app.py

import re
import os
import time
import torch
import shutil
import argparse
import warnings
import gradio as gr
from transformers import GPT2Config
from model import Patchilizer, TunesFormer
from convert import abc2xml, xml2, xml2img
from utils import (
    PATCH_NUM_LAYERS,
    PATCH_LENGTH,
    CHAR_NUM_LAYERS,
    PATCH_SIZE,
    SHARE_WEIGHTS,
    WEIGHTS_PATH,
    TEMP_DIR,
    TEYVAT,
    DEVICE,
)


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",
    )
    return parser.parse_args()


def generate_music(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} Style 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))
    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 audio, midi, pdf, xml, mxl, tunes, jpg

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


def infer(p, k, t, region: str):
    if os.path.exists(TEMP_DIR):
        shutil.rmtree(TEMP_DIR)

    os.makedirs(TEMP_DIR, exist_ok=True)
    parser = argparse.ArgumentParser()
    args = get_args(parser)
    args.top_p = p
    args.top_k = k
    args.temperature = t
    if region == "Natlan":
        region = "Teyvat"

    return generate_music(args, region)


if __name__ == "__main__":
    warnings.filterwarnings("ignore")
    gr.Interface(
        fn=infer,
        inputs=[
            gr.Slider(0.01, 1.0, 0.8, step=0.01, label="Top-P sample"),
            gr.Slider(0, 80, 8, step=1, label="Top-K sample (0=closed)"),
            gr.Slider(0.01, 2.0, 1.2, step=0.01, label="Temperature"),
            gr.Dropdown(
                choices=TEYVAT,
                value="Mondstadt",
                label="Region",
            ),
        ],
        outputs=[
            gr.Audio(label="Audio", type="filepath"),
            gr.File(label="Download MIDI"),
            gr.File(label="Download PDF"),
            gr.File(label="Download MusicXML"),
            gr.File(label="Download MXL"),
            gr.Textbox(label="ABC notation", show_copy_button=True),
            gr.Image(label="Staff", type="filepath", show_share_button=False),
        ],
        flagging_mode="never",
        title="Genshin Music Generation",
        description="""
    Welcome to this space based on the Tunesformer open source project, which is totally free! The current model is still in debugging, the plan is in the Genshin Impact after the main line is killed, all countries and regions after all the characters are open, the second creation of the concert will be complete and the sample is balanced, at that time to re-fine-tune the model and add the reality of the style of screening to assist in the game of each country's output to strengthen the learning in order to enhance the output differentiation and quality. Note: Data engineering on the Star Rail is in operation, and will hopefully be baselined in the future as well with the mainline kill.<br>
    Data source: <a href="https://musescore.org">MuseScore</a> Tags source: <a href="https://genshin-impact.fandom.com/wiki/Genshin_Impact_Wiki">Genshin Impact Wiki | Fandom</a> Model base: <a href="https://github.com/sander-wood/tunesformer">Tunesformer</a>
    """,
    ).launch()