sync

.gitattributes

@@ -1,35 +1,36 @@
 *.7z filter=lfs diff=lfs merge=lfs -text
 *.arrow filter=lfs diff=lfs merge=lfs -text
 *.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
 *.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
 *.ftz filter=lfs diff=lfs merge=lfs -text
 *.gz filter=lfs diff=lfs merge=lfs -text
 *.h5 filter=lfs diff=lfs merge=lfs -text
 *.joblib filter=lfs diff=lfs merge=lfs -text
 *.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
 *.model filter=lfs diff=lfs merge=lfs -text
 *.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
 *.onnx filter=lfs diff=lfs merge=lfs -text
 *.ot filter=lfs diff=lfs merge=lfs -text
 *.parquet filter=lfs diff=lfs merge=lfs -text
 *.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
 *.pt filter=lfs diff=lfs merge=lfs -text
 *.pth filter=lfs diff=lfs merge=lfs -text
 *.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
 saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
 *.tflite filter=lfs diff=lfs merge=lfs -text
 *.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
 *.xz filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*.tfevents* filter=lfs diff=lfs merge=lfs -text
+*.db* filter=lfs diff=lfs merge=lfs -text
+*.ark* filter=lfs diff=lfs merge=lfs -text
+**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text
+**/*ckpt*.meta filter=lfs diff=lfs merge=lfs -text
+**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.sf3 filter=lfs diff=lfs merge=lfs -text
+*.AppImage filter=lfs diff=lfs merge=lfs -textlibnss3.so filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,8 @@
+__pycache__/*
+output/*
+rename.sh
+test.py
+gpt2-abcmusic/*
+*.pth
+flagged/*
+mscore3/*

README.md

@@ -1,10 +1,10 @@
 ---
 title: HoyoGPT
-emoji: 🐢
-colorFrom: pink
-colorTo: purple
+emoji: 🎹
+colorFrom: green
+colorTo: pink
 sdk: gradio
-sdk_version: 4.12.0
+sdk_version: 4.39.0
 app_file: app.py
 pinned: false
 license: mit

app.py

@@ -0,0 +1,243 @@
+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} 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 tunes, midi, pdf, xml, mxl, jpg, audio
+
+    except Exception as e:
+        print(f"Invalid abc generated: {e}, retrying...")
+        return generate_music(args, region)
+
+
+def infer(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)
+    return generate_music(args, TEYVAT[region])
+
+
+if __name__ == "__main__":
+    warnings.filterwarnings("ignore")
+    with gr.Blocks() as demo:
+        gr.Markdown(
+            """
+<center>欢迎使用此创空间, 此创空间由bilibili <a href="https://space.bilibili.com/30620472">@亦真亦幻Studio</a> 基于 Tunesformer 开源项目制作，完全免费。</center>
+<center>Welcome to this space made by bilibili <a href="https://space.bilibili.com/30620472">@MuGeminorum</a> based on the Tunesformer open source project, which is totally free!</center>"""
+        )
+        with gr.Row():
+            with gr.Column():
+                region_opt = gr.Dropdown(
+                    choices=list(TEYVAT.keys()),
+                    value="蒙德 Mondstadt",
+                    label="地区风格 Region",
+                )
+                gen_btn = gr.Button("生成 Generate")
+                gr.Markdown(
+                    """
+    <center>
+    当前模型还在调试中，计划在原神主线杀青后，所有国家地区角色全部开放后，二创音乐会齐全且样本均衡，届时重新微调模型并添加现实风格筛选辅助游戏各国家输出强化学习，以提升输出区分度与质量。<br>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.
+
+    数据来源 (Data source): <a href="https://musescore.org">MuseScore</a><br>
+    Tag 嵌入数据来源 (Tags source): <a href="https://genshin-impact.fandom.com/wiki/Genshin_Impact_Wiki">Genshin Impact Wiki | Fandom</a><br>
+    模型基础 (Model base): <a href="https://github.com/sander-wood/tunesformer">Tunesformer</a>
+
+    注：崩铁方面数据工程正在运作中，未来也希望随主线杀青而基线化。<br>Note: Data engineering on the Star Rail 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="音频 (Audio)", type="filepath")
+                dld_midi = gr.components.File(label="下载 MIDI (Download MIDI)")
+                pdf_score = gr.components.File(label="下载 PDF 乐谱 (Download PDF)")
+                dld_xml = gr.components.File(label="下载 MusicXML (Download MusicXML)")
+                dld_mxl = gr.components.File(label="下载 MXL (Download MXL)")
+                abc_output = gr.Textbox(label="abc notation", show_copy_button=True)
+                img_score = gr.Image(label="五线谱 (Staff)", type="filepath")
+
+        gen_btn.click(
+            infer,
+            inputs=region_opt,
+            outputs=[
+                abc_output,
+                dld_midi,
+                pdf_score,
+                dld_xml,
+                dld_mxl,
+                img_score,
+                wav_output,
+            ],
+        )
+
+    demo.launch()

convert.py

@@ -0,0 +1,61 @@
+import os
+import fitz
+import subprocess
+from PIL import Image
+from music21 import converter
+from utils import MSCORE
+
+
+def abc2xml(abc_content, output_xml_path):
+    score = converter.parse(abc_content, format="abc")
+    score.write("musicxml", fp=output_xml_path, encoding="utf-8")
+    return output_xml_path
+
+
+def xml2(xml_path: str, target_fmt: str):
+    src_fmt = os.path.basename(xml_path).split(".")[-1]
+    if not "." in target_fmt:
+        target_fmt = "." + target_fmt
+
+    target_file = xml_path.replace(f".{src_fmt}", target_fmt)
+    command = [MSCORE, "-o", target_file, xml_path]
+    result = subprocess.run(command)
+    print(result)
+    return target_file
+
+
+def pdf2img(pdf_path: str):
+    output_path = pdf_path.replace(".pdf", ".jpg")
+    doc = fitz.open(pdf_path)
+    # 创建一个图像列表
+    images = []
+    for page_number in range(doc.page_count):
+        page = doc[page_number]
+        # 将页面渲染为图像
+        image = page.get_pixmap()
+        # 将图像添加到列表
+        images.append(
+            Image.frombytes("RGB", [image.width, image.height], image.samples)
+        )
+    # 竖向合并图像
+    merged_image = Image.new(
+        "RGB", (images[0].width, sum(image.height for image in images))
+    )
+    y_offset = 0
+    for image in images:
+        merged_image.paste(image, (0, y_offset))
+        y_offset += image.height
+    # 保存合并后的图像为JPG
+    merged_image.save(output_path, "JPEG")
+    # 关闭PDF文档
+    doc.close()
+    return output_path
+
+
+def xml2img(xml_file: str):
+    ext = os.path.basename(xml_file).split(".")[-1]
+    pdf_score = xml_file.replace(f".{ext}", ".pdf")
+    command = [MSCORE, "-o", pdf_score, xml_file]
+    result = subprocess.run(command)
+    print(result)
+    return pdf_score, pdf2img(pdf_score)

model.py

@@ -0,0 +1,325 @@
+import re
+import torch
+import random
+from tqdm import tqdm
+from unidecode import unidecode
+from torch.utils.data import Dataset
+from transformers import GPT2Model, GPT2LMHeadModel, PreTrainedModel
+from samplings import top_p_sampling, top_k_sampling, temperature_sampling
+from utils import PATCH_SIZE, PATCH_LENGTH, PATCH_SAMPLING_BATCH_SIZE
+
+
+class Patchilizer:
+    """
+    A class for converting music bars to patches and vice versa.
+    """
+
+    def __init__(self):
+        self.delimiters = ["|:", "::", ":|", "[|", "||", "|]", "|"]
+        self.regexPattern = f"({'|'.join(map(re.escape, self.delimiters))})"
+        self.pad_token_id = 0
+        self.bos_token_id = 1
+        self.eos_token_id = 2
+
+    def split_bars(self, body):
+        """
+        Split a body of music into individual bars.
+        """
+        bars = re.split(self.regexPattern, "".join(body))
+        bars = list(filter(None, bars))
+        # remove empty strings
+        if bars[0] in self.delimiters:
+            bars[1] = bars[0] + bars[1]
+            bars = bars[1:]
+
+        bars = [bars[i * 2] + bars[i * 2 + 1] for i in range(len(bars) // 2)]
+        return bars
+
+    def bar2patch(self, bar, patch_size=PATCH_SIZE):
+        """
+        Convert a bar into a patch of specified length.
+        """
+        patch = [self.bos_token_id] + [ord(c) for c in bar] + [self.eos_token_id]
+        patch = patch[:patch_size]
+        patch += [self.pad_token_id] * (patch_size - len(patch))
+        return patch
+
+    def patch2bar(self, patch):
+        """
+        Convert a patch into a bar.
+        """
+        return "".join(
+            chr(idx) if idx > self.eos_token_id else ""
+            for idx in patch
+            if idx != self.eos_token_id
+        )
+
+    def encode(
+        self,
+        abc_code,
+        patch_length=PATCH_LENGTH,
+        patch_size=PATCH_SIZE,
+        add_special_patches=False,
+    ):
+        """
+        Encode music into patches of specified length.
+        """
+        lines = unidecode(abc_code).split("\n")
+        lines = list(filter(None, lines))  # remove empty lines
+        body = ""
+        patches = []
+        for line in lines:
+            if len(line) > 1 and (
+                (line[0].isalpha() and line[1] == ":") or line.startswith("%%score")
+            ):
+                if body:
+                    bars = self.split_bars(body)
+                    patches.extend(
+                        self.bar2patch(
+                            bar + "\n" if idx == len(bars) - 1 else bar, patch_size
+                        )
+                        for idx, bar in enumerate(bars)
+                    )
+                    body = ""
+
+                patches.append(self.bar2patch(line + "\n", patch_size))
+
+            else:
+                body += line + "\n"
+
+        if body:
+            patches.extend(
+                self.bar2patch(bar, patch_size) for bar in self.split_bars(body)
+            )
+
+        if add_special_patches:
+            bos_patch = [self.bos_token_id] * (patch_size - 1) + [self.eos_token_id]
+            eos_patch = [self.bos_token_id] + [self.eos_token_id] * (patch_size - 1)
+            patches = [bos_patch] + patches + [eos_patch]
+
+        return patches[:patch_length]
+
+    def decode(self, patches):
+        """
+        Decode patches into music.
+        """
+        return "".join(self.patch2bar(patch) for patch in patches)
+
+
+class PatchLevelDecoder(PreTrainedModel):
+    """
+    An Patch-level Decoder model for generating patch features in an auto-regressive manner.
+    It inherits PreTrainedModel from transformers.
+    """
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.patch_embedding = torch.nn.Linear(PATCH_SIZE * 128, config.n_embd)
+        torch.nn.init.normal_(self.patch_embedding.weight, std=0.02)
+        self.base = GPT2Model(config)
+
+    def forward(self, patches: torch.Tensor) -> torch.Tensor:
+        """
+        The forward pass of the patch-level decoder model.
+        :param patches: the patches to be encoded
+        :return: the encoded patches
+        """
+        patches = torch.nn.functional.one_hot(patches, num_classes=128).float()
+        patches = patches.reshape(len(patches), -1, PATCH_SIZE * 128)
+        patches = self.patch_embedding(patches.to(self.device))
+        return self.base(inputs_embeds=patches)
+
+
+class CharLevelDecoder(PreTrainedModel):
+    """
+    A Char-level Decoder model for generating the characters within each bar patch sequentially.
+    It inherits PreTrainedModel from transformers.
+    """
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.pad_token_id = 0
+        self.bos_token_id = 1
+        self.eos_token_id = 2
+        self.base = GPT2LMHeadModel(config)
+
+    def forward(
+        self,
+        encoded_patches: torch.Tensor,
+        target_patches: torch.Tensor,
+        patch_sampling_batch_size: int,
+    ):
+        """
+        The forward pass of the char-level decoder model.
+        :param encoded_patches: the encoded patches
+        :param target_patches: the target patches
+        :return: the decoded patches
+        """
+        # preparing the labels for model training
+        target_masks = target_patches == self.pad_token_id
+        labels = target_patches.clone().masked_fill_(target_masks, -100)
+        # masking the labels for model training
+        target_masks = torch.ones_like(labels)
+        target_masks = target_masks.masked_fill_(labels == -100, 0)
+        # select patches
+        if (
+            patch_sampling_batch_size != 0
+            and patch_sampling_batch_size < target_patches.shape[0]
+        ):
+            indices = list(range(len(target_patches)))
+            random.shuffle(indices)
+            selected_indices = sorted(indices[:patch_sampling_batch_size])
+            target_patches = target_patches[selected_indices, :]
+            target_masks = target_masks[selected_indices, :]
+            encoded_patches = encoded_patches[selected_indices, :]
+            labels = labels[selected_indices, :]
+
+        # get input embeddings
+        inputs_embeds = torch.nn.functional.embedding(
+            target_patches, self.base.transformer.wte.weight
+        )
+        # concatenate the encoded patches with the input embeddings
+        inputs_embeds = torch.cat(
+            (encoded_patches.unsqueeze(1), inputs_embeds[:, 1:, :]), dim=1
+        )
+        return self.base(
+            inputs_embeds=inputs_embeds, attention_mask=target_masks, labels=labels
+        )
+
+    def generate(self, encoded_patch: torch.Tensor, tokens: torch.Tensor):
+        """
+        The generate function for generating a patch based on the encoded patch and already generated tokens.
+        :param encoded_patch: the encoded patch
+        :param tokens: already generated tokens in the patch
+        :return: the probability distribution of next token
+        """
+        encoded_patch = encoded_patch.reshape(1, 1, -1)
+        tokens = tokens.reshape(1, -1)
+        # Get input embeddings
+        tokens = torch.nn.functional.embedding(tokens, self.base.transformer.wte.weight)
+        # Concatenate the encoded patch with the input embeddings
+        tokens = torch.cat((encoded_patch, tokens[:, 1:, :]), dim=1)
+        # Get output from model
+        outputs = self.base(inputs_embeds=tokens)
+        # Get probabilities of next token
+        return torch.nn.functional.softmax(outputs.logits.squeeze(0)[-1], dim=-1)
+
+
+class TunesFormer(PreTrainedModel):
+    """
+    TunesFormer is a hierarchical music generation model based on bar patching.
+    It includes a patch-level decoder and a character-level decoder.
+    It inherits PreTrainedModel from transformers.
+    """
+
+    def __init__(self, encoder_config, decoder_config, share_weights=False):
+        super().__init__(encoder_config)
+        self.pad_token_id = 0
+        self.bos_token_id = 1
+        self.eos_token_id = 2
+        if share_weights:
+            max_layers = max(
+                encoder_config.num_hidden_layers, decoder_config.num_hidden_layers
+            )
+            max_context_size = max(encoder_config.max_length, decoder_config.max_length)
+            max_position_embeddings = max(
+                encoder_config.max_position_embeddings,
+                decoder_config.max_position_embeddings,
+            )
+            encoder_config.num_hidden_layers = max_layers
+            encoder_config.max_length = max_context_size
+            encoder_config.max_position_embeddings = max_position_embeddings
+            decoder_config.num_hidden_layers = max_layers
+            decoder_config.max_length = max_context_size
+            decoder_config.max_position_embeddings = max_position_embeddings
+
+        self.patch_level_decoder = PatchLevelDecoder(encoder_config)
+        self.char_level_decoder = CharLevelDecoder(decoder_config)
+        if share_weights:
+            self.patch_level_decoder.base = self.char_level_decoder.base.transformer
+
+    def forward(
+        self,
+        patches: torch.Tensor,
+        patch_sampling_batch_size: int = PATCH_SAMPLING_BATCH_SIZE,
+    ):
+        """
+        The forward pass of the TunesFormer model.
+        :param patches: the patches to be both encoded and decoded
+        :return: the decoded patches
+        """
+        patches = patches.reshape(len(patches), -1, PATCH_SIZE)
+        encoded_patches = self.patch_level_decoder(patches)["last_hidden_state"]
+        return self.char_level_decoder(
+            encoded_patches.squeeze(0)[:-1, :],
+            patches.squeeze(0)[1:, :],
+            patch_sampling_batch_size,
+        )
+
+    def generate(
+        self,
+        patches: torch.Tensor,
+        tokens: torch.Tensor,
+        top_p: float = 1,
+        top_k: int = 0,
+        temperature: float = 1,
+        seed: int = None,
+    ):
+        """
+        The generate function for generating patches based on patches.
+        :param patches: the patches to be encoded
+        :return: the generated patches
+        """
+        patches = patches.reshape(len(patches), -1, PATCH_SIZE)
+        encoded_patches = self.patch_level_decoder(patches)["last_hidden_state"]
+        if tokens == None:
+            tokens = torch.tensor([self.bos_token_id], device=self.device)
+
+        generated_patch = []
+        random.seed(seed)
+        while True:
+            if seed != None:
+                n_seed = random.randint(0, 1000000)
+                random.seed(n_seed)
+
+            else:
+                n_seed = None
+
+            prob = (
+                self.char_level_decoder.generate(encoded_patches[0][-1], tokens)
+                .cpu()
+                .detach()
+                .numpy()
+            )
+            prob = top_p_sampling(prob, top_p=top_p, return_probs=True)
+            prob = top_k_sampling(prob, top_k=top_k, return_probs=True)
+            token = temperature_sampling(prob, temperature=temperature, seed=n_seed)
+            generated_patch.append(token)
+            if token == self.eos_token_id or len(tokens) >= PATCH_SIZE - 1:
+                break
+
+            else:
+                tokens = torch.cat(
+                    (tokens, torch.tensor([token], device=self.device)), dim=0
+                )
+
+        return generated_patch, n_seed
+
+
+class PatchilizedData(Dataset):
+    def __init__(self, items, patchilizer):
+        self.texts = []
+        for item in tqdm(items):
+            text = item["control code"] + "\n".join(
+                item["abc notation"].split("\n")[1:]
+            )
+            input_patch = patchilizer.encode(text, add_special_patches=True)
+            input_patch = torch.tensor(input_patch)
+            if torch.sum(input_patch) != 0:
+                self.texts.append(input_patch)
+
+    def __len__(self):
+        return len(self.texts)
+
+    def __getitem__(self, idx):
+        return self.texts[idx]

requirements.txt

@@ -0,0 +1,9 @@
+transformers==4.18.0
+samplings==0.1.7
+unidecode
+music21
+autopep8
+pillow==9.4.0
+pymupdf
+torch
+modelscope==1.15

utils.py

@@ -0,0 +1,70 @@
+import os
+import sys
+import time
+import torch
+import requests
+import subprocess
+from tqdm import tqdm
+from modelscope import snapshot_download
+
+TEYVAT = {
+    "蒙德 Mondstadt": "Mondstadt",
+    "璃月 Liyue": "Liyue",
+    "稻妻 Inazuma": "Inazuma",
+    "须弥 Sumeru": "Sumeru",
+    "枫丹 Fontaine": "Fontaine",
+}
+WEIGHTS_PATH = (
+    snapshot_download("MuGeminorum/hoyoMusic", cache_dir="./__pycache__")
+    + "/weights.pth"
+)
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+TEMP_DIR = "./flagged"
+PATCH_LENGTH = 128  # Patch Length
+PATCH_SIZE = 32  # Patch Size
+PATCH_NUM_LAYERS = 9  # Number of layers in the encoder
+CHAR_NUM_LAYERS = 3  # Number of layers in the decoder
+# Batch size for patch during training, 0 for full context
+PATCH_SAMPLING_BATCH_SIZE = 0
+# Whether to share weights between the encoder and decoder
+SHARE_WEIGHTS = False
+
+
+def download(filename: str, url: str):
+    try:
+        response = requests.get(url, stream=True)
+        total_size = int(response.headers.get("content-length", 0))
+        chunk_size = 1024
+        with open(filename, "wb") as file, tqdm(
+            desc=f"Downloading {filename} from '{url}'...",
+            total=total_size,
+            unit="B",
+            unit_scale=True,
+            unit_divisor=1024,
+        ) as bar:
+            for data in response.iter_content(chunk_size=chunk_size):
+                size = file.write(data)
+                bar.update(size)
+
+    except Exception as e:
+        print(f"Error: {e}, retrying...")
+        time.sleep(10)
+        download(filename, url)
+
+
+if sys.platform.startswith("linux"):
+    apkname = "MuseScore.AppImage"
+    extra_dir = "squashfs-root"
+    download(
+        filename=apkname,
+        url="https://master.dl.sourceforge.net/project/musescore.mirror/v4.2.0/MuseScore-4.2.0.233521125-x86_64.AppImage?viasf=1",
+    )
+    if not os.path.exists(extra_dir):
+        subprocess.run(["chmod", "+x", f"./{apkname}"])
+        subprocess.run([f"./{apkname}", "--appimage-extract"])
+
+    MSCORE = f"./{extra_dir}/AppRun"
+    os.environ["QT_QPA_PLATFORM"] = "offscreen"
+
+else:
+    MSCORE = "D:/Program Files/MuseScore 3/bin/MuseScore3.exe"