Spaces:
Runtime error
Runtime error
File size: 5,305 Bytes
3a478bf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 |
import os
from concurrent.futures import ThreadPoolExecutor
from tqdm import tqdm
import requests
url_base = "https://huggingface.co/IAHispano/Applio/resolve/main/Resources"
pretraineds_v1_list = [
(
"pretrained_v1/",
[
"D32k.pth",
"D40k.pth",
"D48k.pth",
"G32k.pth",
"G40k.pth",
"G48k.pth",
"f0D32k.pth",
"f0D40k.pth",
"f0D48k.pth",
"f0G32k.pth",
"f0G40k.pth",
"f0G48k.pth",
],
)
]
pretraineds_v2_list = [
(
"pretrained_v2/",
[
"D32k.pth",
"D40k.pth",
"D48k.pth",
"G32k.pth",
"G40k.pth",
"G48k.pth",
"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"])]
linux_executables_list = [("formant/", ["stftpitchshift"])]
executables_list = [
("", ["ffmpeg.exe", "ffprobe.exe"]),
("formant/", ["stftpitchshift.exe"]),
]
folder_mapping_list = {
"pretrained_v1/": "rvc/models/pretraineds/pretrained_v1/",
"pretrained_v2/": "rvc/models/pretraineds/pretrained_v2/",
"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 calculate_total_size(pretraineds_v1, pretraineds_v2, 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:
total_size += get_file_size_if_missing(
executables_list if os.name == "nt" else linux_executables_list
)
if pretraineds_v1:
total_size += get_file_size_if_missing(pretraineds_v1_list)
if pretraineds_v2:
total_size += get_file_size_if_missing(pretraineds_v2_list)
return total_size
def prequisites_download_pipeline(pretraineds_v1, pretraineds_v2, models, exe):
"""
Manage the download pipeline for different categories of files.
"""
total_size = calculate_total_size(pretraineds_v1, pretraineds_v2, 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:
download_mapping_files(
executables_list if os.name == "nt" else linux_executables_list,
global_bar,
)
if pretraineds_v1:
download_mapping_files(pretraineds_v1_list, global_bar)
if pretraineds_v2:
download_mapping_files(pretraineds_v2_list, global_bar)
else:
pass
|