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import librosa
import numpy as np
import torch
from .constants import *
def to_local_average_f0(hidden, center=None, thred=0.03):
idx = torch.arange(N_CLASS, device=hidden.device)[None, None, :] # [B=1, T=1, N]
idx_cents = idx * 20 + CONST # [B=1, N]
if center is None:
center = torch.argmax(hidden, dim=2, keepdim=True) # [B, T, 1]
start = torch.clip(center - 4, min=0) # [B, T, 1]
end = torch.clip(center + 5, max=N_CLASS) # [B, T, 1]
idx_mask = (idx >= start) & (idx < end) # [B, T, N]
weights = hidden * idx_mask # [B, T, N]
product_sum = torch.sum(weights * idx_cents, dim=2) # [B, T]
weight_sum = torch.sum(weights, dim=2) # [B, T]
cents = product_sum / (weight_sum + (weight_sum == 0)) # avoid dividing by zero, [B, T]
f0 = 10 * 2 ** (cents / 1200)
uv = hidden.max(dim=2)[0] < thred # [B, T]
f0 = f0 * ~uv
return f0.squeeze(0).cpu().numpy()
def to_viterbi_f0(hidden, thred=0.03):
# Create viterbi transition matrix
if not hasattr(to_viterbi_f0, 'transition'):
xx, yy = np.meshgrid(range(N_CLASS), range(N_CLASS))
transition = np.maximum(30 - abs(xx - yy), 0)
transition = transition / transition.sum(axis=1, keepdims=True)
to_viterbi_f0.transition = transition
# Convert to probability
prob = hidden.squeeze(0).cpu().numpy()
prob = prob.T
prob = prob / prob.sum(axis=0)
# Perform viterbi decoding
path = librosa.sequence.viterbi(prob, to_viterbi_f0.transition).astype(np.int64)
center = torch.from_numpy(path).unsqueeze(0).unsqueeze(-1).to(hidden.device)
return to_local_average_f0(hidden, center=center, thred=thred)
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