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import os
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import torch
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import hashlib
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import datetime
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from collections import OrderedDict
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def replace_keys_in_dict(d, old_key_part, new_key_part):
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if isinstance(d, OrderedDict):
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updated_dict = OrderedDict()
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else:
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updated_dict = {}
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for key, value in d.items():
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new_key = key.replace(old_key_part, new_key_part)
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if isinstance(value, dict):
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value = replace_keys_in_dict(value, old_key_part, new_key_part)
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updated_dict[new_key] = value
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return updated_dict
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def extract_small_model(path, name, sr, if_f0, version, epoch, step):
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try:
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ckpt = torch.load(path, map_location="cpu")
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pth_file = f"{name}.pth"
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pth_file_old_version_path = os.path.join("logs", f"{pth_file}_old_version.pth")
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opt = OrderedDict(
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weight={
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key: value.half() for key, value in ckpt.items() if "enc_q" not in key
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}
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)
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if "model" in ckpt:
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ckpt = ckpt["model"]
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opt = OrderedDict()
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opt["weight"] = {}
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for key in ckpt.keys():
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if "enc_q" in key:
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continue
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opt["weight"][key] = ckpt[key].half()
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if sr == "40000":
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opt["config"] = [
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1025,
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32,
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192,
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192,
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768,
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2,
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6,
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3,
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0,
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"1",
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[3, 7, 11],
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[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
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[10, 10, 2, 2],
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512,
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[16, 16, 4, 4],
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109,
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256,
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40000,
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]
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elif sr == "48000":
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if version == "v1":
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opt["config"] = [
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1025,
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32,
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192,
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192,
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768,
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2,
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6,
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3,
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0,
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"1",
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[3, 7, 11],
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[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
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[10, 6, 2, 2, 2],
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512,
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[16, 16, 4, 4, 4],
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109,
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256,
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48000,
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]
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else:
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opt["config"] = [
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1025,
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32,
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192,
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192,
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768,
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2,
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6,
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3,
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0,
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"1",
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[3, 7, 11],
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[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
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[12, 10, 2, 2],
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512,
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[24, 20, 4, 4],
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109,
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256,
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48000,
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]
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elif sr == "32000":
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if version == "v1":
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opt["config"] = [
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513,
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32,
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192,
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192,
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768,
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2,
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6,
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3,
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0,
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"1",
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[3, 7, 11],
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[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
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[10, 4, 2, 2, 2],
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512,
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[16, 16, 4, 4, 4],
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109,
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256,
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32000,
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]
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else:
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opt["config"] = [
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513,
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32,
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192,
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192,
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768,
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2,
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6,
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3,
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0,
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"1",
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[3, 7, 11],
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[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
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[10, 8, 2, 2],
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512,
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[20, 16, 4, 4],
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109,
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256,
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32000,
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]
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opt["epoch"] = epoch
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opt["step"] = step
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opt["sr"] = sr
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opt["f0"] = int(if_f0)
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opt["version"] = version
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opt["creation_date"] = datetime.datetime.now().isoformat()
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hash_input = f"{str(ckpt)} {epoch} {step} {datetime.datetime.now().isoformat()}"
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model_hash = hashlib.sha256(hash_input.encode()).hexdigest()
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opt["model_hash"] = model_hash
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model = torch.load(pth_file_old_version_path, map_location=torch.device("cpu"))
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torch.save(
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replace_keys_in_dict(
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replace_keys_in_dict(
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model, ".parametrizations.weight.original1", ".weight_v"
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),
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".parametrizations.weight.original0",
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".weight_g",
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),
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pth_file_old_version_path,
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)
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os.remove(pth_file_old_version_path)
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os.rename(pth_file_old_version_path, pth_file)
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except Exception as error:
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print(error)
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