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# Copyright 2020 Nagoya University (Tomoki Hayashi)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
# Adapted by Florian Lux 2021
import numpy as np
import pyworld
import torch
import torch.nn.functional as F
from scipy.interpolate import interp1d
from Utility.utils import pad_list
class Dio(torch.nn.Module):
"""
F0 estimation with dio + stonemask algortihm.
This is f0 extractor based on dio + stonemask algorithm
introduced in https://doi.org/10.1587/transinf.2015EDP7457
"""
def __init__(self, fs=16000, n_fft=1024, hop_length=256, f0min=40, f0max=400, use_token_averaged_f0=True,
use_continuous_f0=True, use_log_f0=True, reduction_factor=1):
super().__init__()
self.fs = fs
self.n_fft = n_fft
self.hop_length = hop_length
self.frame_period = 1000 * hop_length / fs
self.f0min = f0min
self.f0max = f0max
self.use_token_averaged_f0 = use_token_averaged_f0
self.use_continuous_f0 = use_continuous_f0
self.use_log_f0 = use_log_f0
if use_token_averaged_f0:
assert reduction_factor >= 1
self.reduction_factor = reduction_factor
def output_size(self):
return 1
def get_parameters(self):
return dict(fs=self.fs, n_fft=self.n_fft, hop_length=self.hop_length, f0min=self.f0min, f0max=self.f0max,
use_token_averaged_f0=self.use_token_averaged_f0, use_continuous_f0=self.use_continuous_f0, use_log_f0=self.use_log_f0,
reduction_factor=self.reduction_factor)
def forward(self, input_waves, input_waves_lengths=None, feats_lengths=None, durations=None,
durations_lengths=None, norm_by_average=True):
# If not provided, we assume that the inputs have the same length
if input_waves_lengths is None:
input_waves_lengths = (input_waves.new_ones(input_waves.shape[0], dtype=torch.long) * input_waves.shape[1])
# F0 extraction
pitch = [self._calculate_f0(x[:xl]) for x, xl in zip(input_waves, input_waves_lengths)]
# (Optional): Adjust length to match with the mel-spectrogram
if feats_lengths is not None:
pitch = [self._adjust_num_frames(p, fl).view(-1) for p, fl in zip(pitch, feats_lengths)]
# (Optional): Average by duration to calculate token-wise f0
if self.use_token_averaged_f0:
pitch = [self._average_by_duration(p, d).view(-1) for p, d in zip(pitch, durations)]
pitch_lengths = durations_lengths
else:
pitch_lengths = input_waves.new_tensor([len(p) for p in pitch], dtype=torch.long)
# Padding
pitch = pad_list(pitch, 0.0)
# Return with the shape (B, T, 1)
if norm_by_average:
average = pitch[0][pitch[0] != 0.0].mean()
pitch = pitch / average
return pitch.unsqueeze(-1), pitch_lengths
def _calculate_f0(self, input):
x = input.cpu().numpy().astype(np.double)
f0, timeaxis = pyworld.dio(x, self.fs, f0_floor=self.f0min, f0_ceil=self.f0max, frame_period=self.frame_period)
f0 = pyworld.stonemask(x, f0, timeaxis, self.fs)
if self.use_continuous_f0:
f0 = self._convert_to_continuous_f0(f0)
if self.use_log_f0:
nonzero_idxs = np.where(f0 != 0)[0]
f0[nonzero_idxs] = np.log(f0[nonzero_idxs])
return input.new_tensor(f0.reshape(-1), dtype=torch.float)
@staticmethod
def _adjust_num_frames(x, num_frames):
if num_frames > len(x):
x = F.pad(x, (0, num_frames - len(x)))
elif num_frames < len(x):
x = x[:num_frames]
return x
@staticmethod
def _convert_to_continuous_f0(f0: np.array):
if (f0 == 0).all():
return f0
# padding start and end of f0 sequence
start_f0 = f0[f0 != 0][0]
end_f0 = f0[f0 != 0][-1]
start_idx = np.where(f0 == start_f0)[0][0]
end_idx = np.where(f0 == end_f0)[0][-1]
f0[:start_idx] = start_f0
f0[end_idx:] = end_f0
# get non-zero frame index
nonzero_idxs = np.where(f0 != 0)[0]
# perform linear interpolation
interp_fn = interp1d(nonzero_idxs, f0[nonzero_idxs])
f0 = interp_fn(np.arange(0, f0.shape[0]))
return f0
def _average_by_duration(self, x, d):
assert 0 <= len(x) - d.sum() < self.reduction_factor
d_cumsum = F.pad(d.cumsum(dim=0), (1, 0))
x_avg = [
x[start:end].masked_select(x[start:end].gt(0.0)).mean(dim=0) if len(x[start:end].masked_select(x[start:end].gt(0.0))) != 0 else x.new_tensor(0.0)
for start, end in zip(d_cumsum[:-1], d_cumsum[1:])]
return torch.stack(x_avg)
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