seq = gr.Variable()
output_midi_img = gr.Image(label="output image")
output_midi = gr.File(label="output midi", file_types=[".mid"])
output_audio = gr.Audio(label="output audio", format="wav")
run_event = run_btn.click(run, [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_img, output_midi, output_audio])
stop_btn.click(cancel_run, output_midi_seq, [output_midi, output_audio], cancels=run_event, queue=False)
app.queue(2).launch(server_port=opt.port, share=opt.share, inbrowser=True)