OPENGIT/Retrieval-based-Voice-Conversion-WebUI
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github-actions[bot] 2023-08-03 10:25:05 +08:00 committed by GitHub
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commit 9a20c3b28f
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2 changed files with 29 additions and 19 deletions
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8
infer_cli.py
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@ -234,16 +234,12 @@ def get_vc(model_path):
version = cpt.get("version", "v1")
if version == "v1":
if if_f0 == 1:
net_g = SynthesizerTrnMs256NSFsid(
*cpt["config"], is_half=is_half
)
net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
else:
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
elif version == "v2":
if if_f0 == 1:
net_g = SynthesizerTrnMs768NSFsid(
*cpt["config"], is_half=is_half
)
net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=is_half)
else:
net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
del net_g.enc_q

24
tools/calc_rvc_model_similarity.py
View File

@ -5,6 +5,7 @@ import torch
import torch.nn as nn
import torch.nn.functional as F
def cal_cross_attn(to_q, to_k, to_v, rand_input):
hidden_dim, embed_dim = to_q.shape
attn_to_q = nn.Linear(hidden_dim, embed_dim, bias=False)
@ -16,21 +17,27 @@ def cal_cross_attn(to_q, to_k, to_v, rand_input):
return torch.einsum(
"ik, jk -> ik",
F.softmax(torch.einsum("ij, kj -> ik", attn_to_q(rand_input), attn_to_k(rand_input)), dim=-1),
attn_to_v(rand_input)
F.softmax(
torch.einsum("ij, kj -> ik", attn_to_q(rand_input), attn_to_k(rand_input)),
dim=-1,
),
attn_to_v(rand_input),
)
def model_hash(filename):
try:
with open(filename, "rb") as file:
import hashlib
m = hashlib.sha256()
file.seek(0x100000)
m.update(file.read(0x10000))
return m.hexdigest()[0:8]
except FileNotFoundError:
return 'NOFILE'
return "NOFILE"
def eval(model, n, input):
qk = f"enc_p.encoder.attn_layers.{n}.conv_q.weight"
@ -41,6 +48,7 @@ def eval(model, n, input):
attn = cal_cross_attn(atoq, atok, atov, input)
return attn
def main(path, root):
torch.manual_seed(114514)
model_a = torch.load(path, map_location="cpu")["weight"]
@ -50,7 +58,9 @@ def main(path,root):
map_attn_a = {}
map_rand_input = {}
for n in range(6):
hidden_dim, embed_dim,_ = model_a[f"enc_p.encoder.attn_layers.{n}.conv_v.weight"].shape
hidden_dim, embed_dim, _ = model_a[
f"enc_p.encoder.attn_layers.{n}.conv_v.weight"
].shape
rand_input = torch.randn([embed_dim, hidden_dim])
map_attn_a[n] = eval(model_a, n, rand_input)
@ -70,7 +80,11 @@ def main(path,root):
sim = torch.mean(torch.cosine_similarity(attn_a, attn_b))
sims.append(sim)
print("reference:\t%s\t%s\t%s"%(path,model_hash(path),f"{torch.mean(torch.stack(sims)) * 1e2:.2f}%"))
print(
"reference:\t%s\t%s\t%s"
% (path, model_hash(path), f"{torch.mean(torch.stack(sims)) * 1e2:.2f}%")
)
if __name__ == "__main__":
query_path = r"weights\mi v3.pth"