Upload app_onnx.py

app_onnx.py

@@ -0,0 +1,625 @@
+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()