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import torch
class Stat:
def __init__(self, keep_raw=False):
self.x = 0.0
self.x2 = 0.0
self.z = 0.0 # z = logx
self.z2 = 0.0
self.n = 0.0
self.u = 0.0
self.keep_raw = keep_raw
self.raw = []
def update(self, new_x):
new_z = new_x.log()
self.x += new_x.sum()
self.x2 += (new_x**2).sum()
self.z += new_z.sum()
self.z2 += (new_z**2).sum()
self.n += len(new_x)
self.u += 1
if self.keep_raw:
self.raw.append(new_x)
@property
def mean(self):
return self.x / self.n
@property
def std(self):
return (self.x2 / self.n - self.mean**2) ** 0.5
@property
def mean_log(self):
return self.z / self.n
@property
def std_log(self):
return (self.z2 / self.n - self.mean_log**2) ** 0.5
@property
def n_frms(self):
return self.n
@property
def n_utts(self):
return self.u
@property
def raw_data(self):
assert self.keep_raw, "does not support storing raw data!"
return torch.cat(self.raw)
class F0Stat(Stat):
def update(self, new_x):
# assume unvoiced frames are 0 and consider only voiced frames
if new_x is not None:
super().update(new_x[new_x != 0])
class Accuracy:
def __init__(self):
self.y, self.yhat = [], []
def update(self, yhat, y):
self.yhat.append(yhat)
self.y.append(y)
def acc(self, tol):
yhat = torch.cat(self.yhat)
y = torch.cat(self.y)
acc = torch.abs(yhat - y) <= tol
acc = acc.float().mean().item()
return acc
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