app.py

import re
import os
import time
import torch
import shutil
import argparse
import warnings
import gradio as gr
from config import *
from utils import Patchilizer, TunesFormer, DEVICE
from convert import abc2xml, xml2, xml2img
from modelscope import snapshot_download
from transformers import GPT2Config


# 模型下载
WEIGHTS_PATH = snapshot_download("MuGeminorum/hoyoGPT") + "/weights.pth"


def get_args(parser: argparse.ArgumentParser):
    parser.add_argument(
        "-num_tunes",
        type=int,
        default=1,
        help="the number of independently computed returned tunes",
    )
    parser.add_argument(
        "-max_patch",
        type=int,
        default=128,
        help="integer to define the maximum length in tokens of each tune",
    )
    parser.add_argument(
        "-top_p",
        type=float,
        default=0.8,
        help="float to define the tokens that are within the sample operation of text generation",
    )
    parser.add_argument(
        "-top_k",
        type=int,
        default=8,
        help="integer to define the tokens that are within the sample operation of text generation",
    )
    parser.add_argument(
        "-temperature",
        type=float,
        default=1.2,
        help="the temperature of the sampling operation",
    )
    parser.add_argument("-seed", type=int, default=None, help="seed for randomstate")
    parser.add_argument(
        "-show_control_code",
        type=bool,
        default=False,
        help="whether to show control code",
    )
    args = parser.parse_args()

    return args


def generate_abc(args, region: str):
    patchilizer = Patchilizer()

    patch_config = GPT2Config(
        num_hidden_layers=PATCH_NUM_LAYERS,
        max_length=PATCH_LENGTH,
        max_position_embeddings=PATCH_LENGTH,
        vocab_size=1,
    )

    char_config = GPT2Config(
        num_hidden_layers=CHAR_NUM_LAYERS,
        max_length=PATCH_SIZE,
        max_position_embeddings=PATCH_SIZE,
        vocab_size=128,
    )

    model = TunesFormer(patch_config, char_config, share_weights=SHARE_WEIGHTS)
    checkpoint = torch.load(WEIGHTS_PATH, map_location=torch.device("cpu"))
    model.load_state_dict(checkpoint["model"])
    model = model.to(DEVICE)
    model.eval()
    prompt = f"A:{region}\n"
    tunes = ""
    num_tunes = args.num_tunes
    max_patch = args.max_patch
    top_p = args.top_p
    top_k = args.top_k
    temperature = args.temperature
    seed = args.seed
    show_control_code = args.show_control_code

    print(" Hyper parms ".center(60, "#"), "\n")
    arg_dict: dict = vars(args)
    for key in arg_dict.keys():
        print(f"{key}: {str(arg_dict[key])}")

    print("\n", " Output tunes ".center(60, "#"))
    start_time = time.time()
    for i in range(num_tunes):
        title_artist = f"T:{region} Fragment\nC:Generated by AI\n"
        tune = f"X:{str(i + 1)}\n{title_artist + prompt}"
        lines = re.split(r"(\n)", tune)
        tune = ""
        skip = False
        for line in lines:
            if show_control_code or line[:2] not in ["S:", "B:", "E:"]:
                if not skip:
                    print(line, end="")
                    tune += line

                skip = False

            else:
                skip = True

        input_patches = torch.tensor(
            [patchilizer.encode(prompt, add_special_patches=True)[:-1]], device=DEVICE
        )

        if tune == "":
            tokens = None

        else:
            prefix = patchilizer.decode(input_patches[0])
            remaining_tokens = prompt[len(prefix) :]
            tokens = torch.tensor(
                [patchilizer.bos_token_id] + [ord(c) for c in remaining_tokens],
                device=DEVICE,
            )

        while input_patches.shape[1] < max_patch:
            predicted_patch, seed = model.generate(
                input_patches,
                tokens,
                top_p=top_p,
                top_k=top_k,
                temperature=temperature,
                seed=seed,
            )
            tokens = None

            if predicted_patch[0] != patchilizer.eos_token_id:
                next_bar = patchilizer.decode([predicted_patch])

                if show_control_code or next_bar[:2] not in ["S:", "B:", "E:"]:
                    print(next_bar, end="")
                    tune += next_bar

                if next_bar == "":
                    break

                next_bar = remaining_tokens + next_bar
                remaining_tokens = ""

                predicted_patch = torch.tensor(
                    patchilizer.bar2patch(next_bar), device=DEVICE
                ).unsqueeze(0)

                input_patches = torch.cat(
                    [input_patches, predicted_patch.unsqueeze(0)], dim=1
                )

            else:
                break

        tunes += f"{tune}\n\n"
        print("\n")

    print("Generation time: {:.2f} seconds".format(time.time() - start_time))
    os.makedirs(TEMP_DIR, exist_ok=True)
    timestamp = time.strftime("%a_%d_%b_%Y_%H_%M_%S", time.localtime())
    try:
        xml = abc2xml(tunes, f"{TEMP_DIR}/[{region}]{timestamp}.musicxml")
        midi = xml2(xml, "mid")
        audio = xml2(xml, "wav")
        pdf, jpg = xml2img(xml)
        mxl = xml2(xml, "mxl")

        return tunes, midi, pdf, xml, mxl, jpg, audio

    except Exception as e:
        print(f"Invalid abc generated: {e}, retrying...")
        return generate_abc(args, region)


def inference(region: str):
    if os.path.exists(TEMP_DIR):
        shutil.rmtree(TEMP_DIR)

    parser = argparse.ArgumentParser()
    args = get_args(parser)
    return generate_abc(args, region)


if __name__ == "__main__":
    warnings.filterwarnings("ignore")

    with gr.Blocks() as demo:
        gr.Markdown(
            """<center>Welcome to this space, made by bilibili <a href="https://space.bilibili.com/30620472">@MuGeminorum</a> based on Tunesformer open source project, totally free.</center>"""
        )
        with gr.Row():
            with gr.Column():
                region_opt = gr.Dropdown(
                    choices=["Mondstadt", "Liyue", "Inazuma", "Sumeru", "Fontaine"],
                    value="Mondstadt",
                    label="Region",
                )
                gen_btn = gr.Button("Generate")
                gr.Markdown(
                    """
    <center>
    Currently, the model is still under debugging. Planned in the Genshin main line killed, all countries and regions after all the characters are open, the second creation of the concert will be complete and balanced samples, then re-fine-tune the model and add the reality of the style of screening to assist the game of the various countries output gatekeepers, in order to enhance the output of the differentiation and quality.

    Data source: <a href="https://musescore.org">MuseScore</a><br>
    Tag embedded data source: <a href="https://genshin-impact.fandom.com/wiki/Genshin_Impact_Wiki">Genshin Impact Wiki | Fandom</a><br>
    Base model: <a href="https://github.com/sander-wood/tunesformer">Tunesformer</a>

    Note: Data engineering on the Honkai: Star Rail side is in operation, and will hopefully be baselined in the future as well with the mainline kill.</center>"""
                )

            with gr.Column():
                wav_output = gr.Audio(label="Audio", type="filepath")
                dld_midi = gr.components.File(label="Download MIDI")
                pdf_score = gr.components.File(label="Download PDF score")
                dld_xml = gr.components.File(label="Download MusicXML")
                dld_mxl = gr.components.File(label="Download MXL")
                abc_output = gr.Textbox(label="abc notation", show_copy_button=True)
                img_score = gr.Image(label="Staff", type="filepath")

        gen_btn.click(
            inference,
            inputs=region_opt,
            outputs=[
                abc_output,
                dld_midi,
                pdf_score,
                dld_xml,
                dld_mxl,
                img_score,
                wav_output,
            ],
        )

    demo.launch()