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import os
import sys
import time
import torch
import requests
import subprocess
import modelscope
import huggingface_hub
from tqdm import tqdm
TEMP_DIR = "./__pycache__"
WEIGHTS_DIR = (
huggingface_hub.snapshot_download("monetjoe/EMelodyGen", cache_dir=TEMP_DIR)
if os.getenv("language")
else modelscope.snapshot_download("monetjoe/EMelodyGen", cache_dir=TEMP_DIR)
)
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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
PATCH_SAMPLING_BATCH_SIZE = 0 # Batch size for training patch, 0 for full context
LOAD_FROM_CHECKPOINT = True # Whether to load weights from a checkpoint
SHARE_WEIGHTS = False # Whether to share weights between the encoder and decoder
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}")
time.sleep(10)
download(filename, url)
if sys.platform.startswith("linux"):
apkname = "MuseScore.AppImage"
extra_dir = "squashfs-root"
download(filename=apkname, url=os.getenv("mscore"))
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 = os.getenv("mscore")
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