121 lines
4.4 KiB
Python
121 lines
4.4 KiB
Python
import re
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from typing import IO, List, Tuple
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import unicodedata
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import requests
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import os
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import zipfile
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from ...lib import BASE_CACHE_DIR, BASE_MODELS_DIR
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RVC_DOWNLOAD_LINK = 'https://huggingface.co/datasets/SayanoAI/RVC-Studio/resolve/main/'
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MDX_MODELS = ["MDXNET/UVR-MDX-NET-vocal_FT.onnx"]
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KARAFAN_MODELS = ["karafan/MDX23C-8KFFT-InstVoc_HQ.ckpt"]
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VR_MODELS = ["UVR/UVR-DeEcho-DeReverb.pth","UVR/HP5-vocals+instrumentals.pth"]
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RVC_MODELS = [
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"RVC/Claire.pth",
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"RVC/Sayano.pth",
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"RVC/Mae_v2.pth",
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"RVC/Fuji.pth",
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"RVC/Monika.pth"]
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BASE_MODELS = ["hubert_base.pt", "rmvpe.pt"]
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VITS_MODELS = ["VITS/pretrained_ljs.pth"]
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PRETRAINED_MODELS = [
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"pretrained_v2/D48k.pth",
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"pretrained_v2/G48k.pth",
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"pretrained_v2/D32k.pth",
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"pretrained_v2/G32k.pth",
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"pretrained_v2/D40k.pth",
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"pretrained_v2/G40k.pth",
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"pretrained_v2/f0D48k.pth",
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"pretrained_v2/f0G48k.pth",
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"pretrained_v2/f0D40k.pth",
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"pretrained_v2/f0G40k.pth",
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"pretrained_v2/f0D32k.pth",
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"pretrained_v2/f0G32k.pth",
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"pretrained_v2/f0_RIN_E3_40k_D.pth",
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"pretrained_v2/f0_RIN_E3_40k_G.pth",
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"pretrained_v2/f0Ov2Super32kD.pth",
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"pretrained_v2/f0Ov2Super32kG.pth",
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"pretrained_v2/f0Ov2Super40kD.pth",
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"pretrained_v2/f0Ov2Super40kG.pth",
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]
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LLM_MODELS = [
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"https://huggingface.co/TheBloke/Airoboros-L2-7B-2.1-GGUF/resolve/main/airoboros-l2-7b-2.1.Q4_K_M.gguf",
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"https://huggingface.co/TheBloke/Pygmalion-2-7B-GGUF/resolve/main/pygmalion-2-7b.Q4_K_M.gguf",
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"https://huggingface.co/TheBloke/Zarablend-MX-L2-7B-GGUF/resolve/main/zarablend-mx-l2-7b.Q4_K_M.gguf",
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"https://huggingface.co/TheBloke/MythoMax-L2-Kimiko-v2-13B-GGUF/resolve/main/mythomax-l2-kimiko-v2-13b.Q4_K_M.gguf"
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]
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STT_MODELS = [
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"https://alphacephei.com/vosk/models/vosk-model-en-us-0.22-lgraph.zip"
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]
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def download_file(params: Tuple[str, str]):
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model_path, download_link = params
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if os.path.isfile(model_path): raise FileExistsError(f"{model_path} already exists!")
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os.makedirs(os.path.dirname(model_path), exist_ok=True)
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with requests.get(download_link,stream=True) as r:
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r.raise_for_status()
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with open(model_path, 'wb') as f:
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for chunk in r.iter_content(chunk_size=8192):
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f.write(chunk)
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return True
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def download_link_generator(download_link: str,model_list: List[str]):
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for model in model_list:
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model_path = os.path.join(BASE_MODELS_DIR,model)
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yield (model_path, f"{download_link}{model}")
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def save_file(params: Tuple[str, any]):
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(data_path, datum) = params
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if "zip" in os.path.splitext(data_path)[-1]: return save_zipped_files(params) # unzip
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else:
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try:
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with open(data_path,"wb") as f:
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f.write(datum)
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return f"Successfully saved file to: {data_path}"
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except Exception as e:
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return f"Failed to save file: {e}"
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def save_file_generator(save_dir: str, data: List[IO]):
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for datum in data:
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data_path = os.path.join(save_dir,datum.name)
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yield (data_path, datum.read())
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def save_zipped_files(params: Tuple[str, any]):
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(data_path, datum) = params
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try:
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print(f"saving zip file: {data_path}")
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temp_dir = os.path.join(BASE_CACHE_DIR,"zips")
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os.makedirs(temp_dir,exist_ok=True)
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name = os.path.basename(data_path)
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zip_path = os.path.join(temp_dir,name)
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with open(zip_path,"wb") as f:
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f.write(datum)
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print(f"extracting zip file: {zip_path}")
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with zipfile.ZipFile(zip_path, 'r') as zip_ref:
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zip_ref.extractall(os.path.dirname(data_path))
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print(f"finished extracting zip file")
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os.remove(zip_path) # cleanup
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return f"Successfully saved files to: {data_path}"
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except Exception as e:
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return f"Failed to save files: {e}"
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def slugify_filepath(filepath):
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# Split the path into directory and filename
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directory, filename = os.path.split(filepath)
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# Normalize the filename
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filename = unicodedata.normalize('NFKD', filename)
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# Encode the filename as ASCII and ignore errors
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filename = filename.encode('ascii', 'ignore').decode()
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# Convert the filename to lowercase
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filename = filename.lower()
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# Replace spaces and other unwanted characters with dashes
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filename = re.sub(r'[^a-z0-9.-]+', '-', filename)
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# Join the directory and the slugified filename
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return os.path.join(directory, filename) |