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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 torch
import torch.nn.functional as F
from Layers.STFT import STFT
from Utility.utils import pad_list
class EnergyCalculator(torch.nn.Module):
def __init__(self, fs=16000, n_fft=1024, win_length=None, hop_length=256, window="hann", center=True,
normalized=False, onesided=True, use_token_averaged_energy=True, reduction_factor=1):
super().__init__()
self.fs = fs
self.n_fft = n_fft
self.hop_length = hop_length
self.win_length = win_length
self.window = window
self.use_token_averaged_energy = use_token_averaged_energy
if use_token_averaged_energy:
assert reduction_factor >= 1
self.reduction_factor = reduction_factor
self.stft = STFT(n_fft=n_fft, win_length=win_length, hop_length=hop_length, window=window, center=center, normalized=normalized, onesided=onesided)
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, window=self.window, win_length=self.win_length, center=self.stft.center,
normalized=self.stft.normalized, use_token_averaged_energy=self.use_token_averaged_energy, 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])
# Domain-conversion: e.g. Stft: time -> time-freq
input_stft, energy_lengths = self.stft(input_waves, input_waves_lengths)
assert input_stft.dim() >= 4, input_stft.shape
assert input_stft.shape[-1] == 2, input_stft.shape
# input_stft: (..., F, 2) -> (..., F)
input_power = input_stft[..., 0] ** 2 + input_stft[..., 1] ** 2
# sum over frequency (B, N, F) -> (B, N)
energy = torch.sqrt(torch.clamp(input_power.sum(dim=2), min=1.0e-10))
# (Optional): Adjust length to match with the mel-spectrogram
if feats_lengths is not None:
energy = [self._adjust_num_frames(e[:el].view(-1), fl) for e, el, fl in zip(energy, energy_lengths, feats_lengths)]
energy_lengths = feats_lengths
# (Optional): Average by duration to calculate token-wise energy
if self.use_token_averaged_energy:
energy = [self._average_by_duration(e[:el].view(-1), d) for e, el, d in zip(energy, energy_lengths, durations)]
energy_lengths = durations_lengths
# Padding
if isinstance(energy, list):
energy = pad_list(energy, 0.0)
# Return with the shape (B, T, 1)
if norm_by_average:
average = energy[0][energy[0] != 0.0].mean()
energy = energy / average
return energy.unsqueeze(-1), energy_lengths
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].mean() if len(x[start:end]) != 0 else x.new_tensor(0.0) for start, end in zip(d_cumsum[:-1], d_cumsum[1:])]
return torch.stack(x_avg)
@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
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