app_onnx.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 onnxruntime as rt
import tqdm
from huggingface_hub import hf_hub_download

import MIDI
from midi_synthesizer import MidiSynthesizer
from midi_tokenizer import MIDITokenizer

MAX_SEED = np.iinfo(np.int32).max
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, generator=None):
    if generator is None:
        generator = np.random
    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([generator.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 apply_io_binding(model: rt.InferenceSession, inputs, outputs, batch_size, past_len, cur_len):
    io_binding = model.io_binding()
    for input_ in  model.get_inputs():
        name = input_.name
        if name.startswith("past_key_values"):
            present_name = name.replace("past_key_values", "present")
            if present_name in outputs:
                v = outputs[present_name]
            else:
                v = rt.OrtValue.ortvalue_from_shape_and_type(
                    (batch_size, input_.shape[1], past_len, input_.shape[3]),
                    element_type=np.float32,
                    device_type=device)
            inputs[name] = v
        else:
            v = inputs[name]
        io_binding.bind_ortvalue_input(name, v)

    for output in model.get_outputs():
        name = output.name
        if name.startswith("present"):
            v = rt.OrtValue.ortvalue_from_shape_and_type(
                (batch_size, output.shape[1], cur_len, output.shape[3]),
                element_type=np.float32,
                device_type=device)
            outputs[name] = v
        else:
            v = outputs[name]
        io_binding.bind_ortvalue_output(name, v)
    return io_binding

def generate(model, 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[2]
    if disable_channels is not None:
        disable_channels = [tokenizer.parameter_ids["channel"][c] for c in disable_channels]
    else:
        disable_channels = []
    if generator is None:
        generator = np.random
    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
        input_tensor = input_tensor[None, :, :]
        input_tensor = np.repeat(input_tensor, repeats=batch_size, axis=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 = prompt
    cur_len = input_tensor.shape[1]
    bar = tqdm.tqdm(desc="generating", total=max_len - cur_len, disable=in_space)
    model0_inputs = {}
    model0_outputs = {}
    emb_size = 1024
    for output in model[0].get_outputs():
        if output.name == "hidden":
            emb_size = output.shape[2]
    past_len = 0
    with bar:
        while cur_len < max_len:
            end = [False] * batch_size
            model0_inputs["x"] = rt.OrtValue.ortvalue_from_numpy(input_tensor[:, past_len:], device_type=device)
            model0_outputs["hidden"] = rt.OrtValue.ortvalue_from_shape_and_type(
                (batch_size, cur_len - past_len, emb_size),
                element_type=np.float32,
                device_type=device)
            io_binding = apply_io_binding(model[0], model0_inputs, model0_outputs, batch_size, past_len, cur_len)
            io_binding.synchronize_inputs()
            model[0].run_with_iobinding(io_binding)
            io_binding.synchronize_outputs()

            hidden = model0_outputs["hidden"].numpy()[:, -1:]
            next_token_seq = np.zeros((batch_size, 0), dtype=np.int64)
            event_names = [""] * batch_size
            model1_inputs = {"hidden": rt.OrtValue.ortvalue_from_numpy(hidden, device_type=device)}
            model1_outputs = {}
            for i in range(max_token_seq):
                mask = np.zeros((batch_size, tokenizer.vocab_size), dtype=np.int64)
                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[:, None, :]
                x = next_token_seq
                if i != 0:
                    # cached
                    if i == 1:
                        hidden = np.zeros((batch_size, 0, emb_size), dtype=np.float32)
                        model1_inputs["hidden"] = rt.OrtValue.ortvalue_from_numpy(hidden, device_type=device)
                    x = x[:, -1:]
                model1_inputs["x"] = rt.OrtValue.ortvalue_from_numpy(x, device_type=device)
                model1_outputs["y"] = rt.OrtValue.ortvalue_from_shape_and_type(
                    (batch_size, 1, tokenizer.vocab_size),
                    element_type=np.float32,
                    device_type=device
                )
                io_binding = apply_io_binding(model[1], model1_inputs, model1_outputs, batch_size, i, i+1)
                io_binding.synchronize_inputs()
                model[1].run_with_iobinding(io_binding)
                io_binding.synchronize_outputs()
                logits = model1_outputs["y"].numpy()
                scores = softmax(logits / temp, -1) * mask
                samples = sample_top_p_k(scores, top_p, top_k, 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 = np.concatenate([next_token_seq, samples], axis=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 = 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)
            past_len = cur_len
            cur_len += 1
            bar.update(1)
            yield next_token_seq[:, 0]
            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 // 28
    if "large" in model_name:
        t = gen_events // 20
    return t + 10


@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_base = rt.InferenceSession(model[0], providers=providers)
    model_token = rt.InferenceSession(model[1], providers=providers)
    tokenizer = model[2]
    model = [model_base, model_token, 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 = np.random.RandomState(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][2]
    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][2]
    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][2]
    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


def get_tokenizer(repo_id):
    config_path = hf_hub_download_retry(repo_id=repo_id, filename=f"config.json")
    with open(config_path, "r") as f:
        config = json.load(f)
    tokenizer = MIDITokenizer(config["tokenizer"]["version"])
    tokenizer.set_optimise_midi(config["tokenizer"]["optimise_midi"])
    return tokenizer


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("--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 = {}
    providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
    device = "cuda"

    for name, (repo_id, path, loras) 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")
        tokenizer = get_tokenizer(repo_id)
        models[name] = [model_base_path, model_token_path, tokenizer]
        for lora_name, lora_repo in loras.items():
            model_base_path = hf_hub_download_retry(repo_id=lora_repo, filename=f"onnx/model_base.onnx")
            model_token_path = hf_hub_download_retry(repo_id=lora_repo, filename=f"onnx/model_token.onnx")
            models[f"{name} with {lora_name} lora"] = [model_base_path, model_token_path, tokenizer]

    load_javascript()
    app = gr.Blocks(theme=gr.themes.Soft())
    with app:
        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Midi Composer</h1>")
        gr.Markdown("\n\n"
                    "A modified version of the Midi-Generator for the IAT-360 Course by Ethan Lum\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\n\n"
                    "The current **best** model: generic pretrain model (tv2o-medium) by skytnt"
                    )
        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=0.95)
            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], 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()