app.py

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 = 8
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 // 7 + 5
    else:
        return gen_events // 15 + 5


@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<!-- {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):
    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("<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)"
                    " 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)