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import os
import sys
import json
import shutil
import requests
import tempfile
import gradio as gr
import pandas as pd
from concurrent.futures import ThreadPoolExecutor
from tqdm import tqdm
now_dir = os.getcwd()
sys.path.append(now_dir)
from core import run_download_script
from rvc.lib.utils import format_title
from assets.i18n.i18n import I18nAuto
i18n = I18nAuto()
gradio_temp_dir = os.path.join(tempfile.gettempdir(), "gradio")
if os.path.exists(gradio_temp_dir):
shutil.rmtree(gradio_temp_dir)
def save_drop_model(dropbox):
if "pth" not in dropbox and "index" not in dropbox:
raise gr.Error(
message="The file you dropped is not a valid model file. Please try again."
)
else:
file_name = format_title(os.path.basename(dropbox))
if ".pth" in dropbox:
model_name = format_title(file_name.split(".pth")[0])
else:
if "v2" not in dropbox:
model_name = format_title(
file_name.split("_nprobe_1_")[1].split("_v1")[0]
)
else:
model_name = format_title(
file_name.split("_nprobe_1_")[1].split("_v2")[0]
)
model_path = os.path.join(now_dir, "logs", model_name)
if not os.path.exists(model_path):
os.makedirs(model_path)
if os.path.exists(os.path.join(model_path, file_name)):
os.remove(os.path.join(model_path, file_name))
shutil.move(dropbox, os.path.join(model_path, file_name))
print(f"{file_name} saved in {model_path}")
gr.Info(f"{file_name} saved in {model_path}")
return None
def search_models(name):
url = f"https://cjtfqzjfdimgpvpwhzlv.supabase.co/rest/v1/models?name=ilike.%25{name}%25&order=created_at.desc&limit=15"
headers = {
"apikey": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6ImNqdGZxempmZGltZ3B2cHdoemx2Iiwicm9sZSI6ImFub24iLCJpYXQiOjE3MjY5MjYxMzQsImV4cCI6MjA0MjUwMjEzNH0.OyDXlhvH6D-IsHiWhPAGUtsPGGUvWQynfxUeQwfYToE"
}
response = requests.get(url, headers=headers)
data = response.json()
if len(data) == 0:
gr.Info(i18n("We couldn't find models by that name."))
return None
else:
df = pd.DataFrame(data)[["name", "link", "epochs", "type"]]
df["link"] = df["link"].apply(
lambda x: f'<a href="{x}" target="_blank">{x}</a>'
)
return df
json_url = "https://huggingface.co/IAHispano/Applio/raw/main/pretrains.json"
def fetch_pretrained_data():
pretraineds_custom_path = os.path.join(
"rvc", "models", "pretraineds", "pretraineds_custom"
)
os.makedirs(pretraineds_custom_path, exist_ok=True)
try:
with open(
os.path.join(pretraineds_custom_path, json_url.split("/")[-1]), "r"
) as f:
data = json.load(f)
except:
try:
response = requests.get(json_url)
response.raise_for_status()
data = response.json()
with open(
os.path.join(pretraineds_custom_path, json_url.split("/")[-1]),
"w",
encoding="utf-8",
) as f:
json.dump(
data,
f,
indent=2,
separators=(",", ": "),
ensure_ascii=False,
)
except:
data = {
"Titan": {
"32k": {"D": "null", "G": "null"},
},
}
return data
def get_pretrained_list():
data = fetch_pretrained_data()
return list(data.keys())
def get_pretrained_sample_rates(model):
data = fetch_pretrained_data()
return list(data[model].keys())
def get_file_size(url):
response = requests.head(url)
return int(response.headers.get("content-length", 0))
def download_file(url, destination_path, progress_bar):
os.makedirs(os.path.dirname(destination_path), 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)
progress_bar.update(len(data))
def download_pretrained_model(model, sample_rate):
data = fetch_pretrained_data()
paths = data[model][sample_rate]
pretraineds_custom_path = os.path.join(
"rvc", "models", "pretraineds", "pretraineds_custom"
)
os.makedirs(pretraineds_custom_path, exist_ok=True)
d_url = f"https://huggingface.co/{paths['D']}"
g_url = f"https://huggingface.co/{paths['G']}"
total_size = get_file_size(d_url) + get_file_size(g_url)
gr.Info("Downloading pretrained model...")
with tqdm(
total=total_size, unit="iB", unit_scale=True, desc="Downloading files"
) as progress_bar:
with ThreadPoolExecutor(max_workers=2) as executor:
futures = [
executor.submit(
download_file,
d_url,
os.path.join(pretraineds_custom_path, os.path.basename(paths["D"])),
progress_bar,
),
executor.submit(
download_file,
g_url,
os.path.join(pretraineds_custom_path, os.path.basename(paths["G"])),
progress_bar,
),
]
for future in futures:
future.result()
gr.Info("Pretrained model downloaded successfully!")
print("Pretrained model downloaded successfully!")
def update_sample_rate_dropdown(model):
return {
"choices": get_pretrained_sample_rates(model),
"value": get_pretrained_sample_rates(model)[0],
"__type__": "update",
}
def download_tab():
with gr.Column():
gr.Markdown(value=i18n("## Download Model"))
model_link = gr.Textbox(
label=i18n("Model Link"),
placeholder=i18n("Introduce the model link"),
interactive=True,
)
model_download_output_info = gr.Textbox(
label=i18n("Output Information"),
info=i18n("The output information will be displayed here."),
value="",
max_lines=8,
interactive=False,
)
model_download_button = gr.Button(i18n("Download Model"))
model_download_button.click(
fn=run_download_script,
inputs=[model_link],
outputs=[model_download_output_info],
)
gr.Markdown(value=i18n("## Drop files"))
dropbox = gr.File(
label=i18n(
"Drag your .pth file and .index file into this space. Drag one and then the other."
),
type="filepath",
)
dropbox.upload(
fn=save_drop_model,
inputs=[dropbox],
outputs=[dropbox],
)
gr.Markdown(value=i18n("## Search Model"))
search_name = gr.Textbox(
label=i18n("Model Name"),
placeholder=i18n("Introduce the model name to search."),
interactive=True,
)
search_table = gr.Dataframe(datatype="markdown")
search = gr.Button(i18n("Search"))
search.click(
fn=search_models,
inputs=[search_name],
outputs=[search_table],
)
search_name.submit(search_models, [search_name], search_table)
gr.Markdown(value=i18n("## Download Pretrained Models"))
pretrained_model = gr.Dropdown(
label=i18n("Pretrained"),
info=i18n("Select the pretrained model you want to download."),
choices=get_pretrained_list(),
value="Titan",
interactive=True,
)
pretrained_sample_rate = gr.Dropdown(
label=i18n("Sampling Rate"),
info=i18n("And select the sampling rate."),
choices=get_pretrained_sample_rates(pretrained_model.value),
value="40k",
interactive=True,
allow_custom_value=True,
)
pretrained_model.change(
update_sample_rate_dropdown,
inputs=[pretrained_model],
outputs=[pretrained_sample_rate],
)
download_pretrained = gr.Button(i18n("Download"))
download_pretrained.click(
fn=download_pretrained_model,
inputs=[pretrained_model, pretrained_sample_rate],
outputs=[],
)
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