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import os
from concurrent.futures import ThreadPoolExecutor
from tqdm import tqdm
import requests
url_base = "https://huggingface.co/IAHispano/Applio/resolve/main/Resources"
pretraineds_hifigan_list = [
(
"pretrained_v2/",
[
"f0D32k.pth",
"f0D40k.pth",
"f0D48k.pth",
"f0G32k.pth",
"f0G40k.pth",
"f0G48k.pth",
],
)
]
models_list = [("predictors/", ["rmvpe.pt", "fcpe.pt"])]
embedders_list = [("embedders/contentvec/", ["pytorch_model.bin", "config.json"])]
executables_list = [
("", ["ffmpeg.exe", "ffprobe.exe"]),
]
folder_mapping_list = {
"pretrained_v2/": "rvc/models/pretraineds/hifi-gan/",
"embedders/contentvec/": "rvc/models/embedders/contentvec/",
"predictors/": "rvc/models/predictors/",
"formant/": "rvc/models/formant/",
}
def get_file_size_if_missing(file_list):
"""
Calculate the total size of files to be downloaded only if they do not exist locally.
"""
total_size = 0
for remote_folder, files in file_list:
local_folder = folder_mapping_list.get(remote_folder, "")
for file in files:
destination_path = os.path.join(local_folder, file)
if not os.path.exists(destination_path):
url = f"{url_base}/{remote_folder}{file}"
response = requests.head(url)
total_size += int(response.headers.get("content-length", 0))
return total_size
def download_file(url, destination_path, global_bar):
"""
Download a file from the given URL to the specified destination path,
updating the global progress bar as data is downloaded.
"""
dir_name = os.path.dirname(destination_path)
if dir_name:
os.makedirs(dir_name, exist_ok=True)
response = requests.get(url, stream=True)
block_size = 1024
with open(destination_path, "wb") as file:
for data in response.iter_content(block_size):
file.write(data)
global_bar.update(len(data))
def download_mapping_files(file_mapping_list, global_bar):
"""
Download all files in the provided file mapping list using a thread pool executor,
and update the global progress bar as downloads progress.
"""
with ThreadPoolExecutor() as executor:
futures = []
for remote_folder, file_list in file_mapping_list:
local_folder = folder_mapping_list.get(remote_folder, "")
for file in file_list:
destination_path = os.path.join(local_folder, file)
if not os.path.exists(destination_path):
url = f"{url_base}/{remote_folder}{file}"
futures.append(
executor.submit(
download_file, url, destination_path, global_bar
)
)
for future in futures:
future.result()
def split_pretraineds(pretrained_list):
f0_list = []
non_f0_list = []
for folder, files in pretrained_list:
f0_files = [f for f in files if f.startswith("f0")]
non_f0_files = [f for f in files if not f.startswith("f0")]
if f0_files:
f0_list.append((folder, f0_files))
if non_f0_files:
non_f0_list.append((folder, non_f0_files))
return f0_list, non_f0_list
pretraineds_hifigan_list, _ = split_pretraineds(pretraineds_hifigan_list)
def calculate_total_size(
pretraineds_hifigan,
models,
exe,
):
"""
Calculate the total size of all files to be downloaded based on selected categories.
"""
total_size = 0
if models:
total_size += get_file_size_if_missing(models_list)
total_size += get_file_size_if_missing(embedders_list)
if exe and os.name == "nt":
total_size += get_file_size_if_missing(executables_list)
total_size += get_file_size_if_missing(pretraineds_hifigan)
return total_size
def prequisites_download_pipeline(
pretraineds_hifigan,
models,
exe,
):
"""
Manage the download pipeline for different categories of files.
"""
total_size = calculate_total_size(
pretraineds_hifigan_list if pretraineds_hifigan else [],
models,
exe,
)
if total_size > 0:
with tqdm(
total=total_size, unit="iB", unit_scale=True, desc="Downloading all files"
) as global_bar:
if models:
download_mapping_files(models_list, global_bar)
download_mapping_files(embedders_list, global_bar)
if exe:
if os.name == "nt":
download_mapping_files(executables_list, global_bar)
else:
print("No executables needed")
if pretraineds_hifigan:
download_mapping_files(pretraineds_hifigan_list, global_bar)
else:
pass
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