Spaces:
Running
on
Zero
Running
on
Zero
File size: 5,355 Bytes
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import os
import torch
import hashlib
import datetime
from collections import OrderedDict
def replace_keys_in_dict(d, old_key_part, new_key_part):
# Use OrderedDict if the original is an OrderedDict
if isinstance(d, OrderedDict):
updated_dict = OrderedDict()
else:
updated_dict = {}
for key, value in d.items():
# Replace the key part if found
new_key = key.replace(old_key_part, new_key_part)
# If the value is a dictionary, apply the function recursively
if isinstance(value, dict):
value = replace_keys_in_dict(value, old_key_part, new_key_part)
updated_dict[new_key] = value
return updated_dict
def extract_small_model(
path: str,
name: str,
sr: int,
pitch_guidance: bool,
version: str,
epoch: int,
step: int,
):
try:
ckpt = torch.load(path, map_location="cpu")
pth_file = f"{name}.pth"
pth_file_old_version_path = os.path.join("logs", f"{pth_file}_old_version.pth")
opt = OrderedDict(
weight={
key: value.half() for key, value in ckpt.items() if "enc_q" not in key
}
)
if "model" in ckpt:
ckpt = ckpt["model"]
opt = OrderedDict()
opt["weight"] = {}
for key in ckpt.keys():
if "enc_q" in key:
continue
opt["weight"][key] = ckpt[key].half()
if sr == "40000":
opt["config"] = [
1025,
32,
192,
192,
768,
2,
6,
3,
0,
"1",
[3, 7, 11],
[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
[10, 10, 2, 2],
512,
[16, 16, 4, 4],
109,
256,
40000,
]
elif sr == "48000":
if version == "v1":
opt["config"] = [
1025,
32,
192,
192,
768,
2,
6,
3,
0,
"1",
[3, 7, 11],
[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
[10, 6, 2, 2, 2],
512,
[16, 16, 4, 4, 4],
109,
256,
48000,
]
else:
opt["config"] = [
1025,
32,
192,
192,
768,
2,
6,
3,
0,
"1",
[3, 7, 11],
[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
[12, 10, 2, 2],
512,
[24, 20, 4, 4],
109,
256,
48000,
]
elif sr == "32000":
if version == "v1":
opt["config"] = [
513,
32,
192,
192,
768,
2,
6,
3,
0,
"1",
[3, 7, 11],
[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
[10, 4, 2, 2, 2],
512,
[16, 16, 4, 4, 4],
109,
256,
32000,
]
else:
opt["config"] = [
513,
32,
192,
192,
768,
2,
6,
3,
0,
"1",
[3, 7, 11],
[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
[10, 8, 2, 2],
512,
[20, 16, 4, 4],
109,
256,
32000,
]
opt["epoch"] = epoch
opt["step"] = step
opt["sr"] = sr
opt["f0"] = int(pitch_guidance)
opt["version"] = version
opt["creation_date"] = datetime.datetime.now().isoformat()
hash_input = f"{str(ckpt)} {epoch} {step} {datetime.datetime.now().isoformat()}"
model_hash = hashlib.sha256(hash_input.encode()).hexdigest()
opt["model_hash"] = model_hash
model = torch.load(pth_file_old_version_path, map_location=torch.device("cpu"))
torch.save(
replace_keys_in_dict(
replace_keys_in_dict(
model, ".parametrizations.weight.original1", ".weight_v"
),
".parametrizations.weight.original0",
".weight_g",
),
pth_file_old_version_path,
)
os.remove(pth_file_old_version_path)
os.rename(pth_file_old_version_path, pth_file)
except Exception as error:
print(f"An error occurred extracting the model: {error}")
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